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Sample records for local feature analysis

  1. Rapid Online Analysis of Local Feature Detectors and Their Complementarity

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    Shoaib Ehsan

    2013-08-01

    Full Text Available A vision system that can assess its own performance and take appropriate actions online to maximize its effectiveness would be a step towards achieving the long-cherished goal of imitating humans. This paper proposes a method for performing an online performance analysis of local feature detectors, the primary stage of many practical vision systems. It advocates the spatial distribution of local image features as a good performance indicator and presents a metric that can be calculated rapidly, concurs with human visual assessments and is complementary to existing offline measures such as repeatability. The metric is shown to provide a measure of complementarity for combinations of detectors, correctly reflecting the underlying principles of individual detectors. Qualitative results on well-established datasets for several state-of-the-art detectors are presented based on the proposed measure. Using a hypothesis testing approach and a newly-acquired, larger image database, statistically-significant performance differences are identified. Different detector pairs and triplets are examined quantitatively and the results provide a useful guideline for combining detectors in applications that require a reasonable spatial distribution of image features. A principled framework for combining feature detectors in these applications is also presented. Timing results reveal the potential of the metric for online applications.

  2. Coordination Analysis Using Global Structural Constraints and Alignment-based Local Features

    Science.gov (United States)

    Hara, Kazuo; Shimbo, Masashi; Matsumoto, Yuji

    We propose a hybrid approach to coordinate structure analysis that combines a simple grammar to ensure consistent global structure of coordinations in a sentence, and features based on sequence alignment to capture local symmetry of conjuncts. The weight of the alignment-based features, which in turn determines the score of coordinate structures, is optimized by perceptron training on a given corpus. A bottom-up chart parsing algorithm efficiently finds the best scoring structure, taking both nested or non-overlapping flat coordinations into account. We demonstrate that our approach outperforms existing parsers in coordination scope detection on the Genia corpus.

  3. Consistent Feature Extraction From Vector Fields: Combinatorial Representations and Analysis Under Local Reference Frames

    Energy Technology Data Exchange (ETDEWEB)

    Bhatia, Harsh [Univ. of Utah, Salt Lake City, UT (United States)

    2015-05-01

    This dissertation presents research on addressing some of the contemporary challenges in the analysis of vector fields—an important type of scientific data useful for representing a multitude of physical phenomena, such as wind flow and ocean currents. In particular, new theories and computational frameworks to enable consistent feature extraction from vector fields are presented. One of the most fundamental challenges in the analysis of vector fields is that their features are defined with respect to reference frames. Unfortunately, there is no single “correct” reference frame for analysis, and an unsuitable frame may cause features of interest to remain undetected, thus creating serious physical consequences. This work develops new reference frames that enable extraction of localized features that other techniques and frames fail to detect. As a result, these reference frames objectify the notion of “correctness” of features for certain goals by revealing the phenomena of importance from the underlying data. An important consequence of using these local frames is that the analysis of unsteady (time-varying) vector fields can be reduced to the analysis of sequences of steady (timeindependent) vector fields, which can be performed using simpler and scalable techniques that allow better data management by accessing the data on a per-time-step basis. Nevertheless, the state-of-the-art analysis of steady vector fields is not robust, as most techniques are numerical in nature. The residing numerical errors can violate consistency with the underlying theory by breaching important fundamental laws, which may lead to serious physical consequences. This dissertation considers consistency as the most fundamental characteristic of computational analysis that must always be preserved, and presents a new discrete theory that uses combinatorial representations and algorithms to provide consistency guarantees during vector field analysis along with the uncertainty

  4. Localized scleroderma: imaging features

    International Nuclear Information System (INIS)

    Liu, P.; Uziel, Y.; Chuang, S.; Silverman, E.; Krafchik, B.; Laxer, R.

    1994-01-01

    Localized scleroderma is distinct from the diffuse form of scleroderma and does not show Raynaud's phenomenon and visceral involvement. The imaging features in 23 patients ranging from 2 to 17 years of age (mean 11.1 years) were reviewed. Leg length discrepancy and muscle atrophy were the most common findings (five patients), with two patients also showing modelling deformity of the fibula. One patient with lower extremity involvement showed abnormal bone marrow signals on MR. Disabling joint contracture requiring orthopedic intervention was noted in one patient. In two patients with ''en coup de sabre'' facial deformity, CT and MR scans revealed intracranial calcifications and white matter abnormality in the ipsilateral frontal lobes, with one also showing migrational abnormality. In a third patient, CT revealed white matter abnormality in the ipsilateral parietal lobe. In one patient with progressive facial hemiatrophy, CT and MR scans showed the underlying hypoplastic left maxillary antrum and cheek. Imaging studies of areas of clinical concern revealed positive findings in half our patients. (orig.)

  5. Localized scleroderma: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Liu, P. (Dept. of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON (Canada)); Uziel, Y. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada)); Chuang, S. (Dept. of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON (Canada)); Silverman, E. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada)); Krafchik, B. (Div. of Dermatology, Dept. of Pediatrics, Hospital for Sick Children, Toronto, ON (Canada)); Laxer, R. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada))

    1994-06-01

    Localized scleroderma is distinct from the diffuse form of scleroderma and does not show Raynaud's phenomenon and visceral involvement. The imaging features in 23 patients ranging from 2 to 17 years of age (mean 11.1 years) were reviewed. Leg length discrepancy and muscle atrophy were the most common findings (five patients), with two patients also showing modelling deformity of the fibula. One patient with lower extremity involvement showed abnormal bone marrow signals on MR. Disabling joint contracture requiring orthopedic intervention was noted in one patient. In two patients with ''en coup de sabre'' facial deformity, CT and MR scans revealed intracranial calcifications and white matter abnormality in the ipsilateral frontal lobes, with one also showing migrational abnormality. In a third patient, CT revealed white matter abnormality in the ipsilateral parietal lobe. In one patient with progressive facial hemiatrophy, CT and MR scans showed the underlying hypoplastic left maxillary antrum and cheek. Imaging studies of areas of clinical concern revealed positive findings in half our patients. (orig.)

  6. Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype

    International Nuclear Information System (INIS)

    Knowles, David; Sudar, Damir; Bator, Carol; Bissell, Mina

    2006-01-01

    The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, the distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained using three-dimensional cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from three-dimensional confocal images. Prominent features of fluorescently-stained NuMA were detected using a novel local bright feature analysis technique, and their normalized spatial density calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features as non-neoplastic cells underwent phenotypically normal acinar morphogenesis. In contrast, we did not detect any reorganization of NuMA during the formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating non-neoplastic cells from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues

  7. Automated local bright feature image analysis of nuclear proteindistribution identifies changes in tissue phenotype

    Energy Technology Data Exchange (ETDEWEB)

    Knowles, David; Sudar, Damir; Bator, Carol; Bissell, Mina

    2006-02-01

    The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, the distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained using three-dimensional cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from three-dimensional confocal images. Prominent features of fluorescently-stained NuMA were detected using a novel local bright feature analysis technique, and their normalized spatial density calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features as non-neoplastic cells underwent phenotypically normal acinar morphogenesis. In contrast, we did not detect any reorganization of NuMA during the formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating non-neoplastic cells from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues.

  8. Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

    Directory of Open Access Journals (Sweden)

    Jinlu Sheng

    2016-07-01

    Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

  9. Analysis of Salient Feature Jitter in the Cochlea for Objective Prediction of Temporally Localized Distortion in Synthesized Speech

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    Wenliang Lu

    2009-01-01

    Full Text Available Temporally localized distortions account for the highest variance in subjective evaluation of coded speech signals (Sen (2001 and Hall (2001. The ability to discern and decompose perceptually relevant temporally localized coding noise from other types of distortions is both of theoretical importance as well as a valuable tool for deploying and designing speech synthesis systems. The work described within uses a physiologically motivated cochlear model to provide a tractable analysis of salient feature trajectories as processed by the cochlea. Subsequent statistical analysis shows simple relationships between the jitter of these trajectories and temporal attributes of the Diagnostic Acceptability Measure (DAM.

  10. Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

    Science.gov (United States)

    Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki

    2017-09-01

    Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

  11. A new approach for detecting local features

    DEFF Research Database (Denmark)

    Nguyen, Phuong Giang; Andersen, Hans Jørgen

    2010-01-01

    Local features up to now are often mentioned in the meaning of interest points. A patch around each point is formed to compute descriptors or feature vectors. Therefore, in order to satisfy different invariant imaging conditions such as scales and viewpoints, an input image is often represented i...

  12. Face Alignment via Regressing Local Binary Features.

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    Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian

    2016-03-01

    This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.

  13. Efficient Topological Localization Using Global and Local Feature Matching

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    Junqiu Wang

    2013-03-01

    Full Text Available We present an efficient vision-based global topological localization approach in which different image features are used in a coarse-to-fine matching framework. Orientation Adjacency Coherence Histogram (OACH, a novel image feature, is proposed to improve the coarse localization. The coarse localization results are taken as inputs for the fine localization which is carried out by matching Harris-Laplace interest points characterized by the SIFT descriptor. The computation of OACHs and interest points is efficient due to the fact that these features are computed in an integrated process. The matching of local features is improved by using approximate nearest neighbor searching technique. We have implemented and tested the localization system in real environments. The experimental results demonstrate that our approach is efficient and reliable in both indoor and outdoor environments. This work has also been compared with previous works. The comparison results show that our approach has better performance with higher correct ratio and lower computational complexity.

  14. Pretreatment 18F-FDG PET Textural Features in Locally Advanced Non–Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235

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    Ohri, Nitin; Duan, Fenghai; Snyder, Bradley S.; Wei, Bo; Machtay, Mitchell; Alavi, Abass; Siegel, Barry A.; Johnson, Douglas W.; Bradley, Jeffrey D.; DeNittis, Albert; Werner-Wasik, Maria; El Naqa, Issam

    2016-01-01

    In a secondary analysis of American College of Radiology Imaging Network (ACRIN) 6668/RTOG 0235, high pretreatment metabolic tumor volume (MTV) on 18F-FDG PET was found to be a poor prognostic factor for patients treated with chemoradiotherapy for locally advanced non–small cell lung cancer (NSCLC). Here we utilize the same dataset to explore whether heterogeneity metrics based on PET textural features can provide additional prognostic information. Methods Patients with locally advanced NSCLC underwent 18F-FDG PET prior to treatment. A gradient-based segmentation tool was used to contour each patient’s primary tumor. MTV, maximum SUV, and 43 textural features were extracted for each tumor. To address over-fitting and high collinearity among PET features, the least absolute shrinkage and selection operator (LASSO) method was applied to identify features that were independent predictors of overall survival (OS) after adjusting for MTV. Recursive binary partitioning in a conditional inference framework was utilized to identify optimal thresholds. Kaplan–Meier curves and log-rank testing were used to compare outcomes among patient groups. Results Two hundred one patients met inclusion criteria. The LASSO procedure identified 1 textural feature (SumMean) as an independent predictor of OS. The optimal cutpoint for MTV was 93.3 cm3, and the optimal Sum-Mean cutpoint for tumors above 93.3 cm3 was 0.018. This grouped patients into three categories: low tumor MTV (n = 155; median OS, 22.6 mo), high tumor MTV and high SumMean (n = 23; median OS, 20.0 mo), and high tumor MTV and low SumMean (n = 23; median OS, 6.2 mo; log-rank P textural PET features in the context of established prognostic factors. We have also identified a promising feature that may have prognostic value in locally advanced NSCLC patients with large tumors who are treated with chemoradiotherapy. Validation studies are warranted. PMID:26912429

  15. Transcriptional Analysis and Subcellular Protein Localization Reveal Specific Features of the Essential WalKR System in Staphylococcus aureus.

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    Olivier Poupel

    Full Text Available The WalKR two-component system, controlling cell wall metabolism, is highly conserved among Bacilli and essential for cell viability. In Staphylococcus aureus, walR and walK are followed by three genes of unknown function: walH, walI and walJ. Sequence analysis and transcript mapping revealed a unique genetic structure for this locus in S. aureus: the last gene of the locus, walJ, is transcribed independently, whereas transcription of the tetra-cistronic walRKHI operon occurred from two independent promoters located upstream from walR. Protein topology analysis and protein-protein interactions in E. coli as well as subcellular localization in S. aureus allowed us to show that WalH and WalI are membrane-bound proteins, which associate with WalK to form a complex at the cell division septum. While these interactions suggest that WalH and WalI play a role in activity of the WalKR regulatory pathway, deletion of walH and/or walI did not have a major effect on genes whose expression is strongly dependent on WalKR or on associated phenotypes. No effect of WalH or WalI was seen on tightly controlled WalKR regulon genes such as sle1 or saouhsc_00773, which encodes a CHAP-domain amidase. Of the genes encoding the two major S. aureus autolysins, AtlA and Sle1, only transcription of atlA was increased in the ΔwalH or ΔwalI mutants. Likewise, bacterial autolysis was not increased in the absence of WalH and/or WalI and biofilm formation was lowered rather than increased. Our results suggest that contrary to their major role as WalK inhibitors in B. subtilis, the WalH and WalI proteins have evolved a different function in S. aureus, where they are more accessory. A phylogenomic analysis shows a striking conservation of the 5 gene wal cluster along the evolutionary history of Bacilli, supporting the key importance of this signal transduction system, and indicating that the walH and walI genes were lost in the ancestor of Streptococcaceae, leading to their

  16. Transcriptional Analysis and Subcellular Protein Localization Reveal Specific Features of the Essential WalKR System in Staphylococcus aureus.

    Science.gov (United States)

    Poupel, Olivier; Moyat, Mati; Groizeleau, Julie; Antunes, Luísa C S; Gribaldo, Simonetta; Msadek, Tarek; Dubrac, Sarah

    2016-01-01

    The WalKR two-component system, controlling cell wall metabolism, is highly conserved among Bacilli and essential for cell viability. In Staphylococcus aureus, walR and walK are followed by three genes of unknown function: walH, walI and walJ. Sequence analysis and transcript mapping revealed a unique genetic structure for this locus in S. aureus: the last gene of the locus, walJ, is transcribed independently, whereas transcription of the tetra-cistronic walRKHI operon occurred from two independent promoters located upstream from walR. Protein topology analysis and protein-protein interactions in E. coli as well as subcellular localization in S. aureus allowed us to show that WalH and WalI are membrane-bound proteins, which associate with WalK to form a complex at the cell division septum. While these interactions suggest that WalH and WalI play a role in activity of the WalKR regulatory pathway, deletion of walH and/or walI did not have a major effect on genes whose expression is strongly dependent on WalKR or on associated phenotypes. No effect of WalH or WalI was seen on tightly controlled WalKR regulon genes such as sle1 or saouhsc_00773, which encodes a CHAP-domain amidase. Of the genes encoding the two major S. aureus autolysins, AtlA and Sle1, only transcription of atlA was increased in the ΔwalH or ΔwalI mutants. Likewise, bacterial autolysis was not increased in the absence of WalH and/or WalI and biofilm formation was lowered rather than increased. Our results suggest that contrary to their major role as WalK inhibitors in B. subtilis, the WalH and WalI proteins have evolved a different function in S. aureus, where they are more accessory. A phylogenomic analysis shows a striking conservation of the 5 gene wal cluster along the evolutionary history of Bacilli, supporting the key importance of this signal transduction system, and indicating that the walH and walI genes were lost in the ancestor of Streptococcaceae, leading to their atypical 3 wal gene

  17. Classification of Textures Using Filter Based Local Feature Extraction

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    Bocekci Veysel Gokhan

    2016-01-01

    Full Text Available In this work local features are used in feature extraction process in image processing for textures. The local binary pattern feature extraction method from textures are introduced. Filtering is also used during the feature extraction process for getting discriminative features. To show the effectiveness of the algorithm before the extraction process, three different noise are added to both train and test images. Wiener filter and median filter are used to remove the noise from images. We evaluate the performance of the method with Naïve Bayesian classifier. We conduct the comparative analysis on benchmark dataset with different filtering and size. Our experiments demonstrate that feature extraction process combine with filtering give promising results on noisy images.

  18. Pretreatment 18F-FDG PET Textural Features in Locally Advanced Non-Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235.

    Science.gov (United States)

    Ohri, Nitin; Duan, Fenghai; Snyder, Bradley S; Wei, Bo; Machtay, Mitchell; Alavi, Abass; Siegel, Barry A; Johnson, Douglas W; Bradley, Jeffrey D; DeNittis, Albert; Werner-Wasik, Maria; El Naqa, Issam

    2016-06-01

    In a secondary analysis of American College of Radiology Imaging Network (ACRIN) 6668/RTOG 0235, high pretreatment metabolic tumor volume (MTV) on (18)F-FDG PET was found to be a poor prognostic factor for patients treated with chemoradiotherapy for locally advanced non-small cell lung cancer (NSCLC). Here we utilize the same dataset to explore whether heterogeneity metrics based on PET textural features can provide additional prognostic information. Patients with locally advanced NSCLC underwent (18)F-FDG PET prior to treatment. A gradient-based segmentation tool was used to contour each patient's primary tumor. MTV, maximum SUV, and 43 textural features were extracted for each tumor. To address overfitting and high collinearity among PET features, the least absolute shrinkage and selection operator (LASSO) method was applied to identify features that were independent predictors of overall survival (OS) after adjusting for MTV. Recursive binary partitioning in a conditional inference framework was utilized to identify optimal thresholds. Kaplan-Meier curves and log-rank testing were used to compare outcomes among patient groups. Two hundred one patients met inclusion criteria. The LASSO procedure identified 1 textural feature (SumMean) as an independent predictor of OS. The optimal cutpoint for MTV was 93.3 cm(3), and the optimal SumMean cutpoint for tumors above 93.3 cm(3) was 0.018. This grouped patients into three categories: low tumor MTV (n = 155; median OS, 22.6 mo), high tumor MTV and high SumMean (n = 23; median OS, 20.0 mo), and high tumor MTV and low SumMean (n = 23; median OS, 6.2 mo; log-rank P textural PET features in the context of established prognostic factors. We have also identified a promising feature that may have prognostic value in locally advanced NSCLC patients with large tumors who are treated with chemoradiotherapy. Validation studies are warranted. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

  19. Document localization algorithms based on feature points and straight lines

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    Skoryukina, Natalya; Shemiakina, Julia; Arlazarov, Vladimir L.; Faradjev, Igor

    2018-04-01

    The important part of the system of a planar rectangular object analysis is the localization: the estimation of projective transform from template image of an object to its photograph. The system also includes such subsystems as the selection and recognition of text fields, the usage of contexts etc. In this paper three localization algorithms are described. All algorithms use feature points and two of them also analyze near-horizontal and near- vertical lines on the photograph. The algorithms and their combinations are tested on a dataset of real document photographs. Also the method of localization quality estimation is proposed that allows configuring the localization subsystem independently of the other subsystems quality.

  20. Local Environment Sensitivity of the Cu K-Edge XANES Features in Cu-SSZ-13: Analysis from First-Principles.

    Science.gov (United States)

    Zhang, Renqin; McEwen, Jean-Sabin

    2018-05-22

    Cu K-edge X-ray absorption near-edge spectra (XANES) have been widely used to study the properties of Cu-SSZ-13. In this Letter, the sensitivity of the XANES features to the local environment for a Cu + cation with a linear configuration and a Cu 2+ cation with a square-linear configuration in Cu-SSZ-13 is reported. When a Cu + cation is bonded to H 2 O or NH 3 in a linear configuration, the XANES has a strong peak at around 8983 eV. The intensity of this peak decreases as the linear configuration is broken. As for the Cu 2+ cations in a square-planar configuration with a coordination number of 4, two peaks at around 8986 and 8993 eV are found. An intensity decrease for both peaks at around 8986 and 8993 eV is found in an NH 3 _4_Z 2 Cu model as the N-Cu-N angle changes from 180 to 100°. We correlate these features to the variation of the 4p state by PDOS analysis. In addition, the feature peaks for both the Cu + cation and Cu 2+ cation do not show a dependence on the Cu-N bond length. We further show that the feature peaks also change when the coordination number of the Cu cation is varied, while these feature peaks are independent of the zeolite topology. These findings help elucidate the experimental XANES features at an atomic and an electronic level.

  1. Recognition of handwritten characters using local gradient feature descriptors

    NARCIS (Netherlands)

    Surinta, Olarik; Karaaba, Mahir F.; Schomaker, Lambert R.B.; Wiering, Marco A.

    2015-01-01

    Abstract In this paper we propose to use local gradient feature descriptors, namely the scale invariant feature transform keypoint descriptor and the histogram of oriented gradients, for handwritten character recognition. The local gradient feature descriptors are used to extract feature vectors

  2. LOCAL TREATMENT FEATURES OF PHARYNGITIS IN CHILDREN

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    E. А. Lebedinskaya

    2014-01-01

    Full Text Available This paper reviews causes and treatment methods for inflammatory forms of pharynx pathology in children. It is shown that the primary cause of pharyngitis pathogenesis is the dysfunction of immunogenic resistance of pharyngeal mucous membrane. It is mucosal immune system that blocks viral and bacterial infections. Taking into account functional principles of mucosal immune system allows creating a personal programme of immunoreabilitation based on physiological and complex principles. At the same time in pediatric practice not only drug efficacy, but also its safety, ease of use and flavor become increasingly important. In this regard possibilities and advantages of locally applied antiseptics for acute pharyngitis treatment in children are considered.

  3. RESEARCH ON FEATURE POINTS EXTRACTION METHOD FOR BINARY MULTISCALE AND ROTATION INVARIANT LOCAL FEATURE DESCRIPTOR

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    Hongwei Ying

    2014-08-01

    Full Text Available An extreme point of scale space extraction method for binary multiscale and rotation invariant local feature descriptor is studied in this paper in order to obtain a robust and fast method for local image feature descriptor. Classic local feature description algorithms often select neighborhood information of feature points which are extremes of image scale space, obtained by constructing the image pyramid using certain signal transform method. But build the image pyramid always consumes a large amount of computing and storage resources, is not conducive to the actual applications development. This paper presents a dual multiscale FAST algorithm, it does not need to build the image pyramid, but can extract feature points of scale extreme quickly. Feature points extracted by proposed method have the characteristic of multiscale and rotation Invariant and are fit to construct the local feature descriptor.

  4. Mammographic feature enhancement by multiscale analysis

    International Nuclear Information System (INIS)

    Laine, A.F.; Schuler, S.; Fan, J.; Huda, W.

    1994-01-01

    This paper introduces a novel approach for accomplishing mammographic feature analysis by overcomplete multiresolution representations. The authors show that efficient representations may be identified within a continuum of scale-space and used to enhance features of importance to mammography. Methods of contrast enhancement are described based on three overcomplete multiscale representations: (1) the dyadic wavelet transform (separable), (2) the var-phi-transform (nonseparable, nonorthogonal), and (3) the hexagonal wavelet transform (nonseparable). Multiscale edges identified within distinct levels of transform space provide local support for image enhancement. Mammograms are reconstructed from wavelet coefficients modified at one or more levels by local and global nonlinear operators. In each case, edges and gain parameters are identified adaptively by a measure of energy within each level of scale-space. The authors show quantitatively that transform coefficients, modified by adaptive nonlinear operators, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. The results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. The authors demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology, they can improve chances of early detection while requiring less time to evaluate mammograms for most patients

  5. Biometric feature extraction using local fractal auto-correlation

    International Nuclear Information System (INIS)

    Chen Xi; Zhang Jia-Shu

    2014-01-01

    Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme: (i) using two-dimensional Gabor filter to extract the texture features of biometric images; (ii) calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and (iii) linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach. (condensed matter: structural, mechanical, and thermal properties)

  6. Boosting instance prototypes to detect local dermoscopic features.

    Science.gov (United States)

    Situ, Ning; Yuan, Xiaojing; Zouridakis, George

    2010-01-01

    Local dermoscopic features are useful in many dermoscopic criteria for skin cancer detection. We address the problem of detecting local dermoscopic features from epiluminescence (ELM) microscopy skin lesion images. We formulate the recognition of local dermoscopic features as a multi-instance learning (MIL) problem. We employ the method of diverse density (DD) and evidence confidence (EC) function to convert MIL to a single-instance learning (SIL) problem. We apply Adaboost to improve the classification performance with support vector machines (SVMs) as the base classifier. We also propose to boost the selection of instance prototypes through changing the data weights in the DD function. We validate the methods on detecting ten local dermoscopic features from a dataset with 360 images. We compare the performance of the MIL approach, its boosting version, and a baseline method without using MIL. Our results show that boosting can provide performance improvement compared to the other two methods.

  7. Multiscale wavelet representations for mammographic feature analysis

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    Laine, Andrew F.; Song, Shuwu

    1992-12-01

    This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).

  8. Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

    Science.gov (United States)

    Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

    2013-04-01

    In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

  9. Local features with large spiky non-Gaussianities during inflation

    International Nuclear Information System (INIS)

    Abolhasani, Ali Akbar; Firouzjahi, Hassan; Khosravi, Shahram; Sasaki, Misao

    2012-01-01

    We provide a dynamical mechanism to generate localized features during inflation. The local feature is due to a sharp waterfall phase transition which is coupled to the inflaton field. The key effect is the contributions of waterfall quantum fluctuations which induce a sharp peak on the curvature perturbation which can be as large as the background curvature perturbation from inflaton field. Due to non-Gaussian nature of waterfall quantum fluctuations a large spike non-Gaussianity is produced which is narrowly peaked at modes which leave the Hubble radius at the time of phase transition. The large localized peaks in power spectrum and bispectrum can have interesting consequences on CMB anisotropies

  10. Local Feature Learning for Face Recognition under Varying Poses

    DEFF Research Database (Denmark)

    Duan, Xiaodong; Tan, Zheng-Hua

    2015-01-01

    In this paper, we present a local feature learning method for face recognition to deal with varying poses. As opposed to the commonly used approaches of recovering frontal face images from profile views, the proposed method extracts the subject related part from a local feature by removing the pose...... related part in it on the basis of a pose feature. The method has a closed-form solution, hence being time efficient. For performance evaluation, cross pose face recognition experiments are conducted on two public face recognition databases FERET and FEI. The proposed method shows a significant...... recognition improvement under varying poses over general local feature approaches and outperforms or is comparable with related state-of-the-art pose invariant face recognition approaches. Copyright ©2015 by IEEE....

  11. Prediction of Chemoresistance in Women Undergoing Neo-Adjuvant Chemotherapy for Locally Advanced Breast Cancer: Volumetric Analysis of First-Order Textural Features Extracted from Multiparametric MRI.

    Science.gov (United States)

    Panzeri, M M; Losio, C; Della Corte, A; Venturini, E; Ambrosi, A; Panizza, P; De Cobelli, F

    2018-01-01

    To assess correlations between volumetric first-order texture parameters on baseline MRI and pathological response after neoadjuvant chemotherapy (NAC) for locally advanced breast cancer (BC). 69 patients with locally advanced BC candidate to neoadjuvant chemotherapy underwent MRI within 4 weeks from the start of therapeutic regimen. T2, DWI, and DCE sequences were analyzed and maps were generated for Apparent Diffusion Coefficient (ADC), T2 signal intensity, and the following dynamic parameters: k -trans, peak enhancement, area under curve (AUC), time to maximal enhancement (TME), wash-in rate, and washout rate. Volumetric analysis of these parameters was performed, yielding a histogram analysis including first-order texture kinetics (percentiles, maximum value, minimum value, range, standard deviation, mean, median, mode, skewness, and kurtosis). Finally, correlations between these values and response to NAC (evaluated on the surgical specimen according to RECIST 1.1 criteria) were assessed. Out of 69 tumors, 33 (47.8%) achieved complete pathological response, 26 (37.7%) partial response, and 10 (14.5%) no response. Higher levels of AUCmax ( p value = 0.0338), AUCrange ( p value = 0.0311), and TME 75 ( p value = 0.0452) and lower levels of washout 10 ( p value = 0.0417), washout 20 ( p value = 0.0138), washout 25 ( p value = 0.0114), and washout 30 ( p value = 0.05) were predictive of noncomplete response. Histogram-derived texture analysis of MRI images allows finding quantitative parameters predictive of nonresponse to NAC in women affected by locally advanced BC.

  12. Prediction of Chemoresistance in Women Undergoing Neo-Adjuvant Chemotherapy for Locally Advanced Breast Cancer: Volumetric Analysis of First-Order Textural Features Extracted from Multiparametric MRI

    Directory of Open Access Journals (Sweden)

    M. M. Panzeri

    2018-01-01

    Full Text Available Purpose. To assess correlations between volumetric first-order texture parameters on baseline MRI and pathological response after neoadjuvant chemotherapy (NAC for locally advanced breast cancer (BC. Materials and Methods. 69 patients with locally advanced BC candidate to neoadjuvant chemotherapy underwent MRI within 4 weeks from the start of therapeutic regimen. T2, DWI, and DCE sequences were analyzed and maps were generated for Apparent Diffusion Coefficient (ADC, T2 signal intensity, and the following dynamic parameters: k-trans, peak enhancement, area under curve (AUC, time to maximal enhancement (TME, wash-in rate, and washout rate. Volumetric analysis of these parameters was performed, yielding a histogram analysis including first-order texture kinetics (percentiles, maximum value, minimum value, range, standard deviation, mean, median, mode, skewness, and kurtosis. Finally, correlations between these values and response to NAC (evaluated on the surgical specimen according to RECIST 1.1 criteria were assessed. Results. Out of 69 tumors, 33 (47.8% achieved complete pathological response, 26 (37.7% partial response, and 10 (14.5% no response. Higher levels of AUCmax (p value = 0.0338, AUCrange (p value = 0.0311, and TME75 (p value = 0.0452 and lower levels of washout10 (p value = 0.0417, washout20 (p value = 0.0138, washout25 (p value = 0.0114, and washout30 (p value = 0.05 were predictive of noncomplete response. Conclusion. Histogram-derived texture analysis of MRI images allows finding quantitative parameters predictive of nonresponse to NAC in women affected by locally advanced BC.

  13. Iris recognition using possibilistic fuzzy matching on local features.

    Science.gov (United States)

    Tsai, Chung-Chih; Lin, Heng-Yi; Taur, Jinshiuh; Tao, Chin-Wang

    2012-02-01

    In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.

  14. Adapting Local Features for Face Detection in Thermal Image

    Directory of Open Access Journals (Sweden)

    Chao Ma

    2017-11-01

    Full Text Available A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses. We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

  15. The contribution of local features to familiarity judgments in music.

    Science.gov (United States)

    Bigand, Emmanuel; Gérard, Yannick; Molin, Paul

    2009-07-01

    The contributions of local and global features to object identification depend upon the context. For example, while local features play an essential role in identification of words and objects, the global features are more influential in face recognition. In order to evaluate the respective strengths of local and global features for face recognition, researchers usually ask participants to recognize human faces (famous or learned) in normal and scrambled pictures. In this paper, we address a similar issue in music. We present the results of an experiment in which musically untrained participants were asked to differentiate famous from unknown musical excerpts that were presented in normal or scrambled ways. Manipulating the size of the temporal window on which the scrambling procedure was applied allowed us to evaluate the minimal length of time necessary for participants to make a familiarity judgment. Quite surprisingly, the minimum duration for differentiation of famous from unknown pieces is extremely short. This finding highlights the contribution of very local features to music memory.

  16. Face detection and facial feature localization using notch based templates

    International Nuclear Information System (INIS)

    Qayyum, U.

    2007-01-01

    We present a real time detection off aces from the video with facial feature localization as well as the algorithm capable of differentiating between the face/non-face patterns. The need of face detection and facial feature localization arises in various application of computer vision, so a lot of research is dedicated to come up with a real time solution. The algorithm should remain simple to perform real time whereas it should not compromise on the challenges encountered during the detection and localization phase, keeping simplicity and all challenges i.e. algorithm invariant to scale, translation, and (+-45) rotation transformations. The proposed system contains two parts. Visual guidance and face/non-face classification. The visual guidance phase uses the fusion of motion and color cues to classify skin color. Morphological operation with union-structure component labeling algorithm extracts contiguous regions. Scale normalization is applied by nearest neighbor interpolation method to avoid the effect of different scales. Using the aspect ratio of width and height size. Region of Interest (ROI) is obtained and then passed to face/non-face classifier. Notch (Gaussian) based templates/ filters are used to find circular darker regions in ROI. The classified face region is handed over to facial feature localization phase, which uses YCbCr eyes/lips mask for face feature localization. The empirical results show an accuracy of 90% for five different videos with 1000 face/non-face patterns and processing rate of proposed algorithm is 15 frames/sec. (author)

  17. Refined estimates of local recurrence risks by DCIS score adjusting for clinicopathological features: a combined analysis of ECOG-ACRIN E5194 and Ontario DCIS cohort studies.

    Science.gov (United States)

    Rakovitch, E; Gray, R; Baehner, F L; Sutradhar, R; Crager, M; Gu, S; Nofech-Mozes, S; Badve, S S; Hanna, W; Hughes, L L; Wood, W C; Davidson, N E; Paszat, L; Shak, S; Sparano, J A; Solin, L J

    2018-06-01

    Better tools are needed to estimate local recurrence (LR) risk after breast-conserving surgery (BCS) for DCIS. The DCIS score (DS) was validated as a predictor of LR in E5194 and Ontario DCIS cohort (ODC) after BCS. We combined data from E5194 and ODC adjusting for clinicopathological factors to provide refined estimates of the 10-year risk of LR after treatment by BCS alone. Data from E5194 and ODC were combined. Patients with positive margins or multifocality were excluded. Identical Cox regression models were fit for each study. Patient-specific meta-analysis was used to calculate precision-weighted estimates of 10-year LR risk by DS, age, tumor size and year of diagnosis. The combined cohort includes 773 patients. The DS and age at diagnosis, tumor size and year of diagnosis provided independent prognostic information on the 10-year LR risk (p ≤ 0.009). Hazard ratios from E5194 and ODC cohorts were similar for the DS (2.48, 1.95 per 50 units), tumor size ≤ 1 versus  > 1-2.5 cm (1.45, 1.47), age ≥ 50 versus  15%) 10-year LR risk after BCS alone compared to utilization of DS alone or clinicopathological factors alone. The combined analysis provides refined estimates of 10-year LR risk after BCS for DCIS. Adding information on tumor size and age at diagnosis to the DS adjusting for year of diagnosis provides improved LR risk estimates to guide treatment decision making.

  18. Attending to global versus local stimulus features modulates neural processing of low versus high spatial frequencies: An analysis with event-related brain potentials.

    Directory of Open Access Journals (Sweden)

    Anastasia V Flevaris

    2014-04-01

    Full Text Available Spatial frequency (SF selection has long been recognized to play a role in global and local processing, though the nature of the relationship between SF processing and global/local perception is debated. Previous studies have shown that attention to relatively lower SFs facilitates global perception, and that attention to relatively higher SFs facilitates local perception. Here we recorded event-related brain potentials (ERPs to investigate whether processing of low versus high SFs is modulated automatically during global and local perception, and to examine the time course of any such effects. Participants compared bilaterally presented hierarchical letter stimuli and attended to either the global or local levels. Irrelevant SF grating probes flashed at the center of the display 200 ms after the onset of the hierarchical letter stimuli could either be low or high in SF. It was found that ERPs elicited by the SF grating probes differed as a function of attended level (global vs. local. ERPs elicited by low SF grating probes were more positive in the interval 196-236 ms during global than local attention, and this difference was greater over the right occipital scalp. In contrast, ERPs elicited by the high SF gratings were more positive in the interval 250-290 ms during local than global attention, and this difference was bilaterally distributed over the occipital scalp. These results indicate that directing attention to global versus local levels of a hierarchical display facilitates automatic perceptual processing of low versus high SFs, respectively, and this facilitation is not limited to the locations occupied by the hierarchical display. The relatively long latency of these attention-related ERP modulations suggests that initial (early SF processing is not affected by attention to hierarchical level, lending support to theories positing a higher level mechanism to underlie the relationship between SF processing and global versus local

  19. Palm Vein Verification Using Multiple Features and Locality Preserving Projections

    Directory of Open Access Journals (Sweden)

    Ali Mohsin Al-juboori

    2014-01-01

    Full Text Available Biometrics is defined as identifying people by their physiological characteristic, such as iris pattern, fingerprint, and face, or by some aspects of their behavior, such as voice, signature, and gesture. Considerable attention has been drawn on these issues during the last several decades. And many biometric systems for commercial applications have been successfully developed. Recently, the vein pattern biometric becomes increasingly attractive for its uniqueness, stability, and noninvasiveness. A vein pattern is the physical distribution structure of the blood vessels underneath a person’s skin. The palm vein pattern is very ganglion and it shows a huge number of vessels. The attitude of the palm vein vessels stays in the same location for the whole life and its pattern is definitely unique. In our work, the matching filter method is proposed for the palm vein image enhancement. New palm vein features extraction methods, global feature extracted based on wavelet coefficients and locality preserving projections (WLPP, and local feature based on local binary pattern variance and locality preserving projections (LBPV_LPP have been proposed. Finally, the nearest neighbour matching method has been proposed that verified the test palm vein images. The experimental result shows that the EER to the proposed method is 0.1378%.

  20. Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

    Science.gov (United States)

    Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

    2014-08-01

    Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature

  1. Probabilistic Slow Features for Behavior Analysis

    NARCIS (Netherlands)

    Zafeiriou, Lazaros; Nicolaou, Mihalis A.; Zafeiriou, Stefanos; Nikitidis, Symeon; Pantic, Maja

    A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis is the so-called slow feature analysis (SFA). SFA is a deterministic component analysis technique for multidimensional sequences that, by minimizing the variance of the first-order time derivative

  2. Learning slow features for behavior analysis

    NARCIS (Netherlands)

    Zafeiriou, Lazaros; Nicolaou, Mihalis A.; Zafeiriou, Stefanos; Nikitids, Symeon; Pantic, Maja

    2013-01-01

    A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative

  3. Template match using local feature with view invariance

    Science.gov (United States)

    Lu, Cen; Zhou, Gang

    2013-10-01

    Matching the template image in the target image is the fundamental task in the field of computer vision. Aiming at the deficiency in the traditional image matching methods and inaccurate matching in scene image with rotation, illumination and view changing, a novel matching algorithm using local features are proposed in this paper. The local histograms of the edge pixels (LHoE) are extracted as the invariable feature to resist view and brightness changing. The merits of the LHoE is that the edge points have been little affected with view changing, and the LHoE can resist not only illumination variance but also the polution of noise. For the process of matching are excuded only on the edge points, the computation burden are highly reduced. Additionally, our approach is conceptually simple, easy to implement and do not need the training phase. The view changing can be considered as the combination of rotation, illumination and shear transformation. Experimental results on simulated and real data demonstrated that the proposed approach is superior to NCC(Normalized cross-correlation) and Histogram-based methods with view changing.

  4. An age estimation method using brain local features for T1-weighted images.

    Science.gov (United States)

    Kondo, Chihiro; Ito, Koichi; Kai Wu; Sato, Kazunori; Taki, Yasuyuki; Fukuda, Hiroshi; Aoki, Takafumi

    2015-08-01

    Previous statistical analysis studies using large-scale brain magnetic resonance (MR) image databases have examined that brain tissues have age-related morphological changes. This fact indicates that one can estimate the age of a subject from his/her brain MR image by evaluating morphological changes with healthy aging. This paper proposes an age estimation method using local features extracted from T1-weighted MR images. The brain local features are defined by volumes of brain tissues parcellated into local regions defined by the automated anatomical labeling atlas. The proposed method selects optimal local regions to improve the performance of age estimation. We evaluate performance of the proposed method using 1,146 T1-weighted images from a Japanese MR image database. We also discuss the medical implication of selected optimal local regions.

  5. Features of Local Murders and Murderers (Latvia Case Study

    Directory of Open Access Journals (Sweden)

    Aron Shneyer

    2012-11-01

    Full Text Available This article examines the subject of collaborationism. The author for the first time examines the materials of the trial, held in Riga in October 1965. Some documents of the criminal trials against former SS guards, held in the USSR in 1945-1965 are also introduced for the scientific use for the first time. Special attention is attached to the collaborators' personal participation in single, localized murders.The circumstances and sites of these murders are usually little-known and remain beyond the scope of scientific research. This is explained by the fact that, unlike Babi Yar or Zmievskaya Balka, the number of victims of these localized murders is incomparably small and therefore, could never acquire a symbolic meaning and be used for propaganda purposes. The author draws attention to certain behavioral features of some of the crimes participants. In the author's view, the politicized claims of some researchers regarding the prominent role of certain ethnicities in Nazi crimes are debatable. The article emphasizes that the ethnic composition of local Nazi collaborators within the occupied territories depended on the ethnic composition of the population of those territories. Crimes and criminals transcend national boundaries. As one of the proofs of his thesis, the author cites the ethnic composition of the SS guards in the Stutthof concentration camp and in the Trawniki training camp.This article may stimulate researchers' interest to the problem of localized murders, including the possibilities to determine crime sites, number of victims and their names, the crime participants and perpetuate the memory of the dead.

  6. Exposing region duplication through local geometrical color invariant features

    Science.gov (United States)

    Gong, Jiachang; Guo, Jichang

    2015-05-01

    Many advanced image-processing softwares are available for tampering images. How to determine the authenticity of an image has become an urgent problem. Copy-move is one of the most common image forgery operations. Many methods have been proposed for copy-move forgery detection (CMFD). However, most of these methods are designed for grayscale images without any color information used. They are usually not suitable when the duplicated regions have little structure or have undergone various transforms. We propose a CMFD method using local geometrical color invariant features to detect duplicated regions. The method starts by calculating the color gradient of the inspected image. Then, we directly take the color gradient as the input for scale invariant features transform (SIFT) to extract color-SIFT descriptors. Finally, keypoints are matched and clustered before their geometrical relationship is estimated to expose the duplicated regions. We evaluate the detection performance and computational complexity of the proposed method together with several popular CMFD methods on a public database. Experimental results demonstrate the efficacy of the proposed method in detecting duplicated regions with various transforms and poor structure.

  7. CMB anomalies and the effects of local features of the inflaton potential

    Energy Technology Data Exchange (ETDEWEB)

    Cadavid, Alexander Gallego [Kyoto University, Yukawa Institute for Theoretical Physics, Kyoto (Japan); ICRANet, Pescara (Italy); Universidad de Antioquia, Instituto de Fisica, Medellin (Colombia); Romano, Antonio Enea [Kyoto University, Yukawa Institute for Theoretical Physics, Kyoto (Japan); University of Torino, Department of Physics, Turin (Italy); Universidad de Antioquia, Instituto de Fisica, Medellin (Colombia); Gariazzo, Stefano [University of Torino, Department of Physics, Turin (Italy); INFN, Sezione di Torino, Turin (Italy); Instituto de Fisica Corpuscular (CSIC-Universitat de Valencia), Paterna, Valencia (Spain)

    2017-04-15

    Recent analysis of the WMAP and Planck data have shown the presence of a dip and a bump in the spectrum of primordial perturbations at the scales k = 0.002 Mpc{sup -1}, respectively. We analyze for the first time the effects of a local feature in the inflaton potential to explain the observed deviations from scale invariance in the primordial spectrum. We perform a best-fit analysis of the cosmic microwave background (CMB) radiation temperature and polarization data. The effects of the features can improve the agreement with observational data respect to the featureless model. The best-fit local feature affects the primordial curvature spectrum mainly in the region of the bump, leaving the spectrum unaffected on other scales. (orig.)

  8. a Performance Comparison of Feature Detectors for Planetary Rover Mapping and Localization

    Science.gov (United States)

    Wan, W.; Peng, M.; Xing, Y.; Wang, Y.; Liu, Z.; Di, K.; Teng, B.; Mao, X.; Zhao, Q.; Xin, X.; Jia, M.

    2017-07-01

    Feature detection and matching are key techniques in computer vision and robotics, and have been successfully implemented in many fields. So far there is no performance comparison of feature detectors and matching methods for planetary mapping and rover localization using rover stereo images. In this research, we present a comprehensive evaluation and comparison of six feature detectors, including Moravec, Förstner, Harris, FAST, SIFT and SURF, aiming for optimal implementation of feature-based matching in planetary surface environment. To facilitate quantitative analysis, a series of evaluation criteria, including distribution evenness of matched points, coverage of detected points, and feature matching accuracy, are developed in the research. In order to perform exhaustive evaluation, stereo images, simulated under different baseline, pitch angle, and interval of adjacent rover locations, are taken as experimental data source. The comparison results show that SIFT offers the best overall performance, especially it is less sensitive to changes of image taken at adjacent locations.

  9. A PERFORMANCE COMPARISON OF FEATURE DETECTORS FOR PLANETARY ROVER MAPPING AND LOCALIZATION

    Directory of Open Access Journals (Sweden)

    W. Wan

    2017-07-01

    Full Text Available Feature detection and matching are key techniques in computer vision and robotics, and have been successfully implemented in many fields. So far there is no performance comparison of feature detectors and matching methods for planetary mapping and rover localization using rover stereo images. In this research, we present a comprehensive evaluation and comparison of six feature detectors, including Moravec, Förstner, Harris, FAST, SIFT and SURF, aiming for optimal implementation of feature-based matching in planetary surface environment. To facilitate quantitative analysis, a series of evaluation criteria, including distribution evenness of matched points, coverage of detected points, and feature matching accuracy, are developed in the research. In order to perform exhaustive evaluation, stereo images, simulated under different baseline, pitch angle, and interval of adjacent rover locations, are taken as experimental data source. The comparison results show that SIFT offers the best overall performance, especially it is less sensitive to changes of image taken at adjacent locations.

  10. Critical feature analysis of a radiotherapy machine

    International Nuclear Information System (INIS)

    Rae, Andrew; Jackson, Daniel; Ramanan, Prasad; Flanz, Jay; Leyman, Didier

    2005-01-01

    The software implementation of the emergency shutdown feature in a major radiotherapy system was analyzed, using a directed form of code review based on module dependences. Dependences between modules are labelled by particular assumptions; this allows one to trace through the code, and identify those fragments responsible for critical features. An 'assumption tree' is constructed in parallel, showing the assumptions which each module makes about others. The root of the assumption tree is the critical feature of interest, and its leaves represent assumptions which, if not valid, might cause the critical feature to fail. The analysis revealed some unexpected assumptions that motivated improvements to the code

  11. Cytokine profiles in localized scleroderma and relationship to clinical features.

    Science.gov (United States)

    Kurzinski, Katherine; Torok, Kathryn S

    2011-08-01

    Localized scleroderma (LS) is a disfiguring autoimmune disease of the skin and underlying tissue that mainly affects the pediatric population. Inflammation of the tissue leads to fibrosis and atrophy, causing physical and psychological disability that can continue throughout childhood into adulthood. Available therapies for LS have had variable effects and are associated with morbidity themselves. A better understanding of the pathophysiology of LS, especially during the active inflammatory phase, would lead to more directed and efficacious therapies. As in systemic sclerosis (SSc), the other form of scleroderma, T-helper (Th) cells and their associated cytokines have been suggested to contribute significantly to the pathophysiology of LS supported by the presence of cytokines from these lineages in the sera and tissue of LS patients. It is postulated that the imbalance between Th1/Th2/Th17 cell subsets drives inflammation in the early stages of disease (Th1 and Th17 predominant) and fibrosis in the later stages of scleroderma (Th2 predominant). We review the available experimental data regarding cytokines in LS and compare them to available clinical disease severity and activity features. This provides the platform to launch further investigations into the role of select cytokines in the pathogenesis of LS and to provide directed therapeutic options in the future. Published by Elsevier Ltd.

  12. Geomagnetic secular variation in India-regional and local features

    International Nuclear Information System (INIS)

    Srivastava, B.J.; Abbas, H.

    1977-01-01

    A study of the secular variation in the geomagnetic elements H, Z, F and D at Colaba (Bombay)-Alibag for the period 1848-1973, has been made. Fifth degree polynomials are fitted to the data of annual mean values of H, Z and F, and third degree to D, and the residuals discussed. The trends are also examined at the six Indian observatories using the data for 1960-1974. The increasing trend of Z at Alibag is found to decrease from about 1937, while that of H and F from 1965 at 20-30 nT/year, it being of the same order at Sabhawala and Hyderabad but smaller at the equatorial stations, particularly for Z component. The westward annual change in D swings eastward again around 1965 at all the Indian stations. This reversal of the secular variation trend in India after 1965 emerges as an important regional feature connected with a southward migration of the dip equator in India from 1968. The secular change in D at Alibag (+0.4'/year) is somewhat anomalous in the sense that it is reduced as compared to Hyderabad and other stations (+1.6'/year), probably due to the local magnetic anomaly of the Deccan lavas, and calls for detailed investigations. (auth.)

  13. ECG Identification System Using Neural Network with Global and Local Features

    Science.gov (United States)

    Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

    2016-01-01

    This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

  14. The analysis of image feature robustness using cometcloud

    Directory of Open Access Journals (Sweden)

    Xin Qi

    2012-01-01

    Full Text Available The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

  15. Facial expression recognition under partial occlusion based on fusion of global and local features

    Science.gov (United States)

    Wang, Xiaohua; Xia, Chen; Hu, Min; Ren, Fuji

    2018-04-01

    Facial expression recognition under partial occlusion is a challenging research. This paper proposes a novel framework for facial expression recognition under occlusion by fusing the global and local features. In global aspect, first, information entropy are employed to locate the occluded region. Second, principal Component Analysis (PCA) method is adopted to reconstruct the occlusion region of image. After that, a replace strategy is applied to reconstruct image by replacing the occluded region with the corresponding region of the best matched image in training set, Pyramid Weber Local Descriptor (PWLD) feature is then extracted. At last, the outputs of SVM are fitted to the probabilities of the target class by using sigmoid function. For the local aspect, an overlapping block-based method is adopted to extract WLD features, and each block is weighted adaptively by information entropy, Chi-square distance and similar block summation methods are then applied to obtain the probabilities which emotion belongs to. Finally, fusion at the decision level is employed for the data fusion of the global and local features based on Dempster-Shafer theory of evidence. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the effectiveness and fault tolerance of this method.

  16. Coding Local and Global Binary Visual Features Extracted From Video Sequences

    Science.gov (United States)

    Baroffio, Luca; Canclini, Antonio; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

    2015-11-01

    Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks, while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW) model. Several applications, including for example visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget, while attaining a target level of efficiency. In this paper we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the Compress-Then-Analyze (CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: homography estimation and content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with CTA, especially in bandwidth limited scenarios.

  17. Coding Local and Global Binary Visual Features Extracted From Video Sequences.

    Science.gov (United States)

    Baroffio, Luca; Canclini, Antonio; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

    2015-11-01

    Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the bag-of-visual word model. Several applications, including, for example, visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget while attaining a target level of efficiency. In this paper, we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can conveniently be adopted to support the analyze-then-compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs the visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the compress-then-analyze (CTA) paradigm. In this paper, we experimentally compare the ATC and the CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: 1) homography estimation and 2) content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with the CTA, especially in bandwidth limited scenarios.

  18. Core analysis: new features and applications

    International Nuclear Information System (INIS)

    Edenius, M.; Kurcyusz, E.; Molina, D.; Wiksell, G.

    1995-01-01

    Today, core analysis may be performed with sophisticated software capable of both steady state and transient analysis using a common methodology for BWRs and PWRs. General trends in core analysis software development are: improved accuracy, automated engineering functions; three-dimensional transient capability; graphical user interfaces. As a demonstration of such software, new features of Studsvik-CMS (Core management system) and examples of applications are discussed in this article. 2 figs., 8 refs

  19. Slow feature analysis: unsupervised learning of invariances.

    Science.gov (United States)

    Wiskott, Laurenz; Sejnowski, Terrence J

    2002-04-01

    Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

  20. Adaptive weighted local textural features for illumination, expression, and occlusion invariant face recognition

    Science.gov (United States)

    Cui, Chen; Asari, Vijayan K.

    2014-03-01

    Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image

  1. Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring

    Directory of Open Access Journals (Sweden)

    Ramakrishnan Mukundan

    2018-02-01

    Full Text Available This paper presents novel feature descriptors and classification algorithms for the automated scoring of HER2 in Whole Slide Images (WSI of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC-stained slides. The first descriptor is called a characteristic curve, which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is a local binary pattern (LBP feature curve, which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and a comparative analysis.

  2. Local appearance features for robust MRI brain structure segmentation across scanning protocols

    DEFF Research Database (Denmark)

    Achterberg, H.C.; Poot, Dirk H. J.; van der Lijn, Fedde

    2013-01-01

    Segmentation of brain structures in magnetic resonance images is an important task in neuro image analysis. Several papers on this topic have shown the benefit of supervised classification based on local appearance features, often combined with atlas-based approaches. These methods require...... a representative annotated training set and therefore often do not perform well if the target image is acquired on a different scanner or with a different acquisition protocol than the training images. Assuming that the appearance of the brain is determined by the underlying brain tissue distribution...... with substantially different imaging protocols and on different scanners. While a combination of conventional appearance features trained on data from a different scanner with multiatlas segmentation performed poorly with an average Dice overlap of 0.698, the local appearance model based on the new acquisition...

  3. Joint and collaborative representation with local Volterra kernels convolution feature for face recognition

    Science.gov (United States)

    Feng, Guang; Li, Hengjian; Dong, Jiwen; Chen, Xi; Yang, Huiru

    2018-04-01

    In this paper, we proposed a joint and collaborative representation with Volterra kernel convolution feature (JCRVK) for face recognition. Firstly, the candidate face images are divided into sub-blocks in the equal size. The blocks are extracted feature using the two-dimensional Voltera kernels discriminant analysis, which can better capture the discrimination information from the different faces. Next, the proposed joint and collaborative representation is employed to optimize and classify the local Volterra kernels features (JCR-VK) individually. JCR-VK is very efficiently for its implementation only depending on matrix multiplication. Finally, recognition is completed by using the majority voting principle. Extensive experiments on the Extended Yale B and AR face databases are conducted, and the results show that the proposed approach can outperform other recently presented similar dictionary algorithms on recognition accuracy.

  4. Universal features of localized eigenstates in disordered systems

    International Nuclear Information System (INIS)

    Ludlam, J J; Taraskin, S N; Elliott, S R; Drabold, D A

    2005-01-01

    Localization-delocalization transitions occur in problems ranging from semiconductor-device physics to propagation of disease in plants and viruses on the internet. Here, we report calculations of localized electronic and vibrational eigenstates for remarkably different, mostly realistic, disordered systems and point out similar characteristics in the cases studied. We show in each case that the eigenstates may be decomposed into exponentially localized islands which may appear in many different eigenstates. In all cases, the decay length of the islands increases only modestly near the localization-delocalization transition; the eigenstates become extended primarily by proliferation (growth in number) of islands near the transition. Recently, microphotoluminescence experiments (Guillet et al 2003 Phys. Rev. B 68 045319) have imaged exciton states in disordered quantum wires, and these bear a strong qualitative resemblance to the island structure of eigenstates that we have studied theoretically. (letter to the editor)

  5. A Local Asynchronous Distributed Privacy Preserving Feature Selection Algorithm for Large Peer-to-Peer Networks

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used...

  6. Artificially intelligent recognition of Arabic speaker using voice print-based local features

    Science.gov (United States)

    Mahmood, Awais; Alsulaiman, Mansour; Muhammad, Ghulam; Akram, Sheeraz

    2016-11-01

    Local features for any pattern recognition system are based on the information extracted locally. In this paper, a local feature extraction technique was developed. This feature was extracted in the time-frequency plain by taking the moving average on the diagonal directions of the time-frequency plane. This feature captured the time-frequency events producing a unique pattern for each speaker that can be viewed as a voice print of the speaker. Hence, we referred to this technique as voice print-based local feature. The proposed feature was compared to other features including mel-frequency cepstral coefficient (MFCC) for speaker recognition using two different databases. One of the databases used in the comparison is a subset of an LDC database that consisted of two short sentences uttered by 182 speakers. The proposed feature attained 98.35% recognition rate compared to 96.7% for MFCC using the LDC subset.

  7. Cloning, chromosome localization and features of a novel human ...

    Indian Academy of Sciences (India)

    We report cloning and some features of a novel human gene, MATH2, which encodes a protein of 337 amino acid residues with a basic helix–loop–helix domain ... State Key Laboratory of Genetic Engineering, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai 200433, People's Republic of China ...

  8. Understanding Protein-Protein Interactions Using Local Structural Features

    DEFF Research Database (Denmark)

    Planas-Iglesias, Joan; Bonet, Jaume; García-García, Javier

    2013-01-01

    Protein-protein interactions (PPIs) play a relevant role among the different functions of a cell. Identifying the PPI network of a given organism (interactome) is useful to shed light on the key molecular mechanisms within a biological system. In this work, we show the role of structural features...... interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation...... to score the likelihood of the interaction between two proteins and to develop a method for the prediction of PPIs. We have tested our method on several sets with unbalanced ratios of interactions and non-interactions to simulate real conditions, obtaining accuracies higher than 25% in the most unfavorable...

  9. URBAN FEATURES AND ENERGY CONSUMPTION AT LOCAL LEVEL

    Directory of Open Access Journals (Sweden)

    Ali Soltani

    2012-12-01

    Full Text Available There has been a growing interest in discovering the human effects on the environment and energy consumption in recent decades. It is estimated that the share of energy consumed in transportation and housing systems are around 20 and 30 percent of total energy consumption respectively. Furthermore, the residential greenhouse emissions depend on urban form and structure. This paper explores the effects of urban features on residential energy consumption at neighborhood level using data collected through household questionnaire (n=140. Two residential districts in metropolitan Shiraz, south of Iran, were selected as case study areas. Different features of two areas were compared including building density, typology, housing location, parcel size, floor area and construction materials. Ordinary linear regression was used to discover the impact of explanatory variables on energy consumption. It was found that some physical variables such as parcel size, setback and number of floors played significant roles in explaining the variances exist in energy use level. The results can be used by governmental agencies to modify land use policies and subdivision rules in hope of saving energy and achieving a sustainable community.

  10. Uniform competency-based local feature extraction for remote sensing images

    Science.gov (United States)

    Sedaghat, Amin; Mohammadi, Nazila

    2018-01-01

    Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

  11. Nevada local government revenues analysis

    International Nuclear Information System (INIS)

    1988-06-01

    This report analyzes the major sources of revenue for Nevada local government for purposes of estimating the impacts associated with the siting of a nuclear waste repository at Yucca Mountain. Each major revenue source is analyzed separately to identify relationships between the economic or demographic base, the revenue base and the revenues generated. Trends and changes in the rates and/or base are highlighted. A model is developed for each component to allow impact estimation. This report is a companion to the report Nevada State Revenues Analysis

  12. Apollo 15 clastic materials and their relationship to local geologic features

    Science.gov (United States)

    Fruchter, J. S.; Stoeser, J. W.; Lindstrom, M. M.; Goles, G. G.

    1973-01-01

    Ninety sub-samples of Apollo 15 materials have been analyzed by instrumental neutron activation analysis techniques for as many as 21 elements. Soil and soil breccia compositions show considerable variation from station to station although at any given station the soils and soil breccias were compositionally very similar to one another. Mixing model calculations show that the station-to-station variations can be related to important local geologic features. These features include the Apennine Front, Hadley Rille and the ray from the craters Aristillus or Autolycus. Compositional similarities between soils and soil breccias at the Apollo 15 site indicate that the breccias and soils are related in some fundamental way, although the exact nature of this relationship is not yet fully understood.

  13. Localization of Outdoor Mobile Robots Using Curb Features in Urban Road Environments

    Directory of Open Access Journals (Sweden)

    Hyunsuk Lee

    2014-01-01

    Full Text Available Urban road environments that have pavement and curb are characterized as semistructured road environments. In semistructured road environments, the curb provides useful information for robot navigation. In this paper, we present a practical localization method for outdoor mobile robots using the curb features in semistructured road environments. The curb features are especially useful in urban environment, where the GPS failures take place frequently. A curb extraction is conducted on the basis of the Kernel Fisher Discriminant Analysis (KFDA to minimize false detection. We adopt the Extended Kalman Filter (EKF to combine the curb information with odometry and Differential Global Positioning System (DGPS. The uncertainty models for the sensors are quantitatively analyzed to provide a practical solution.

  14. Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

    Directory of Open Access Journals (Sweden)

    Lijun Wang

    2013-01-01

    Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

  15. Analysis of bone mellow density in adults of domestic local area using multi-detector computed tomography: Focus on correlation about eating habits, lifestyle, physical features and social characteristics

    International Nuclear Information System (INIS)

    Lee, Tae Hui; Kim, Tae Hyung; So, Woon Young; Lim, Hei Gyeom; Lim, Cheong Hwan; Park, Myeong Hwan; Cheon, Myung Ki

    2016-01-01

    This study analyzed the correlation between BMD (bone mineral density) value calculated in the MDCT(multidetector computed tomography) and lifestyle, physical features and social characteristics. From July 15 2015 to June 6 2016, we converted from HU (hounsfield unit) value measured by using MDCT to T-score for BMD of 141 patients (male: 63, female: 78) in W medical center. We measured the 2nd, 3rd and 4th lumbar spine and analyzed the correlation between gender differences in BMD and lifestyle, physical features and social characteristics. Statistical significance was validated using independent sample T test with one way Anova. Gender BMD was confirmed that a statistically significant difference (p<0.05). BMD values decreased with increasing age but for the statistically men, there was no significant difference from 20s to 50s, it only showed a significant difference in 20s and 60s (p<0.001). For the statistically women, there was no significant difference from 20s to 40s. but since 50s BMD was decreased rapidly, which showed a significant difference(p<0.001). women showed significant differences for the menstruation and menopause, childbirth, alcohol, cereals and greasy food in bone mineral density (p<0.05) but there were no significant differences in men. The bone mineral density values calculated by the MDCT and lifestyle, physical features and social characteristics correlation analysis method is considered to be used as a basis for estimating the state in BMD and osteoporosis management

  16. Analysis of bone mellow density in adults of domestic local area using multi-detector computed tomography: Focus on correlation about eating habits, lifestyle, physical features and social characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Tae Hui [Wonju Medical Center, Wonju (Korea, Republic of); Kim, Tae Hyung; So, Woon Young; Lim, Hei Gyeom [Kangwon National University Graduate School, Wonju (Korea, Republic of); Lim, Cheong Hwan [Hanseo University, Seosan (Korea, Republic of); Park, Myeong Hwan [Daegu Health College, Daegu (Korea, Republic of); Cheon, Myung Ki [Soongsil University, Seoul (Korea, Republic of)

    2016-12-15

    This study analyzed the correlation between BMD (bone mineral density) value calculated in the MDCT(multidetector computed tomography) and lifestyle, physical features and social characteristics. From July 15 2015 to June 6 2016, we converted from HU (hounsfield unit) value measured by using MDCT to T-score for BMD of 141 patients (male: 63, female: 78) in W medical center. We measured the 2nd, 3rd and 4th lumbar spine and analyzed the correlation between gender differences in BMD and lifestyle, physical features and social characteristics. Statistical significance was validated using independent sample T test with one way Anova. Gender BMD was confirmed that a statistically significant difference (p<0.05). BMD values decreased with increasing age but for the statistically men, there was no significant difference from 20s to 50s, it only showed a significant difference in 20s and 60s (p<0.001). For the statistically women, there was no significant difference from 20s to 40s. but since 50s BMD was decreased rapidly, which showed a significant difference(p<0.001). women showed significant differences for the menstruation and menopause, childbirth, alcohol, cereals and greasy food in bone mineral density (p<0.05) but there were no significant differences in men. The bone mineral density values calculated by the MDCT and lifestyle, physical features and social characteristics correlation analysis method is considered to be used as a basis for estimating the state in BMD and osteoporosis management.

  17. Glocal Features of In-flight Magazines: when Local Becomes Global. An Explorative Study

    Directory of Open Access Journals (Sweden)

    Stefania Maria Maci

    2012-02-01

    Full Text Available In-flights are magazines distributed by commercial airlines to their passengers and contain news items concerning travel, business and general-interest features, including tourist resorts. The choice of resorts to be described in in-flight magazines seems to depend on the destinations reached by the flights and apparently reflects a cultural and business tendency to focus tourists’ attention not just on popular destinations but also on less frequently advertised or less traditional tourism localities, and to invest in the rediscovery of local identities. Such rediscovery allows the exportation of local tourism to an international audience, thus providing considerable financial advantages. It is the purpose of this paper to investigate the multimodal and linguistic strategies adopted by in-flight magazines so as to allow the local to become global. The analysis, based on a corpus of ten monthly in-flight magazines published in English and collected between 2009 and 2010, will try to define the linguistic conventions and constraints of this genre. In addition, attention will focus on the extent to which iconicity and interdiscursivity permeate the discourse of tourism in in-flight magazines. The resulting data seem to suggest that the airline industry tends to adopt marketing strategies aimed at promoting and differentiating national interests in an international context. The easiest way to do so is to present themselves as global. By highlighting this characteristic, airline companies construct a global reality which the international, and therefore global, traveller experiences

  18. Achievement motivation features of female servants of government and local self-government bodies

    Directory of Open Access Journals (Sweden)

    T.U. Kulakovsk

    2018-03-01

    Full Text Available The article makes the theoretical analysis of the features of professional performance of civil servants and officials of local self-government bodies. The significant level of staff turnover among civil servants and the significant level of registered unemployed among the persons who had previously worked in these structures was revealed. The article carries out the theoretical analysis of psychological researches, which testify to the existence of direct correlation between the level of achievement motivation and success in an entrepreneurial activity. The purpose has been set to study the peculiar properties of achievement motivation of civil servants and officials of local self-government bodies. The researches have shown that the level of motivation for the achievement has a straightforward connection with the success of entrepreneurship. The purpose is to study the peculiarities of the motivation of achievement of civil servants and officials of local self-government bodies. There is the possibility for engagement of fired civil servants to entrepreneurial activities. The empirical study of achievement motivation peculiarities of female employees has been conducted. A statistically significant inverse correlation between the level of achievement motivation and the length of civil service has been established. The paper substantiates the need in further research aimed at uncovering the factors responsible for the inverse correlation between the level of achievement motivation and the term of service in the civil service.

  19. Cell array-based intracellular localization screening reveals novel functional features of human chromosome 21 proteins

    Directory of Open Access Journals (Sweden)

    Kahlem Pascal

    2006-06-01

    Full Text Available Abstract Background Trisomy of human chromosome 21 (Chr21 results in Down's syndrome, a complex developmental and neurodegenerative disease. Molecular analysis of Down's syndrome, however, poses a particular challenge, because the aneuploid region of Chr21 contains many genes of unknown function. Subcellular localization of human Chr21 proteins may contribute to further understanding of the functions and regulatory mechanisms of the genes that code for these proteins. Following this idea, we used a transfected-cell array technique to perform a rapid and cost-effective analysis of the intracellular distribution of Chr 21 proteins. Results We chose 89 genes that were distributed over the majority of 21q, ranging from RBM11 (14.5 Mb to MCM3AP (46.6 Mb, with part of them expressed aberrantly in the Down's syndrome mouse model. Open reading frames of these genes were cloned into a mammalian expression vector with an amino-terminal His6 tag. All of the constructs were arrayed on glass slides and reverse transfected into HEK293T cells for protein expression. Co-localization detection using a set of organelle markers was carried out for each Chr21 protein. Here, we report the subcellular localization properties of 52 proteins. For 34 of these proteins, their localization is described for the first time. Furthermore, the alteration in cell morphology and growth as a result of protein over-expression for claudin-8 and claudin-14 genes has been characterized. Conclusion The cell array-based protein expression and detection approach is a cost-effective platform for large-scale functional analyses, including protein subcellular localization and cell phenotype screening. The results from this study reveal novel functional features of human Chr21 proteins, which should contribute to further understanding of the molecular pathology of Down's syndrome.

  20. Textural features for radar image analysis

    Science.gov (United States)

    Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.

    1981-01-01

    Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

  1. Relating Local to Global Spatial Knowledge: Heuristic Influence of Local Features on Direction Estimates

    Science.gov (United States)

    Phillips, Daniel W.; Montello, Daniel R.

    2015-01-01

    Previous research has examined heuristics--simplified decision-making rules-of-thumb--for geospatial reasoning. This study examined at two locations the influence of beliefs about local coastline orientation on estimated directions to local and distant places; estimates were made immediately or after fifteen seconds. This study goes beyond…

  2. Analysis of wheezes using wavelet higher order spectral features.

    Science.gov (United States)

    Taplidou, Styliani A; Hadjileontiadis, Leontios J

    2010-07-01

    Wheezes are musical breath sounds, which usually imply an existing pulmonary obstruction, such as asthma and chronic obstructive pulmonary disease (COPD). Although many studies have addressed the problem of wheeze detection, a limited number of scientific works has focused in the analysis of wheeze characteristics, and in particular, their time-varying nonlinear characteristics. In this study, an effort is made to reveal and statistically analyze the nonlinear characteristics of wheezes and their evolution over time, as they are reflected in the quadratic phase coupling of their harmonics. To this end, the continuous wavelet transform (CWT) is used in combination with third-order spectra to define the analysis domain, where the nonlinear interactions of the harmonics of wheezes and their time variations are revealed by incorporating instantaneous wavelet bispectrum and bicoherence, which provide with the instantaneous biamplitude and biphase curves. Based on this nonlinear information pool, a set of 23 features is proposed for the nonlinear analysis of wheezes. Two complementary perspectives, i.e., general and detailed, related to average performance and to localities, respectively, were used in the construction of the feature set, in order to embed trends and local behaviors, respectively, seen in the nonlinear interaction of the harmonic elements of wheezes over time. The proposed feature set was evaluated on a dataset of wheezes, acquired from adult patients with diagnosed asthma and COPD from a lung sound database. The statistical evaluation of the feature set revealed discrimination ability between the two pathologies for all data subgroupings. In particular, when the total breathing cycle was examined, all 23 features, but one, showed statistically significant difference between the COPD and asthma pathologies, whereas for the subgroupings of inspiratory and expiratory phases, 18 out of 23 and 22 out of 23 features exhibited discrimination power, respectively

  3. Iris-based medical analysis by geometric deformation features.

    Science.gov (United States)

    Ma, Lin; Zhang, D; Li, Naimin; Cai, Yan; Zuo, Wangmeng; Wang, Kuanguan

    2013-01-01

    Iris analysis studies the relationship between human health and changes in the anatomy of the iris. Apart from the fact that iris recognition focuses on modeling the overall structure of the iris, iris diagnosis emphasizes the detecting and analyzing of local variations in the characteristics of irises. This paper focuses on studying the geometrical structure changes in irises that are caused by gastrointestinal diseases, and on measuring the observable deformations in the geometrical structures of irises that are related to roundness, diameter and other geometric forms of the pupil and the collarette. Pupil and collarette based features are defined and extracted. A series of experiments are implemented on our experimental pathological iris database, including manual clustering of both normal and pathological iris images, manual classification by non-specialists, manual classification by individuals with a medical background, classification ability verification for the proposed features, and disease recognition by applying the proposed features. The results prove the effectiveness and clinical diagnostic significance of the proposed features and a reliable recognition performance for automatic disease diagnosis. Our research results offer a novel systematic perspective for iridology studies and promote the progress of both theoretical and practical work in iris diagnosis.

  4. Featureous: A Tool for Feature-Centric Analysis of Java Software

    DEFF Research Database (Denmark)

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2010-01-01

    Feature-centric comprehension of source code is necessary for incorporating user-requested modifications during software evolution and maintenance. However, such comprehension is difficult to achieve in case of large object-oriented programs due to the size, complexity, and implicit character...... of mappings between features and source code. To support programmers in overcoming these difficulties, we present a feature-centric analysis tool, Featureous. Our tool extends the NetBeans IDE with mechanisms for efficient location of feature implementations in legacy source code, and an extensive analysis...

  5. Featureous: an Integrated Approach to Location, Analysis and Modularization of Features in Java Applications

    DEFF Research Database (Denmark)

    Olszak, Andrzej

    , it is essential that features are properly modularized within the structural organization of software systems. Nevertheless, in many object-oriented applications, features are not represented explicitly. Consequently, features typically end up scattered and tangled over multiple source code units......, such as architectural layers, packages and classes. This lack of modularization is known to make application features difficult to locate, to comprehend and to modify in isolation from one another. To overcome these problems, this thesis proposes Featureous, a novel approach to location, analysis and modularization...... quantitative and qualitative results suggest that Featureous succeeds at efficiently locating features in unfamiliar codebases, at aiding feature-oriented comprehension and modification, and at improving modularization of features using Java packages....

  6. A study on feature analysis for musical instrument classification.

    Science.gov (United States)

    Deng, Jeremiah D; Simmermacher, Christian; Cranefield, Stephen

    2008-04-01

    In tackling data mining and pattern recognition tasks, finding a compact but effective set of features has often been found to be a crucial step in the overall problem-solving process. In this paper, we present an empirical study on feature analysis for recognition of classical instrument, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes. It is revealed that there is significant redundancy between and within feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.

  7. Online 3D Ear Recognition by Combining Global and Local Features.

    Science.gov (United States)

    Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

    2016-01-01

    The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%.

  8. Online 3D Ear Recognition by Combining Global and Local Features.

    Directory of Open Access Journals (Sweden)

    Yahui Liu

    Full Text Available The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles and local feature class (points, lines, and areas. These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%.

  9. Classification of Mls Point Clouds in Urban Scenes Using Detrended Geometric Features from Supervoxel-Based Local Contexts

    Science.gov (United States)

    Sun, Z.; Xu, Y.; Hoegner, L.; Stilla, U.

    2018-05-01

    In this work, we propose a classification method designed for the labeling of MLS point clouds, with detrended geometric features extracted from the points of the supervoxel-based local context. To achieve the analysis of complex 3D urban scenes, acquired points of the scene should be tagged with individual labels of different classes. Thus, assigning a unique label to the points of an object that belong to the same category plays an essential role in the entire 3D scene analysis workflow. Although plenty of studies in this field have been reported, this work is still a challenging task. Specifically, in this work: 1) A novel geometric feature extraction method, detrending the redundant and in-salient information in the local context, is proposed, which is proved to be effective for extracting local geometric features from the 3D scene. 2) Instead of using individual point as basic element, the supervoxel-based local context is designed to encapsulate geometric characteristics of points, providing a flexible and robust solution for feature extraction. 3) Experiments using complex urban scene with manually labeled ground truth are conducted, and the performance of proposed method with respect to different methods is analyzed. With the testing dataset, we have obtained a result of 0.92 for overall accuracy for assigning eight semantic classes.

  10. Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features.

    Science.gov (United States)

    Youji Feng; Lixin Fan; Yihong Wu

    2016-01-01

    The essence of image-based localization lies in matching 2D key points in the query image and 3D points in the database. State-of-the-art methods mostly employ sophisticated key point detectors and feature descriptors, e.g., Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT), to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast. The naive usage of binary features, however, does not lend itself to significant speedup of localization, since existing indexing approaches, such as hierarchical clustering trees and locality sensitive hashing, are not efficient enough in indexing binary features and matching binary features turns out to be much slower than matching SIFT features. To overcome this, we propose a much more efficient indexing approach for approximate nearest neighbor search of binary features. This approach resorts to randomized trees that are constructed in a supervised training process by exploiting the label information derived from that multiple features correspond to a common 3D point. In the tree construction process, node tests are selected in a way such that trees have uniform leaf sizes and low error rates, which are two desired properties for efficient approximate nearest neighbor search. To further improve the search efficiency, a probabilistic priority search strategy is adopted. Apart from the label information, this strategy also uses non-binary pixel intensity differences available in descriptor extraction. By using the proposed indexing approach, matching binary features is no longer much slower but slightly faster than matching SIFT features. Consequently, the overall localization speed is significantly improved due to the much faster key

  11. A Probabilistic Feature Map-Based Localization System Using a Monocular Camera.

    Science.gov (United States)

    Kim, Hyungjin; Lee, Donghwa; Oh, Taekjun; Choi, Hyun-Taek; Myung, Hyun

    2015-08-31

    Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments.

  12. A Probabilistic Feature Map-Based Localization System Using a Monocular Camera

    Directory of Open Access Journals (Sweden)

    Hyungjin Kim

    2015-08-01

    Full Text Available Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments

  13. Featureous: infrastructure for feature-centric analysis of object-oriented software

    DEFF Research Database (Denmark)

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2010-01-01

    The decentralized nature of collaborations between objects in object-oriented software makes it difficult to understand how user-observable program features are implemented and how their implementations relate to each other. It is worthwhile to improve this situation, since feature-centric program...... understanding and modification are essential during software evolution and maintenance. In this paper, we present an infrastructure built on top of the NetBeans IDE called Featureous that allows for rapid construction of tools for feature-centric analysis of object-oriented software. Our infrastructure...... encompasses a lightweight feature location mechanism, a number of analytical views and an API allowing for addition of third-party extensions. To form a common conceptual framework for future feature-centric extensions, we propose to structure feature centric analysis along three dimensions: perspective...

  14. An integration of minimum local feature representation methods to recognize large variation of foods

    Science.gov (United States)

    Razali, Mohd Norhisham bin; Manshor, Noridayu; Halin, Alfian Abdul; Mustapha, Norwati; Yaakob, Razali

    2017-10-01

    Local invariant features have shown to be successful in describing object appearances for image classification tasks. Such features are robust towards occlusion and clutter and are also invariant against scale and orientation changes. This makes them suitable for classification tasks with little inter-class similarity and large intra-class difference. In this paper, we propose an integrated representation of the Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors, using late fusion strategy. The proposed representation is used for food recognition from a dataset of food images with complex appearance variations. The Bag of Features (BOF) approach is employed to enhance the discriminative ability of the local features. Firstly, the individual local features are extracted to construct two kinds of visual vocabularies, representing SURF and SIFT. The visual vocabularies are then concatenated and fed into a Linear Support Vector Machine (SVM) to classify the respective food categories. Experimental results demonstrate impressive overall recognition at 82.38% classification accuracy based on the challenging UEC-Food100 dataset.

  15. Feature Point Extraction from the Local Frequency Map of an Image

    Directory of Open Access Journals (Sweden)

    Jesmin Khan

    2012-01-01

    Full Text Available We propose a novel technique for detecting rotation- and scale-invariant interest points from the local frequency representation of an image. Local or instantaneous frequency is the spatial derivative of the local phase, where the local phase of any signal can be found from its Hilbert transform. Local frequency estimation can detect edge, ridge, corner, and texture information at the same time, and it shows high values at those dominant features of an image. For each pixel, we select an appropriate width of the window for computing the derivative of the phase. In order to select the width of the window for any given pixel, we make use of the measure of the extent to which the phases, in the neighborhood of that pixel, are in the same direction. The local frequency map, thus obtained, is then thresholded by employing a global thresholding approach to detect the interest or feature points. Repeatability rate, a performance evaluation criterion for an interest point detector, is used to check the geometric stability of the proposed method under different transformations. We present simulation results of the detection of feature points from image utilizing the suggested technique and compare the proposed method with five existing approaches that yield good results. The results prove the efficacy of the proposed feature point detection algorithm. Moreover, in terms of repeatability rate; the results show that the performance of the proposed method with respect to different aspect is compatible with the existing methods.

  16. A Smartphone Indoor Localization Algorithm Based on WLAN Location Fingerprinting with Feature Extraction and Clustering.

    Science.gov (United States)

    Luo, Junhai; Fu, Liang

    2017-06-09

    With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS), which is collected from Access Points (APs). The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA) is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC) algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML) estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.

  17. A Smartphone Indoor Localization Algorithm Based on WLAN Location Fingerprinting with Feature Extraction and Clustering

    Directory of Open Access Journals (Sweden)

    Junhai Luo

    2017-06-01

    Full Text Available With the development of communication technology, the demand for location-based services is growing rapidly. This paper presents an algorithm for indoor localization based on Received Signal Strength (RSS, which is collected from Access Points (APs. The proposed localization algorithm contains the offline information acquisition phase and online positioning phase. Firstly, the AP selection algorithm is reviewed and improved based on the stability of signals to remove useless AP; secondly, Kernel Principal Component Analysis (KPCA is analyzed and used to remove the data redundancy and maintain useful characteristics for nonlinear feature extraction; thirdly, the Affinity Propagation Clustering (APC algorithm utilizes RSS values to classify data samples and narrow the positioning range. In the online positioning phase, the classified data will be matched with the testing data to determine the position area, and the Maximum Likelihood (ML estimate will be employed for precise positioning. Eventually, the proposed algorithm is implemented in a real-world environment for performance evaluation. Experimental results demonstrate that the proposed algorithm improves the accuracy and computational complexity.

  18. Handwriting: Feature Correlation Analysis for Biometric Hashes

    Science.gov (United States)

    Vielhauer, Claus; Steinmetz, Ralf

    2004-12-01

    In the application domain of electronic commerce, biometric authentication can provide one possible solution for the key management problem. Besides server-based approaches, methods of deriving digital keys directly from biometric measures appear to be advantageous. In this paper, we analyze one of our recently published specific algorithms of this category based on behavioral biometrics of handwriting, the biometric hash. Our interest is to investigate to which degree each of the underlying feature parameters contributes to the overall intrapersonal stability and interpersonal value space. We will briefly discuss related work in feature evaluation and introduce a new methodology based on three components: the intrapersonal scatter (deviation), the interpersonal entropy, and the correlation between both measures. Evaluation of the technique is presented based on two data sets of different size. The method presented will allow determination of effects of parameterization of the biometric system, estimation of value space boundaries, and comparison with other feature selection approaches.

  19. Handwriting: Feature Correlation Analysis for Biometric Hashes

    Directory of Open Access Journals (Sweden)

    Ralf Steinmetz

    2004-04-01

    Full Text Available In the application domain of electronic commerce, biometric authentication can provide one possible solution for the key management problem. Besides server-based approaches, methods of deriving digital keys directly from biometric measures appear to be advantageous. In this paper, we analyze one of our recently published specific algorithms of this category based on behavioral biometrics of handwriting, the biometric hash. Our interest is to investigate to which degree each of the underlying feature parameters contributes to the overall intrapersonal stability and interpersonal value space. We will briefly discuss related work in feature evaluation and introduce a new methodology based on three components: the intrapersonal scatter (deviation, the interpersonal entropy, and the correlation between both measures. Evaluation of the technique is presented based on two data sets of different size. The method presented will allow determination of effects of parameterization of the biometric system, estimation of value space boundaries, and comparison with other feature selection approaches.

  20. SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

    International Nuclear Information System (INIS)

    Andrews, M; Abazeed, M; Woody, N; Stephans, K; Videtic, G; Xia, P; Zhuang, T

    2016-01-01

    Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.

  1. SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

    Energy Technology Data Exchange (ETDEWEB)

    Andrews, M; Abazeed, M; Woody, N; Stephans, K; Videtic, G; Xia, P; Zhuang, T [The Cleveland Clinic Foundation, Cleveland, OH (United States)

    2016-06-15

    Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.

  2. Evaluation Methodology between Globalization and Localization Features Approaches for Skin Cancer Lesions Classification

    Science.gov (United States)

    Ahmed, H. M.; Al-azawi, R. J.; Abdulhameed, A. A.

    2018-05-01

    Huge efforts have been put in the developing of diagnostic methods to skin cancer disease. In this paper, two different approaches have been addressed for detection the skin cancer in dermoscopy images. The first approach uses a global method that uses global features for classifying skin lesions, whereas the second approach uses a local method that uses local features for classifying skin lesions. The aim of this paper is selecting the best approach for skin lesion classification. The dataset has been used in this paper consist of 200 dermoscopy images from Pedro Hispano Hospital (PH2). The achieved results are; sensitivity about 96%, specificity about 100%, precision about 100%, and accuracy about 97% for globalization approach while, sensitivity about 100%, specificity about 100%, precision about 100%, and accuracy about 100% for Localization Approach, these results showed that the localization approach achieved acceptable accuracy and better than globalization approach for skin cancer lesions classification.

  3. Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

    Directory of Open Access Journals (Sweden)

    Minh-Tan Pham

    2017-10-01

    Full Text Available A novel efficient method for content-based image retrieval (CBIR is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

  4. Modeling vehicle emissions in different types of Chinese cities: Importance of vehicle fleet and local features

    International Nuclear Information System (INIS)

    Huo Hong; Zhang Qiang; He Kebin; Yao Zhiliang; Wang Xintong; Zheng Bo; Streets, David G.; Wang Qidong; Ding Yan

    2011-01-01

    We propose a method to simulate vehicle emissions in Chinese cities of different sizes and development stages. Twenty two cities are examined in this study. The target year is 2007. Among the cities, the vehicle emission factors were remarkably different (the highest is 50-90% higher than the lowest) owing to their distinct local features and vehicle technology levels, and the major contributors to total vehicle emissions were also different. A substantial increase in vehicle emissions is foreseeable unless stronger measures are implemented because the benefit of current policies can be quickly offset by the vehicle growth. Major efforts should be focused on all cities, especially developing cities where the requirements are lenient. This work aims a better understanding of vehicle emissions in all types of Chinese cities. The proposed method could benefit national emission inventory studies in improving accuracy and help in designing national and local policies for vehicle emission control. - Highlights: → We examine vehicle emissions in 22 Chinese cities of different types and locations. → Vehicle emission factors of the cities differ by 50-90% due to distinct local features. → Each vehicle type contributes differently to total emissions among the cities. → A substantial increase in vehicle emissions in most Chinese cities is foreseeable. → City-specific fleet and local features are important in research and policy making. - Vehicle emission characteristics of Chinese cities are remarkably different, and local features need to be taken into account in vehicle emission studies and control strategy.

  5. Improving scale invariant feature transform with local color contrastive descriptor for image classification

    Science.gov (United States)

    Guo, Sheng; Huang, Weilin; Qiao, Yu

    2017-01-01

    Image representation and classification are two fundamental tasks toward version understanding. Shape and texture provide two key features for visual representation and have been widely exploited in a number of successful local descriptors, e.g., scale invariant feature transform (SIFT), local binary pattern descriptor, and histogram of oriented gradient. Unlike these gradient-based descriptors, this paper presents a simple yet efficient local descriptor, named local color contrastive descriptor (LCCD), which captures the contrastive aspects among local regions or color channels for image representation. LCCD is partly inspired by the neural science facts that color contrast plays important roles in visual perception and there exist strong linkages between color and shape. We leverage f-divergence as a robust measure to estimate the contrastive features between different spatial locations and multiple channels. Our descriptor enriches local image representation with both color and contrast information. Due to that LCCD does not explore any gradient information, individual LCCD does not yield strong performance. But we verified experimentally that LCCD can compensate strongly SIFT. Extensive experimental results on image classification show that our descriptor improves the performance of SIFT substantially by combination on three challenging benchmarks, including MIT Indoor-67 database, SUN397, and PASCAL VOC 2007.

  6. Local Area Network Material Accounting System (LANMAS) Functions and Features Overview

    International Nuclear Information System (INIS)

    Robichaux, J.J.

    1998-07-01

    The Local Area Network Material Accounting System (LANMAS) application is a standardized approach to comply with the DOE Order 5633.3B, control and Accountability of Nuclear Material, material accounting requirements. This paper provides a general overview of the functions and features included in the LANMAS application

  7. Features of the repetition frequency of edge localized modes in EAST

    DEFF Research Database (Denmark)

    Jiang, M.; Xiao, C.; Xu, G.S.

    2012-01-01

    This paper presents the features of the edge localized modes (ELMs) observed in the 2010 experimental campaign on the Experimental Advanced Superconducting Tokamak (EAST). The first high-confinement mode (H-mode) at an H-factor of HIPB98(y, 2)~1 has been obtained with about 1 MW lower hybrid wave...

  8. Toward fast feature adaptation and localization for real-time face recognition systems

    NARCIS (Netherlands)

    Zuo, F.; With, de P.H.N.; Ebrahimi, T.; Sikora, T.

    2003-01-01

    In a home environment, video surveillance employing face detection and recognition is attractive for new applications. Facial feature (e.g. eyes and mouth) localization in the face is an essential task for face recognition because it constitutes an indispensable step for face geometry normalization.

  9. Compounding local invariant features and global deformable geometry for medical image registration.

    Directory of Open Access Journals (Sweden)

    Jianhua Zhang

    Full Text Available Using deformable models to register medical images can result in problems of initialization of deformable models and robustness and accuracy of matching of inter-subject anatomical variability. To tackle these problems, a novel model is proposed in this paper by compounding local invariant features and global deformable geometry. This model has four steps. First, a set of highly-repeatable and highly-robust local invariant features, called Key Features Model (KFM, are extracted by an effective matching strategy. Second, local features can be matched more accurately through the KFM for the purpose of initializing a global deformable model. Third, the positional relationship between the KFM and the global deformable model can be used to precisely pinpoint all landmarks after initialization. And fourth, the final pose of the global deformable model is determined by an iterative process with a lower time cost. Through the practical experiments, the paper finds three important conclusions. First, it proves that the KFM can detect the matching feature points well. Second, the precision of landmark locations adjusted by the modeled relationship between KFM and global deformable model is greatly improved. Third, regarding the fitting accuracy and efficiency, by observation from the practical experiments, it is found that the proposed method can improve 6~8% of the fitting accuracy and reduce around 50% of the computational time compared with state-of-the-art methods.

  10. Analysis of localized damage in creep rupture

    International Nuclear Information System (INIS)

    Wang Zhengdong; Wu Dongdi

    1992-01-01

    Continuum Damage Mechanics studies the effect of distributed defects, whereas the failure of engineering structures is usually caused by local damage. In this paper, an analysis of localized damage in creep rupture is carried out. The material tested is a 2 1/4Cr-1Mo pressure vessel steel and the material constants necessary for damage analysis are evaluated. Notched specimens are used to reflect localized damage in creep rupture and the amount of damage is measured using DCPD method. Through FEM computation, stress components and effective stress in the region of notch root are evaluated and it is found that the von Mises effective stress can represent the damage effective stress in the analysis of localized creep damage. It is possible to develop a method for the assessment of safety of pressure vessels under creep through localized creep damage analysis. (orig.)

  11. Prominent feature extraction for review analysis: an empirical study

    Science.gov (United States)

    Agarwal, Basant; Mittal, Namita

    2016-05-01

    Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

  12. Local-Scale Simulations of Nucleate Boiling on Micrometer Featured Surfaces: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Sitaraman, Hariswaran [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Moreno, Gilberto [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Narumanchi, Sreekant V [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dede, Ercan M. [Toyota Research Institute of North America; Joshi, Shailesh N. [Toyota Research Institute of North America; Zhou, Feng [Toyota Research Institute of North America

    2017-08-03

    A high-fidelity computational fluid dynamics (CFD)-based model for bubble nucleation of the refrigerant HFE7100 on micrometer-featured surfaces is presented in this work. The single-fluid incompressible Navier-Stokes equations, along with energy transport and natural convection effects are solved on a featured surface resolved grid. An a priori cavity detection method is employed to convert raw profilometer data of a surface into well-defined cavities. The cavity information and surface morphology are represented in the CFD model by geometric mesh deformations. Surface morphology is observed to initiate buoyancy-driven convection in the liquid phase, which in turn results in faster nucleation of cavities. Simulations pertaining to a generic rough surface show a trend where smaller size cavities nucleate with higher wall superheat. This local-scale model will serve as a self-consistent connection to larger device scale continuum models where local feature representation is not possible.

  13. Local-Scale Simulations of Nucleate Boiling on Micrometer-Featured Surfaces

    Energy Technology Data Exchange (ETDEWEB)

    Sitaraman, Hariswaran [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Moreno, Gilberto [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Narumanchi, Sreekant V [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dede, Ercan M. [Toyota Research Institute of North America; Joshi, Shailesh N. [Toyota Research Institute of North America; Zhou, Feng [Toyota Research Institute of North America

    2017-07-12

    A high-fidelity computational fluid dynamics (CFD)-based model for bubble nucleation of the refrigerant HFE7100 on micrometer-featured surfaces is presented in this work. The single-fluid incompressible Navier-Stokes equations, along with energy transport and natural convection effects are solved on a featured surface resolved grid. An a priori cavity detection method is employed to convert raw profilometer data of a surface into well-defined cavities. The cavity information and surface morphology are represented in the CFD model by geometric mesh deformations. Surface morphology is observed to initiate buoyancy-driven convection in the liquid phase, which in turn results in faster nucleation of cavities. Simulations pertaining to a generic rough surface show a trend where smaller size cavities nucleate with higher wall superheat. This local-scale model will serve as a self-consistent connection to larger device scale continuum models where local feature representation is not possible.

  14. Passive Forensics for Region Duplication Image Forgery Based on Harris Feature Points and Local Binary Patterns

    Directory of Open Access Journals (Sweden)

    Jie Zhao

    2013-01-01

    Full Text Available Nowadays the demand for identifying the authenticity of an image is much increased since advanced image editing software packages are widely used. Region duplication forgery is one of the most common and immediate tampering attacks which are frequently used. Several methods to expose this forgery have been developed to detect and locate the tampered region, while most methods do fail when the duplicated region undergoes rotation or flipping before being pasted. In this paper, an efficient method based on Harris feature points and local binary patterns is proposed. First, the image is filtered with a pixelwise adaptive Wiener method, and then dense Harris feature points are employed in order to obtain a sufficient number of feature points with approximately uniform distribution. Feature vectors for a circle patch around each feature point are extracted using local binary pattern operators, and the similar Harris points are matched based on their representation feature vectors using the BBF algorithm. Finally, RANSAC algorithm is employed to eliminate the possible erroneous matches. Experiment results demonstrate that the proposed method can effectively detect region duplication forgery, even when an image was distorted by rotation, flipping, blurring, AWGN, JPEG compression, and their mixed operations, especially resistant to the forgery with the flat area of little visual structures.

  15. Feature Extraction Method for High Impedance Ground Fault Localization in Radial Power Distribution Networks

    DEFF Research Database (Denmark)

    Jensen, Kåre Jean; Munk, Steen M.; Sørensen, John Aasted

    1998-01-01

    A new approach to the localization of high impedance ground faults in compensated radial power distribution networks is presented. The total size of such networks is often very large and a major part of the monitoring of these is carried out manually. The increasing complexity of industrial...... of three phase voltages and currents. The method consists of a feature extractor, based on a grid description of the feeder by impulse responses, and a neural network for ground fault localization. The emphasis of this paper is the feature extractor, and the detection of the time instance of a ground fault...... processes and communication systems lead to demands for improved monitoring of power distribution networks so that the quality of power delivery can be kept at a controlled level. The ground fault localization method for each feeder in a network is based on the centralized frequency broadband measurement...

  16. Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.

    Science.gov (United States)

    Xu, Jiucheng; Mu, Huiyu; Wang, Yun; Huang, Fangzhou

    2018-01-01

    The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC 2 ), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

  17. Gas Classification Using Combined Features Based on a Discriminant Analysis for an Electronic Nose

    Directory of Open Access Journals (Sweden)

    Sang-Il Choi

    2016-01-01

    Full Text Available This paper proposes a gas classification method for an electronic nose (e-nose system, for which combined features that have been configured through discriminant analysis are used. First, each global feature is extracted from the entire measurement section of the data samples, while the same process is applied to the local features of the section that corresponds to the stabilization, exposure, and purge stages. The discriminative information amounts in the individual features are then measured based on the discriminant analysis, and the combined features are subsequently composed by selecting the features that have a large amount of discriminative information. Regarding a variety of volatile organic compound data, the results of the experiment show that, in a noisy environment, the proposed method exhibits classification performance that is relatively excellent compared to the other feature types.

  18. Face recognition using slow feature analysis and contourlet transform

    Science.gov (United States)

    Wang, Yuehao; Peng, Lingling; Zhe, Fuchuan

    2018-04-01

    In this paper we propose a novel face recognition approach based on slow feature analysis (SFA) in contourlet transform domain. This method firstly use contourlet transform to decompose the face image into low frequency and high frequency part, and then takes technological advantages of slow feature analysis for facial feature extraction. We named the new method combining the slow feature analysis and contourlet transform as CT-SFA. The experimental results on international standard face database demonstrate that the new face recognition method is effective and competitive.

  19. Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination.

    Science.gov (United States)

    Qiao, Yongliang

    2017-10-25

    Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period). In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns) to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision-recall performance against the state-of-the-art SeqSLAM algorithm.

  20. Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination

    Directory of Open Access Journals (Sweden)

    Yongliang Qiao

    2017-10-01

    Full Text Available Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS. However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period. In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision–recall performance against the state-of-the-art SeqSLAM algorithm.

  1. Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    2017-04-01

    Full Text Available Device-free localization (DFL is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF DFL system, radio transmitters (RTs and radio receivers (RXs are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN, support vector machine (SVM, back propagation neural network (BPNN, as well as the well-known radio tomographic imaging (RTI DFL approach.

  2. Safety analysis of accident localization system

    International Nuclear Information System (INIS)

    1999-01-01

    A complex safety analysis of accident localization system of Ignalina NPP was performed. Calculation results obtained, results of non-destruct ing testing and experimental data of reinforced concrete testing of buildings does not revealed deficiencies of buildings of accident localization system at unit 1 of Ignalina NPP. Calculations were performed using codes NEPTUNE, ALGOR, CONTAIN

  3. Improving Remote Sensing Scene Classification by Integrating Global-Context and Local-Object Features

    Directory of Open Access Journals (Sweden)

    Dan Zeng

    2018-05-01

    Full Text Available Recently, many researchers have been dedicated to using convolutional neural networks (CNNs to extract global-context features (GCFs for remote-sensing scene classification. Commonly, accurate classification of scenes requires knowledge about both the global context and local objects. However, unlike the natural images in which the objects cover most of the image, objects in remote-sensing images are generally small and decentralized. Thus, it is hard for vanilla CNNs to focus on both global context and small local objects. To address this issue, this paper proposes a novel end-to-end CNN by integrating the GCFs and local-object-level features (LOFs. The proposed network includes two branches, the local object branch (LOB and global semantic branch (GSB, which are used to generate the LOFs and GCFs, respectively. Then, the concatenation of features extracted from the two branches allows our method to be more discriminative in scene classification. Three challenging benchmark remote-sensing datasets were extensively experimented on; the proposed approach outperformed the existing scene classification methods and achieved state-of-the-art results for all three datasets.

  4. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

    Science.gov (United States)

    Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

    2018-01-01

    Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

  5. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

    Directory of Open Access Journals (Sweden)

    Xu Yu

    2018-01-01

    Full Text Available Cross-domain collaborative filtering (CDCF solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR. We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

  6. A review of feature detection and match algorithms for localization and mapping

    Science.gov (United States)

    Li, Shimiao

    2017-09-01

    Localization and mapping is an essential ability of a robot to keep track of its own location in an unknown environment. Among existing methods for this purpose, vision-based methods are more effective solutions for being accurate, inexpensive and versatile. Vision-based methods can generally be categorized as feature-based approaches and appearance-based approaches. The feature-based approaches prove higher performance in textured scenarios. However, their performance depend highly on the applied feature-detection algorithms. In this paper, we surveyed algorithms for feature detection, which is an essential step in achieving vision-based localization and mapping. In this pater, we present mathematical models of the algorithms one after another. To compare the performances of the algorithms, we conducted a series of experiments on their accuracy, speed, scale invariance and rotation invariance. The results of the experiments showed that ORB is the fastest algorithm in detecting and matching features, the speed of which is more than 10 times that of SURF and approximately 40 times that of SIFT. And SIFT, although with no advantage in terms of speed, shows the most correct matching pairs and proves its accuracy.

  7. LSAH: a fast and efficient local surface feature for point cloud registration

    Science.gov (United States)

    Lu, Rongrong; Zhu, Feng; Wu, Qingxiao; Kong, Yanzi

    2018-04-01

    Point cloud registration is a fundamental task in high level three dimensional applications. Noise, uneven point density and varying point cloud resolutions are the three main challenges for point cloud registration. In this paper, we design a robust and compact local surface descriptor called Local Surface Angles Histogram (LSAH) and propose an effectively coarse to fine algorithm for point cloud registration. The LSAH descriptor is formed by concatenating five normalized sub-histograms into one histogram. The five sub-histograms are created by accumulating a different type of angle from a local surface patch respectively. The experimental results show that our LSAH is more robust to uneven point density and point cloud resolutions than four state-of-the-art local descriptors in terms of feature matching. Moreover, we tested our LSAH based coarse to fine algorithm for point cloud registration. The experimental results demonstrate that our algorithm is robust and efficient as well.

  8. FEATUREOUS: AN INTEGRATED ENVIRONMENT FOR FEATURE-CENTRIC ANALYSIS AND MODIFICATION OF OBJECT-ORIENTED SOFTWARE

    DEFF Research Database (Denmark)

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2011-01-01

    The decentralized nature of collaborations between objects in object-oriented software makes it difficult to understand the implementations of user-observable program features and their respective interdependencies. As feature-centric program understanding and modification are essential during...... software maintenance and evolution, this situation needs to change. In this paper, we present Featureous, an integrated development environment built on top of the NetBeans IDE that facilitates feature-centric analysis of object-oriented software. Our integrated development environment encompasses...... a lightweight feature location mechanism, a number of reusable analytical views, and necessary APIs for supporting future extensions. The base of the integrated development environment is a conceptual framework comprising of three complementary dimensions of comprehension: perspective, abstraction...

  9. Data analysis for physical scientists featuring Excel

    CERN Document Server

    Kirkup, Les

    2012-01-01

    The ability to summarise data, compare models and apply computer-based analysis tools are vital skills necessary for studying and working in the physical sciences. This textbook supports undergraduate students as they develop and enhance these skills. Introducing data analysis techniques, this textbook pays particular attention to the internationally recognised guidelines for calculating and expressing measurement uncertainty. This new edition has been revised to incorporate Excel® 2010. It also provides a practical approach to fitting models to data using non-linear least squares, a powerful technique which can be applied to many types of model. Worked examples using actual experimental data help students understand how the calculations apply to real situations. Over 200 in-text exercises and end-of-chapter problems give students the opportunity to use the techniques themselves and gain confidence in applying them. Answers to the exercises and problems are given at the end of the book.

  10. Determining local and contextual features describing appearance of difficult to identify mitotic figures

    Science.gov (United States)

    Gandomkar, Ziba; Brennan, Patrick C.; Mello-Thoms, Claudia

    2017-03-01

    Mitotic count is helpful in determining the aggressiveness of breast cancer. In previous studies, it was shown that the agreement among pathologists for grading mitotic index is fairly modest, as mitoses have a large variety of appearances and they could be mistaken for other similar objects. In this study, we determined local and contextual features that differ significantly between easily identifiable mitoses and challenging ones. The images were obtained from the Mitosis-Atypia 2014 challenge. In total, the dataset contained 453 mitotic figures. Two pathologists annotated each mitotic figure. In case of disagreement, an opinion from a third pathologist was requested. The mitoses were grouped into three categories, those recognized as "a true mitosis" by both pathologists ,those labelled as "a true mitosis" by only one of the first two readers and also the third pathologist, and those annotated as "probably a mitosis" by all readers or the majority of them. After color unmixing, the mitoses were segmented from H channel. Shape-based features along with intensity-based and textural features were extracted from H-channel, blue ratio channel and five different color spaces. Holistic features describing each image were also considered. The Kruskal-Wallis H test was used to identify significantly different features. Multiple comparisons were done using the rank-based version of Tukey-Kramer test. The results indicated that there are local and global features which differ significantly among different groups. In addition, variations between mitoses in different groups were captured in the features from HSL and LCH color space more than other ones.

  11. A Novel Feature-Level Data Fusion Method for Indoor Autonomous Localization

    Directory of Open Access Journals (Sweden)

    Minxiang Liu

    2013-01-01

    Full Text Available We present a novel feature-level data fusion method for autonomous localization in an inactive multiple reference unknown indoor environment. Since monocular sensors cannot provide the depth information directly, the proposed method incorporates the edge information of images from a camera with homologous depth information received from an infrared sensor. Real-time experimental results demonstrate that the accuracies of position and orientation are greatly improved by using the proposed fusion method in an unknown complex indoor environment. Compared to monocular localization, the proposed method is found to have up to 70 percent improvement in accuracy.

  12. MetaFIND: A feature analysis tool for metabolomics data

    Directory of Open Access Journals (Sweden)

    Cunningham Pádraig

    2008-11-01

    Full Text Available Abstract Background Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or features, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data. Results In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations. Conclusion Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.

  13. A blur-invariant local feature for motion blurred image matching

    Science.gov (United States)

    Tong, Qiang; Aoki, Terumasa

    2017-07-01

    Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

  14. Using features of local densities, statistics and HMM toolkit (HTK for offline Arabic handwriting text recognition

    Directory of Open Access Journals (Sweden)

    El Moubtahij Hicham

    2017-12-01

    Full Text Available This paper presents an analytical approach of an offline handwritten Arabic text recognition system. It is based on the Hidden Markov Models (HMM Toolkit (HTK without explicit segmentation. The first phase is preprocessing, where the data is introduced in the system after quality enhancements. Then, a set of characteristics (features of local densities and features statistics are extracted by using the technique of sliding windows. Subsequently, the resulting feature vectors are injected to the Hidden Markov Model Toolkit (HTK. The simple database “Arabic-Numbers” and IFN/ENIT are used to evaluate the performance of this system. Keywords: Hidden Markov Models (HMM Toolkit (HTK, Sliding windows

  15. Secondary iris recognition method based on local energy-orientation feature

    Science.gov (United States)

    Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing

    2015-01-01

    This paper proposes a secondary iris recognition based on local features. The application of the energy-orientation feature (EOF) by two-dimensional Gabor filter to the extraction of the iris goes before the first recognition by the threshold of similarity, which sets the whole iris database into two categories-a correctly recognized class and a class to be recognized. Therefore, the former are accepted and the latter are transformed by histogram to achieve an energy-orientation histogram feature (EOHF), which is followed by a second recognition with the chi-square distance. The experiment has proved that the proposed method, because of its higher correct recognition rate, could be designated as the most efficient and effective among its companion studies in iris recognition algorithms.

  16. New Keypoint Matching Method Using Local Convolutional Features for Power Transmission Line Icing Monitoring

    Directory of Open Access Journals (Sweden)

    Qiangliang Guo

    2018-02-01

    Full Text Available Power transmission line icing (PTLI problems, which cause tremendous damage to the power grids, has drawn much attention. Existing three-dimensional measurement methods based on binocular stereo vision was recently introduced to measure the ice thickness in PTLI, but failed to meet requirements of practical applications due to inefficient keypoint matching in the complex PTLI scene. In this paper, a new keypoint matching method is proposed based on the local multi-layer convolutional neural network (CNN features, termed Local Convolutional Features (LCFs. LCFs are deployed to extract more discriminative features than the conventional CNNs. Particularly in LCFs, a multi-layer features fusion scheme is exploited to boost the matching performance. Together with a location constraint method, the correspondence of neighboring keypoints is further refined. Our approach achieves 1.5%, 5.3%, 13.1%, 27.3% improvement in the average matching precision compared with SIFT, SURF, ORB and MatchNet on the public Middlebury dataset, and the measurement accuracy of ice thickness can reach 90.9% compared with manual measurement on the collected PTLI dataset.

  17. Performance Analysis of Local Ensemble Kalman Filter

    Science.gov (United States)

    Tong, Xin T.

    2018-03-01

    Ensemble Kalman filter (EnKF) is an important data assimilation method for high-dimensional geophysical systems. Efficient implementation of EnKF in practice often involves the localization technique, which updates each component using only information within a local radius. This paper rigorously analyzes the local EnKF (LEnKF) for linear systems and shows that the filter error can be dominated by the ensemble covariance, as long as (1) the sample size exceeds the logarithmic of state dimension and a constant that depends only on the local radius; (2) the forecast covariance matrix admits a stable localized structure. In particular, this indicates that with small system and observation noises, the filter error will be accurate in long time even if the initialization is not. The analysis also reveals an intrinsic inconsistency caused by the localization technique, and a stable localized structure is necessary to control this inconsistency. While this structure is usually taken for granted for the operation of LEnKF, it can also be rigorously proved for linear systems with sparse local observations and weak local interactions. These theoretical results are also validated by numerical implementation of LEnKF on a simple stochastic turbulence in two dynamical regimes.

  18. SVM-based glioma grading. Optimization by feature reduction analysis

    International Nuclear Information System (INIS)

    Zoellner, Frank G.; Schad, Lothar R.; Emblem, Kyrre E.; Harvard Medical School, Boston, MA; Oslo Univ. Hospital

    2012-01-01

    We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∝87%) while reducing the number of features by up to 98%. (orig.)

  19. A Local Texture-Based Superpixel Feature Coding for Saliency Detection Combined with Global Saliency

    Directory of Open Access Journals (Sweden)

    Bingfei Nan

    2015-12-01

    Full Text Available Because saliency can be used as the prior knowledge of image content, saliency detection has been an active research area in image segmentation, object detection, image semantic understanding and other relevant image-based applications. In the case of saliency detection from cluster scenes, the salient object/region detected needs to not only be distinguished clearly from the background, but, preferably, to also be informative in terms of complete contour and local texture details to facilitate the successive processing. In this paper, a Local Texture-based Region Sparse Histogram (LTRSH model is proposed for saliency detection from cluster scenes. This model uses a combination of local texture patterns and color distribution as well as contour information to encode the superpixels to characterize the local feature of image for region contrast computing. Combining the region contrast as computed with the global saliency probability, a full-resolution salient map, in which the salient object/region detected adheres more closely to its inherent feature, is obtained on the bases of the corresponding high-level saliency spatial distribution as well as on the pixel-level saliency enhancement. Quantitative comparisons with five state-of-the-art saliency detection methods on benchmark datasets are carried out, and the comparative results show that the method we propose improves the detection performance in terms of corresponding measurements.

  20. Infrared and visible images registration with adaptable local-global feature integration for rail inspection

    Science.gov (United States)

    Tang, Chaoqing; Tian, Gui Yun; Chen, Xiaotian; Wu, Jianbo; Li, Kongjing; Meng, Hongying

    2017-12-01

    Active thermography provides infrared images that contain sub-surface defect information, while visible images only reveal surface information. Mapping infrared information to visible images offers more comprehensive visualization for decision-making in rail inspection. However, the common information for registration is limited due to different modalities in both local and global level. For example, rail track which has low temperature contrast reveals rich details in visible images, but turns blurry in the infrared counterparts. This paper proposes a registration algorithm called Edge-Guided Speeded-Up-Robust-Features (EG-SURF) to address this issue. Rather than sequentially integrating local and global information in matching stage which suffered from buckets effect, this algorithm adaptively integrates local and global information into a descriptor to gather more common information before matching. This adaptability consists of two facets, an adaptable weighting factor between local and global information, and an adaptable main direction accuracy. The local information is extracted using SURF while the global information is represented by shape context from edges. Meanwhile, in shape context generation process, edges are weighted according to local scale and decomposed into bins using a vector decomposition manner to provide more accurate descriptor. The proposed algorithm is qualitatively and quantitatively validated using eddy current pulsed thermography scene in the experiments. In comparison with other algorithms, better performance has been achieved.

  1. Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features.

    Science.gov (United States)

    Pesteie, Mehran; Abolmaesumi, Purang; Ashab, Hussam Al-Deen; Lessoway, Victoria A; Massey, Simon; Gunka, Vit; Rohling, Robert N

    2015-06-01

    Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections. A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification. The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image. A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for

  2. Naive Bayesian classifiers for multinomial features: a theoretical analysis

    CSIR Research Space (South Africa)

    Van Dyk, E

    2007-11-01

    Full Text Available The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density...

  3. Feature selection using genetic algorithms for fetal heart rate analysis

    International Nuclear Information System (INIS)

    Xu, Liang; Redman, Christopher W G; Georgieva, Antoniya; Payne, Stephen J

    2014-01-01

    The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies. (paper)

  4. Special Features of Strain Localization and Nanodipoles of Partial Disclinations in the Region of Elastic Distortions

    Science.gov (United States)

    Tyumentsev, A. N.; Ditenberg, I. A.; Sukhanov, I. I.

    2018-02-01

    In the zones of strain localization in the region of elastic distortions and nanodipoles of partial disclinations representing the defects of elastically deformed medium, a theoretical analysis of the elastically stressed state and the energy of these defects, including the cases of their transformation into more complex ensembles of interrelated disclinations, is performed. Using the analytical results, the mechanisms of strain localization are discussed in the stages of nucleation and propagation of the bands of elastic and plastic strain localization formed in these zones (including the cases of nanocrystalline structure formation).

  5. Features Analysis of Dry Stone Walls of Tuscany (Italy

    Directory of Open Access Journals (Sweden)

    Mauro Agnoletti

    2015-10-01

    Full Text Available Terraced systems currently represent an indubitable added value for Tuscany, as well as for other Italian regions and for several Mediterranean countries. This value goes beyond their original function of hosting new areas for cultivation. The hydrological functions performed by these systems, including control of erosion, stabilisation of the slopes, prolongation of runoff times and the possible reduction of the volumes of surface runoff, are well-known. In addition, they also play a strategic role in the conservation of biodiversity and in maintaining local identity. At a national level, the terraced agricultural systems fall within the scope of actions scheduled in the National Strategic Plan for Rural Development 2007–2013, and the standards of Good Agricultural and Environmental Conditions (GAECs envisages that they be maintained through the granting of economic aid as laid down in the Rural Development Plans 2007–2013 and 2014–2020. Eighteen sample areas, previously selected on the basis of the terracing intensity index (defined as the ratio between the lines representing the walls and the surface of 1 ha, were subjected for on-site surveys to determine the geo-typological features through the identification and measurement of the main technical-construction parameters of the dry stone walls. This analysis also enabled assessments of the overall state of conservation of the dry stone walls in order to suggest operations for safeguarding and protection.

  6. License plate localization in complex scenes based on oriented FAST and rotated BRIEF feature

    Science.gov (United States)

    Wang, Ran; Xia, Yuanchun; Wang, Guoyou; Tian, Jiangmin

    2015-09-01

    Within intelligent transportation systems, fast and robust license plate localization (LPL) in complex scenes is still a challenging task. Real-world scenes introduce complexities such as variation in license plate size and orientation, uneven illumination, background clutter, and nonplate objects. These complexities lead to poor performance using traditional LPL features, such as color, edge, and texture. Recently, state-of-the-art performance in LPL has been achieved by applying the scale invariant feature transform (SIFT) descriptor to LPL for visual matching. However, for applications that require fast processing, such as mobile phones, SIFT does not meet the efficiency requirement due to its relatively slow computational speed. To address this problem, a new approach for LPL, which uses the oriented FAST and rotated BRIEF (ORB) feature detector, is proposed. The feature extraction in ORB is much more efficient than in SIFT and is invariant to scale and grayscale as well as rotation changes, and hence is able to provide superior performance for LPL. The potential regions of a license plate are detected by considering spatial and color information simultaneously, which is different from previous approaches. The experimental results on a challenging dataset demonstrate the effectiveness and efficiency of the proposed method.

  7. Contact-free palm-vein recognition based on local invariant features.

    Directory of Open Access Journals (Sweden)

    Wenxiong Kang

    Full Text Available Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs, respectively, which demonstrate the effectiveness of the proposed approach.

  8. Contact-free palm-vein recognition based on local invariant features.

    Science.gov (United States)

    Kang, Wenxiong; Liu, Yang; Wu, Qiuxia; Yue, Xishun

    2014-01-01

    Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach.

  9. Feature Selection and Kernel Learning for Local Learning-Based Clustering.

    Science.gov (United States)

    Zeng, Hong; Cheung, Yiu-ming

    2011-08-01

    The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

  10. An alternative to scale-space representation for extracting local features in image recognition

    DEFF Research Database (Denmark)

    Andersen, Hans Jørgen; Nguyen, Phuong Giang

    2012-01-01

    In image recognition, the common approach for extracting local features using a scale-space representation has usually three main steps; first interest points are extracted at different scales, next from a patch around each interest point the rotation is calculated with corresponding orientation...... and compensation, and finally a descriptor is computed for the derived patch (i.e. feature of the patch). To avoid the memory and computational intensive process of constructing the scale-space, we use a method where no scale-space is required This is done by dividing the given image into a number of triangles...... with sizes dependent on the content of the image, at the location of each triangle. In this paper, we will demonstrate that by rotation of the interest regions at the triangles it is possible in grey scale images to achieve a recognition precision comparable with that of MOPS. The test of the proposed method...

  11. Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric

    Science.gov (United States)

    He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Chen, Ying

    2014-05-01

    A multimodal biometric system has been considered a promising technique to overcome the defects of unimodal biometric systems. We have introduced a fusion scheme to gain a better understanding and fusion method for a face-iris-fingerprint multimodal biometric system. In our case, we use particle swarm optimization to train a set of adaptive Gabor filters in order to achieve the proper Gabor basic functions for each modality. For a closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next, all matching scores of the two Gabor features for each modality are projected to a single-scalar score via a trained, supported, vector regression model for a final decision. A large-scale dataset is formed to validate the proposed scheme using the Facial Recognition Technology database-fafb and CASIA-V3-Interval together with FVC2004-DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multimodalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state-of-the-art individual methods and other fusion approaches for face-iris-fingerprint multimodal biometric systems.

  12. A New Curve Tracing Algorithm Based on Local Feature in the Vectorization of Paper Seismograms

    Directory of Open Access Journals (Sweden)

    Maofa Wang

    2014-02-01

    Full Text Available History paper seismograms are very important information for earthquake monitoring and prediction. The vectorization of paper seismograms is an import problem to be resolved. Auto tracing of waveform curves is a key technology for the vectorization of paper seismograms. It can transform an original scanning image into digital waveform data. Accurately tracing out all the key points of each curve in seismograms is the foundation for vectorization of paper seismograms. In the paper, we present a new curve tracing algorithm based on local feature, applying to auto extraction of earthquake waveform in paper seismograms.

  13. Modeling vehicle emissions in different types of Chinese cities: importance of vehicle fleet and local features.

    Science.gov (United States)

    Huo, Hong; Zhang, Qiang; He, Kebin; Yao, Zhiliang; Wang, Xintong; Zheng, Bo; Streets, David G; Wang, Qidong; Ding, Yan

    2011-10-01

    We propose a method to simulate vehicle emissions in Chinese cities of different sizes and development stages. Twenty two cities are examined in this study. The target year is 2007. Among the cities, the vehicle emission factors were remarkably different (the highest is 50-90% higher than the lowest) owing to their distinct local features and vehicle technology levels, and the major contributors to total vehicle emissions were also different. A substantial increase in vehicle emissions is foreseeable unless stronger measures are implemented because the benefit of current policies can be quickly offset by the vehicle growth. Major efforts should be focused on all cities, especially developing cities where the requirements are lenient. This work aims a better understanding of vehicle emissions in all types of Chinese cities. The proposed method could benefit national emission inventory studies in improving accuracy and help in designing national and local policies for vehicle emission control. Copyright © 2011 Elsevier Ltd. All rights reserved.

  14. Global-local feature attention network with reranking strategy for image caption generation

    Science.gov (United States)

    Wu, Jie; Xie, Si-ya; Shi, Xin-bao; Chen, Yao-wen

    2017-11-01

    In this paper, a novel framework, named as global-local feature attention network with reranking strategy (GLAN-RS), is presented for image captioning task. Rather than only adopting unitary visual information in the classical models, GLAN-RS explores the attention mechanism to capture local convolutional salient image maps. Furthermore, we adopt reranking strategy to adjust the priority of the candidate captions and select the best one. The proposed model is verified using the Microsoft Common Objects in Context (MSCOCO) benchmark dataset across seven standard evaluation metrics. Experimental results show that GLAN-RS significantly outperforms the state-of-the-art approaches, such as multimodal recurrent neural network (MRNN) and Google NIC, which gets an improvement of 20% in terms of BLEU4 score and 13 points in terms of CIDER score.

  15. Low temperature features of the local structure of Sm1-xYxS

    International Nuclear Information System (INIS)

    Menushenkov, A. P.; Chernikov, R. V.; Sidorov, V. V.; Klementiev, K. V.; Alekseev, P. A.; Rybina, A. V.

    2007-01-01

    The particular features of the local electronic and local crystal structures of the mixed-valence compound Sm 1-x Y x S are studied by the XAFS spectroscopy methods in the temperature range 20-300 K for the yttrium concentration x = 0.17, 0.25, 0.33, and 0.45. The temperature behavior of the valence of Sm, as well as of the lengths and the Debye-Waller factors of the bonds Sm-S, Sm-Sm(Y), Y-S, and Y-Sm(Y), has been determined. The violation of the Vegard law has been observed. A model for the estimation of the energy width of the 4f level and of its position with respect to the Fermi level is proposed

  16. Semi-Local DFT Functionals with Exact-Exchange-Like Features: Beyond the AK13

    Science.gov (United States)

    Armiento, Rickard

    The Armiento-Kümmel functional from 2013 (AK13) is a non-empirical semi-local exchange functional on generalized gradient approximation form (GGA) in Kohn-Sham (KS) density functional theory (DFT). Recent works have established that AK13 gives improved electronic-structure exchange features over other semi-local methods, with a qualitatively improved orbital description and band structure. For example, the Kohn-Sham band gap is greatly extended, as it is for exact exchange. This talk outlines recent efforts towards new exchange-correlation functionals based on, and extending, the AK13 design ideas. The aim is to improve the quantitative accuracy, the description of energetics, and to address other issues found with the original formulation. Swedish e-Science Research Centre (SeRC).

  17. A comparative analysis of image features between weave embroidered Thangka and piles embroidered Thangka

    Science.gov (United States)

    Li, Zhenjiang; Wang, Weilan

    2018-04-01

    Thangka is a treasure of Tibetan culture. In its digital protection, most of the current research focuses on the content of Thangka images, not the fabrication process. For silk embroidered Thangka of "Guo Tang", there are two craft methods, namely, weave embroidered and piles embroidered. The local texture of weave embroidered Thangka is rough, and that of piles embroidered Thangka is more smooth. In order to distinguish these two kinds of fabrication processes from images, a effectively segmentation algorithm of color blocks is designed firstly, and the obtained color blocks contain the local texture patterns of Thangka image; Secondly, the local texture features of the color block are extracted and screened; Finally, the selected features are analyzed experimentally. The experimental analysis shows that the proposed features can well reflect the difference between methods of weave embroidered and piles embroidered.

  18. Relationship between local deformation behavior and crystallographic features of as-quenched lath martensite during uniaxial tensile deformation

    International Nuclear Information System (INIS)

    Michiuchi, M.; Nambu, S.; Ishimoto, Y.; Inoue, J.; Koseki, T.

    2009-01-01

    Electron backscattering diffraction patterns were used to investigate the relationship between local deformation behavior and the crystallographic features of as-quenched lath martensite of low-carbon steel during uniform elongation in tensile tests. The slip system operating during the deformation up to a strain of 20% was estimated by comparing the crystal rotation of each martensite block after deformation of 20% strain with predictions by the Taylor and Sachs models. The results indicate that the in-lath-plane slip system was preferentially activated compared to the out-of-lath-plane system up to this strain level. Further detailed analysis of crystal rotation at intervals of approximately 5% strain confirmed that the constraint on the operative slip system by the lath structure begins at a strain of 8% and that the local strain hardening of the primary slip systems occurred at approximately 15% strain.

  19. Opinion mining feature-level using Naive Bayes and feature extraction based analysis dependencies

    Science.gov (United States)

    Sanda, Regi; Baizal, Z. K. Abdurahman; Nhita, Fhira

    2015-12-01

    Development of internet and technology, has major impact and providing new business called e-commerce. Many e-commerce sites that provide convenience in transaction, and consumers can also provide reviews or opinions on products that purchased. These opinions can be used by consumers and producers. Consumers to know the advantages and disadvantages of particular feature of the product. Procuders can analyse own strengths and weaknesses as well as it's competitors products. Many opinions need a method that the reader can know the point of whole opinion. The idea emerged from review summarization that summarizes the overall opinion based on sentiment and features contain. In this study, the domain that become the main focus is about the digital camera. This research consisted of four steps 1) giving the knowledge to the system to recognize the semantic orientation of an opinion 2) indentify the features of product 3) indentify whether the opinion gives a positive or negative 4) summarizing the result. In this research discussed the methods such as Naï;ve Bayes for sentiment classification, and feature extraction algorithm based on Dependencies Analysis, which is one of the tools in Natural Language Processing (NLP) and knowledge based dictionary which is useful for handling implicit features. The end result of research is a summary that contains a bunch of reviews from consumers on the features and sentiment. With proposed method, accuration for sentiment classification giving 81.2 % for positive test data, 80.2 % for negative test data, and accuration for feature extraction reach 90.3 %.

  20. A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm.

    Science.gov (United States)

    Wang, Yun-Ting; Peng, Chao-Chung; Ravankar, Ankit A; Ravankar, Abhijeet

    2018-04-23

    In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR) device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP) with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision.

  1. A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm

    Directory of Open Access Journals (Sweden)

    Yun-Ting Wang

    2018-04-01

    Full Text Available In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision.

  2. Feature-Based Statistical Analysis of Combustion Simulation Data

    Energy Technology Data Exchange (ETDEWEB)

    Bennett, J; Krishnamoorthy, V; Liu, S; Grout, R; Hawkes, E; Chen, J; Pascucci, V; Bremer, P T

    2011-11-18

    We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion

  3. LOCAL LINE BINARY PATTERN FOR FEATURE EXTRACTION ON PALM VEIN RECOGNITION

    Directory of Open Access Journals (Sweden)

    Jayanti Yusmah Sari

    2015-08-01

    Full Text Available In recent years, palm vein recognition has been studied to overcome problems in conventional systems in biometrics technology (finger print, face, and iris. Those problems in biometrics includes convenience and performance. However, due to the clarity of the palm vein image, the veins could not be segmented properly. To overcome this problem, we propose a palm vein recognition system using Local Line Binary Pattern (LLBP method that can extract robust features from the palm vein images that has unclear veins. LLBP is an advanced method of Local Binary Pattern (LBP, a texture descriptor based on the gray level comparison of a neighborhood of pixels. There are four major steps in this paper, Region of Interest (ROI detection, image preprocessing, features extraction using LLBP method, and matching using Fuzzy k-NN classifier. The proposed method was applied on the CASIA Multi-Spectral Image Database. Experimental results showed that the proposed method using LLBP has a good performance with recognition accuracy of 97.3%. In the future, experiments will be conducted to observe which parameter that could affect processing time and recognition accuracy of LLBP is needed

  4. Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

    Directory of Open Access Journals (Sweden)

    Su-Zhe Wang

    2016-01-01

    Full Text Available Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario.

  5. Underwater Broadband Source Localization Based on Modal Filtering and Features Extraction

    Directory of Open Access Journals (Sweden)

    Dominique Fattaccioli

    2010-01-01

    Full Text Available Passive source localization is a crucial issue in underwater acoustics. In this paper, we focus on shallow water environment (0 to 400 m and broadband Ultra-Low Frequency acoustic sources (1 to 100 Hz. In this configuration and at a long range, the acoustic propagation can be described by normal mode theory. The propagating signal breaks up into a series of depth-dependent modes. These modes carry information about the source position. Mode excitation factors and mode phases analysis allow, respectively, localization in depth and distance. We propose two different approaches to achieve the localization: multidimensional approach (using a horizontal array of hydrophones based on frequency-wavenumber transform (F-K method and monodimensional approach (using a single hydrophone based on adapted spectral representation (FTa method. For both approaches, we propose first complete tools for modal filtering, and then depth and distance estimators. We show that adding mode sign and source spectrum informations improves considerably the localization performance in depth. The reference acoustic field needed for depth localization is simulated with the new realistic propagation modelMoctesuma. The feasibility of both approaches, F-K and FTa, are validated on data simulated in shallow water for different configurations. The performance of localization, in depth and distance, is very satisfactory.

  6. Underwater Broadband Source Localization Based on Modal Filtering and Features Extraction

    Directory of Open Access Journals (Sweden)

    Cristol Xavier

    2010-01-01

    Full Text Available Passive source localization is a crucial issue in underwater acoustics. In this paper, we focus on shallow water environment (0 to 400 m and broadband Ultra-Low Frequency acoustic sources (1 to 100 Hz. In this configuration and at a long range, the acoustic propagation can be described by normal mode theory. The propagating signal breaks up into a series of depth-dependent modes. These modes carry information about the source position. Mode excitation factors and mode phases analysis allow, respectively, localization in depth and distance. We propose two different approaches to achieve the localization: multidimensional approach (using a horizontal array of hydrophones based on frequency-wavenumber transform ( method and monodimensional approach (using a single hydrophone based on adapted spectral representation ( method. For both approaches, we propose first complete tools for modal filtering, and then depth and distance estimators. We show that adding mode sign and source spectrum informations improves considerably the localization performance in depth. The reference acoustic field needed for depth localization is simulated with the new realistic propagation modelMoctesuma. The feasibility of both approaches, and , are validated on data simulated in shallow water for different configurations. The performance of localization, in depth and distance, is very satisfactory.

  7. Hierarchical representation of shapes in visual cortex - from localized features to figural shape segregation

    Directory of Open Access Journals (Sweden)

    Stephan eTschechne

    2014-08-01

    Full Text Available Visual structures in the environment are effortlessly segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. At this stage, highly articulated changes in shape boundary as well as very subtle curvature changes contribute to the perception of an object.We propose a recurrent computational network architecture that utilizes a hierarchical distributed representation of shape features to encode boundary features over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback from representations generated at higher stages. In so doing, global configurational as well as local information is available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. This combines separate findings about the generation of cortical shape representation using hierarchical representations with figure-ground segregation mechanisms.Our model is probed with a selection of artificial and real world images to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

  8. [Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

    Science.gov (United States)

    Zhou, Jinzhi; Tang, Xiaofang

    2015-08-01

    In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

  9. Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors.

    Science.gov (United States)

    Yang, Wei; Zhong, Liming; Chen, Yang; Lin, Liyan; Lu, Zhentai; Liu, Shupeng; Wu, Yao; Feng, Qianjin; Chen, Wufan

    2018-04-01

    Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.

  10. Analysis of Feature Extraction Methods for Speaker Dependent Speech Recognition

    Directory of Open Access Journals (Sweden)

    Gurpreet Kaur

    2017-02-01

    Full Text Available Speech recognition is about what is being said, irrespective of who is saying. Speech recognition is a growing field. Major progress is taking place on the technology of automatic speech recognition (ASR. Still, there are lots of barriers in this field in terms of recognition rate, background noise, speaker variability, speaking rate, accent etc. Speech recognition rate mainly depends on the selection of features and feature extraction methods. This paper outlines the feature extraction techniques for speaker dependent speech recognition for isolated words. A brief survey of different feature extraction techniques like Mel-Frequency Cepstral Coefficients (MFCC, Linear Predictive Coding Coefficients (LPCC, Perceptual Linear Prediction (PLP, Relative Spectra Perceptual linear Predictive (RASTA-PLP analysis are presented and evaluation is done. Speech recognition has various applications from daily use to commercial use. We have made a speaker dependent system and this system can be useful in many areas like controlling a patient vehicle using simple commands.

  11. Joint Tensor Feature Analysis For Visual Object Recognition.

    Science.gov (United States)

    Wong, Wai Keung; Lai, Zhihui; Xu, Yong; Wen, Jiajun; Ho, Chu Po

    2015-11-01

    Tensor-based object recognition has been widely studied in the past several years. This paper focuses on the issue of joint feature selection from the tensor data and proposes a novel method called joint tensor feature analysis (JTFA) for tensor feature extraction and recognition. In order to obtain a set of jointly sparse projections for tensor feature extraction, we define the modified within-class tensor scatter value and the modified between-class tensor scatter value for regression. The k-mode optimization technique and the L(2,1)-norm jointly sparse regression are combined together to compute the optimal solutions. The convergent analysis, computational complexity analysis and the essence of the proposed method/model are also presented. It is interesting to show that the proposed method is very similar to singular value decomposition on the scatter matrix but with sparsity constraint on the right singular value matrix or eigen-decomposition on the scatter matrix with sparse manner. Experimental results on some tensor datasets indicate that JTFA outperforms some well-known tensor feature extraction and selection algorithms.

  12. SVM-based glioma grading. Optimization by feature reduction analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zoellner, Frank G.; Schad, Lothar R. [University Medical Center Mannheim, Heidelberg Univ., Mannheim (Germany). Computer Assisted Clinical Medicine; Emblem, Kyrre E. [Massachusetts General Hospital, Charlestown, A.A. Martinos Center for Biomedical Imaging, Boston MA (United States). Dept. of Radiology; Harvard Medical School, Boston, MA (United States); Oslo Univ. Hospital (Norway). The Intervention Center

    2012-11-01

    We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values ({proportional_to}87%) while reducing the number of features by up to 98%. (orig.)

  13. [Localized Scleroderma of Lower Extremities:Clinical and Magnetic Resonance Imaging Features].

    Science.gov (United States)

    Wang, Feng-dan; Wang, Hong-wei; Wu, Zhi-hong; Hou, Bo; Jiang, Bo; Zhang, Yan; Feng, Feng; Jin, Zheng-yu; Yuan, Xie

    2015-08-01

    To evaluate the clinical and musculoskeletal characteristics of localized scleroderma with lower extremities affected. All the localized scleroderma patients,who received magnetic resonance (MR ) examinations of affected lower extremities at Peking Union Medical College Hospital from April 2013 to June 2014,were retrospectively reviewed. Their clinical data and laboratory results of antinuclear antibody,anti-double stranded-DNA antibody, and anti-extractable nuclear antigen antibody were collected and analyzed. All the MR examinations were non-contrast imaging using Siemens Skyra 3.0T MR scanner. There were 16 localized scleroderma patients with lower extremities affected, 11 of whom were linear scleroderma, 4 generalized morphea, and 1 deep morphea. Female to male ratio was 1:2.2. The mean age was 22.5 years. The mean time span was 7.4 years. Four of the 14 patients (28.6%) who received antinuclear antibody test were positive. All the 10 patients who received anti-double stranded-DNA antibody test and the 7 patients who received anti-extractable nuclear antigen antibody test were negative. The most common musculoskeletal MR features were subcutaneous septal thickening (16/16) and fascial thickening (11/16). The thickened speta and fascia could either be hypointenstiy or hyperintensity on turbo inversion recovery magnitude/proton density weighted imaging. Other MR manifestations were intramuscular speta thickening (3/16), muscular abnormal signals (1/16), and bone marrow abnormal signals (2/16). Musculoskeletal manifestations of the lower extremities with localized scleroderma can be well revealed using MR imaging.

  14. Numerical analysis of the Anderson localization

    International Nuclear Information System (INIS)

    Markos, P.

    2006-01-01

    The aim of this paper is to demonstrate, by simple numerical simulations, the main transport properties of disordered electron systems. These systems undergo the metal insulator transition when either Fermi energy crosses the mobility edge or the strength of the disorder increases over critical value. We study how disorder affects the energy spectrum and spatial distribution of electronic eigenstates in the diffusive and insulating regime, as well as in the critical region of the metal-insulator transition. Then, we introduce the transfer matrix and conductance, and we discuss how the quantum character of the electron propagation influences the transport properties of disordered samples. In the weakly disordered systems, the weak localization and anti-localization as well as the universal conductance fluctuation are numerically simulated and discussed. The localization in the one dimensional system is described and interpreted as a purely quantum effect. Statistical properties of the conductance in the critical and localized regimes are demonstrated. Special attention is given to the numerical study of the transport properties of the critical regime and to the numerical verification of the single parameter scaling theory of localization. Numerical data for the critical exponent in the orthogonal models in dimension 2 < d ≤ 5 are compared with theoretical predictions. We argue that the discrepancy between the theory and numerical data is due to the absence of the self-averaging of transmission quantities. This complicates the analytical analysis of the disordered systems. Finally, theoretical methods of description of weakly disordered systems are explained and their possible generalization to the localized regime is discussed. Since we concentrate on the one-electron propagation at zero temperature, no effects of electron-electron interaction and incoherent scattering are discussed in the paper (Author)

  15. An application of locally linear model tree algorithm with combination of feature selection in credit scoring

    Science.gov (United States)

    Siami, Mohammad; Gholamian, Mohammad Reza; Basiri, Javad

    2014-10-01

    Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets - Australian and German - from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.

  16. Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

    Science.gov (United States)

    Tschechne, Stephan; Neumann, Heiko

    2014-01-01

    Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

  17. A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images.

    Science.gov (United States)

    Acharya, U Rajendra; Bhat, Shreya; Koh, Joel E W; Bhandary, Sulatha V; Adeli, Hojjat

    2017-09-01

    Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. MRI features of the complete histopathological response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Franklin, J.M., E-mail: jamiemfranklin@hotmail.com [Churchill Hospital, Headington, Oxford (United Kingdom); Anderson, E.M.; Gleeson, F.V. [Churchill Hospital, Headington, Oxford (United Kingdom)

    2012-06-15

    Aim: To describe the post-chemoradiotherapy magnetic resonance imaging (MRI) features of locally advanced rectal carcinoma (LARC) in which there has been a complete histopathological response to neoadjuvant chemoradiotherapy (CRT). Materials and methods: This retrospective cohort study was performed between January 2005 and November 2009 at a regional cancer centre. Consecutive patients with LARC and a histopathological complete response to long-course CRT were identified. Pre- and post-treatment MRI images were reviewed using a proforma for predefined features and response criteria. ymrT0 was defined as the absence of residual abnormality on MRI. Results: Twenty patients were included in the study. Seven (35%) ypT0 tumours were ymrT0. All 13 ypT0 tumours not achieving ymrT0 appearances had a good radiological response, with at least 65% tumour reduction. The appearances were heterogeneous: in 11/13 patients the tumour was replaced by a region of at least 50% low signal on MRI, with 8/13 having {>=}80% low signal, and 3/13 with 100% low signal. Conclusion: MRI may be useful in identifying a complete histopathological response. However, the MRI appearances of ypT0 tumours are heterogeneous and conventional MRI complete response criteria will not detect the majority of patients with a complete histopathological response.

  19. MRI features of the complete histopathological response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy

    International Nuclear Information System (INIS)

    Franklin, J.M.; Anderson, E.M.; Gleeson, F.V.

    2012-01-01

    Aim: To describe the post-chemoradiotherapy magnetic resonance imaging (MRI) features of locally advanced rectal carcinoma (LARC) in which there has been a complete histopathological response to neoadjuvant chemoradiotherapy (CRT). Materials and methods: This retrospective cohort study was performed between January 2005 and November 2009 at a regional cancer centre. Consecutive patients with LARC and a histopathological complete response to long-course CRT were identified. Pre- and post-treatment MRI images were reviewed using a proforma for predefined features and response criteria. ymrT0 was defined as the absence of residual abnormality on MRI. Results: Twenty patients were included in the study. Seven (35%) ypT0 tumours were ymrT0. All 13 ypT0 tumours not achieving ymrT0 appearances had a good radiological response, with at least 65% tumour reduction. The appearances were heterogeneous: in 11/13 patients the tumour was replaced by a region of at least 50% low signal on MRI, with 8/13 having ≥80% low signal, and 3/13 with 100% low signal. Conclusion: MRI may be useful in identifying a complete histopathological response. However, the MRI appearances of ypT0 tumours are heterogeneous and conventional MRI complete response criteria will not detect the majority of patients with a complete histopathological response.

  20. 3D face analysis by using Mesh-LBP feature

    Science.gov (United States)

    Wang, Haoyu; Yang, Fumeng; Zhang, Yuming; Wu, Congzhong

    2017-11-01

    Objective: Face Recognition is one of the widely application of image processing. Corresponding two-dimensional limitations, such as the pose and illumination changes, to a certain extent restricted its accurate rate and further development. How to overcome the pose and illumination changes and the effects of self-occlusion is the research hotspot and difficulty, also attracting more and more domestic and foreign experts and scholars to study it. 3D face recognition fusing shape and texture descriptors has become a very promising research direction. Method: Our paper presents a 3D point cloud based on mesh local binary pattern grid (Mesh-LBP), then feature extraction for 3D face recognition by fusing shape and texture descriptors. 3D Mesh-LBP not only retains the integrity of the 3D geometry, is also reduces the need for recognition process of normalization steps, because the triangle Mesh-LBP descriptor is calculated on 3D grid. On the other hand, in view of multi-modal consistency in face recognition advantage, construction of LBP can fusing shape and texture information on Triangular Mesh. In this paper, some of the operators used to extract Mesh-LBP, Such as the normal vectors of the triangle each face and vertex, the gaussian curvature, the mean curvature, laplace operator and so on. Conclusion: First, Kinect devices obtain 3D point cloud face, after the pretreatment and normalization, then transform it into triangular grid, grid local binary pattern feature extraction from face key significant parts of face. For each local face, calculate its Mesh-LBP feature with Gaussian curvature, mean curvature laplace operator and so on. Experiments on the our research database, change the method is robust and high recognition accuracy.

  1. Assessment of global and local region-based bilateral mammographic feature asymmetry to predict short-term breast cancer risk

    Science.gov (United States)

    Li, Yane; Fan, Ming; Cheng, Hu; Zhang, Peng; Zheng, Bin; Li, Lihua

    2018-01-01

    This study aims to develop and test a new imaging marker-based short-term breast cancer risk prediction model. An age-matched dataset of 566 screening mammography cases was used. All ‘prior’ images acquired in the two screening series were negative, while in the ‘current’ screening images, 283 cases were positive for cancer and 283 cases remained negative. For each case, two bilateral cranio-caudal view mammograms acquired from the ‘prior’ negative screenings were selected and processed by a computer-aided image processing scheme, which segmented the entire breast area into nine strip-based local regions, extracted the element regions using difference of Gaussian filters, and computed both global- and local-based bilateral asymmetrical image features. An initial feature pool included 190 features related to the spatial distribution and structural similarity of grayscale values, as well as of the magnitude and phase responses of multidirectional Gabor filters. Next, a short-term breast cancer risk prediction model based on a generalized linear model was built using an embedded stepwise regression analysis method to select features and a leave-one-case-out cross-validation method to predict the likelihood of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) values significantly increased from 0.5863  ±  0.0237 to 0.6870  ±  0.0220 when the model trained by the image features extracted from the global regions and by the features extracted from both the global and the matched local regions (p  =  0.0001). The odds ratio values monotonically increased from 1.00-8.11 with a significantly increasing trend in slope (p  =  0.0028) as the model-generated risk score increased. In addition, the AUC values were 0.6555  ±  0.0437, 0.6958  ±  0.0290, and 0.7054  ±  0.0529 for the three age groups of 37

  2. Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.

    Science.gov (United States)

    Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; Chen, Huiling; He, Fei; Pang, Yutong

    2014-01-01

    For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

  3. Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture

    Science.gov (United States)

    West, Phillip B [Idaho Falls, ID; Novascone, Stephen R [Idaho Falls, ID; Wright, Jerry P [Idaho Falls, ID

    2011-09-27

    Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture are described. According to one embodiment, an earth analysis method includes engaging a device with the earth, analyzing the earth in a single substantially lineal direction using the device during the engaging, and providing information regarding a subsurface feature of the earth using the analysis.

  4. Linear feature selection in texture analysis - A PLS based method

    DEFF Research Database (Denmark)

    Marques, Joselene; Igel, Christian; Lillholm, Martin

    2013-01-01

    We present a texture analysis methodology that combined uncommitted machine-learning techniques and partial least square (PLS) in a fully automatic framework. Our approach introduces a robust PLS-based dimensionality reduction (DR) step to specifically address outliers and high-dimensional feature...... and considering all CV groups, the methods selected 36 % of the original features available. The diagnosis evaluation reached a generalization area-under-the-ROC curve of 0.92, which was higher than established cartilage-based markers known to relate to OA diagnosis....

  5. LINGUISTIC FEATURES ANALYSIS OF THE ENGLISH ELECTRONIC COMMERCE WEBSITES

    Directory of Open Access Journals (Sweden)

    Siti Nurani

    2014-06-01

    Full Text Available This research aims at identifying linguistic features used in the English electronic commerce websites used in correlation with the field, tenor and mode of discourse as parts of Systemic Functional Linguistics (SFL approach. Findings have shown that in the field of discourse, the linguistic features are largely appeared in the experiential domain analysis which shows that all terms of registers function as technical terms, of which the two major forms of nouns and verbs were the most frequent categories among other kinds of technical terms. The goal orientation is considered to be as a long term and the social activity is exchange. In the tenor of discourse, the linguistic features are highly appeared in the social distance analysis which shows that the social distance between participants is considered minimal. The agentive role is said to be equal and the social role is considered as non-hierarchic. In the mode of discourse, the linguistic features are excessively occurred in the language role analysis which exists equally of both constitutive and ancillary. The channel is in graphic mode. The medium is in written with a visual contact as its device.

  6. Multi-scale Analysis of High Resolution Topography: Feature Extraction and Identification of Landscape Characteristic Scales

    Science.gov (United States)

    Passalacqua, P.; Sangireddy, H.; Stark, C. P.

    2015-12-01

    With the advent of digital terrain data, detailed information on terrain characteristics and on scale and location of geomorphic features is available over extended areas. Our ability to observe landscapes and quantify topographic patterns has greatly improved, including the estimation of fluxes of mass and energy across landscapes. Challenges still remain in the analysis of high resolution topography data; the presence of features such as roads, for example, challenges classic methods for feature extraction and large data volumes require computationally efficient extraction and analysis methods. Moreover, opportunities exist to define new robust metrics of landscape characterization for landscape comparison and model validation. In this presentation we cover recent research in multi-scale and objective analysis of high resolution topography data. We show how the analysis of the probability density function of topographic attributes such as slope, curvature, and topographic index contains useful information for feature localization and extraction. The analysis of how the distributions change across scales, quantified by the behavior of modal values and interquartile range, allows the identification of landscape characteristic scales, such as terrain roughness. The methods are introduced on synthetic signals in one and two dimensions and then applied to a variety of landscapes of different characteristics. Validation of the methods includes the analysis of modeled landscapes where the noise distribution is known and features of interest easily measured.

  7. Towards the maturity model for feature oriented domain analysis

    Directory of Open Access Journals (Sweden)

    Muhammad Javed

    2014-09-01

    Full Text Available Assessing the quality of a model has always been a challenge for researchers in academia and industry. The quality of a feature model is a prime factor because it is used in the development of products. A degraded feature model leads the development of low quality products. Few efforts have been made on improving the quality of feature models. This paper is an effort to present our ongoing work i.e. development of FODA (Feature Oriented Domain Analysis maturity model which will help to evaluate the quality of a given feature model. In this paper, we provide the quality levels along with their descriptions. The proposed model consists of four levels starting from level 0 to level 3. Design of each level is based on the severity of errors, whereas severity of errors decreases from level 0 to level 3. We elaborate each level with the help of examples. We borrowed all examples from the material published by the research community of Software Product Lines (SPL for the application of our framework.

  8. Expanding the boundaries of local similarity analysis.

    Science.gov (United States)

    Durno, W Evan; Hanson, Niels W; Konwar, Kishori M; Hallam, Steven J

    2013-01-01

    Pairwise comparison of time series data for both local and time-lagged relationships is a computationally challenging problem relevant to many fields of inquiry. The Local Similarity Analysis (LSA) statistic identifies the existence of local and lagged relationships, but determining significance through a p-value has been algorithmically cumbersome due to an intensive permutation test, shuffling rows and columns and repeatedly calculating the statistic. Furthermore, this p-value is calculated with the assumption of normality -- a statistical luxury dissociated from most real world datasets. To improve the performance of LSA on big datasets, an asymptotic upper bound on the p-value calculation was derived without the assumption of normality. This change in the bound calculation markedly improved computational speed from O(pm²n) to O(m²n), where p is the number of permutations in a permutation test, m is the number of time series, and n is the length of each time series. The bounding process is implemented as a computationally efficient software package, FASTLSA, written in C and optimized for threading on multi-core computers, improving its practical computation time. We computationally compare our approach to previous implementations of LSA, demonstrate broad applicability by analyzing time series data from public health, microbial ecology, and social media, and visualize resulting networks using the Cytoscape software. The FASTLSA software package expands the boundaries of LSA allowing analysis on datasets with millions of co-varying time series. Mapping metadata onto force-directed graphs derived from FASTLSA allows investigators to view correlated cliques and explore previously unrecognized network relationships. The software is freely available for download at: http://www.cmde.science.ubc.ca/hallam/fastLSA/.

  9. SIMS analysis of extended impact features on LDEF experiment

    Science.gov (United States)

    Amari, S.; Foote, J.; Jessberger, E. K.; Simon, C.; Stadermann, F. J.; Swan, P.; Walker, R.; Zinner, E.

    1991-01-01

    Discussed here are the first Secondary Ion Mass Spectroscopy (SIMS) analysis of projectile material deposited in extended impact features on Ge wafers from the trailing edge. Although most capture cells lost their plastic film covers, they contain extended impact features that apparently were produced by high velocity impacts when the plastic foils were still intact. Detailed optical scanning of all bare capture cells from the trailing edge revealed more than 100 impacts. Fifty-eight were selected by scanning electron microscope (SEM) inspection as prime candidates for SIMS analysis. Preliminary SIMS measurements were made on 15 impacts. More than half showed substantial enhancements of Mg, Al, Si, Ca, and Fe in the impact region, indicating micrometeorites as the projectiles.

  10. Research and Analysis on Energy Consumption Features of Civil Airports

    Science.gov (United States)

    Li, Bo; Zhang, Wen; Wang, Jianping; Xu, Junku; Su, Jixiang

    2017-11-01

    Civil aviation is an important part of China’s transportation system, and also the fastest-growing field of comprehensive transportation. Airports, as a key infrastructure of the air transportation system, are the junctions of air and ground transportation. Large airports are generally comprehensive transportation hubs that integrate various modes of transportation, serving as important functional zones of cities. Compared with other transportation hubs, airports cover a wide area, with plenty of functional sections, complex systems and strong specialization, while airport buildings represented by terminals have exhibited characteristics of large space, massive energy consumption, high requirement for safety and comfort, as well as concentrated and rapidly changing passenger flows. Through research and analysis on energy consumption features of civil airports, and analysis on energy consumption features of airports with different sizes or in different climate regions, this article has drawn conclusions therefrom.

  11. A Study of Feature Extraction Using Divergence Analysis of Texture Features

    Science.gov (United States)

    Hallada, W. A.; Bly, B. G.; Boyd, R. K.; Cox, S.

    1982-01-01

    An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.

  12. Enhancement of Local Climate Analysis Tool

    Science.gov (United States)

    Horsfall, F. M.; Timofeyeva, M. M.; Dutton, J.

    2012-12-01

    The National Oceanographic and Atmospheric Administration (NOAA) National Weather Service (NWS) will enhance its Local Climate Analysis Tool (LCAT) to incorporate specific capabilities to meet the needs of various users including energy, health, and other communities. LCAT is an online interactive tool that provides quick and easy access to climate data and allows users to conduct analyses at the local level such as time series analysis, trend analysis, compositing, correlation and regression techniques, with others to be incorporated as needed. LCAT uses principles of Artificial Intelligence in connecting human and computer perceptions on application of data and scientific techniques in multiprocessing simultaneous users' tasks. Future development includes expanding the type of data currently imported by LCAT (historical data at stations and climate divisions) to gridded reanalysis and General Circulation Model (GCM) data, which are available on global grids and thus will allow for climate studies to be conducted at international locations. We will describe ongoing activities to incorporate NOAA Climate Forecast System (CFS) reanalysis data (CFSR), NOAA model output data, including output from the National Multi Model Ensemble Prediction System (NMME) and longer term projection models, and plans to integrate LCAT into the Earth System Grid Federation (ESGF) and its protocols for accessing model output and observational data to ensure there is no redundancy in development of tools that facilitate scientific advancements and use of climate model information in applications. Validation and inter-comparison of forecast models will be included as part of the enhancement to LCAT. To ensure sustained development, we will investigate options for open sourcing LCAT development, in particular, through the University Corporation for Atmospheric Research (UCAR).

  13. Clinical and laboratory features of systemic sclerosis complicated with localized scleroderma.

    Science.gov (United States)

    Toki, Sayaka; Motegi, Sei-ichiro; Yamada, Kazuya; Uchiyama, Akihiko; Kanai, Sahori; Yamanaka, Masayoshi; Ishikawa, Osamu

    2015-03-01

    Localized scleroderma (LSc) primarily affects skin, whereas systemic sclerosis (SSc) affects skin and various internal organs. LSc and SSc are considered to be basically different diseases, and there is no transition between them. However, LSc and SSc have several common characteristics, including endothelial cell dysfunction, immune activation, and excess fibrosis of the skin, and there exist several SSc cases complicated with LSc during the course of SSc. Clinical and laboratory characteristics of SSc patients with LSc remain unclear. We investigated the clinical and laboratory features of 8 SSc patients with LSc among 220 SSc patients (3.6%). The types of LSc included plaque (5/8), guttate (2/8), and linear type (1/8). All cases were diagnosed as having SSc within 5 years before or after the appearance of LSc. In three cases of SSc with LSc (37.5%), LSc skin lesions preceded clinical symptoms of SSc. Young age, negative antinuclear antibody, and positive anti-RNA polymerase III antibody were significantly prevalent in SSc patients with LSc. The positivity of anticentromere antibody tended to be prevalent in SSc patients without LSc. No significant difference in the frequency of complications, such as interstitial lung disease, reflux esophagitis, and pulmonary artery hypertension, was observed. The awareness of these characteristic of SSc with LSc are essential to establish an early diagnosis and treatment. © 2015 Japanese Dermatological Association.

  14. Clinical features and outcome analysis of intracranial hydatid cysts

    International Nuclear Information System (INIS)

    Khan, M.M.; Shah, M.; Ayub, S.; Ahmad, A.; Aman, A.; Shah, M.A.; Rehman, R.U.

    2016-01-01

    Background: Hydatid cyst of the brain is a serious zoonotic parasitic infection which have profound health consequences if left untreated. The surgical excision of the cysts are rewarding for both the patient the neurosurgeon. Methods: The study was conducted prospectively at Department of Neurosurgery Hayatabad Medical Complex Peshawar from January 2013 to December 2014. Patients with a diagnosis of intracranial hydatid cysts were included, clinical and radiological features recorded, intervention and postoperative outcome were analysed. Results: Eleven patients with a male to female ratio of 1.7:1. Mean age was 12.4 (SD±6.5) years with median GCS on arrival of 10 (SD±2.5). Clinical features were headache (81.8 percentage), vomiting (90.9 percentage), seizures (36.4 percentage), focal deficits (54.5 percentage) and papilloedema (72.7 percentage). The median GCS on discharge was 13 (SD±1.1) while GOS at 1 month follow up was 4 (SD±0.7). The bivariate analysis showed inverse correlation (R2=-0.68; p=0.02) between duration of symptoms and outcome while GCS on admission was positively correlated (rs=0.75; p=0.007) with the outcome. There was no mortality. Conclusion: Despite its rarity the clinical features are non-specific while radiological features help in establishing diagnosis. Earlier diagnosis and prompt intervention is the key to favourable outcome. (author)

  15. Analysis of a cryolava flow-like feature on Titan

    Science.gov (United States)

    Le, Corre L.; Le, Mouelic S.; Sotin, Christophe; Combe, J.-P.; Rodriguez, S.; Barnes, J.W.; Brown, R.H.; Buratti, B.J.; Jaumann, R.; Soderblom, J.; Soderblom, L.A.; Clark, R.; Baines, K.H.; Nicholson, P.D.

    2009-01-01

    This paper reports on the analysis of the highest spatial resolution hyperspectral images acquired by the Visual and Infrared Mapping Spectrometer (VIMS) onboard the Cassini spacecraft during its prime mission. A bright area matches a flow-like feature coming out of a caldera-like feature observed in Synthetic Aperture Radar (SAR) data recorded by the Cassini radar experiment [Lopes et al., 2007. Cryovolcanic features on Titan's surface as revealed by the Cassini Titan Radar Mapper. Icarus 186, 395-412, doi:10.1016/j.icarus.2006.09.006]. In this SAR image, the flow extends about 160 km east of the caldera. The contrast in brightness between the flow and the surroundings progressively vanishes, suggesting alteration or evolution of the composition of the cryolava during the lifetime of the eruptions. Dunes seem to cover part of this flow on its eastern end. We analyze the different terrains using the Spectral Mixing Analysis (SMA) approach of the Multiple-Endmember Linear Unmixing Model (MELSUM, Combe et al., 2008). The study area can be fully modeled by using only two types of terrains. Then, the VIMS spectra are compared with laboratory spectra of known materials in the relevant atmospheric windows (from 1 to 2.78 ??m). We considered simple molecules that could be produced during cryovolcanic events, including H2O, CO2 (using two different grain sizes), CH4 and NH3. We find that the mean spectrum of the cryoflow-like feature is not consistent with pure water ice. It can be best fitted by linear combinations of spectra of the candidate materials, showing that its composition is compatible with a mixture of H2O, CH4 and CO2.. ?? 2009 Elsevier Ltd.

  16. Remodularizing Java Programs for Improved Locality of Feature Implementations in Source Code

    DEFF Research Database (Denmark)

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2011-01-01

    Explicit traceability between features and source code is known to help programmers to understand and modify programs during maintenance tasks. However, the complex relations between features and their implementations are not evident from the source code of object-oriented Java programs....... Consequently, the implementations of individual features are difficult to locate, comprehend, and modify in isolation. In this paper, we present a novel remodularization approach that improves the representation of features in the source code of Java programs. Both forward- and reverse restructurings...... are supported through on-demand bidirectional restructuring between feature-oriented and object-oriented decompositions. The approach includes a feature location phase based of tracing program execution, a feature representation phase that reallocates classes into a new package structure based on single...

  17. WCET Analysis of Java Bytecode Featuring Common Execution Environments

    DEFF Research Database (Denmark)

    Luckow, Kasper Søe; Thomsen, Bent; Frost, Christian

    2011-01-01

    We present a novel tool for statically determining the Worst Case Execution Time (WCET) of Java Bytecode-based programs called Tool for Execution Time Analysis of Java bytecode (TetaJ). This tool differentiates itself from existing tools by separating the individual constituents of the execution...... environment into independent components. The prime benefit is that it can be used for execution environments featuring common embedded processors and software implementations of the JVM. TetaJ employs a model checking approach for statically determining WCET where the Java program, the JVM, and the hardware...

  18. Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

    International Nuclear Information System (INIS)

    Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

    2017-01-01

    Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification. (paper)

  19. Cascade detection for the extraction of localized sequence features; specificity results for HIV-1 protease and structure-function results for the Schellman loop.

    Science.gov (United States)

    Newell, Nicholas E

    2011-12-15

    The extraction of the set of features most relevant to function from classified biological sequence sets is still a challenging problem. A central issue is the determination of expected counts for higher order features so that artifact features may be screened. Cascade detection (CD), a new algorithm for the extraction of localized features from sequence sets, is introduced. CD is a natural extension of the proportional modeling techniques used in contingency table analysis into the domain of feature detection. The algorithm is successfully tested on synthetic data and then applied to feature detection problems from two different domains to demonstrate its broad utility. An analysis of HIV-1 protease specificity reveals patterns of strong first-order features that group hydrophobic residues by side chain geometry and exhibit substantial symmetry about the cleavage site. Higher order results suggest that favorable cooperativity is weak by comparison and broadly distributed, but indicate possible synergies between negative charge and hydrophobicity in the substrate. Structure-function results for the Schellman loop, a helix-capping motif in proteins, contain strong first-order features and also show statistically significant cooperativities that provide new insights into the design of the motif. These include a new 'hydrophobic staple' and multiple amphipathic and electrostatic pair features. CD should prove useful not only for sequence analysis, but also for the detection of multifactor synergies in cross-classified data from clinical studies or other sources. Windows XP/7 application and data files available at: https://sites.google.com/site/cascadedetect/home. nacnewell@comcast.net Supplementary information is available at Bioinformatics online.

  20. Detection and analysis of diamond fingerprinting feature and its application

    Energy Technology Data Exchange (ETDEWEB)

    Li Xin; Huang Guoliang; Li Qiang; Chen Shengyi, E-mail: tshgl@tsinghua.edu.cn [Department of Biomedical Engineering, the School of Medicine, Tsinghua University, Beijing, 100084 (China)

    2011-01-01

    Before becoming a jewelry diamonds need to be carved artistically with some special geometric features as the structure of the polyhedron. There are subtle differences in the structure of this polyhedron in each diamond. With the spatial frequency spectrum analysis of diamond surface structure, we can obtain the diamond fingerprint information which represents the 'Diamond ID' and has good specificity. Based on the optical Fourier Transform spatial spectrum analysis, the fingerprinting identification of surface structure of diamond in spatial frequency domain was studied in this paper. We constructed both the completely coherent diamond fingerprinting detection system illuminated by laser and the partially coherent diamond fingerprinting detection system illuminated by led, and analyzed the effect of the coherence of light source to the diamond fingerprinting feature. We studied rotation invariance and translation invariance of the diamond fingerprinting and verified the feasibility of real-time and accurate identification of diamond fingerprint. With the profit of this work, we can provide customs, jewelers and consumers with a real-time and reliable diamonds identification instrument, which will curb diamond smuggling, theft and other crimes, and ensure the healthy development of the diamond industry.

  1. Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

    Science.gov (United States)

    Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

    2018-04-01

    Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

  2. Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

    Science.gov (United States)

    Shi, Wenzhong; Deng, Susu; Xu, Wenbing

    2018-02-01

    For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should

  3. Analysis of local subassembly accident in KALIMER

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Young Min; Jeong, Kwan Seong; Hahn, Do Hee

    2000-10-01

    Subassembly Accidents (S-A) in the Liquid Metal Reactor (LMR) may cause extensive clad and fuel melting and are thus regarded as a potential whole core accident initiator. The possibility of S-A occurrence must be very low frequency by the design features, and reactor must have specific instrumentation to interrupt the S-A sequences by causing a reactor shutdown. The evaluation of the relevant initiators, the event sequences which follow them, and their detection are the essence of the safety issue. Particularly, the phenomena of flow blockage caused by foreign materials and/or the debris from the failed fuel pin have been researched world-widely. The foreign strategies for dealing with the S-A and the associated safety issues with experimental and theoretical R and D results are reviewed. This report aims at obtaining information to reasonably evaluate the thermal-hydraulic effect of S-A for a wire-wrapped LMR fuel pin bundle. The mechanism of blockage formation and growth within a pin bundle and at the subassembly entrance is reviewed in the phenomenological aspect. Knowledge about the recent LMR subassembly design and operation procedure to prevent flow blockage will be reflected for KALIMER design later. The blockage analysis method including computer codes and related analytical models are reviewed. Especially SABRE4 code is discussed in detail. Preliminary analyses of flow blockage within a 271-pin driver subassembly have been performed using the SABRE4 computer code. As a result no sodium boiling occurred for the central 24-subchannel blockage as well as 6-subchannel blockage.

  4. General tensor discriminant analysis and gabor features for gait recognition.

    Science.gov (United States)

    Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

    2007-10-01

    The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine

  5. Dissociative features in posttraumatic stress disorder: A latent profile analysis.

    Science.gov (United States)

    Műllerová, Jana; Hansen, Maj; Contractor, Ateka A; Elhai, Jon D; Armour, Cherie

    2016-09-01

    The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) characterizes the dissociative subtype of posttraumatic stress disorder (PTSD) in terms of the individual meeting the criteria for PTSD and additionally reporting symptoms of depersonalization and/or derealization. The current study aimed to examine whether a dissociative PTSD profile may include alternative features of dissociation and whether it could be differentiated from a nondissociative PTSD profile on certain psychopathologies and demographics. Data from 309 trauma-exposed participants, collected through Amazon Mechanical Turk, were subjected to latent profile analysis. Regression analyses were used to examine the predictors of latent classes. Three discrete profiles named Baseline, PTSD, and Dissociative profile were uncovered. All examined features of dissociation were significantly elevated in the Dissociative profile. Anxiety, male sex, being employed, and having a minority racial background significantly predicted the Dissociative profile relative to the PTSD profile. The study points to the importance of alternative symptoms of dissociation in the dissociative PTSD subtype beyond the symptoms of depersonalization and derealization. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  6. Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD (Open Access)

    Science.gov (United States)

    2016-02-18

    the database, we propose per -bundle vector of locally aggregated de- scriptors (PBVLAD), where each maximally stable region is described by a vector of...a large area. We propose a per -bundle vector of locally aggregated descriptors (PBVLAD) for fea- ture representation, where each maximally stable

  7. Extended local binary pattern features for improving settlement type classification of quickbird images

    CSIR Research Space (South Africa)

    Mdakane, L

    2012-11-01

    Full Text Available Despite the fact that image texture features extracted from high-resolution remotely sensed images over urban areas have demonstrated their ability to distinguish different classes, they are still far from being ideal. Multiresolution grayscale...

  8. Local strains in waste tank deflagration analysis

    International Nuclear Information System (INIS)

    Bryan, B.J.; Flanders, H.E. Jr.

    1993-01-01

    In recent years extensive effort has been expended to qualify buried nuclear waste storage tanks under accident conditions. One of these conditions is deflagration of the combustible gases which may build up over time. While much work has been done to calculate the general strain state, less effort has been made to address the local strains at structural discontinuities. An analytical method is presented for calculating these local strains and combining them with the general strain state. A closed form solution of the local strains is compared to a finite element solution

  9. Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

    Science.gov (United States)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

    2018-03-01

    Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p breast cancer detected in the next subsequent mammography screening.

  10. Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

    Directory of Open Access Journals (Sweden)

    Kai Liu

    2018-03-01

    Full Text Available The separation of coal and gangue is an important process of the coal preparation technology. The conventional way of manual selection and separation of gangue from the raw coal can be replaced by computer vision technology. In the literature, research on image recognition and classification of coal and gangue is mainly based on the grayscale and texture features of the coal and gangue. However, there are few studies on characteristics of coal and gangue from the perspective of their outline differences. Therefore, the multifractal detrended fluctuation analysis (MFDFA method is introduced in this paper to extract the geometric features of coal and gangue. Firstly, the outline curves of coal and gangue in polar coordinates are detected and achieved along the centroid, thereby the multifractal characteristics of the series are analyzed and compared. Subsequently, the modified local singular spectrum widths Δ h of the outline curve series are extracted as the characteristic variables of the coal and gangue for pattern recognition. Finally, the extracted geometric features by MFDFA combined with the grayscale and texture features of the images are compared with other methods, indicating that the recognition rate of coal gangue images can be increased by introducing the geometric features.

  11. Arabic Feature-Based Level Sentiment Analysis Using Lexicon ...

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... structured reviews being prior knowledge for mining unstructured reviews. ... FDSO has been introduced, which defines a space of product features ... polarity of a review using feature ontology and sentiment lexicons.

  12. Prediction of human protein function from post-translational modifications and localization features

    DEFF Research Database (Denmark)

    Jensen, Lars Juhl; Gupta, Ramneek; Blom, Nikolaj

    2002-01-01

    a number of functional attributes that are more directly related to the linear sequence of amino acids, and hence easier to predict, than protein structure. These attributes include features associated with post-translational modifications and protein sorting, but also much simpler aspects......We have developed an entirely sequence-based method that identifies and integrates relevant features that can be used to assign proteins of unknown function to functional classes, and enzyme categories for enzymes. We show that strategies for the elucidation of protein function may benefit from...

  13. Analysis of Non Local Image Denoising Methods

    Science.gov (United States)

    Pardo, Álvaro

    Image denoising is probably one of the most studied problems in the image processing community. Recently a new paradigm on non local denoising was introduced. The Non Local Means method proposed by Buades, Morel and Coll attracted the attention of other researches who proposed improvements and modifications to their proposal. In this work we analyze those methods trying to understand their properties while connecting them to segmentation based on spectral graph properties. We also propose some improvements to automatically estimate the parameters used on these methods.

  14. The westernmost locality of Macrosciadium alatum (Apiaceae in Europe and a new diagnostic feature of the species

    Directory of Open Access Journals (Sweden)

    Proćkуw Jarosław

    2014-01-01

    Full Text Available A new locality of Macrosciadium alatum in the Western Bieszczady Mts. (Duszatyn, Komańcza district, Sanok county is described in this paper. The locality is currently the westernmost point of distribution of the species. As Macrosciadium alatum is an invasive species, it is advisable to monitor it cyclically in this area. A newly discovered diagnostic feature of this species, not included in descriptions of this plant so far, is conically elongated cells, i.e. papillae on the upper side of the petals. It is suggested that this feature be used in the identification of representatives of the Apiaceae family in Poland and Europe. The distribution map of the species has been updated in this work.

  15. Featuring Multiple Local Optima to Assist the User in the Interpretation of Induced Bayesian Network Models

    DEFF Research Database (Denmark)

    Dalgaard, Jens; Pena, Jose; Kocka, Tomas

    2004-01-01

    We propose a method to assist the user in the interpretation of the best Bayesian network model indu- ced from data. The method consists in extracting relevant features from the model (e.g. edges, directed paths and Markov blankets) and, then, assessing the con¯dence in them by studying multiple...

  16. Preliminary safety analysis for key design features of KALIMER

    Energy Technology Data Exchange (ETDEWEB)

    Hahn, D. H.; Kwon, Y. M.; Chang, W. P.; Suk, S. D.; Lee, S. O.; Lee, Y. B.; Jeong, K. S

    2000-07-01

    KAERI is currently developing the conceptual design of a liquid metal reactor, KALIMER(Korea Advanced Liquid Metal Reactor) under the long-term nuclear R and D program. In this report, descriptions of the KALIMER safety design features and safety analyses results for selected ATWS accidents are presented. First, the basic approach to achieve the safety goal is introduced in chapter 1, and the safety evaluation procedure for the KALIMER design is described in chapter 2. It includes event selection, event categorization, description of design basis events, and beyond design basis events. In chapter 3, results of inherent safety evaluations for the KALIMER conceptual design are presented. The KALIMER core and plant system are designed to assure design performance during a selected set of events without either reactor control or protection system intervention. Safety analyses for the postulated anticipated transient without scram(ATWS) have been performed to investigate the KALIMER system response to the events. They are categorized as bounding events(BEs) because of their low probability of occurrence. In chapter 4, the design of the KALIMER containment dome and the results of its performance analysis are presented. The designs of the existing LMR containment and the KALIMER containment dome have been compared in this chapter. Procedure of the containment performance analysis and the analysis results are described along with the accident scenario and source terms. Finally, a simple methodology is introduced to investigate the core kinetics and hydraulic behavior during HCDA in chapter 5. Mathematical formulations have been developed in the framework of the modified bethe-tait method, and scoping analyses have been performed for the KALIMER core behavior during super-prompt critical excursions.

  17. Datum Feature Extraction and Deformation Analysis Method Based on Normal Vector of Point Cloud

    Science.gov (United States)

    Sun, W.; Wang, J.; Jin, F.; Liang, Z.; Yang, Y.

    2018-04-01

    In order to solve the problem lacking applicable analysis method in the application of three-dimensional laser scanning technology to the field of deformation monitoring, an efficient method extracting datum feature and analysing deformation based on normal vector of point cloud was proposed. Firstly, the kd-tree is used to establish the topological relation. Datum points are detected by tracking the normal vector of point cloud determined by the normal vector of local planar. Then, the cubic B-spline curve fitting is performed on the datum points. Finally, datum elevation and the inclination angle of the radial point are calculated according to the fitted curve and then the deformation information was analyzed. The proposed approach was verified on real large-scale tank data set captured with terrestrial laser scanner in a chemical plant. The results show that the method could obtain the entire information of the monitor object quickly and comprehensively, and reflect accurately the datum feature deformation.

  18. Fungal ABC Transporter Deletion and Localization Analysis

    NARCIS (Netherlands)

    Kovalchuk, A.; Weber, S.S.; Nijland, J.G.; Bovenberg, R.A.L.; Driessen, A.J.M.

    2012-01-01

    Fungal cells are highly complex as their metabolism is compartmentalized harboring various types of subcellular organelles that are bordered by one or more membranes. Knowledge about the intracellular localization of transporter proteins is often required for the understanding of their biological

  19. Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

    International Nuclear Information System (INIS)

    Wang Xiaojia; Mao Qirong; Zhan Yongzhao

    2008-01-01

    There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions. The experiments show that this method can improve the recognition rate and the time of feature extraction

  20. Development of local TDC model in core thermal hydraulic analysis

    International Nuclear Information System (INIS)

    Kwon, H.S.; Park, J.R.; Hwang, D.H.; Lee, S.K.

    2004-01-01

    The local TDC model consisting of natural mixing and forced mixing part was developed to obtain more realistic local fluid properties in the core subchannel analysis. To evaluate the performance of local TDC model, the CHF prediction capability was tested with the various CHF correlations and local fluid properties at CHF location which are based on the local TDC model. The results show that the standard deviation of measured to predicted CHF ratio (M/P) based on local TDC model can be reduced by about 7% compared to those based on global TDC model when the CHF correlation has no term to account for distance from the spacer grid. (author)

  1. Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

    Science.gov (United States)

    Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

    2016-09-01

    The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

  2. Smoothed Analysis of Local Search Algorithms

    NARCIS (Netherlands)

    Manthey, Bodo; Dehne, Frank; Sack, Jörg-Rüdiger; Stege, Ulrike

    2015-01-01

    Smoothed analysis is a method for analyzing the performance of algorithms for which classical worst-case analysis fails to explain the performance observed in practice. Smoothed analysis has been applied to explain the performance of a variety of algorithms in the last years. One particular class of

  3. SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

    International Nuclear Information System (INIS)

    Soufi, M; Arimura, H; Toyofuku, F; Nakamura, K; Hirose, T; Umezu, Y; Shioyama, Y

    2016-01-01

    Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

  4. SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Soufi, M; Arimura, H; Toyofuku, F [Kyushu University, Fukuoka, Fukuoka (Japan); Nakamura, K [Hamamatsu University School of Medicine, Hamamatsu, Shizuoka (Japan); Hirose, T; Umezu, Y [Kyushu University Hospital, Fukuoka, Fukuoka (Japan); Shioyama, Y [Saga Heavy Ion Medical Accelerator in Tosu, Tosu, Saga (Japan)

    2016-06-15

    Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

  5. Learned Compact Local Feature Descriptor for Tls-Based Geodetic Monitoring of Natural Outdoor Scenes

    Science.gov (United States)

    Gojcic, Z.; Zhou, C.; Wieser, A.

    2018-05-01

    The advantages of terrestrial laser scanning (TLS) for geodetic monitoring of man-made and natural objects are not yet fully exploited. Herein we address one of the open challenges by proposing feature-based methods for identification of corresponding points in point clouds of two or more epochs. We propose a learned compact feature descriptor tailored for point clouds of natural outdoor scenes obtained using TLS. We evaluate our method both on a benchmark data set and on a specially acquired outdoor dataset resembling a simplified monitoring scenario where we successfully estimate 3D displacement vectors of a rock that has been displaced between the scans. We show that the proposed descriptor has the capacity to generalize to unseen data and achieves state-of-the-art performance while being time efficient at the matching step due the low dimension.

  6. Digital Image Forgery Detection Using JPEG Features and Local Noise Discrepancies

    Directory of Open Access Journals (Sweden)

    Bo Liu

    2014-01-01

    Full Text Available Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image.

  7. Analysis on CT features of tumor-like gastric schwannomas

    International Nuclear Information System (INIS)

    Zhang Yu; Chen Jie

    2011-01-01

    Objective: To analyze CT imaging features of tumor-like gastric schwannomas. Methods: Ten patients with gastric schwannomas were retrospectively analyzed. All were scanned with pre-and pro-enhanced CT. Analysis of the CT findings included evaluation of the volume, number, location, contour, growth pattern, border, enhancement pattern, and enhancement grade as well as the presence of surface dimpling, integrity of overlying mucosa. All cases were confirmed by pathology. Results: In every case, simple tumor was present. The largest was about 5.7 cm in the diameter, the smallest was 2.3 cm. All tumors were round or oval, and one tumor was slightly lobulated. Endoluminal growth pattern was defined in two cases, exoluminal growth pattern was defined in one case, and a mixed growth pattern was noted in the rest. The borders of tumors were clear. In arterial phase, no visible enhancement was present in eight cases and mild enhancement in two cases. All cases were constantly enhanced in portal phase. Superficial ulcers were present in four cases. Conclusion: CT findings of tumor-like gastric schwannomas are distinctive to a certain degree. It can be used to guide clinical therapy. (authors)

  8. Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

    Directory of Open Access Journals (Sweden)

    Ting-quan Deng

    2014-01-01

    Full Text Available In the visual tracking scenarios, if there are multiple objects, due to the interference of similar objects, tracking may fail in the progress of occlusion to separation. To address this problem, this paper proposed a visual tracking algorithm with discrimination through multimanifold learning. Color-gradient-based feature tensor was used to describe object appearance for accommodation of partial occlusion. A prior multimanifold tensor dataset is established through the template matching tracking algorithm. For the purpose of discrimination, tensor distance was defined to determine the intramanifold and intermanifold neighborhood relationship in multimanifold space. Then multimanifold discriminate analysis was employed to construct multilinear projection matrices of submanifolds. Finally, object states were obtained by combining with sequence inference. Meanwhile, the multimanifold dataset and manifold learning embedded projection should be updated online. Experiments were conducted on two real visual surveillance sequences to evaluate the proposed algorithm with three state-of-the-art tracking methods qualitatively and quantitatively. Experimental results show that the proposed algorithm can achieve effective and robust effect in multi-similar-object mutual occlusion scenarios.

  9. Acne image analysis: lesion localization and classification

    Science.gov (United States)

    Abas, Fazly Salleh; Kaffenberger, Benjamin; Bikowski, Joseph; Gurcan, Metin N.

    2016-03-01

    Acne is a common skin condition present predominantly in the adolescent population, but may continue into adulthood. Scarring occurs commonly as a sequel to severe inflammatory acne. The presence of acne and resultant scars are more than cosmetic, with a significant potential to alter quality of life and even job prospects. The psychosocial effects of acne and scars can be disturbing and may be a risk factor for serious psychological concerns. Treatment efficacy is generally determined based on an invalidated gestalt by the physician and patient. However, the validated assessment of acne can be challenging and time consuming. Acne can be classified into several morphologies including closed comedones (whiteheads), open comedones (blackheads), papules, pustules, cysts (nodules) and scars. For a validated assessment, the different morphologies need to be counted independently, a method that is far too time consuming considering the limited time available for a consultation. However, it is practical to record and analyze images since dermatologists can validate the severity of acne within seconds after uploading an image. This paper covers the processes of region-ofinterest determination using entropy-based filtering and thresholding as well acne lesion feature extraction. Feature extraction methods using discrete wavelet frames and gray-level co-occurence matrix were presented and their effectiveness in separating the six major acne lesion classes were discussed. Several classifiers were used to test the extracted features. Correct classification accuracy as high as 85.5% was achieved using the binary classification tree with fourteen principle components used as descriptors. Further studies are underway to further improve the algorithm performance and validate it on a larger database.

  10. Analysis of clinical features and visual outcomes of pars planitis.

    Science.gov (United States)

    Berker, Nilufer; Sen, Emine; Elgin, Ufuk; Atilgan, Cemile Ucgul; Dursun, Erdem; Yilmazbas, Pelin

    2018-04-01

    To evaluate the demographic characteristics, clinical features, treatment and outcomes of patients with pars planitis in a tertiary referral center in Turkey. Medical records of patients with pars planitis were retrospectively reviewed. The data including demographic and ocular features and treatment outcomes were recorded. The distribution of clinical findings and complications were evaluated according to age and gender groups. The changes in final BCVA compared to the initial BCVA were noted. Statistical analysis was performed using SPSS software (Version 18.0, SPSS Inc., Chicago, USA). Twenty-seven patients (54 eyes) were included in this study. 16 patients were male (59.3%), and 11 were female (40.7%). Mean age at diagnosis was 12.84 ± 8.26 (range 4-36) years. Mean follow-up period was 61.3 ± 52.15 (range 9-172) months. Mean BCVA was 0.58 ± 0.36 (range 0.03-1.00) (0.40 ± 0.45 logMAR) at presentation, and 0.81 ± 0.28 (range 0.10-1.00) (0.14 ± 0.27 logMAR) at final visit (P = 0.001). Vitreous inflammation (100%), vitreous haze (92.6%), snowballs (74.1%), snowbanks (66.7%), anterior chamber cells (66.7%) and peripheral retinal vascular sheathing (48.1%) were the most common presentations. Ocular complications included vitreous condensation (51.9%), cystoid macular edema (22.2%), cataract (18.5%), inferior peripheral retinal detachment (11.1%), glaucoma (5.6%) and vitreous hemorrhage (3.7%). Treatments included topical, periocular, intravitreal and systemic corticosteroids, immunosuppressives, peripheral laser photocoagulation and pars plana vitrectomy when needed. Pars planitis is an idiopathic chronic intermediate uveitis mostly affecting children and adolescents. In spite of its chronic nature with high potential of causing ocular complications, adequate treatment and close follow-up lead to favorable visual outcomes.

  11. Analysis of Local Financial Management Transparency Based on Websites on Local Government in Java

    Directory of Open Access Journals (Sweden)

    Anissa Adriana

    2018-03-01

    Full Text Available The aim of this research is to analyze financial management transparency of local governments in Java using scoring and rating. The financial management transparency of the local governments is scored based on presentation of local financial information uploaded on each local government’s official website in Jawa in the fiscal years 2016.This research is a qualitative research with the object of research is all local government in Java. Data analysis in two levels, namely the transparency of local government financial management and identification of local government characteristics based on transparency of financial management. Data analysis in two levels, namely the transparency of local government financial management and identification of local government characteristics based on transparency of financial management. The results show that the Special Capital Region of Jakarta obtained the highest transparency index, at 58, 02% whereas Madiun Regency received the lowest transparency index, at 3, 40%. The average transparency index in Jawa for the fiscal years 2016 was still low, at only 19, 59%.The conclusion of this research is that Java regional governments consider the transparency of local financial management using less important websites because it is considered as a better thing not delivered to the public.

  12. Yucca Mountain Feature, Event, and Process (FEP) Analysis

    International Nuclear Information System (INIS)

    Freeze, G.

    2005-01-01

    A Total System Performance Assessment (TSPA) model was developed for the U.S. Department of Energy (DOE) Yucca Mountain Project (YMP) to help demonstrate compliance with applicable postclosure regulatory standards and support the License Application (LA). Two important precursors to the development of the TSPA model were (1) the identification and screening of features, events, and processes (FEPs) that might affect the Yucca Mountain disposal system (i.e., FEP analysis), and (2) the formation of scenarios from screened in (included) FEPs to be evaluated in the TSPA model (i.e., scenario development). YMP FEP analysis and scenario development followed a five-step process: (1) Identify a comprehensive list of FEPs potentially relevant to the long-term performance of the disposal system. (2) Screen the FEPs using specified criteria to identify those FEPs that should be included in the TSPA analysis and those that can be excluded from the analysis. (3) Form scenarios from the screened in (included) FEPs. (4) Screen the scenarios using the same criteria applied to the FEPs to identify any scenarios that can be excluded from the TSPA, as appropriate. (5) Specify the implementation of the scenarios in the computational modeling for the TSPA, and document the treatment of included FEPs. This paper describes the FEP analysis approach (Steps 1 and 2) for YMP, with a brief discussion of scenario formation (Step 3). Details of YMP scenario development (Steps 3 and 4) and TSPA modeling (Step 5) are beyond scope of this paper. The identification and screening of the YMP FEPs was an iterative process based on site-specific information, design, and regulations. The process was iterative in the sense that there were multiple evaluation and feedback steps (e.g., separate preliminary, interim, and final analyses). The initial YMP FEP list was compiled from an existing international list of FEPs from other radioactive waste disposal programs and was augmented by YMP site- and design

  13. A comparative analysis of DNA barcode microarray feature size

    Directory of Open Access Journals (Sweden)

    Smith Andrew M

    2009-10-01

    Full Text Available Abstract Background Microarrays are an invaluable tool in many modern genomic studies. It is generally perceived that decreasing the size of microarray features leads to arrays with higher resolution (due to greater feature density, but this increase in resolution can compromise sensitivity. Results We demonstrate that barcode microarrays with smaller features are equally capable of detecting variation in DNA barcode intensity when compared to larger feature sizes within a specific microarray platform. The barcodes used in this study are the well-characterized set derived from the Yeast KnockOut (YKO collection used for screens of pooled yeast (Saccharomyces cerevisiae deletion mutants. We treated these pools with the glycosylation inhibitor tunicamycin as a test compound. Three generations of barcode microarrays at 30, 8 and 5 μm features sizes independently identified the primary target of tunicamycin to be ALG7. Conclusion We show that the data obtained with 5 μm feature size is of comparable quality to the 30 μm size and propose that further shrinking of features could yield barcode microarrays with equal or greater resolving power and, more importantly, higher density.

  14. Job Analysis: A Local Government's Experience.

    Science.gov (United States)

    Urbanek, Steve J.

    1997-01-01

    A county personnel department undertook reclassification of all positions by collecting and using job analysis data to rewrite job descriptions. External pay equity and validated selection procedures resulted with only a modest increase in payroll costs. (SK)

  15. Predominant membrane localization is an essential feature of the bacterial signal recognition particle receptor

    Directory of Open Access Journals (Sweden)

    Graumann Peter

    2009-11-01

    Full Text Available Abstract Background The signal recognition particle (SRP receptor plays a vital role in co-translational protein targeting, because it connects the soluble SRP-ribosome-nascent chain complex (SRP-RNCs to the membrane bound Sec translocon. The eukaryotic SRP receptor (SR is a heterodimeric protein complex, consisting of two unrelated GTPases. The SRβ subunit is an integral membrane protein, which tethers the SRP-interacting SRα subunit permanently to the endoplasmic reticulum membrane. The prokaryotic SR lacks the SRβ subunit and consists of only the SRα homologue FtsY. Strikingly, although FtsY requires membrane contact for functionality, cell fractionation studies have localized FtsY predominantly to the cytosolic fraction of Escherichia coli. So far, the exact function of the soluble SR in E. coli is unknown, but it has been suggested that, in contrast to eukaryotes, the prokaryotic SR might bind SRP-RNCs already in the cytosol and only then initiates membrane targeting. Results In the current study we have determined the contribution of soluble FtsY to co-translational targeting in vitro and have re-analysed the localization of FtsY in vivo by fluorescence microscopy. Our data show that FtsY can bind to SRP-ribosome nascent chains (RNCs in the absence of membranes. However, these soluble FtsY-SRP-RNC complexes are not efficiently targeted to the membrane. In contrast, we observed effective targeting of SRP-RNCs to membrane-bond FtsY. These data show that soluble FtsY does not contribute significantly to cotranslational targeting in E. coli. In agreement with this observation, our in vivo analyses of FtsY localization in bacterial cells by fluorescence microscopy revealed that the vast majority of FtsY was localized to the inner membrane and that soluble FtsY constituted only a negligible species in vivo. Conclusion The exact function of the SRP receptor (SR in bacteria has so far been enigmatic. Our data show that the bacterial SR is

  16. Imaging features in calcinosis circumscripta, a rare type of subcutaneous calcification in localized scleroderma

    Directory of Open Access Journals (Sweden)

    Pratiksha Yadav

    2013-01-01

    Full Text Available Calcinosis cutis circumscripta is a rare condition in which abnormal deposition of calcium seen in the dermis and subcutaneous tissue, it is associated with localized scleroderma. A 30-year-old female presented with an area of extensive calcification involving the right gluteal region, lateral aspect of right thigh and a small area on left thigh detected on radiograph with atrophy of subcutaneous tissue. Magnetic resonance imaging and computed tomography were done for further evaluation and the findings were of calcification and atrophy involving the skin and subcutaneous tissue.

  17. Fault feature analysis of cracked gear based on LOD and analytical-FE method

    Science.gov (United States)

    Wu, Jiateng; Yang, Yu; Yang, Xingkai; Cheng, Junsheng

    2018-01-01

    At present, there are two main ideas for gear fault diagnosis. One is the model-based gear dynamic analysis; the other is signal-based gear vibration diagnosis. In this paper, a method for fault feature analysis of gear crack is presented, which combines the advantages of dynamic modeling and signal processing. Firstly, a new time-frequency analysis method called local oscillatory-characteristic decomposition (LOD) is proposed, which has the attractive feature of extracting fault characteristic efficiently and accurately. Secondly, an analytical-finite element (analytical-FE) method which is called assist-stress intensity factor (assist-SIF) gear contact model, is put forward to calculate the time-varying mesh stiffness (TVMS) under different crack states. Based on the dynamic model of the gear system with 6 degrees of freedom, the dynamic simulation response was obtained for different tooth crack depths. For the dynamic model, the corresponding relation between the characteristic parameters and the degree of the tooth crack is established under a specific condition. On the basis of the methods mentioned above, a novel gear tooth root crack diagnosis method which combines the LOD with the analytical-FE is proposed. Furthermore, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) are contrasted with the LOD by gear crack fault vibration signals. The analysis results indicate that the proposed method performs effectively and feasibility for the tooth crack stiffness calculation and the gear tooth crack fault diagnosis.

  18. Numerical prediction of local transitional features of turbulent forced gas flows in circular tubes with strong heating

    International Nuclear Information System (INIS)

    Ezato, Koichiro; Kunugi, Tomoaki; Shehata, A.M.; McEligot, D.M.

    1997-03-01

    Previous numerical simulation for the laminarization due to heating of the turbulent flow in pipe were assessed by comparison with only macroscopic characteristics such as heat transfer coefficient and pressure drop, since no experimental data on the local distributions of the velocity and temperature in such flow situation was available. Recently, Shehata and McEligot reported the first measurements of local distributions of velocity and temperature for turbulent forced air flow in a vertical circular tube with strongly heating. They carried out the experiments in three situations from turbulent flow to laminarizing flow according to the heating rate. In the present study, we analyzed numerically the local transitional features of turbulent flow evolving laminarizing due to strong heating in their experiments by using the advanced low-Re two-equation turbulence model. As the result, we successfully predicted the local distributions of velocity and temperature as well as macroscopic characteristics in three turbulent flow conditions. By the present study, a numerical procedure has been established to predict the local characteristics such as velocity distribution of the turbulent flow with large thermal-property variation and laminarizing flow due to strong heating with enough accuracy. (author). 60 refs

  19. Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters

    Science.gov (United States)

    He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Dong, Hongxing

    2014-12-01

    Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.

  20. Analysis of computed tomography features of fungal sinusitis and ...

    African Journals Online (AJOL)

    CT) features of fungal sinusitis and to correlate them with nasal endoscopy and histopathological findings. Materials and Methods: Our study included 16 patients of either sex and any age group who presented in the otorhinolaryngology clinic at ...

  1. Numerical Analysis for Relevant Features in Intrusion Detection (NARFid)

    Science.gov (United States)

    2009-03-01

    Error and Average Correlation Coefficient. Mucciardi and Gose [63] discuss seven methods for selecting features. These methods seek to overcome the...POEmaxPOEmin). (2.37) With each iteration of selecting the next feature, ACC is also normalized in the same fashion. As stated by Mucciardi and Gose ...lan’s discussion [70] as described in Section 2.3.1. Mucciardi and Gose [63] provide the POEACC parameters that perform well in their experiments. As

  2. Proto-object categorisation and local gist vision using low-level spatial features.

    Science.gov (United States)

    Martins, Jaime A; Rodrigues, J M F; du Buf, J M H

    2015-09-01

    Object categorisation is a research area with significant challenges, especially in conditions with bad lighting, occlusions, different poses and similar objects. This makes systems that rely on precise information unable to perform efficiently, like a robotic arm that needs to know which objects it can reach. We propose a biologically inspired object detection and categorisation framework that relies on robust low-level object shape. Using only edge conspicuity and disparity features for scene figure-ground segregation and object categorisation, a trained neural network classifier can quickly categorise broad object families and consequently bootstrap a low-level scene gist system. We argue that similar processing is possibly located in the parietal pathway leading to the LIP cortex and, via areas V5/MT and MST, providing useful information to the superior colliculus for eye and head control. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  3. Analysis of iris surface features in populations of diverse ancestry

    Science.gov (United States)

    Edwards, Melissa; Cha, David; Krithika, S.; Johnson, Monique; Parra, Esteban J.

    2016-01-01

    There are many textural elements that can be found in the human eye, including Fuchs’ crypts, Wolfflin nodules, pigment spots, contraction furrows and conjunctival melanosis. Although iris surface features have been well-studied in populations of European ancestry, the worldwide distribution of these traits is poorly understood. In this paper, we develop a new method of characterizing iris features from photographs of the iris. We then apply this method to a diverse sample of East Asian, European and South Asian ancestry. All five iris features showed significant differences in frequency between the three populations, indicating that iris features are largely population dependent. Although none of the features were correlated with each other in the East and South Asian groups, Fuchs’ crypts were significantly correlated with contraction furrows and pigment spots and contraction furrows were significantly associated with pigment spots in the European group. The genetic marker SEMA3A rs10235789 was significantly associated with Fuchs’ crypt grade in the European, East Asian and South Asian samples and a borderline association between TRAF3IP1 rs3739070 and contraction furrow grade was found in the European sample. The study of iris surface features in diverse populations may provide valuable information of forensic, biomedical and ophthalmological interest. PMID:26909168

  4. A Stable Metal-Organic Framework Featuring a Local Buffer Environment for Carbon Dioxide Fixation.

    Science.gov (United States)

    He, Hongming; Sun, Qi; Gao, Wenyang; Perman, Jason A; Sun, Fuxing; Zhu, Guangshan; Aguila, Briana; Forrest, Katherine; Space, Brian; Ma, Shengqian

    2018-04-16

    A majority of metal-organic frameworks (MOFs) fail to preserve their physical and chemical properties after exposure to acidic, neutral, or alkaline aqueous solutions, therefore limiting their practical applications in many areas. The strategy demonstrated herein is the design and synthesis of an organic ligand that behaves as a buffer to drastically boost the aqueous stability of a porous MOF (JUC-1000), which maintains its structural integrity at low and high pH values. The local buffer environment resulting from the weak acid-base pairs of the custom-designed organic ligand also greatly facilitates the performance of JUC-1000 in the chemical fixation of carbon dioxide under ambient conditions, outperforming a series of benchmark catalysts. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Clinical features of patients with subarachnoid hemorrhage arriving through a referral from a local primary hospital

    International Nuclear Information System (INIS)

    Yamazaki, Takaaki; Kubota, Tsukasa; Shimazaki, Mitsunori

    2010-01-01

    We investigated the current state of and problems in patients with subarachnoid hemorrhage initially diagnosed and treated by general physicians at a local referring hospital and subsequently transferred to our hospital for neurosurgical treatment. We studied 37 consecutive patients with subarachnoid hemorrhage over a 7-year period from April 2001 to March 2008. A total of 7 men and 30 women aged 50 to 89 years (average: 71.2±9.5 years) were included in this study. Thirteen patients (35.1%) were referred to our hospital with diagnoses other than subarachnoid hemorrhage. Twenty-three of 27 patients who had CT scans were diagnosed correctly in the referring hospital, while only 1 of 10 patients was correctly diagnosed without CT. Time from the onset to admission to our hospital ranged from 85 minutes to 144 hours (average: 15.3±29 hours). The reasons of delay in patients who took more than 12 hours to reach us were patients' delay in visiting the referring hospital in 3 cases and uncertain initial diagnosis in 6. All 6 cases complained of sudden headache, but did not undergo CT. All patients were transferred by an ambulance car, and the duration of transfer ranged from 60 to 120 minutes (average: 85.4±15.7 minutes). None of the patients experienced rebleeding during transfer. Subarachnoid hemorrhage can be diagnosed correctly at the local primary hospital with CT, allowing appropriate primary treatments. Initial misdiagnosis is the major cause of delay in transferring patients to neurosurgical facilities. (author)

  6. HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

    Science.gov (United States)

    Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

    2014-01-01

    Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

  7. local

    Directory of Open Access Journals (Sweden)

    Abílio Amiguinho

    2005-01-01

    Full Text Available The process of socio-educational territorialisation in rural contexts is the topic of this text. The theme corresponds to a challenge to address it having as main axis of discussion either the problem of social exclusion or that of local development. The reasons to locate the discussion in this last field of analysis are discussed in the first part of the text. Theoretical and political reasons are there articulated because the question is about projects whose intentions and practices call for the political both in the theoretical debate and in the choices that anticipate intervention. From research conducted for several years, I use contributions that aim at discuss and enlighten how school can be a potential locus of local development. Its identification and recognition as local institution (either because of those that work and live in it or because of those that act in the surrounding context are crucial steps to progressively constitute school as a partner for development. The promotion of the local values and roots, the reconstruction of socio-personal and local identities, the production of sociabilities and the equation and solution of shared problems were the dimensions of a socio-educative intervention, markedly globalising. This scenario, as it is argued, was also, intentionally, one of transformation and of deliberate change of school and of the administration of the educative territoires.

  8. Automatic localization of cerebral cortical malformations using fractal analysis.

    Science.gov (United States)

    De Luca, A; Arrigoni, F; Romaniello, R; Triulzi, F M; Peruzzo, D; Bertoldo, A

    2016-08-21

    Malformations of cortical development (MCDs) encompass a variety of brain disorders affecting the normal development and organization of the brain cortex. The relatively low incidence and the extreme heterogeneity of these disorders hamper the application of classical group level approaches for the detection of lesions. Here, we present a geometrical descriptor for a voxel level analysis based on fractal geometry, then define two similarity measures to detect the lesions at single subject level. The pipeline was applied to 15 normal children and nine pediatric patients affected by MCDs following two criteria, maximum accuracy (WACC) and minimization of false positives (FPR), and proved that our lesion detection algorithm is able to detect and locate abnormalities of the brain cortex with high specificity (WACC  =  85%, FPR  =  96%), sensitivity (WACC  =  83%, FPR  =  63%) and accuracy (WACC  =  85%, FPR  =  90%). The combination of global and local features proves to be effective, making the algorithm suitable for the detection of both focal and diffused malformations. Compared to other existing algorithms, this method shows higher accuracy and sensitivity.

  9. Automatic localization of cerebral cortical malformations using fractal analysis

    Science.gov (United States)

    De Luca, A.; Arrigoni, F.; Romaniello, R.; Triulzi, F. M.; Peruzzo, D.; Bertoldo, A.

    2016-08-01

    Malformations of cortical development (MCDs) encompass a variety of brain disorders affecting the normal development and organization of the brain cortex. The relatively low incidence and the extreme heterogeneity of these disorders hamper the application of classical group level approaches for the detection of lesions. Here, we present a geometrical descriptor for a voxel level analysis based on fractal geometry, then define two similarity measures to detect the lesions at single subject level. The pipeline was applied to 15 normal children and nine pediatric patients affected by MCDs following two criteria, maximum accuracy (WACC) and minimization of false positives (FPR), and proved that our lesion detection algorithm is able to detect and locate abnormalities of the brain cortex with high specificity (WACC  =  85%, FPR  =  96%), sensitivity (WACC  =  83%, FPR  =  63%) and accuracy (WACC  =  85%, FPR  =  90%). The combination of global and local features proves to be effective, making the algorithm suitable for the detection of both focal and diffused malformations. Compared to other existing algorithms, this method shows higher accuracy and sensitivity.

  10. On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis

    Directory of Open Access Journals (Sweden)

    Asriyanti Indah Pratiwi

    2018-01-01

    Full Text Available Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of analysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an enormous number of features and provide better sentiment classification, an information-based feature selection and classification are proposed. The proposed method reduces more than 90% unnecessary features while the proposed classification scheme achieves 96% accuracy of sentiment classification. From the experimental results, it can be concluded that the combination of proposed feature selection and classification achieves the best performance so far.

  11. PDF analysis of PuAl alloys local structure

    Energy Technology Data Exchange (ETDEWEB)

    Platteau, C. [CEA Valduc, 21120 Is-sur-Tille (France)], E-mail: platteau.cyril@yahoo.fr; Bruckel, P.; Ravat, B.; Delaunay, F. [CEA Valduc, 21120 Is-sur-Tille (France)

    2009-03-15

    For understanding singular properties of plutonium, there is a need in studying the average and local atomic structure in Pu alloys. To study the local structure of the {delta} phase, a pair distribution function (PDF) analysis was done and has shown some significant differences with the average structure.

  12. Gastrointestinal stromal tumor of large size, extragastrointestinal localization and different morphological features

    Directory of Open Access Journals (Sweden)

    Shpon’ka I.S.

    2015-09-01

    Full Text Available The problems of accurate verification of the gastro¬intestinal stromal tumor are relevant today for many reasons. Thus, the histological diagnosis is complicated by the morphological similarity of other gastrointestinal tract mesenchymal neoplasms and by histologicaly different zones within the same investigation. We present the situation with the above issues: the differential diagnosis includes an analysis of morphological criteria and received immunohisto-chemical reactions. Between immunophenotypes of histologicaly different zones principal difference is not revealed.

  13. Analysis of Conserved Structural Features of Selenoprotein K | Al ...

    African Journals Online (AJOL)

    Selenium plays important roles in human health and these roles may be exerted through its presence in selenoproteins. Among the 25 selenoproteins in human is selenoprotein K (SelK) whose exact function is still unclear. Here, we investigated the conserved structural features of SelK using bioinformatics as an approach ...

  14. Pulmonary nodule characterization, including computer analysis and quantitative features.

    Science.gov (United States)

    Bartholmai, Brian J; Koo, Chi Wan; Johnson, Geoffrey B; White, Darin B; Raghunath, Sushravya M; Rajagopalan, Srinivasan; Moynagh, Michael R; Lindell, Rebecca M; Hartman, Thomas E

    2015-03-01

    Pulmonary nodules are commonly detected in computed tomography (CT) chest screening of a high-risk population. The specific visual or quantitative features on CT or other modalities can be used to characterize the likelihood that a nodule is benign or malignant. Visual features on CT such as size, attenuation, location, morphology, edge characteristics, and other distinctive "signs" can be highly suggestive of a specific diagnosis and, in general, be used to determine the probability that a specific nodule is benign or malignant. Change in size, attenuation, and morphology on serial follow-up CT, or features on other modalities such as nuclear medicine studies or MRI, can also contribute to the characterization of lung nodules. Imaging analytics can objectively and reproducibly quantify nodule features on CT, nuclear medicine, and magnetic resonance imaging. Some quantitative techniques show great promise in helping to differentiate benign from malignant lesions or to stratify the risk of aggressive versus indolent neoplasm. In this article, we (1) summarize the visual characteristics, descriptors, and signs that may be helpful in management of nodules identified on screening CT, (2) discuss current quantitative and multimodality techniques that aid in the differentiation of nodules, and (3) highlight the power, pitfalls, and limitations of these various techniques.

  15. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET.

    Science.gov (United States)

    Tixier, Florent; Hatt, Mathieu; Le Rest, Catherine Cheze; Le Pogam, Adrien; Corcos, Laurent; Visvikis, Dimitris

    2012-05-01

    (18)F-FDG PET measurement of standardized uptake value (SUV) is increasingly used for monitoring therapy response and predicting outcome. Alternative parameters computed through textural analysis were recently proposed to quantify the heterogeneity of tracer uptake by tumors as a significant predictor of response. The primary objective of this study was to evaluate the reproducibility of these heterogeneity measurements. Double baseline (18)F-FDG PET scans were acquired within 4 d of each other for 16 patients before any treatment was considered. A Bland-Altman analysis was performed on 8 parameters based on histogram measurements and 17 parameters based on textural heterogeneity features after discretization with values between 8 and 128. The reproducibility of maximum and mean SUV was similar to that in previously reported studies, with a mean percentage difference of 4.7% ± 19.5% and 5.5% ± 21.2%, respectively. By comparison, better reproducibility was measured for some textural features describing local heterogeneity of tracer uptake, such as entropy and homogeneity, with a mean percentage difference of -2% ± 5.4% and 1.8% ± 11.5%, respectively. Several regional heterogeneity parameters such as variability in the intensity and size of regions of homogeneous activity distribution had reproducibility similar to that of SUV measurements, with 95% confidence intervals of -22.5% to 3.1% and -1.1% to 23.5%, respectively. These parameters were largely insensitive to the discretization range. Several parameters derived from textural analysis describing heterogeneity of tracer uptake by tumors on local and regional scales had reproducibility similar to or better than that of simple SUV measurements. These reproducibility results suggest that these (18)F-FDG PET-derived parameters, which have already been shown to have predictive and prognostic value in certain cancer models, may be used to monitor therapy response and predict patient outcome.

  16. Economic Analysis Of Yam Marketing In Obubra Local Government ...

    African Journals Online (AJOL)

    Economic Analysis Of Yam Marketing In Obubra Local Government Area Of Cross River State, Nigeria. ... Characteristics of the sellers, marketing channels, marketing margin and efficiency were also ... EMAIL FULL TEXT EMAIL FULL TEXT

  17. Face recognition based on matching of local features on 3D dynamic range sequences

    Science.gov (United States)

    Echeagaray-Patrón, B. A.; Kober, Vitaly

    2016-09-01

    3D face recognition has attracted attention in the last decade due to improvement of technology of 3D image acquisition and its wide range of applications such as access control, surveillance, human-computer interaction and biometric identification systems. Most research on 3D face recognition has focused on analysis of 3D still data. In this work, a new method for face recognition using dynamic 3D range sequences is proposed. Experimental results are presented and discussed using 3D sequences in the presence of pose variation. The performance of the proposed method is compared with that of conventional face recognition algorithms based on descriptors.

  18. KALMAN FILTER BASED FEATURE ANALYSIS FOR TRACKING PEOPLE FROM AIRBORNE IMAGES

    Directory of Open Access Journals (Sweden)

    B. Sirmacek

    2012-09-01

    Full Text Available Recently, analysis of man events in real-time using computer vision techniques became a very important research field. Especially, understanding motion of people can be helpful to prevent unpleasant conditions. Understanding behavioral dynamics of people can also help to estimate future states of underground passages, shopping center like public entrances, or streets. In order to bring an automated solution to this problem, we propose a novel approach using airborne image sequences. Although airborne image resolutions are not enough to see each person in detail, we can still notice a change of color components in the place where a person exists. Therefore, we propose a color feature detection based probabilistic framework in order to detect people automatically. Extracted local features behave as observations of the probability density function (pdf of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding pdf. First, we use estimated pdf to detect boundaries of dense crowds. After that, using background information of dense crowds and previously extracted local features, we detect other people in non-crowd regions automatically for each image in the sequence. We benefit from Kalman filtering to track motion of detected people. To test our algorithm, we use a stadium entrance image data set taken from airborne camera system. Our experimental results indicate possible usage of the algorithm in real-life man events. We believe that the proposed approach can also provide crucial information to police departments and crisis management teams to achieve more detailed observations of people in large open area events to prevent possible accidents or unpleasant conditions.

  19. Mammographic Features of Local Recurrence after Conservative Surgery and Radiation Therapy: Comparison with that of the Primary Tumor

    International Nuclear Information System (INIS)

    Guenhan-Bilgen, I.; Oktay, A.

    2007-01-01

    Purpose: To compare the mammographic features of recurrent breast cancer with those of the primary tumor and to determine whether certain mammographic features are associated with a higher risk of local recurrence after breast-conserving therapy. Material and Methods: A retrospective review of mammograms of 421 patients who were treated with conservative surgery and radiotherapy revealed 41 recurrent tumors. Mammographic findings, location, and histopathologic characteristics were retrospectively compared between primary and recurrent tumors. Results: Recurrent tumors were similar in mammographic appearance to primary tumors in 27 (66%) cases. Of 27 primary tumors that occurred as masses without calcifications, 19 (70%) recurred as a mass, and of the six isolated calcifications, five (83%) recurred with calcifications. Ten (53%) of the 19 recurrent masses and five (100%) of the five recurrent calcifications had morphologic features that were similar to those of the primary tumor. Ninety-two percent (11/12) of the recurrences containing microcalcifications (isolated or associated with a mass) had microcalcifications in their primary tumor. Of 27 masses that recurred, the morphology of the primary tumor was obscured in 13 (48%), ill defined in 10 (37%), and spiculated in four (15%) of the masses. Seventy-six percent (31/41) of recurrences were within the lumpectomy quadrant. In 25 (61%) cases, the histologic findings from the primary tumor and the recurrence were identical. Conclusion: The majority of recurrent tumors appear to be mammographically similar to primary tumors. Therefore, it is important to review preoperative mammograms during follow-up of these patients. Although the study population is small, it was noted that mass with spiculated contour is associated with a lower risk for local recurrence

  20. Chromatic Information and Feature Detection in Fast Visual Analysis.

    Directory of Open Access Journals (Sweden)

    Maria M Del Viva

    Full Text Available The visual system is able to recognize a scene based on a sketch made of very simple features. This ability is likely crucial for survival, when fast image recognition is necessary, and it is believed that a primal sketch is extracted very early in the visual processing. Such highly simplified representations can be sufficient for accurate object discrimination, but an open question is the role played by color in this process. Rich color information is available in natural scenes, yet artist's sketches are usually monochromatic; and, black-and-white movies provide compelling representations of real world scenes. Also, the contrast sensitivity of color is low at fine spatial scales. We approach the question from the perspective of optimal information processing by a system endowed with limited computational resources. We show that when such limitations are taken into account, the intrinsic statistical properties of natural scenes imply that the most effective strategy is to ignore fine-scale color features and devote most of the bandwidth to gray-scale information. We find confirmation of these information-based predictions from psychophysics measurements of fast-viewing discrimination of natural scenes. We conclude that the lack of colored features in our visual representation, and our overall low sensitivity to high-frequency color components, are a consequence of an adaptation process, optimizing the size and power consumption of our brain for the visual world we live in.

  1. A meta-analysis of local adaptation in plants.

    Directory of Open Access Journals (Sweden)

    Roosa Leimu

    Full Text Available Local adaptation is of fundamental importance in evolutionary, population, conservation, and global-change biology. The generality of local adaptation in plants and whether and how it is influenced by specific species, population and habitat characteristics have, however, not been quantitatively reviewed. Therefore, we examined published data on the outcomes of reciprocal transplant experiments using two approaches. We conducted a meta-analysis to compare the performance of local and foreign plants at all transplant sites. In addition, we analysed frequencies of pairs of plant origin to examine whether local plants perform better than foreign plants at both compared transplant sites. In both approaches, we also examined the effects of population size, and of the habitat and species characteristics that are predicted to affect local adaptation. We show that, overall, local plants performed significantly better than foreign plants at their site of origin: this was found to be the case in 71.0% of the studied sites. However, local plants performed better than foreign plants at both sites of a pair-wise comparison (strict definition of local adaption only in 45.3% of the 1032 compared population pairs. Furthermore, we found local adaptation much more common for large plant populations (>1000 flowering individuals than for small populations (<1000 flowering individuals for which local adaptation was very rare. The degree of local adaptation was independent of plant life history, spatial or temporal habitat heterogeneity, and geographic scale. Our results suggest that local adaptation is less common in plant populations than generally assumed. Moreover, our findings reinforce the fundamental importance of population size for evolutionary theory. The clear role of population size for the ability to evolve local adaptation raises considerable doubt on the ability of small plant populations to cope with changing environments.

  2. Analysis of radiological features relative to pathology in pelvic chondrosarcoma

    International Nuclear Information System (INIS)

    Zhou Jianjun; Ding Jianguo; Wang Jianhua; Zeng Mengsu; Yan Fuhua; Zhou Kangrong; Ji Yuan

    2008-01-01

    Objective: To Explore the imaging features relative to pathology of pelvic chondrosarcoma and to evaluate the clinical value. Methods: All 12 cases patients with primary pelvic chondrosarcoma confirmed by pathological examination underwent radiography, spiral CT plain scanning, MR SE-T 1 WI, FSE-T 2 WI and SE-T 1 WI enhancement scanning before operation. The imaging data was reviewed and analyzed retrospectively to compare with surgical and pathological results. Results: Eleven conventional chondrosarcoma and one dedifferentiated chondrosarcoma were located in different parts of pelvis. The diameters of the tumors ranged from 4.7 to 17.0 cm with one case less than 5.0 cm, 6 cases being 5.0-10.0 cm and 5 cases more than 10.0 cm. The CT value of 5 eases was identical or inferior to muscle with mild to moderate 'ring-and-arc' mineralization and soft mass. MR imaging depict the high water content of these lesions as very high signal intensity was detected on T 2 WI. Six cases showed typical 'ting- and-arc' fibrous tissue which enhanced persistently. Aggressive features of deep endosteal scalloping and soft-tissue extension was also found in these cases. Conclusions: Radiographic findings can suggest the diagnosis of pelvic chondrosarcoma when there is typical 'ring-and-arc' fibrous tissue, mineralization, aggressive features of deep endosteal scalloping and large soft-tissue extension. MR imaging reflect directly this pathologic structure, superior to that of CT and radiography. CT is optimal to detect the matrix mineralization, particularly when it is subtle or when the lesion is located in anatomically complex pelvic areas. (authors)

  3. Aplastic anemia: clinico haematological features, treatment and outcome analysis

    International Nuclear Information System (INIS)

    Wali, R.; Fadoo, Z.; Naqvi, M.A.

    2011-01-01

    To determine the clinico haematological features, treatment and outcome of children diagnosed with aplastic anemia at a single institution. Study Design: Observational study. Place and Duration of Study: The Aga Khan University Hospital, Karachi, from January 1999 till December 2008. Methodology: Medical records of children aged less than 15 years of age diagnosed with aplastic anemia were reviewed. Clinico haematological features, treatment and its response to therapy and outcome were recorded. Results were described in percentages. Results: Ninety patients were diagnosed to have aplastic anemia (AA); 65 were male during the study period. Age ranged from 1 to 15 years. Fever in 65 patients (72.2%), pallor in 53 (58.8%), skin bleeding in 49 (54.4%) and epistaxis in 31(34.4%) were the most common and frequent presenting features. Congenital (Fanconi's) anemia was found in 15 (16.6%) and acquired idiopathic in 75 (83.4%) of patients. Very severe aplastic anemia (VSAA) was seen in 29 (32.2%), 26 (28.9%) had severe AA and 17 (18.9%) had moderate AA. Eight patients (8.9%) underwent haematopoietic stem cell transplantation (HSCT), 12 (13.3%) received immunosuppressive therapy (IST) and 70 patients (77.7%) received other and supportive therapy. Five (62.5%) patients showed complete response to HSCT and 3 (37.5%) failed to engraft. IST showed complete response in 3 (25%), partial response in 5 (41.6%) and no response in 4 (33.3%). Twenty two patients (24.4%) expired either due to infection in 16 (72.7%, fungal in 6, bacterial in 10) and intracranial haemorrhage in 6 (27.3%) cases. Conclusion: Majority of cases with AA were acquired and idiopathic in etiology. VSAA and SAA were frequent. Response to HSCT and IST was sub-optimal. (author)

  4. Feature Extraction and Analysis of Breast Cancer Specimen

    Science.gov (United States)

    Bhattacharyya, Debnath; Robles, Rosslin John; Kim, Tai-Hoon; Bandyopadhyay, Samir Kumar

    In this paper, we propose a method to identify abnormal growth of cells in breast tissue and suggest further pathological test, if necessary. We compare normal breast tissue with malignant invasive breast tissue by a series of image processing steps. Normal ductal epithelial cells and ductal / lobular invasive carcinogenic cells also consider for comparison here in this paper. In fact, features of cancerous breast tissue (invasive) are extracted and analyses with normal breast tissue. We also suggest the breast cancer recognition technique through image processing and prevention by controlling p53 gene mutation to some greater extent.

  5. Chemical analysis and base-promoted hydrolysis of locally ...

    African Journals Online (AJOL)

    Abstract. The study was on the chemical analysis and base- promoted hydrolysis of extracted shea nut fat. The local method of extraction of the shea nut oil was employed in comparison with literature report. A simple cold-process alkali hydrolysis of the shea nut oil was used in producing the soap. The chemical analysis of ...

  6. Local crystallography analysis for atomically resolved scanning tunneling microscopy images

    International Nuclear Information System (INIS)

    Lin, Wenzhi; Li, Qing; Belianinov, Alexei; Gai, Zheng; Baddorf, Arthur P; Pan, Minghu; Jesse, Stephen; Kalinin, Sergei V; Sales, Brian C; Sefat, Athena

    2013-01-01

    Scanning probe microscopy has emerged as a powerful and flexible tool for atomically resolved imaging of surface structures. However, due to the amount of information extracted, in many cases the interpretation of such data is limited to being qualitative and semi-quantitative in nature. At the same time, much can be learned from local atom parameters, such as distances and angles, that can be analyzed and interpreted as variations of local chemical bonding, or order parameter fields. Here, we demonstrate an iterative algorithm for indexing and determining atomic positions that allows the analysis of inhomogeneous surfaces. This approach is further illustrated by local crystallographic analysis of several real surfaces, including highly ordered pyrolytic graphite and an Fe-based superconductor FeTe 0.55 Se 0.45 . This study provides a new pathway to extract and quantify local properties for scanning probe microscopy images. (paper)

  7. The cryptogenic organizing pneumonia: the analysis of CT features

    International Nuclear Information System (INIS)

    Zhu Xiaohua; Li Tiannv; You Zhengqian; Ma Jun; Jiang Sen

    2007-01-01

    Objective: To improve our understanding concerning radiographic manifestations of cryptogenic organizing pneumonia (COP). Methods: The diagnosis of cryptogenic organizing pneumonia was made based on clinical and radiological features, and was verified with lung biopsy and pathological examination in 23 cases. All data were analyzed and relevant literatures were reviewed. Results: CT scans revealed multi- patch shadows, patchy air-space consolidations in 15 cases, often located in predominantly subpleural and(or) both inferior lungs, with or ground-glass opacities, bronchiectasis, and cords. Lesion sites changed over time in some patients. Corticosteroid treatment led to significant improvement in most cases. Conclusions: The diagnosis of cryptogenic organizing pneumonia required the converging evidence from clinical and radiological manifestations as well as pathologies. It is important to appreciate CT manifestations of COP. (authors)

  8. Global/local methods for probabilistic structural analysis

    Science.gov (United States)

    Millwater, H. R.; Wu, Y.-T.

    1993-04-01

    A probabilistic global/local method is proposed to reduce the computational requirements of probabilistic structural analysis. A coarser global model is used for most of the computations with a local more refined model used only at key probabilistic conditions. The global model is used to establish the cumulative distribution function (cdf) and the Most Probable Point (MPP). The local model then uses the predicted MPP to adjust the cdf value. The global/local method is used within the advanced mean value probabilistic algorithm. The local model can be more refined with respect to the g1obal model in terms of finer mesh, smaller time step, tighter tolerances, etc. and can be used with linear or nonlinear models. The basis for this approach is described in terms of the correlation between the global and local models which can be estimated from the global and local MPPs. A numerical example is presented using the NESSUS probabilistic structural analysis program with the finite element method used for the structural modeling. The results clearly indicate a significant computer savings with minimal loss in accuracy.

  9. Textural features of 18F-FDG PET after two cycles of neoadjuvant chemotherapy can predict pCR in patients with locally advanced breast cancer.

    Science.gov (United States)

    Cheng, Lin; Zhang, Jianping; Wang, Yujie; Xu, Xiaoli; Zhang, Yongping; Zhang, Yingjian; Liu, Guangyu; Cheng, Jingyi

    2017-08-01

    This study was designed to evaluate the utility of textural features for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Sixty-one consecutive patients with locally advanced breast cancer underwent 18 F-FDG PET/CT scanning at baseline and after the second course of NAC. Changes to imaging parameters [maximum standardized uptake value (SUV max ), metabolic tumor volume (MTV), total lesion glycolysis (TLG)] and textural features (entropy, coarseness, skewness) between the 2 scans were measured by two independent radiologists. Pathological responses were reviewed by one pathologist, and the significance of the predictive value of each parameter was analyzed using a Chi-squared test. Receiver operating characteristic curve analysis was used to compare the area under the curve (AUC) for each parameter. pCR was observed more often in patients with HER2-positive tumors (22 patients) than in patients with HER2-negative tumors (5 patients) (71.0 vs. 16.7%, p textural features of 18 F-FDG PET images after two cycles of NAC are predictive of pCR in both HER2-negative and HER2-positive patients; this evidence warrants confirmation by further research.

  10. Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm

    Science.gov (United States)

    Auat Cheein, Fernando A.; Carelli, Ricardo

    2011-01-01

    This paper introduces several non-arbitrary feature selection techniques for a Simultaneous Localization and Mapping (SLAM) algorithm. The feature selection criteria are based on the determination of the most significant features from a SLAM convergence perspective. The SLAM algorithm implemented in this work is a sequential EKF (Extended Kalman filter) SLAM. The feature selection criteria are applied on the correction stage of the SLAM algorithm, restricting it to correct the SLAM algorithm with the most significant features. This restriction also causes a decrement in the processing time of the SLAM. Several experiments with a mobile robot are shown in this work. The experiments concern the map reconstruction and a comparison between the different proposed techniques performance. The experiments were carried out at an outdoor environment composed by trees, although the results shown herein are not restricted to a special type of features. PMID:22346568

  11. Analysis of clinical features and risk factors for infective endocarditis

    International Nuclear Information System (INIS)

    Wang Li; Zhao Liangping; Xu Weiting; Chen Jianchang; Tong Guangming; Hong Xiaosu

    2012-01-01

    Objective: To analyze the clinical features of infective endocarditis (IE) and explore the risk factors for it's prognosis. Methods: Clinical data of 65 patients with IE were acquired retrospectively, and its causes, clinical characteristics, pathogenic microorganism, clinical outcomes were analyzed. Results: The major occurring heart diseases for IE in all patients were rheumatic heart disease, congenital heart disease, and there was no any previously known heart disease. The major clinical manifestations included fever and anemia. The major pathogenic bacteria is streptococcus, but percentage of other bacteria increased gradually. Thirteen patients were refractory, in hospital. Haematoglobin and seralbumin were significantly lower, and leucocyte, hsCRP, erythrocyte sedimentation were significantly higher in refractory group. Anaemia, lower seralbumin, higher hsCRP were independent predictors for bad prognosis. Conclusion: The proportion of rheumatic heart disease is decreasing as one of the risk factors for IE in recent years. Streptococcus is major pathogen of IE, and the mortality of IE is still very high. Anaemia, lower seralbumin, higher hsCRP are independent predictors for bad prognosis. (authors)

  12. AUTOMATIC LUNG NODULE SEGMENTATION USING AUTOSEED REGION GROWING WITH MORPHOLOGICAL MASKING (ARGMM AND FEATURE EX-TRACTION THROUGH COMPLETE LOCAL BINARY PATTERN AND MICROSCOPIC INFORMATION PATTERN

    Directory of Open Access Journals (Sweden)

    Senthil Kumar

    2015-04-01

    Full Text Available An efficient Autoseed Region Growing with Morphological Masking(ARGMM is imple-mented in this paper on the Lung CT Slice to segment the 'Lung Nodules',which may be the potential indicator for the Lung Cancer. The segmentation of lung nodules car-ried out in this paper through Multi-Thresholding, ARGMM and Level Set Evolution. ARGMM takes twice the time compared to Level Set, but still the number of suspected segmented nodules are doubled, which make sure that no potential cancerous nodules go unnoticed at the earlier stages of diagnosis. It is very important not to panic the patient by finding the presence of nodules from Lung CT scan. Only 40 percent of nod-ules can be cancerous. Hence, in this paper an efficient Shape and Texture analysis is computed to quantitatively describe the segmented lung nodules. The Frequency spectrum of the lung nodules is developed and its frequency domain features are com-puted. The Complete Local binary pattern of lung nodules is computed in this paper by constructing the combine histogram of Sign and Magnitude Local Binary Patterns. Lo-cal Configuration Pattern is also determined in this work for lung nodules to numeri-cally model the microscopic information of nodules pattern.

  13. A new similarity index for nonlinear signal analysis based on local extrema patterns

    Science.gov (United States)

    Niknazar, Hamid; Motie Nasrabadi, Ali; Shamsollahi, Mohammad Bagher

    2018-02-01

    Common similarity measures of time domain signals such as cross-correlation and Symbolic Aggregate approximation (SAX) are not appropriate for nonlinear signal analysis. This is because of the high sensitivity of nonlinear systems to initial points. Therefore, a similarity measure for nonlinear signal analysis must be invariant to initial points and quantify the similarity by considering the main dynamics of signals. The statistical behavior of local extrema (SBLE) method was previously proposed to address this problem. The SBLE similarity index uses quantized amplitudes of local extrema to quantify the dynamical similarity of signals by considering patterns of sequential local extrema. By adding time information of local extrema as well as fuzzifying quantized values, this work proposes a new similarity index for nonlinear and long-term signal analysis, which extends the SBLE method. These new features provide more information about signals and reduce noise sensitivity by fuzzifying them. A number of practical tests were performed to demonstrate the ability of the method in nonlinear signal clustering and classification on synthetic data. In addition, epileptic seizure detection based on electroencephalography (EEG) signal processing was done by the proposed similarity to feature the potentials of the method as a real-world application tool.

  14. Localization particularities and development features of cancer in the medial and low thoracic parts of the esophagus

    International Nuclear Information System (INIS)

    Ragimov, R.N.

    2001-01-01

    The role of the initial localization of esophagus cancer and its growth character is important for timely diagnostics and the choice of treatment technique. The results of clinical, radiological and endoscopic examination of 195 cases of esophagus cancer are assessed. The diagnosis is verified on the basis of biopsy and histology. All 195 patients underwent the radiotherapy at ROCUS-M and AGAT-R facilities. Out of 195 cases, esophagus cancer is localized in cervical, medial and lower parts in 7, 125 and 63 patients, respectively. Macroscopically, cancer was of the exophyte from in 105 patients and the endophyte-infiltrative form in 63 patients. The analysis of gamma-therapy results is shown that the complete tumor regression is ascertained in 91 of 105 patients with the exophyte tumor following the curative radiotherapy (60-70 Gy) [ru

  15. Time series analysis of diverse extreme phenomena: universal features

    Science.gov (United States)

    Eftaxias, K.; Balasis, G.

    2012-04-01

    The field of study of complex systems holds that the dynamics of complex systems are founded on universal principles that may used to describe a great variety of scientific and technological approaches of different types of natural, artificial, and social systems. We suggest that earthquake, epileptic seizures, solar flares, and magnetic storms dynamics can be analyzed within similar mathematical frameworks. A central property of aforementioned extreme events generation is the occurrence of coherent large-scale collective behavior with very rich structure, resulting from repeated nonlinear interactions among the corresponding constituents. Consequently, we apply the Tsallis nonextensive statistical mechanics as it proves an appropriate framework in order to investigate universal principles of their generation. First, we examine the data in terms of Tsallis entropy aiming to discover common "pathological" symptoms of transition to a significant shock. By monitoring the temporal evolution of the degree of organization in time series we observe similar distinctive features revealing significant reduction of complexity during their emergence. Second, a model for earthquake dynamics coming from a nonextensive Tsallis formalism, starting from first principles, has been recently introduced. This approach leads to an energy distribution function (Gutenberg-Richter type law) for the magnitude distribution of earthquakes, providing an excellent fit to seismicities generated in various large geographic areas usually identified as seismic regions. We show that this function is able to describe the energy distribution (with similar non-extensive q-parameter) of solar flares, magnetic storms, epileptic and earthquake shocks. The above mentioned evidence of a universal statistical behavior suggests the possibility of a common approach for studying space weather, earthquakes and epileptic seizures.

  16. Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

    Directory of Open Access Journals (Sweden)

    Araabi Babak N

    2010-12-01

    Full Text Available Abstract Background It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes. Results We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%. Conclusions We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the

  17. Clinical features and genetic analysis of tuberous sclerosis pedigrees

    Directory of Open Access Journals (Sweden)

    LI Ya-qin

    2012-06-01

    Full Text Available Objective In order to understand tuberous sclerosis complex better, the clinical manifestation, imaging characteristics, and genetic characteristics of tuberous sclerosis complex from 3 pedigrees were investigated. Methods The clinical data of patients from 3 tuberous sclerosis families were collected. The gene mutation type of TSC2 of proband in pedigree one was determined by PCR and direct gene sequencing. Results All of the 3 probands went to our clinic for the reason of epilepsy. Brain imaging examination noted intracranial nodular calcification. EEG showed comprehensive spines and slow waves, sharp waves. The pedigree 1 has family history, two male patients and 3 female patients, all had facial angiofibromas and epilepsy. Gene mutation analysis of TSC2 demonstrated the c.1444-2A > C mutation in index patient. All the 3 index patients had mental retardation, autism and hypopigmented macule. Conclusion For infants and young children with epilepsy as the first symptom, accompanied by mental retardation, autism, facial angiofibromas or hypopigmented macule and other skin abnormalities, brain imaging examination noted intracranial nodular calcification are highly suggestive of tuberous sclerosis complex. TSC1 and TSC2 gene analysis contribute to the diagnosis of this disease, genentic counseling and prenatal diagnosis.

  18. Non-local model analysis of heat pulse propagation

    International Nuclear Information System (INIS)

    Iwasaki, Takuya; Itoh, Sanae-I.; Yagi, Masatoshi

    1998-01-01

    A new theoretical model equation which includes the non-local effect in the heat flux is proposed to study the transient transport phenomena. A non-local heat flux, which is expressed in terms of the integral equation, is superimposed on the conventional form of the heat flux. This model is applied to describe the experimental results from the power switching [Stroth U, et al 1996 Plasma Phys. Control. Fusion 38 1087] and the power modulation experiments [Giannone L, et al 1992 Nucl. Fusion 32 1985] in the W7-AS stellarator. A small fraction of non-local component in the heat flux is found to be very effective in modifying the response against an external modulation. The transient feature of the transport property, which are observed in the response of heat pulse propagation, are qualitatively reproduced by the transport simulations based on this model. A possibility is discussed to determine the correlation length of the non-local effect experimentally by use of the results of transport simulations. (author)

  19. X linked exudative vitreoretinopathy: clinical features and genetic linkage analysis.

    Science.gov (United States)

    Fullwood, P; Jones, J; Bundey, S; Dudgeon, J; Fielder, A R; Kilpatrick, M W

    1993-03-01

    A four generation family in which familial exudative vitreoretinopathy is inherited as an X linked condition is described. Essentially the condition is one of abnormal vascularisation and signs at birth are those of a retinopathy superficially resembling retinopathy of prematurity, retinal folds, or, in advanced cases, enophthalmos or even phthisis. Prognosis depends on the progression of the retinal changes. The family members, including seven affected males and five obligate carrier females, have been types for 20 DNA markers, and linkage analysis suggests a gene locus either at Xq21.3 or at Xp11. As the latter region includes the locus for the gene for Norrie disease, it is possible that this and X linked vitreoretinopathy are allelic. We can further speculate that the differences in severity of the clinical manifestations are dependent only upon the timing of the insult.

  20. Detection of Abnormal Events via Optical Flow Feature Analysis

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2015-03-01

    Full Text Available In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm.

  1. Detection of Abnormal Events via Optical Flow Feature Analysis

    Science.gov (United States)

    Wang, Tian; Snoussi, Hichem

    2015-01-01

    In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. PMID:25811227

  2. HYDROLOGIC AND FEATURE-BASED SURFACE ANALYSIS FOR TOOL MARK INVESTIGATION ON ARCHAEOLOGICAL FINDS

    Directory of Open Access Journals (Sweden)

    K. Kovács

    2012-07-01

    Full Text Available The improvement of detailed surface documentation methods provides unique tool mark-study opportunities in the field of archaeological researches. One of these data collection techniques is short-range laser scanning, which creates a digital copy of the object’s morphological characteristics from high-resolution datasets. The aim of our work was the accurate documentation of a Bronze Age sluice box from Mitterberg, Austria with a spatial resolution of 0.2 mm. Furthermore, the investigation of the entirely preserved tool marks on the surface of this archaeological find was also accomplished by these datasets. The methodology of this tool mark-study can be summarized in the following way: At first, a local hydrologic analysis has been applied to separate the various patterns of tools on the finds’ surface. As a result, the XYZ coordinates of the special points, which represent the edge lines of the sliding tool marks, were calculated by buffer operations in a GIS environment. During the second part of the workflow, these edge points were utilized to manually clip the triangle meshes of these patterns in reverse engineering software. Finally, circle features were generated and analysed to determine the different sections along these sliding tool marks. In conclusion, the movement of the hand tool could be reproduced by the spatial analysis of the created features, since the horizontal and vertical position of the defined circle centre points indicated the various phases of the movements. This research shows an exact workflow to determine the fine morphological structures on the surface of the archaeological find.

  3. Global and Local Sensitivity Analysis Methods for a Physical System

    Science.gov (United States)

    Morio, Jerome

    2011-01-01

    Sensitivity analysis is the study of how the different input variations of a mathematical model influence the variability of its output. In this paper, we review the principle of global and local sensitivity analyses of a complex black-box system. A simulated case of application is given at the end of this paper to compare both approaches.…

  4. Harmonic analysis on local fields and adelic spaces. I

    International Nuclear Information System (INIS)

    Osipov, D V; Parshin, A N

    2008-01-01

    We develop harmonic analysis on the objects of a category C 2 of infinite-dimensional filtered vector spaces over a finite field. This category includes two-dimensional local fields and adelic spaces of algebraic surfaces defined over a finite field. As the main result, we construct the theory of the Fourier transform on these objects and obtain two-dimensional Poisson formulae

  5. A Local Approach Methodology for the Analysis of Ultimate Strength ...

    African Journals Online (AJOL)

    The local approach methodology in contrast to classical fracture mechanics can be used to predict the onset of tearing fracture, and the effects of geometry in tubular joints. Finite element analysis of T-joints plate geometries, and tubular joints has been done. The parameters of constraint, equivalent stress, plastic strain and ...

  6. Runoff Analysis Considering Orographical Features Using Dual Polarization Radar Rainfall

    Science.gov (United States)

    Noh, Hui-seong; Shin, Hyun-seok; Kang, Na-rae; Lee, Choong-Ke; Kim, Hung-soo

    2013-04-01

    Recently, the necessity for rainfall estimation and forecasting using the radar is being highlighted, due to the frequent occurrence of torrential rainfall resulting from abnormal changes of weather. Radar rainfall data represents temporal and spatial distributions properly and replace the existing rain gauge networks. It is also frequently applied in many hydrologic field researches. However, the radar rainfall data has an accuracy limitation since it estimates rainfall, by monitoring clouds and precipitation particles formed around the surface of the earth(1.5-3km above the surface) or the atmosphere. In a condition like Korea where nearly 70% of the land is covered by mountainous areas, there are lots of restrictions to use rainfall radar, because of the occurrence of beam blocking areas by topography. This study is aiming at analyzing runoff and examining the applicability of (R(Z), R(ZDR) and R(KDP)) provided by the Han River Flood Control Office(HRFCO) based on the basin elevation of Nakdong river watershed. For this purpose, the amount of radar rainfall of each rainfall event was estimated according to three sub-basins of Nakdong river watershed with the average basin elevation above 400m which are Namgang dam, Andong dam and Hapcheon dam and also another three sub-basins with the average basin elevation below 150m which are Waegwan, Changryeong and Goryeong. After runoff analysis using a distribution model, Vflo model, the results were reviewed and compared with the observed runoff. This study estimated the rainfall by using the radar-rainfall transform formulas, (R(Z), R(Z,ZDR) and R(Z,ZDR,KDP) for four stormwater events and compared the results with the point rainfall of the rain gauge. As the result, it was overestimated or underestimated, depending on rainfall events. Also, calculation indicates that the values from R(Z,ZDR) and R(Z,ZDR,KDP) relatively showed the most similar results. Moreover the runoff analysis using the estimated radar rainfall is

  7. Detecting PHG frames in wireless capsule endoscopy video by integrating rough global dominate-color with fine local texture features

    Science.gov (United States)

    Liu, Xiaoqi; Wang, Chengliang; Bai, Jianying; Liao, Guobin

    2018-02-01

    Portal hypertensive gastropathy (PHG) is common in gastrointestinal (GI) diseases, and a severe stage of PHG (S-PHG) is a source of gastrointestinal active bleeding. Generally, the diagnosis of PHG is made visually during endoscopic examination; compared with traditional endoscopy, (wireless capsule endoscopy) WCE with noninvasive and painless is chosen as a prevalent tool for visual observation of PHG. However, accurate measurement of WCE images with PHG is a difficult task due to faint contrast and confusing variations in background gastric mucosal tissue for physicians. Therefore, this paper proposes a comprehensive methodology to automatically detect S-PHG images in WCE video to help physicians accurately diagnose S-PHG. Firstly, a rough dominatecolor-tone extraction approach is proposed for better describing global color distribution information of gastric mucosa. Secondly, a hybrid two-layer texture acquisition model is designed by integrating co-occurrence matrix into local binary pattern to depict complex and unique gastric mucosal microstructure local variation. Finally, features of mucosal color and microstructure texture are merged into linear support vector machine to accomplish this automatic classification task. Experiments were implemented on an annotated data set including 1,050 SPHG and 1,370 normal images collected from 36 real patients of different nationalities, ages and genders. By comparison with three traditional texture extraction methods, our method, combined with experimental results, performs best in detection of S-PHG images in WCE video: the maximum of accuracy, sensitivity and specificity reach 0.90, 0.92 and 0.92 respectively.

  8. Analysis of Local Chicken Entreprise in DAS Serayu Banyumas

    Directory of Open Access Journals (Sweden)

    N Noor Hidayat

    2000-01-01

    Full Text Available The Objectives of this research was to know income and efficiency level of local chicken entreprise. Beside that, to know potency of local chicken enterprise developing in DAS Serayu, Banyumas and know factors can effect level of that income and efficiency. Methode that used at this research is survey method to farmer families. Take of research data by random sampling.The data is analysed by multiple regression analysis. The results of this research showed that income level of local chicken entreprise at DAS Serayu is Rp 277.375,00 / year and economi efficiency 2.80 , that means the farmers get return Rp 2.80 for every one unit cost addition. The age of farmers and total of chicken possession effect at efficiency of  local chicken entreprise. Potency of local chicken developing very big if showed from power of area and human resources. Very important to increase entreprise capital and increase knowledge for farmer. Beside that more important present motivation and support for develop there enterprise (Animal Production 2(1: 13-17 (2000 Key Words: local chicken, farmers income, economic efficiency

  9. Group-theoretical and topological analysis of localized rotation-vibration states

    International Nuclear Information System (INIS)

    Sadovskii, D.A.; Zhilinskii, B.I.

    1993-01-01

    A general scheme of qualitative analysis is applied to molecular rovibrational problems. The classical-quantum correspondence provides a description of different classes of localized quantum rotation-vibration states associated with localized classical motion. A description of qualitative features, such as localized motion, and of qualitative changes, such as localization phenomena, is based on the concept of the simplest Hamiltonian. It uses only the topological properties of the compact reduced phase space and the action of the symmetry group on this space. The qualitative changes of the simplest Hamiltonian are analyzed as bifurcations caused by rotational or vibrational excitation. The relation between the stationary points of the classical Hamiltonian function on the reduced phase space and the principal periodic trajectories in the coordinate space is analyzed for vibrational Hamiltonians. In particular, the relation between the nonlinear normal modes, proposed by Montaldi, Roberts, and Stewart [Philos. Trans. R. Soc. London, Ser. A 325, 237 (1988)], and normal- and local-mode models widely used in molecular physics is discussed. Along with a general consideration of localized rotational and vibrational states a more detailed analysis of the vibrational dynamics of an X 3 molecule with the D 3h symmetry, such as the H 3 + molecular ion, is given

  10. Local buckling failure analysis of high-strength pipelines

    Institute of Scientific and Technical Information of China (English)

    Yan Li; Jian Shuai; Zhong-Li Jin; Ya-Tong Zhao; Kui Xu

    2017-01-01

    Pipelines in geological disaster regions typically suffer the risk of local buckling failure because of slender structure and complex load.This paper is meant to reveal the local buckling behavior of buried pipelines with a large diameter and high strength,which are under different conditions,including pure bending and bending combined with internal pressure.Finite element analysis was built according to previous data to study local buckling behavior of pressurized and unpressurized pipes under bending conditions and their differences in local buckling failure modes.In parametric analysis,a series of parameters,including pipe geometrical dimension,pipe material properties and internal pressure,were selected to study their influences on the critical bending moment,critical compressive stress and critical compressive strain of pipes.Especially the hardening exponent of pipe material was introduced to the parameter analysis by using the Ramberg-Osgood constitutive model.Results showed that geometrical dimensions,material and internal pressure can exert similar effects on the critical bending moment and critical compressive stress,which have different,even reverse effects on the critical compressive strain.Based on these analyses,more accurate design models of critical bending moment and critical compressive stress have been proposed for high-strength pipelines under bending conditions,which provide theoretical methods for highstrength pipeline engineering.

  11. Analysis of local warm forming of high strength steel using near infrared ray energy

    Energy Technology Data Exchange (ETDEWEB)

    Yang, W. H., E-mail: whyang21@hyundai.com [Hyundai Motor Company, 700 Yeompo-ro, Buk-Gu, Ulsan, 683-791 (Korea, Republic of); Lee, K., E-mail: klee@deform.co.kr [Solution Lab, 502, 102, Dunsan-daero 117 beon-gil, Seo-Gu, Daejeon, 302-834 (Korea, Republic of); Lee, E. H., E-mail: mtgs2@kaist.ac.kr, E-mail: dyyang@kaist.ac.kr; Yang, D. Y., E-mail: mtgs2@kaist.ac.kr, E-mail: dyyang@kaist.ac.kr [KAIST, Science Town291, Daehak-ro, Yuseong-Gu, Daejeon 305-701 (Korea, Republic of)

    2013-12-16

    The automotive industry has been pressed to satisfy more rigorous fuel efficiency requirements to promote energy conservation, safety features and cost containment. To satisfy this need, high strength steel has been developed and used for many different vehicle parts. The use of high strength steels, however, requires careful analysis and creativity in order to accommodate its relatively high springback behavior. An innovative method, called local warm forming with near infrared ray, has been developed to help promote the use of high strength steels in sheet metal forming. For this method, local regions of the work piece are heated using infrared ray energy, thereby promoting the reduction of springback behavior. In this research, a V-bend test is conducted with DP980. After springback, the bend angles for specimens without local heating are compared to those with local heating. Numerical analysis has been performed using the commercial program, DEFORM-2D. This analysis is carried out with the purpose of understanding how changes to the local stress distribution will affect the springback during the unloading process. The results between experimental and computational approaches are evaluated to assure the accuracy of the simulation. Subsequent numerical simulation studies are performed to explore best practices with respect to thermal boundary conditions, timing, and applicability to the production environment.

  12. Analysis of local warm forming of high strength steel using near infrared ray energy

    International Nuclear Information System (INIS)

    Yang, W. H.; Lee, K.; Lee, E. H.; Yang, D. Y.

    2013-01-01

    The automotive industry has been pressed to satisfy more rigorous fuel efficiency requirements to promote energy conservation, safety features and cost containment. To satisfy this need, high strength steel has been developed and used for many different vehicle parts. The use of high strength steels, however, requires careful analysis and creativity in order to accommodate its relatively high springback behavior. An innovative method, called local warm forming with near infrared ray, has been developed to help promote the use of high strength steels in sheet metal forming. For this method, local regions of the work piece are heated using infrared ray energy, thereby promoting the reduction of springback behavior. In this research, a V-bend test is conducted with DP980. After springback, the bend angles for specimens without local heating are compared to those with local heating. Numerical analysis has been performed using the commercial program, DEFORM-2D. This analysis is carried out with the purpose of understanding how changes to the local stress distribution will affect the springback during the unloading process. The results between experimental and computational approaches are evaluated to assure the accuracy of the simulation. Subsequent numerical simulation studies are performed to explore best practices with respect to thermal boundary conditions, timing, and applicability to the production environment

  13. Factoring local sequence composition in motif significance analysis.

    Science.gov (United States)

    Ng, Patrick; Keich, Uri

    2008-01-01

    We recently introduced a biologically realistic and reliable significance analysis of the output of a popular class of motif finders. In this paper we further improve our significance analysis by incorporating local base composition information. Relying on realistic biological data simulation, as well as on FDR analysis applied to real data, we show that our method is significantly better than the increasingly popular practice of using the normal approximation to estimate the significance of a finder's output. Finally we turn to leveraging our reliable significance analysis to improve the actual motif finding task. Specifically, endowing a variant of the Gibbs Sampler with our improved significance analysis we demonstrate that de novo finders can perform better than has been perceived. Significantly, our new variant outperforms all the finders reviewed in a recently published comprehensive analysis of the Harbison genome-wide binding location data. Interestingly, many of these finders incorporate additional information such as nucleosome positioning and the significance of binding data.

  14. Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

    Science.gov (United States)

    Yu, Jianbo

    2016-11-01

    The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

  15. The analysis of cultural architectural trends in Crisan locality

    Directory of Open Access Journals (Sweden)

    SELA Florentina

    2010-09-01

    Full Text Available The paper presents data about the identification and analyse of the traditional architectural elements in Crisan locality knowing that the tourism activity is in a continuous development. The field research (during November 2007 enabled us to develop a qualitative and quantitative analysis in terms of identification of traditional architecture elements, their conservation status, and frequency of traditional building materials use, decorative elements and specificcolors used in construction architecture. Further, based on collected data, was realized the chart - Distribution for TraditionalArchitecture Index (TAI on the distance from the center of Crisan locality, showing that in Crisan locality the houses were and are built without taking into account any rule, destroying thus traditional architecture.

  16. Binary naive Bayesian classifiers for correlated Gaussian features: a theoretical analysis

    CSIR Research Space (South Africa)

    Van Dyk, E

    2008-11-01

    Full Text Available classifier with Gaussian features while using any quadratic decision boundary. Therefore, the analysis is not restricted to Naive Bayesian classifiers alone and can, for instance, be used to calculate the Bayes error performance. We compare the analytical...

  17. Laser Applications to Chemical, Security, and Environmental Analysis: introduction to the feature issue

    International Nuclear Information System (INIS)

    Dreizler, Andreas; Fried, Alan; Gord, James R.

    2007-01-01

    This Applied Optics feature issue on Laser Applications to Chemical, Security,and Environmental Analysis (LACSEA) highlights papers presented at theLACSEA 2006 Tenth Topical Meeting sponsored by the Optical Society ofAmerica

  18. Laser applications to chemical, security, and environmental analysis: introduction to the feature issue.

    Science.gov (United States)

    Seeger, Thomas; Dreier, Thomas; Chen, Weidong; Kearny, Sean; Kulatilaka, Waruna

    2017-04-10

    This Applied Optics feature issue on laser applications to chemical, security, and environmental analysis (LACSEA) highlights papers presented at the LACSEA 2016 Fifteenth Topical Meeting sponsored by the Optical Society of America.

  19. Prostate cancer multi-feature analysis using trans-rectal ultrasound images

    International Nuclear Information System (INIS)

    Mohamed, S S; Salama, M M A; Kamel, M; El-Saadany, E F; Rizkalla, K; Chin, J

    2005-01-01

    This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN. (note)

  20. COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS

    Directory of Open Access Journals (Sweden)

    K. Seetharaman

    2015-08-01

    Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

  1. A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

    Science.gov (United States)

    Yao, Hao-Dong; Ma, Jia-Li; Fu, Bin-Bin; Wang, Hai-Yang; Dong, Ming-Chui

    2015-07-01

    Auscultation of heart sound (HS) signals serves as an important primary approach to diagnose cardiovascular diseases (CVDs) for centuries. Confronting the intrinsic drawbacks of traditional HS auscultation, computer-aided automatic HS auscultation based on feature extraction technique has witnessed explosive development. Yet, most existing HS feature extraction methods adopt acoustic or time-frequency features which exhibit poor relationship with diagnostic information, thus restricting the performance of further interpretation and analysis. Tackling such a bottleneck problem, this paper innovatively proposes a novel murmur-based HS feature extraction method since murmurs contain massive pathological information and are regarded as the first indications of pathological occurrences of heart valves. Adapting discrete wavelet transform (DWT) and Shannon envelope, the envelope-morphological characteristics of murmurs are obtained and three features are extracted accordingly. Validated by discriminating normal HS and 5 various abnormal HS signals with extracted features, the proposed method provides an attractive candidate in automatic HS auscultation.

  2. Analysis of locally controlled esophageal carcinomas treated with radiotherapy

    International Nuclear Information System (INIS)

    Gotoh, Yasuo; Yamada, Shogo; Takai, Yoshihiro; Nemoto, Kenji; Ogawa, Yoshihiro; Hoshi, Akihiko; Ariga, Hisanori; Sakamoto, Kiyohiko

    1996-01-01

    Of 227 esophageal carcinomas treated with a radiation dose of 60 Gy or more, 100 patients had no tumor or ulceration (with or without stenosis) of the esophagus after irradiation. We analyzed local control factors of these 100 patients to determine the need for further treatment. The cumulative local control rate at five years was 40% in all cases, 37% in 21 cases without any stenosis of the esophagus and 40% in 79 cases with stenosis. The presence of stenosis of the esophagus after irradiation was not a critical factor in predicting final local control. Local recurrence of tumors with findings of Borrmann III or Borrmann IV by the pretreatment esophageal barium study, tumors controlled after a total dose of more than 80 Gy, tumors without low dose rate telecobalt therapy (LDRT: 1 Gy/hour, 5 to 7 Gy/day, a total dose of 12 to 15 Gy) as boost therapy, and apparently controlled tumors with a stenotic ratio of 60% or more or with 5 cm or more length of stenosis of the esophagus after irradiation was significantly higher than that of the others (p<0.05). Multivariate analysis revealed that findings of pretreatment barium study, total dose, with or without LDRT, and length of stenosis of the esophagus after irradiation were significantly important factors in local control. Members of the high risk group of apparently controlled tumors should undertake surgical treatment or further intensive chemotherapy. (author)

  3. Minimization of local impact of energy systems through exergy analysis

    International Nuclear Information System (INIS)

    Cassetti, Gabriele; Colombo, Emanuela

    2013-01-01

    Highlights: • The model proposed aims at minimizing local impact of energy systems. • The model is meant to minimize the impact starting from system thermodynamics. • The formulation combines exergy analysis and quantitative risk analysis. • The approach of the model is dual to Thermoeconomics. - Abstract: For the acceptability of energy systems, environmental impacts are becoming more and more important. One primary way for reducing impacts related to processes is by improving efficiency of plants. A key instrument currently used to verify such improvements is exergy analysis, extended to include also the environmental externalities generated by systems. Through exergy-based analyses, it is possible indeed to evaluate the overall amount of resources consumed along all the phases of the life cycle of a system, from construction to dismantling. However, resource consumption is a dimension of the impact of a system at global level, while it may not be considered a measure of its local impact. In the paper a complementary approach named Combined Risk and Exergy Analysis (CRExA) to assess impacts from major accidents in energy systems is proposed, based on the combination of classical exergy analysis and quantitative risk analysis (QRA). Impacts considered are focused on effects on human health. The approach leads to the identification of solutions to minimize damages of major accidents by acting on the energy system design

  4. The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis.

    Science.gov (United States)

    Siemann, Julia; Herrmann, Manfred; Galashan, Daniela

    2018-01-25

    The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).

  5. Analysis of syntactic and semantic features for fine-grained event-spatial understanding in outbreak news reports

    Directory of Open Access Journals (Sweden)

    Chanlekha Hutchatai

    2010-03-01

    Full Text Available Abstract Background Previous studies have suggested that epidemiological reasoning needs a fine-grained modelling of events, especially their spatial and temporal attributes. While the temporal analysis of events has been intensively studied, far less attention has been paid to their spatial analysis. This article aims at filling the gap concerning automatic event-spatial attribute analysis in order to support health surveillance and epidemiological reasoning. Results In this work, we propose a methodology that provides a detailed analysis on each event reported in news articles to recover the most specific locations where it occurs. Various features for recognizing spatial attributes of the events were studied and incorporated into the models which were trained by several machine learning techniques. The best performance for spatial attribute recognition is very promising; 85.9% F-score (86.75% precision/85.1% recall. Conclusions We extended our work on event-spatial attribute recognition by focusing on machine learning techniques, which are CRF, SVM, and Decision tree. Our approach avoided the costly development of an external knowledge base by employing the feature sources that can be acquired locally from the analyzed document. The results showed that the CRF model performed the best. Our study indicated that the nearest location and previous event location are the most important features for the CRF and SVM model, while the location extracted from the verb's subject is the most important to the Decision tree model.

  6. Influence of local habitat features on disease avoidance by Caribbean spiny lobsters in a casita-enhanced bay.

    Science.gov (United States)

    Briones-Fourzán, Patricia; Candia-Zulbarán, Rebeca I; Negrete-Soto, Fernando; Barradas-Ortiz, Cecilia; Huchin-Mian, Juan P; Lozano-Álvarez, Enrique

    2012-08-27

    In Bahía de la Ascensión, Mexico, 'casitas' (large artificial shelters) are extensively used to harvest Caribbean spiny lobsters Panulirus argus. After the discovery of a pathogenic virus, Panulirus argus virus 1 (PaV1), in these lobsters, laboratory experiments revealed that PaV1 could be transmitted by contact and through water, and that lobsters avoided shelters harboring diseased conspecifics. To examine these issues in the context of casitas, which typically harbor multiple lobsters of all sizes, we examined the distribution and aggregation patterns of lobsters in the absence/presence of diseased conspecifics (i.e. visibly infected with PaV1) in 531 casitas distributed over 3 bay zones, 1 poorly vegetated ('Vigía Chico', average depth: 1.5 m) and 2 more extensively vegetated ('Punta Allen': 2.5 m; 'Los Cayos': 2.4 m). All zones had relatively high indices of predation risk. Using several statistical approaches, we found that distribution parameters of lobsters were generally not affected by the presence of diseased conspecifics in casitas. Interestingly, however, in the shallower and less vegetated zone (Vigía Chico), individual casitas harbored more lobsters and lobsters were actually more crowded in casitas containing diseased conspecifics, yet disease prevalence was the lowest in lobsters of all sizes. These results suggest that (1) investment in disease avoidance by lobsters is partially modulated by local habitat features, (2) contact transmission rates of PaV1 may be lower in nature than in the laboratory, and (3) water-borne transmission rates may be lower in shallow, poorly vegetated habitats more exposed to solar ultraviolet radiation, which can damage viral particles.

  7. Molecular features contributing to virus-independent intracellular localization and dynamic behavior of the herpesvirus transport protein US9.

    Directory of Open Access Journals (Sweden)

    Manuela Pedrazzi

    Full Text Available Reaching the right destination is of vital importance for molecules, proteins, organelles, and cargoes. Thus, intracellular traffic is continuously controlled and regulated by several proteins taking part in the process. Viruses exploit this machinery, and viral proteins regulating intracellular transport have been identified as they represent valuable tools to understand and possibly direct molecules targeting and delivery. Deciphering the molecular features of viral proteins contributing to (or determining this dynamic phenotype can eventually lead to a virus-independent approach to control cellular transport and delivery. From this virus-independent perspective we looked at US9, a virion component of Herpes Simplex Virus involved in anterograde transport of the virus inside neurons of the infected host. As the natural cargo of US9-related vesicles is the virus (or its parts, defining its autonomous, virus-independent role in vesicles transport represents a prerequisite to make US9 a valuable molecular tool to study and possibly direct cellular transport. To assess the extent of this autonomous role in vesicles transport, we analyzed US9 behavior in the absence of viral infection. Based on our studies, Us9 behavior appears similar in different cell types; however, as expected, the data we obtained in neurons best represent the virus-independent properties of US9. In these primary cells, transfected US9 mostly recapitulates the behavior of US9 expressed from the viral genome. Additionally, ablation of two major phosphorylation sites (i.e. Y32Y33 and S34ES36 have no effect on protein incorporation on vesicles and on its localization on both proximal and distal regions of the cells. These results support the idea that, while US9 post-translational modification may be important to regulate cargo loading and, consequently, virion export and delivery, no additional viral functions are required for US9 role in intracellular transport.

  8. Typing Local Control and State Using Flow Analysis

    Science.gov (United States)

    Guha, Arjun; Saftoiu, Claudiu; Krishnamurthi, Shriram

    Programs written in scripting languages employ idioms that confound conventional type systems. In this paper, we highlight one important set of related idioms: the use of local control and state to reason informally about types. To address these idioms, we formalize run-time tags and their relationship to types, and use these to present a novel strategy to integrate typing with flow analysis in a modular way. We demonstrate that in our separation of typing and flow analysis, each component remains conventional, their composition is simple, but the result can handle these idioms better than either one alone.

  9. Analysis of Kerch by Local Indicators of Sustainable Development

    Science.gov (United States)

    Mazygula, E.; Kharlamova, M.; Kozlova, E.

    2017-11-01

    This article presents an analysis of the city of Kerch (Crimea Republic, Kerch Peninsula) in accordance with the local sustainable development indicators. The authors carried out the assessment of the existing environmental problems in the city which was necessary for the further development of the environmentally oriented infrastructure under various development scenarios. Due to the natural and economic factors, Kerch can develop both as an industrial and recreational centre of the peninsula. The analysis of the atmospheric air condition, use of water and energy resources and the waste management system was conducted. The presented results showed the presence of major environmental problems in almost all spheres.

  10. Reliability in content analysis: The case of semantic feature norms classification.

    Science.gov (United States)

    Bolognesi, Marianna; Pilgram, Roosmaryn; van den Heerik, Romy

    2017-12-01

    Semantic feature norms (e.g., STIMULUS: car → RESPONSE: ) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses-that is, by using taxonomies of feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated semantic feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme-that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated semantic feature norms.

  11. MRI features can predict EGFR expression in lower grade gliomas. A voxel-based radiomic analysis

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yiming; Liu, Xing; Qian, Zenghui; Fan, Xing; Li, Shaowu; Jiang, Tao [Capital Medical University, Beijing Neurosurgical Institute, Beijing (China); Xu, Kaibin [Chinese Academy of Sciences, Institute of Automation, Beijing (China); Wang, Kai [Beijing Tiantan Hospital, Department of Neuroradiology, Beijing (China); Wang, Yinyan [Beijing Tiantan Hospital, Department of Neuroradiology, Beijing (China); Beijing Tiantan Hospital, Capital Medical University, Department of Neurosurgery, Beijing (China)

    2018-01-15

    To identify the magnetic resonance imaging (MRI) features associated with epidermal growth factor (EGFR) expression level in lower grade gliomas using radiomic analysis. 270 lower grade glioma patients with known EGFR expression status were randomly assigned into training (n=200) and validation (n=70) sets, and were subjected to feature extraction. Using a logistic regression model, a signature of MRI features was identified to be predictive of the EGFR expression level in lower grade gliomas in the training set, and the accuracy of prediction was assessed in the validation set. A signature of 41 MRI features achieved accuracies of 82.5% (area under the curve [AUC] = 0.90) in the training set and 90.0% (AUC = 0.95) in the validation set. This radiomic signature consisted of 25 first-order statistics or related wavelet features (including range, standard deviation, uniformity, variance), one shape and size-based feature (spherical disproportion), and 15 textural features or related wavelet features (including sum variance, sum entropy, run percentage). A radiomic signature allowing for the prediction of the EGFR expression level in patients with lower grade glioma was identified, suggesting that using tumour-derived radiological features for predicting genomic information is feasible. (orig.)

  12. A data skimming service for locally resident analysis data

    International Nuclear Information System (INIS)

    Cranshaw, J; Gieraltowski, J; Malon, D; May, E; Gardner, R W; Mambelli, M

    2008-01-01

    A Data Skimming Service (DSS) is a site-level service for rapid event filtering and selection from locally resident datasets based on metadata queries to associated 'tag' databases. In US ATLAS, we expect most if not all of the AOD-based datasets to be replicated to each of the five Tier 2 regional facilities in the US Tier 1 'cloud' coordinated by Brookhaven National Laboratory. Entire datasets will consist of on the order of several terabytes of data, and providing easy, quick access to skimmed subsets of these data will be vital to physics working groups. Typically, physicists will be interested in portions of the complete datasets, selected according to event-level attributes (number of jets, missing Et, etc) and content (specific analysis objects for subsequent processing). In this paper we describe methods used to classify data (metadata tag generation) and to store these results in a local database. Next we discuss a general framework which includes methods for accessing this information, defining skims, specifying event output content, accessing locally available storage through a variety of interfaces (SRM, dCache/dccp, gridftp), accessing remote storage elements as specified, and user job submission tools through local or grid schedulers. The advantages of the DSS are the ability to quickly 'browse' datasets and design skims, for example, pre-adjusting cuts to get to a desired skim level with minimal use of compute resources, and to encode these analysis operations in a database for re-analysis and archival purposes. Additionally the framework has provisions to operate autonomously in the event that external, central resources are not available, and to provide, as a reduced package, a minimal skimming service tailored to the needs of small Tier 3 centres or individual users

  13. A Meta-Analysis of Local Climate Change Adaptation Actions ...

    Science.gov (United States)

    Local governments are beginning to take steps to address the consequences of climate change, such as sea level rise and heat events. However, we do not have a clear understanding of what local governments are doing -- the extent to which they expect climate change to affect their community, the types of actions they have in place to address climate change, and the resources at their disposal for implementation. Several studies have been conducted by academics, non-governmental organizations, and public agencies to assess the status of local climate change adaptation. This project collates the findings from dozens of such studies to conduct a meta-analysis of local climate change adaptation actions. The studies will be characterized along several dimensions, including (a) methods used, (b) timing and geographic scope, (c) topics covered, (d) types of adaptation actions identified, (e) implementation status, and (f) public engagement and environmental justice dimensions considered. The poster presents the project's rationale and approach and some illustrative findings from early analyses. [Note: The document being reviewed is an abstract in which a poster is being proposed. The poster will enter clearance if the abstract is accepted] The purpose of this poster is to present the research framework and approaches I am developing for my ORISE postdoctoral project, and to get feedback on early analyses.

  14. Asymptotically optimal data analysis for rejecting local realism

    International Nuclear Information System (INIS)

    Zhang, Yanbao; Glancy, Scott; Knill, Emanuel

    2011-01-01

    Reliable experimental demonstrations of violations of local realism are highly desirable for fundamental tests of quantum mechanics. One can quantify the violation witnessed by an experiment in terms of a statistical p value, which can be defined as the maximum probability according to local realism of a violation at least as high as that witnessed. Thus, high violation corresponds to small p value. We propose a prediction-based-ratio (PBR) analysis protocol whose p values are valid even if the prepared quantum state varies arbitrarily and local realistic models can depend on previous measurement settings and outcomes. It is therefore not subject to the memory loophole [J. Barrett et al., Phys. Rev. A 66, 042111 (2002)]. If the prepared state does not vary in time, the p values are asymptotically optimal. For comparison, we consider protocols derived from the number of standard deviations of violation of a Bell inequality and from martingale theory [R. Gill, e-print arXiv:quant-ph/0110137]. We find that the p values of the former can be too small and are therefore not statistically valid, while those derived from the latter are suboptimal. PBR p values do not require a predetermined Bell inequality and can be used to compare results from different tests of local realism independent of experimental details.

  15. Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns †

    Directory of Open Access Journals (Sweden)

    Prerna Singh

    2017-12-01

    Full Text Available Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modeling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

  16. Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.

    Science.gov (United States)

    Zhou, Hang; Yang, Yang; Shen, Hong-Bin

    2017-03-15

    Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models. In this paper, we propose a new amino acid sequence-based human protein subcellular location prediction approach Hum-mPLoc 3.0, which covers 12 human subcellular localizations. The sequences are represented by multi-view complementary features, i.e. context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. To systematically reflect the structural hierarchy of the domain knowledge bases, we propose a novel feature representation protocol denoted as HCM (Hidden Correlation Modeling), which will create more compact and discriminative feature vectors by modeling the hidden correlations between annotation terms. Experimental results on four benchmark datasets show that HCM improves prediction accuracy by 5-11% and F 1 by 8-19% compared with conventional GO-based methods. A large-scale application of Hum-mPLoc 3.0 on the whole human proteome reveals proteins co-localization preferences in the cell. www.csbio.sjtu.edu.cn/bioinf/Hum-mPLoc3/. hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  17. Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis

    Directory of Open Access Journals (Sweden)

    Zare Habil

    2013-01-01

    Full Text Available Abstract One challenge in applying bioinformatic tools to clinical or biological data is high number of features that might be provided to the learning algorithm without any prior knowledge on which ones should be used. In such applications, the number of features can drastically exceed the number of training instances which is often limited by the number of available samples for the study. The Lasso is one of many regularization methods that have been developed to prevent overfitting and improve prediction performance in high-dimensional settings. In this paper, we propose a novel algorithm for feature selection based on the Lasso and our hypothesis is that defining a scoring scheme that measures the "quality" of each feature can provide a more robust feature selection method. Our approach is to generate several samples from the training data by bootstrapping, determine the best relevance-ordering of the features for each sample, and finally combine these relevance-orderings to select highly relevant features. In addition to the theoretical analysis of our feature scoring scheme, we provided empirical evaluations on six real datasets from different fields to confirm the superiority of our method in exploratory data analysis and prediction performance. For example, we applied FeaLect, our feature scoring algorithm, to a lymphoma dataset, and according to a human expert, our method led to selecting more meaningful features than those commonly used in the clinics. This case study built a basis for discovering interesting new criteria for lymphoma diagnosis. Furthermore, to facilitate the use of our algorithm in other applications, the source code that implements our algorithm was released as FeaLect, a documented R package in CRAN.

  18. Analysis of local influences in structural details of the bridges

    Directory of Open Access Journals (Sweden)

    Adam RUDZIK

    2015-03-01

    Full Text Available The article analyses the problems of local influences in structural details of bridges as the critical locations, whose damages or excessive force may directly affect the safety of users. These analyses are shown on selected examples. Presented is the example of local changes in the forms of proper vibrations in the node of the truss bridge that can be used in expert issues concerning the causes of damages. The second example are the changes in stresses in the stay cable anchorage element including the nonlinear material models. Models of this type can be successfully used by engineers as they allow for analysis of selected structural details without the need for detailed mapping of the entire structure, but only a selected section.

  19. Four-Channel Biosignal Analysis and Feature Extraction for Automatic Emotion Recognition

    Science.gov (United States)

    Kim, Jonghwa; André, Elisabeth

    This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user's emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results.

  20. Preoperative localization strategies for primary hyperparathyroidism: an economic analysis.

    Science.gov (United States)

    Lubitz, Carrie C; Stephen, Antonia E; Hodin, Richard A; Pandharipande, Pari

    2012-12-01

    Strategies for localizing parathyroid pathology preoperatively vary in cost and accuracy. Our purpose was to compute and compare comprehensive costs associated with common localization strategies. A decision-analytic model was developed to evaluate comprehensive, short-term costs of parathyroid localization strategies for patients with primary hyperparathyroidism. Eight strategies were compared. Probabilities of accurate localization were extracted from the literature, and costs associated with each strategy were based on 2011 Medicare reimbursement schedules. Differential cost considerations included outpatient versus inpatient surgeries, operative time, and costs of imaging. Sensitivity analyses were performed to determine effects of variability in key model parameters upon model results. Ultrasound (US) followed by 4D-CT was the least expensive strategy ($5,901), followed by US alone ($6,028), and 4D-CT alone ($6,110). Strategies including sestamibi (SM) were more expensive, with associated expenditures of up to $6,329 for contemporaneous US and SM. Four-gland, bilateral neck exploration (BNE) was the most expensive strategy ($6,824). Differences in cost were dependent upon differences in the sensitivity of each strategy for detecting single-gland disease, which determined the proportion of patients able to undergo outpatient minimally invasive parathyroidectomy. In sensitivity analysis, US alone was preferred over US followed by 4D-CT only when both the sensitivity of US alone for detecting an adenoma was ≥ 94 %, and the sensitivity of 4D-CT following negative US was ≤ 39 %. 4D-CT alone was the least costly strategy when US sensitivity was ≤ 31 %. Among commonly used strategies for preoperative localization of parathyroid pathology, US followed by selective 4D-CT is the least expensive.

  1. Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles

    Directory of Open Access Journals (Sweden)

    Yi Hou

    2017-01-01

    Full Text Available Efficient and robust visual localization is important for autonomous vehicles. By achieving impressive localization accuracy under conditions of significant changes, ConvNet landmark-based approach has attracted the attention of people in several research communities including autonomous vehicles. Such an approach relies heavily on the outstanding discrimination power of ConvNet features to match detected landmarks between images. However, a major challenge of this approach is how to extract discriminative ConvNet features efficiently. To address this challenging, inspired by the high efficiency of the region of interest (RoI pooling layer, we propose a Multiple RoI (MRoI pooling technique, an enhancement of RoI, and a simple yet efficient ConvNet feature extraction method. Our idea is to leverage MRoI pooling to exploit multilevel and multiresolution information from multiple convolutional layers and then fuse them to improve the discrimination capacity of the final ConvNet features. The main advantages of our method are (a high computational efficiency for real-time applications; (b GPU memory efficiency for mobile applications; and (c use of pretrained model without fine-tuning or retraining for easy implementation. Experimental results on four datasets have demonstrated not only the above advantages but also the high discriminating power of the extracted ConvNet features with state-of-the-art localization accuracy.

  2. Feature Analysis of the “Customer Relationship Management” Systems for Higher Education Institutions

    Directory of Open Access Journals (Sweden)

    Hugo de Juan-Jordán

    2018-03-01

    This article summarizes the features a CRM system should possess to make educational institutions thrive in the current digital era, and points out the future trends on this topic. The final objective is neither an analysis of the applications available on the market nor a selection guide, but a recommendation for the end users to utilize a CRM system when considering achieving some of the business needs implied in the features available on these CRMs.

  3. A Framework for the Analysis of Localized Corrosion at the Proposed Yucca Mountain Repository

    International Nuclear Information System (INIS)

    Payer, J H; Carroll, S A; Gdowski, G E; Rebak, R B; Michels, T C; Miller, M C; Henson, V E

    2006-01-01

    The proposed Repository presents a familiar materials performance application that is regularly encountered in energy, transportation and other industries. The widely accepted approach to dealing with materials performance is to identify the performance requirements, to determine the operating conditions to which materials will be exposed and to select materials of construction that perform well in those conditions. A special feature of the proposed Yucca Mountain Repository is the extremely long time frame of interest, i.e. 10,000's of years and longer. Thus, the time evolution of the environment in contact with waste package surfaces and the time evolution of corrosion damage that may result are of primary interest in the determination of expected performance. An approach is presented to the analysis of localized corrosion during a time period when it is possible for waters from drips and seepage to contact the waste package surfaces, and the analysis is demonstrated for the water chemistry of mixed salt solutions and a set of time-temperature-relative humidity profiles for a hot, mid and cool temperature waste package. Based on the analysis, there are large time periods when localized corrosion can not be supported, and no corrosion damage will occur. Further analysis can then focus on time periods when it is possible for localized corrosion to occur and the determination of the evolution of any corrosion damage

  4. Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Jae Kwon Kim

    2017-01-01

    Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

  5. Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

    Science.gov (United States)

    Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

    2017-09-01

    Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

  6. FEATURES OF FORMATTING IN FORMATION COMPETENCE OF FUTURE TRANSLATORS IN ASPECT OF TRAINING FOR LOCALIZATION OF SOFTWARE PRODUCTS

    Directory of Open Access Journals (Sweden)

    Svitlana M. Amelina

    2016-07-01

    Full Text Available The article deals with the formation of information competence of translators for the agricultural sector. Tasks of translators in the context of the preparation of new types of translation work, in particular localization, are defined. The ways of formation of information competence of translators in foreign universities are studied. The activities of translators especially for localization to meet the needs of agricultural sector are identified. The content of the module to develop the software localization process and web-sites using specialized software is concretized. It focuses on the need for skills of mastering the use of CAT systems for software localization and web sites.

  7. Morphometric and Phylogenic Analysis of Six Population Indonesian Local Goats

    Directory of Open Access Journals (Sweden)

    A. Batubara

    2011-12-01

    Full Text Available The research objectives were to characterize morphometric and genetic distance between populations of Indonesian local goats. The morphological discriminant and canonical analysis were carried out to estimate the phylogenic relationship and determine the discriminant variable between Benggala goats (n= 96, Marica (n= 60, Jawarandu (n= 94, (Kacang (n= 217, Muara (n= 30 and Samosir (n= 42. Discriminant analysis used to clasify body weight and body measurements. In the analysis of variance showed that body weight and body measurement (body length, height at withers, thorax width, thorax height, hert girth, skull width and height, tail length and width, ear length and width of Muara goats was higher (P<0.05 compared to the other groups, and the lowest was in Marica goats. The smallest genetic distance was between Marica and Samosir (11.207 and the highest were between Muara and Benggala (255.110. The highest similarity between individual within population was found in Kacang (99.28% and the lowest in Samosir (82.50%. The canonical analysis showed high correlation on canon circumference, body weight, skull width, skull height, and tail width variables so these six variables can be used as distinguishing variables among population. The result from Mahalonobis distance for phenogram tree and canonical analysis showed that six populations of Indonesian local goats were divided into six breed of goats: the first was Muara, the second was Jawarandu, the third was Kacang, the fourth was Benggala, the fifth was Samosir and the sixth was Marica goats. The diversity of body size and body weight of goats was observed quite large, so the chances of increasing productivity could be made through selection and mating programs.

  8. Feature-Space Clustering for fMRI Meta-Analysis

    DEFF Research Database (Denmark)

    Goutte, Cyril; Hansen, Lars Kai; Liptrot, Mathew G.

    2001-01-01

    MRI sequences containing several hundreds of images, it is sometimes necessary to invoke feature extraction to reduce the dimensionality of the data space. A second interesting application is in the meta-analysis of fMRI experiment, where features are obtained from a possibly large number of single......-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular......, shows interesting differences between individual voxel analysis performed with traditional methods. © 2001 Wiley-Liss, Inc....

  9. Applying computational geometry techniques for advanced feature analysis in atom probe data

    International Nuclear Information System (INIS)

    Felfer, Peter; Ceguerra, Anna; Ringer, Simon; Cairney, Julie

    2013-01-01

    In this paper we present new methods for feature analysis in atom probe tomography data that have useful applications in materials characterisation. The analysis works on the principle of Voronoi subvolumes and piecewise linear approximations, and feature delineation based on the distance to the centre of mass of a subvolume (DCOM). Based on the coordinate systems defined by these approximations, two examples are shown of the new types of analyses that can be performed. The first is the analysis of line-like-objects (i.e. dislocations) using both proxigrams and line-excess plots. The second is interfacial excess mapping of an InGaAs quantum dot. - Highlights: • Computational geometry is used to detect and analyse features within atom probe data. • Limitations of conventional feature detection are overcome by using atomic density gradients. • 0D, 1D, 2D and 3D features can be analysed by using Voronoi tessellation for spatial binning. • New, robust analysis methods are demonstrated, including line and interfacial excess mapping

  10. Systematic Review and Meta-Analysis of CT Features for Differentiating Complicated and Uncomplicated Appendicitis.

    Science.gov (United States)

    Kim, Hae Young; Park, Ji Hoon; Lee, Yoon Jin; Lee, Sung Soo; Jeon, Jong-June; Lee, Kyoung Ho

    2018-04-01

    Purpose To perform a systematic review and meta-analysis to identify computed tomographic (CT) features for differentiating complicated appendicitis in patients suspected of having appendicitis and to summarize their diagnostic accuracy. Materials and Methods Studies on diagnostic accuracy of CT features for differentiating complicated appendicitis (perforated or gangrenous appendicitis) in patients suspected of having appendicitis were searched in Ovid-MEDLINE, EMBASE, and the Cochrane Library. Overlapping descriptors used in different studies to denote the same image finding were subsumed under a single CT feature. Pooled diagnostic accuracy of the CT features was calculated by using a bivariate random effects model. CT features with pooled diagnostic odds ratios with 95% confidence intervals not including 1 were considered as informative. Results Twenty-three studies were included, and 184 overlapping descriptors for various CT findings were subsumed under 14 features. Of these, 10 features were informative for complicated appendicitis. There was a general tendency for these features to show relatively high specificity but low sensitivity. Extraluminal appendicolith, abscess, appendiceal wall enhancement defect, extraluminal air, ileus, periappendiceal fluid collection, ascites, intraluminal air, and intraluminal appendicolith showed pooled specificity greater than 70% (range, 74%-100%), but sensitivity was limited (range, 14%-59%). Periappendiceal fat stranding was the only feature that showed high sensitivity (94%; 95% confidence interval: 86%, 98%) but low specificity (40%; 95% confidence interval, 23%, 60%). Conclusion Ten informative CT features for differentiating complicated appendicitis were identified in this study, nine of which showed high specificity, but low sensitivity. © RSNA, 2017 Online supplemental material is available for this article.

  11. Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling.

    Directory of Open Access Journals (Sweden)

    Daniel P Riordan

    Full Text Available Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the

  12. Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

    International Nuclear Information System (INIS)

    Lee, Juhun; Nishikawa, Robert M.; Reiser, Ingrid; Boone, John M.; Lindfors, Karen K.

    2015-01-01

    Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C_T) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C_T yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C_T were conducted. The results showed that C_T was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure

  13. TU-AB-BRA-10: Prognostic Value of Intra-Radiation Treatment FDG-PET and CT Imaging Features in Locally Advanced Head and Neck Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Song, J; Pollom, E; Durkee, B; Aggarwal, S; Bui, T; Le, Q; Loo, B; Hara, W [Stanford University, Palo Alto, CA (United States); Cui, Y [Hokkaido University, Global Institute for Collaborative Research and Educat, Sapporo, Hokkaido (Japan); Li, R [Stanford University, Palo Alto, CA (United States); Hokkaido University, Global Institute for Collaborative Research and Educat, Sapporo, Hokkaido (Japan)

    2015-06-15

    Purpose: To predict response to radiation treatment using computational FDG-PET and CT images in locally advanced head and neck cancer (HNC). Methods: 68 patients with State III-IVB HNC treated with chemoradiation were included in this retrospective study. For each patient, we analyzed primary tumor and lymph nodes on PET and CT scans acquired both prior to and during radiation treatment, which led to 8 combinations of image datasets. From each image set, we extracted high-throughput, radiomic features of the following types: statistical, morphological, textural, histogram, and wavelet, resulting in a total of 437 features. We then performed unsupervised redundancy removal and stability test on these features. To avoid over-fitting, we trained a logistic regression model with simultaneous feature selection based on least absolute shrinkage and selection operator (LASSO). To objectively evaluate the prediction ability, we performed 5-fold cross validation (CV) with 50 random repeats of stratified bootstrapping. Feature selection and model training was solely conducted on the training set and independently validated on the holdout test set. Receiver operating characteristic (ROC) curve of the pooled Result and the area under the ROC curve (AUC) was calculated as figure of merit. Results: For predicting local-regional recurrence, our model built on pre-treatment PET of lymph nodes achieved the best performance (AUC=0.762) on 5-fold CV, which compared favorably with node volume and SUVmax (AUC=0.704 and 0.449, p<0.001). Wavelet coefficients turned out to be the most predictive features. Prediction of distant recurrence showed a similar trend, in which pre-treatment PET features of lymph nodes had the highest AUC of 0.705. Conclusion: The radiomics approach identified novel imaging features that are predictive to radiation treatment response. If prospectively validated in larger cohorts, they could aid in risk-adaptive treatment of HNC.

  14. Feature extraction with deep neural networks by a generalized discriminant analysis.

    Science.gov (United States)

    Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

    2012-04-01

    We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

  15. Local cell metrics: a novel method for analysis of cell-cell interactions.

    Science.gov (United States)

    Su, Jing; Zapata, Pedro J; Chen, Chien-Chiang; Meredith, J Carson

    2009-10-23

    The regulation of many cell functions is inherently linked to cell-cell contact interactions. However, effects of contact interactions among adherent cells can be difficult to detect with global summary statistics due to the localized nature and noise inherent to cell-cell interactions. The lack of informatics approaches specific for detecting cell-cell interactions is a limitation in the analysis of large sets of cell image data, including traditional and combinatorial or high-throughput studies. Here we introduce a novel histogram-based data analysis strategy, termed local cell metrics (LCMs), which addresses this shortcoming. The new LCM method is demonstrated via a study of contact inhibition of proliferation of MC3T3-E1 osteoblasts. We describe how LCMs can be used to quantify the local environment of cells and how LCMs are decomposed mathematically into metrics specific to each cell type in a culture, e.g., differently-labelled cells in fluorescence imaging. Using this approach, a quantitative, probabilistic description of the contact inhibition effects in MC3T3-E1 cultures has been achieved. We also show how LCMs are related to the naïve Bayes model. Namely, LCMs are Bayes class-conditional probability functions, suggesting their use for data mining and classification. LCMs are successful in robust detection of cell contact inhibition in situations where conventional global statistics fail to do so. The noise due to the random features of cell behavior was suppressed significantly as a result of the focus on local distances, providing sensitive detection of cell-cell contact effects. The methodology can be extended to any quantifiable feature that can be obtained from imaging of cell cultures or tissue samples, including optical, fluorescent, and confocal microscopy. This approach may prove useful in interpreting culture and histological data in fields where cell-cell interactions play a critical role in determining cell fate, e.g., cancer, developmental

  16. Assessment of features for automatic CTG analysis based on expert annotation.

    Science.gov (United States)

    Chudácek, Vacláv; Spilka, Jirí; Lhotská, Lenka; Janku, Petr; Koucký, Michal; Huptych, Michal; Bursa, Miroslav

    2011-01-01

    Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. Number of acceleration and deceleration, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

  17. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    International Nuclear Information System (INIS)

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin; Hollingsworth, Alan B.; Qian, Wei

    2015-01-01

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy

  18. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin, E-mail: Bin.Zheng-1@ou.edu [School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019 (United States); Hollingsworth, Alan B. [Mercy Women’s Center, Mercy Health Center, Oklahoma City, Oklahoma 73120 (United States); Qian, Wei [Department of Electrical and Computer Engineering, University of Texas, El Paso, Texas 79968 (United States)

    2015-11-15

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

  19. Feature-level analysis of a novel smartphone application for smoking cessation.

    Science.gov (United States)

    Heffner, Jaimee L; Vilardaga, Roger; Mercer, Laina D; Kientz, Julie A; Bricker, Jonathan B

    2015-01-01

    Currently, there are over 400 smoking cessation smartphone apps available, downloaded an estimated 780,000 times per month. No prior studies have examined how individuals engage with specific features of cessation apps and whether use of these features is associated with quitting. Using data from a pilot trial of a novel smoking cessation app, we examined: (i) the 10 most-used app features, and (ii) prospective associations between feature usage and quitting. Participants (n = 76) were from the experimental arm of a randomized, controlled pilot trial of an app for smoking cessation called "SmartQuit," which includes elements of both Acceptance and Commitment Therapy (ACT) and traditional cognitive behavioral therapy (CBT). Utilization data were automatically tracked during the 8-week treatment phase. Thirty-day point prevalence smoking abstinence was assessed at 60-day follow-up. The most-used features - quit plan, tracking, progress, and sharing - were mostly CBT. Only two of the 10 most-used features were prospectively associated with quitting: viewing the quit plan (p = 0.03) and tracking practice of letting urges pass (p = 0.03). Tracking ACT skill practice was used by fewer participants (n = 43) but was associated with cessation (p = 0.01). In this exploratory analysis without control for multiple comparisons, viewing a quit plan (CBT) as well as tracking practice of letting urges pass (ACT) were both appealing to app users and associated with successful quitting. Aside from these features, there was little overlap between a feature's popularity and its prospective association with quitting. Tests of causal associations between feature usage and smoking cessation are now needed.

  20. Edge enhancement and noise suppression for infrared image based on feature analysis

    Science.gov (United States)

    Jiang, Meng

    2018-06-01

    Infrared images are often suffering from background noise, blurred edges, few details and low signal-to-noise ratios. To improve infrared image quality, it is essential to suppress noise and enhance edges simultaneously. To realize it in this paper, we propose a novel algorithm based on feature analysis in shearlet domain. Firstly, as one of multi-scale geometric analysis (MGA), we introduce the theory and superiority of shearlet transform. Secondly, after analyzing the defects of traditional thresholding technique to suppress noise, we propose a novel feature extraction distinguishing image structures from noise well and use it to improve the traditional thresholding technique. Thirdly, with computing the correlations between neighboring shearlet coefficients, the feature attribute maps identifying the weak detail and strong edges are completed to improve the generalized unsharped masking (GUM). At last, experiment results with infrared images captured in different scenes demonstrate that the proposed algorithm suppresses noise efficiently and enhances image edges adaptively.

  1. Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

    International Nuclear Information System (INIS)

    Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi

    2010-01-01

    Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)

  2. Feature-based analysis for quality assessment of x-ray computed tomography measurements

    International Nuclear Information System (INIS)

    Nardelli, Vitor C; Arenhart, Francisco A; Donatelli, Gustavo D; Porath, Maurício C; Niggemann, Christian; Schmitt, Robert

    2012-01-01

    This paper presents an approach to assess the quality of the data extracted with computed tomography (CT) measuring systems to perform geometrical evaluations. The approach consists in analyzing the error features introduced by the CT measuring system during the extraction operation. The analysis of the features is performed qualitatively (using graphical analysis tools) and/or quantitatively (by means of the root-mean-square deviation parameter of the error features). The approach was used to analyze four sets of measurements performed with an industrial x-ray cone beam CT measuring system. Three test parts were used in the experiments: a high accuracy manufacturing multi-wave standard, a calibrated step cylinder and a calibrated production part. The results demonstrate the usefulness of the approach to gain knowledge on CT measuring processes and improve the quality of CT geometrical evaluations. Advantages and limitations of the approach are discussed. (paper)

  3. FEATURES OF FORMATTING IN FORMATION COMPETENCE OF FUTURE TRANSLATORS IN ASPECT OF TRAINING FOR LOCALIZATION OF SOFTWARE PRODUCTS

    OpenAIRE

    Svitlana M. Amelina; Rostyslav O. Tarasenko

    2016-01-01

    The article deals with the formation of information competence of translators for the agricultural sector. Tasks of translators in the context of the preparation of new types of translation work, in particular localization, are defined. The ways of formation of information competence of translators in foreign universities are studied. The activities of translators especially for localization to meet the needs of agricultural sector are identified. The content of the module to develop the soft...

  4. The Use of a Modified Semantic Features Analysis Approach in Aphasia

    Science.gov (United States)

    Hashimoto, Naomi; Frome, Amber

    2011-01-01

    Several studies have reported improved naming using the semantic feature analysis (SFA) approach in individuals with aphasia. Whether the SFA can be modified and still produce naming improvements in aphasia is unknown. The present study was designed to address this question by using a modified version of the SFA approach. Three, rather than the…

  5. A Quantitative Features Analysis of Recommended No- and Low-Cost Preschool E-Books

    Science.gov (United States)

    Parette, Howard P.; Blum, Craig; Luthin, Katie

    2015-01-01

    In recent years, recommended e-books have drawn increasing attention from early childhood education professionals. This study applied a quantitative descriptive features analysis of cost (n = 70) and no-cost (n = 60) e-books recommended by the Texas Computer Education Association. While t tests revealed no statistically significant differences…

  6. Computer-Aided Diagnosis of Solid Breast Lesions Using an Ultrasonic Multi-Feature Analysis Procedure

    Science.gov (United States)

    2011-01-01

    ultrasound. 1. BACKGROUND AND INTRODUCTION Breast cancer affects one of every eight women, it kills one of 29 women in the United States, and is the leading...feature analysis procedure for computer-aided diagnosis of solid breast lesions,” Ultrason Imag, 2010 (In Press). 22. C. B. Shakespeare , personal

  7. SSVEP recognition using common feature analysis in brain-computer interface.

    Science.gov (United States)

    Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

    2015-04-15

    Canonical correlation analysis (CCA) has been successfully applied to steady-state visual evoked potential (SSVEP) recognition for brain-computer interface (BCI) application. Although the CCA method outperforms the traditional power spectral density analysis through multi-channel detection, it requires additionally pre-constructed reference signals of sine-cosine waves. It is likely to encounter overfitting in using a short time window since the reference signals include no features from training data. We consider that a group of electroencephalogram (EEG) data trials recorded at a certain stimulus frequency on a same subject should share some common features that may bear the real SSVEP characteristics. This study therefore proposes a common feature analysis (CFA)-based method to exploit the latent common features as natural reference signals in using correlation analysis for SSVEP recognition. Good performance of the CFA method for SSVEP recognition is validated with EEG data recorded from ten healthy subjects, in contrast to CCA and a multiway extension of CCA (MCCA). Experimental results indicate that the CFA method significantly outperformed the CCA and the MCCA methods for SSVEP recognition in using a short time window (i.e., less than 1s). The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder

    2015-01-01

    We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis, and it is for...

  9. Management and performance features of cancer centers in Europe: A fuzzy-set analysis

    NARCIS (Netherlands)

    Wind, Anke; Lobo, Mariana Fernandes; van Dijk, Joris; Lepage-Nefkens, Isabelle; Laranja-Pontes, Jose; da Conceicao Goncalves, Vitor; van Harten, Willem H.; Rocha-Goncalves, Francisco Nuno

    2016-01-01

    The specific aim of this study is to identify the performance features of cancer centers in the European Union by using a fuzzy-set qualitative comparative analysis (fsQCA). The fsQCA method represents cases (cancer centers) as a combination of explanatory and outcome conditions. This study uses

  10. An Application of Discriminant Analysis to Pattern Recognition of Selected Contaminated Soil Features in Thin Sections

    DEFF Research Database (Denmark)

    Ribeiro, Alexandra B.; Nielsen, Allan Aasbjerg

    1997-01-01

    qualitative microprobe results: present elements Al, Si, Cr, Fe, As (associated with others). Selected groups of calibrated images (same light conditions and magnification) submitted to discriminant analysis, in order to find a pattern of recognition in the soil features corresponding to contamination already...

  11. Taxometric Analysis of the Antisocial Features Scale of the Personality Assessment Inventory in Federal Prison Inmates

    Science.gov (United States)

    Walters, Glenn D.; Diamond, Pamela M.; Magaletta, Philip R.; Geyer, Matthew D.; Duncan, Scott A.

    2007-01-01

    The Antisocial Features (ANT) scale of the Personality Assessment Inventory (PAI) was subjected to taxometric analysis in a group of 2,135 federal prison inmates. Scores on the three ANT subscales--Antisocial Behaviors (ANT-A), Egocentricity (ANT-E), and Stimulus Seeking (ANT-S)--served as indicators in this study and were evaluated using the…

  12. A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer

    Directory of Open Access Journals (Sweden)

    Pattichis Marios S

    2007-11-01

    Full Text Available Abstract Background In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i the distance from the tissue (panoramic vs close up, (ii difference in viewing angles and (iii color correction. Methods We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i a variety of testing targets from a color palette with a known color distribution, (ii different viewing angles, (iv two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i Statistical Features (SF, (ii Spatial Gray Level Dependence Matrices (SGLDM, and (iii Gray Level Difference Statistics (GLDS. All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. Results For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better

  13. Analysis of Local Economic Development Capacity in Hungarian Rural Settlements

    Directory of Open Access Journals (Sweden)

    Ritter Krisztián

    2017-11-01

    Full Text Available Besides local economic development (LED theories, especially LED practices have a growing importance nowadays. By a primary research involving more than 400 actors (local governments, local entrepreneurs, local agencies, the necessary competencies, practical experiences, and the field of further skills and extension concerning cooperation in economic development of localities were analysed. Summing up the research results, both local governments and local entrepreneurs have certain lack of competence that has to be improved, while the need of this exercise (and LED as a whole for an appropriate financial background and a national strategy/policy is well-emphasized by the answers of the actors.

  14. Features of an advanced human reliability analysis method, AGAPE-ET

    International Nuclear Information System (INIS)

    Kim, Jae Whan; Jung, Won Dea; Park, Jin Kyun

    2005-01-01

    This paper presents the main features of an advanced human reliability analysis (HRA) method, AGAPE-ET. It has the capabilities to deal with the diagnosis failures and the errors of commission (EOC), which have not been normally treated in the conventional HRAs. For the analysis of the potential for diagnosis failures, an analysis framework, which is called the misdiagnosis tree analysis (MDTA), and a taxonomy of the misdiagnosis causes with appropriate quantification schemes are provided. For the identification of the EOC events from the misdiagnosis, some procedural guidance is given. An example of the application of the method is also provided

  15. Features of an advanced human reliability analysis method, AGAPE-ET

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jae Whan; Jung, Won Dea; Park, Jin Kyun [Korea Atomic Energy Research Institute, Taejeon (Korea, Republic of)

    2005-11-15

    This paper presents the main features of an advanced human reliability analysis (HRA) method, AGAPE-ET. It has the capabilities to deal with the diagnosis failures and the errors of commission (EOC), which have not been normally treated in the conventional HRAs. For the analysis of the potential for diagnosis failures, an analysis framework, which is called the misdiagnosis tree analysis (MDTA), and a taxonomy of the misdiagnosis causes with appropriate quantification schemes are provided. For the identification of the EOC events from the misdiagnosis, some procedural guidance is given. An example of the application of the method is also provided.

  16. Analysis of Time n Frequency EEG Feature Extraction Methods for Mental Task Classification

    Directory of Open Access Journals (Sweden)

    Caglar Uyulan

    2017-01-01

    Full Text Available Many endogenous and external components may affect the physiological, mental and behavioral states in humans. Monitoring tools are required to evaluate biomarkers, identify biological events, and predict their outcomes. Being one of the valuable indicators, brain biomarkers derived from temporal or spectral electroencephalography (EEG signals processing, allow for the classification of mental disorders and mental tasks. An EEG signal has a nonstationary nature and individual frequency feature, hence it can be concluded that each subject has peculiar timing and data to extract unique features. In order to classify data, which are collected by performing four mental task (reciting the alphabet backwards, imagination of rotation of a cube, imagination of right hand movements (open/close and performing mathematical operations, discriminative features were extracted using four competitive time-frequency techniques; Wavelet Packet Decomposition (WPD, Morlet Wavelet Transform (MWT, Short Time Fourier Transform (STFT and Wavelet Filter Bank (WFB, respectively. The extracted features using both time and frequency domain information were then reduced using a principal component analysis for subset reduction. Finally, the reduced subsets were fed into a multi-layer perceptron neural network (MP-NN trained with back propagation (BP algorithm to generate a predictive model. This study mainly focuses on comparing the relative performance of time-frequency feature extraction methods that are used to classify mental tasks. The real-time (RT conducted experimental results underlined that the WPD feature extraction method outperforms with 92% classification accuracy compared to three other aforementioned methods for four different mental tasks.

  17. Consumers’ Preferences for Electronic Nicotine Delivery System Product Features: A Structured Content Analysis

    Directory of Open Access Journals (Sweden)

    Christine E. Kistler

    2017-06-01

    Full Text Available To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18–25, n = 11; middle-age group aged 26–64, n = 9; and women’s group aged 26–64, n = 9. We conducted five individual older adult interviews (aged 68–80. Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1 user experience; (2 social acceptability; (3 cost; (4 health risks/benefits; (5 ease of use; (6 flavors; (7 smoking cessation aid; (8 nicotine content; (9 modifiability; (10 ENDS regulation; (11 bridge between tobacco cigarettes; (12 collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women’s group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts.

  18. Visual scan-path analysis with feature space transient fixation moments

    Science.gov (United States)

    Dempere-Marco, Laura; Hu, Xiao-Peng; Yang, Guang-Zhong

    2003-05-01

    The study of eye movements provides useful insight into the cognitive processes underlying visual search tasks. The analysis of the dynamics of eye movements has often been approached from a purely spatial perspective. In many cases, however, it may not be possible to define meaningful or consistent dynamics without considering the features underlying the scan paths. In this paper, the definition of the feature space has been attempted through the concept of visual similarity and non-linear low dimensional embedding, which defines a mapping from the image space into a low dimensional feature manifold that preserves the intrinsic similarity of image patterns. This has enabled the definition of perceptually meaningful features without the use of domain specific knowledge. Based on this, this paper introduces a new concept called Feature Space Transient Fixation Moments (TFM). The approach presented tackles the problem of feature space representation of visual search through the use of TFM. We demonstrate the practical values of this concept for characterizing the dynamics of eye movements in goal directed visual search tasks. We also illustrate how this model can be used to elucidate the fundamental steps involved in skilled search tasks through the evolution of transient fixation moments.

  19. Automatic evaluation of intrapartum fetal heart rate recordings: a comprehensive analysis of useful features.

    Science.gov (United States)

    Chudáček, V; Spilka, J; Janků, P; Koucký, M; Lhotská, L; Huptych, M

    2011-08-01

    Cardiotocography is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO), used routinely since the 1960s by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. We assess the features on a large data set (552 records) and unlike in other published papers we use three-class expert evaluation of the records instead of the pH values. We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. The number of accelerations and decelerations, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

  20. Automatic evaluation of intrapartum fetal heart rate recordings: a comprehensive analysis of useful features

    International Nuclear Information System (INIS)

    Chudáček, V; Spilka, J; Lhotská, L; Huptych, M; Janků, P; Koucký, M

    2011-01-01

    Cardiotocography is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO), used routinely since the 1960s by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. We assess the features on a large data set (552 records) and unlike in other published papers we use three-class expert evaluation of the records instead of the pH values. We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. The number of accelerations and decelerations, interval index, as well as Lempel–Ziv complexity and Higuchi's fractal dimension are among the top five features

  1. Radiotherapy in desmoid tumors. Treatment response, local control, and analysis of local failures

    Energy Technology Data Exchange (ETDEWEB)

    Santti, Kirsi; Beule, Annette; Tuomikoski, Laura; Jaeaeskelaeinen, Anna-Stina; Saarilahti, Kauko; Tarkkanen, Maija; Blomqvist, Carl [Helsinki University Hospital and University of Helsinki, Comprehensive Cancer Center, Helsinki (Finland); Roenty, Mikko [HUSLAB and University of Helsinki, Department of Pathology, Helsinki (Finland); Ihalainen, Hanna [Helsinki University Hospital and University of Helsinki, Department of Plastic Surgery, Helsinki (Finland)

    2017-04-15

    Desmoid tumors (aggressive fibromatosis) are rare soft tissue tumors which frequently recur after surgery. Desmoid tumors arise from musculoaponeurotic tissue in the extremities, head and neck, abdominal wall, or intra-abdominally. Our aim was to examine the outcome of radiotherapy of desmoid tumors in a single institution series. We evaluated 41 patients with desmoid tumors treated with 49 radiotherapies between 1987 and 2012. Radiologic images for response evaluation were reassessed and responses to treatment registered according to RECIST criteria 1.1. For patients with local failures radiation dose distribution was determined in each local failure volume using image co-registration. Recurrences were classified as in-target, marginal, or out-of-target. Prognostic factors for radiotherapy treatment failure were evaluated. Radiotherapy doses varied from 20-63 Gy (median 50 Gy) with a median fraction size of 2 Gy. The objective response rate to definitive radiotherapy was 55% (12/22 patients). Median time to response was 14 months. A statistically significant dose-response relation for definitive and postoperative radiotherapy was observed both in univariate (p-value 0.002) and in multivariate analysis (p-value 0.02) adjusted for potential confounding factors. Surgery before radiotherapy or surgical margin had no significant effect on time to progression. Nine of 11 (82%) local failures were classified as marginal and two of 11 (18%) in-target. None of the recurrences occurred totally out-of-target. Radiotherapy is a valuable option for treating desmoid tumors. Radiotherapy dose appears to be significantly associated to local control. (orig.) [German] Desmoide (aggressive Fibromatosen) sind seltene Weichteiltumore der muskulaeren Membranen von Kopf, Hals, Extremitaeten und Bauchwand. Ziel war es, die Wirksamkeit der Strahlentherapie bei aggressiver Fibromatose an einer einzelnen Klinik zu untersuchen. Ausgewertet wurden 41 Patienten mit aggressiver Fibromatose, die

  2. AGSuite: Software to conduct feature analysis of artificial grammar learning performance.

    Science.gov (United States)

    Cook, Matthew T; Chubala, Chrissy M; Jamieson, Randall K

    2017-10-01

    To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.

  3. Locality-Driven Parallel Static Analysis for Power Delivery Networks

    KAUST Repository

    Zeng, Zhiyu

    2011-06-01

    Large VLSI on-chip Power Delivery Networks (PDNs) are challenging to analyze due to the sheer network complexity. In this article, a novel parallel partitioning-based PDN analysis approach is presented. We use the boundary circuit responses of each partition to divide the full grid simulation problem into a set of independent subgrid simulation problems. Instead of solving exact boundary circuit responses, a more efficient scheme is proposed to provide near-exact approximation to the boundary circuit responses by exploiting the spatial locality of the flip-chip-type power grids. This scheme is also used in a block-based iterative error reduction process to achieve fast convergence. Detailed computational cost analysis and performance modeling is carried out to determine the optimal (or near-optimal) number of partitions for parallel implementation. Through the analysis of several large power grids, the proposed approach is shown to have excellent parallel efficiency, fast convergence, and favorable scalability. Our approach can solve a 16-million-node power grid in 18 seconds on an IBM p5-575 processing node with 16 Power5+ processors, which is 18.8X faster than a state-of-the-art direct solver. © 2011 ACM.

  4. Local porosity analysis of pore structure in cement paste

    International Nuclear Information System (INIS)

    Hu Jing; Stroeven, Piet

    2005-01-01

    Three-dimensional (3-D) local porosity theory (LPT) was originally proposed by Hilfer and recently used for the analysis of pore space geometry in model sandstone. LPT pursues to define the probability density functions of porosity and porosity connectivity. In doing so, heterogeneity differences in various sandstone samples were assessed. However, fundamental issues as to the stochastic concept of geometric heterogeneity are ignored in Hilfer's LPT theory. This paper focuses on proper sampling procedures that should be based on stochastic approaches to multistage sampling and geometric heterogeneity. Standard LPT analysis provides a 3-D microscopic modeling approach to materials. Traditional experimental techniques yield two-dimensional (2-D) section images, however. Therefore, this paper replaces the method for assessing material data in standard LPT theory to a more practical one, based on stereological, 3-D interpretation of quantitative image analysis data. The developed methodology is used to characterize the pore structure in hardened cement paste with various water/cement ratios (w/c) at different hydration stages

  5. Feature selectivity of the gamma-band of the local field potential in primate primary visual cortex

    Directory of Open Access Journals (Sweden)

    Philipp Berens

    2008-12-01

    Full Text Available Extra-cellular voltage fluctuations (local field potentials; LFPs reflecting neural mass action are ubiquitous across species and brain regions. Numerous studies have characterized the properties of LFP signals in the cortex to study sensory and motor computations as well as cognitive processes like attention, perception and memory. In addition, its extracranial counterpart – the electroencelphalogram (EEG – is widely used in clinical applications. However, the link between LFP signals and the underlying activity of local populations of neurons remains largely elusive. Here, we review recent work elucidating the relationship between spiking activity of local neural populations and LFP signals. We focus on oscillations in the gamma-band (30-90Hz of the local field potential in the primary visual cortex (V1 of the macaque that dominate during visual stimulation. Given that in area V1 much is known about the properties of single neurons and the cortical architecture, it provides an excellent opportunity to study the mechanisms underlying the generation of the local field potential.

  6. Obtaining local reciprocal lattice vectors from finite-element analysis.

    Science.gov (United States)

    Sutter, John P; Connolley, Thomas; Hill, Tim P; Huang, Houcheng; Sharp, Doug W; Drakopoulos, Michael

    2008-11-01

    Finite-element analysis is frequently used by engineers at synchrotron beamlines to calculate the elastic deformation of a single crystal undergoing mechanical bending or thermal load. ANSYS Workbench software is widely used for such simulations. However, although ANSYS Workbench software provides useful information on the displacements, strains and stresses within the crystal, it does not yield the local reciprocal lattice vectors that would be required for X-ray diffraction calculations. To bridge this gap, a method based on the shape functions and interpolation procedures of the software itself has been developed. An application to the double-crystal bent Laue monochromator being designed for the I12 (JEEP) wiggler beamline at the Diamond Light Source is presented.

  7. Spatial analysis of geologic and hydrologic features relating to sinkhole occurrence in Jefferson County, West Virginia

    Science.gov (United States)

    Doctor, Daniel H.; Doctor, Katarina Z.

    2012-01-01

    In this study the influence of geologic features related to sinkhole susceptibility was analyzed and the results were mapped for the region of Jefferson County, West Virginia. A model of sinkhole density was constructed using Geographically Weighted Regression (GWR) that estimated the relations among discrete geologic or hydrologic features and sinkhole density at each sinkhole location. Nine conditioning factors on sinkhole occurrence were considered as independent variables: distance to faults, fold axes, fracture traces oriented along bedrock strike, fracture traces oriented across bedrock strike, ponds, streams, springs, quarries, and interpolated depth to groundwater. GWR model parameter estimates for each variable were evaluated for significance, and the results were mapped. The results provide visual insight into the influence of these variables on localized sinkhole density, and can be used to provide an objective means of weighting conditioning factors in models of sinkhole susceptibility or hazard risk.

  8. Characterizing structural transitions using localized free energy landscape analysis.

    Directory of Open Access Journals (Sweden)

    Nilesh K Banavali

    Full Text Available Structural changes in molecules are frequently observed during biological processes like replication, transcription and translation. These structural changes can usually be traced to specific distortions in the backbones of the macromolecules involved. Quantitative energetic characterization of such distortions can greatly advance the atomic-level understanding of the dynamic character of these biological processes.Molecular dynamics simulations combined with a variation of the Weighted Histogram Analysis Method for potential of mean force determination are applied to characterize localized structural changes for the test case of cytosine (underlined base flipping in a GTCAGCGCATGG DNA duplex. Free energy landscapes for backbone torsion and sugar pucker degrees of freedom in the DNA are used to understand their behavior in response to the base flipping perturbation. By simplifying the base flipping structural change into a two-state model, a free energy difference of upto 14 kcal/mol can be attributed to the flipped state relative to the stacked Watson-Crick base paired state. This two-state classification allows precise evaluation of the effect of base flipping on local backbone degrees of freedom.The calculated free energy landscapes of individual backbone and sugar degrees of freedom expectedly show the greatest change in the vicinity of the flipping base itself, but specific delocalized effects can be discerned upto four nucleotide positions away in both 5' and 3' directions. Free energy landscape analysis thus provides a quantitative method to pinpoint the determinants of structural change on the atomic scale and also delineate the extent of propagation of the perturbation along the molecule. In addition to nucleic acids, this methodology is anticipated to be useful for studying conformational changes in all macromolecules, including carbohydrates, lipids, and proteins.

  9. Text localization using standard deviation analysis of structure elements and support vector machines

    Directory of Open Access Journals (Sweden)

    Zagoris Konstantinos

    2011-01-01

    Full Text Available Abstract A text localization technique is required to successfully exploit document images such as technical articles and letters. The proposed method detects and extracts text areas from document images. Initially a connected components analysis technique detects blocks of foreground objects. Then, a descriptor that consists of a set of suitable document structure elements is extracted from the blocks. This is achieved by incorporating an algorithm called Standard Deviation Analysis of Structure Elements (SDASE which maximizes the separability between the blocks. Another feature of the SDASE is that its length adapts according to the requirements of the application. Finally, the descriptor of each block is used as input to a trained support vector machines that classify the block as text or not. The proposed technique is also capable of adjusting to the text structure of the documents. Experimental results on benchmarking databases demonstrate the effectiveness of the proposed method.

  10. Advanced GPR imaging of sedimentary features: integrated attribute analysis applied to sand dunes

    Science.gov (United States)

    Zhao, Wenke; Forte, Emanuele; Fontolan, Giorgio; Pipan, Michele

    2018-04-01

    We evaluate the applicability and the effectiveness of integrated GPR attribute analysis to image the internal sedimentary features of the Piscinas Dunes, SW Sardinia, Italy. The main objective is to explore the limits of GPR techniques to study sediment-bodies geometry and to provide a non-invasive high-resolution characterization of the different subsurface domains of dune architecture. On such purpose, we exploit the high-quality Piscinas data-set to extract and test different attributes of the GPR trace. Composite displays of multi-attributes related to amplitude, frequency, similarity and textural features are displayed with overlays and RGB mixed models. A multi-attribute comparative analysis is used to characterize different radar facies to better understand the characteristics of internal reflection patterns. The results demonstrate that the proposed integrated GPR attribute analysis can provide enhanced information about the spatial distribution of sediment bodies, allowing an enhanced and more constrained data interpretation.

  11. Learning representative features for facial images based on a modified principal component analysis

    Science.gov (United States)

    Averkin, Anton; Potapov, Alexey

    2013-05-01

    The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

  12. Microstructure evolution of ceramics during sintering: an analysis based on local image analysis measurements in the vicinity of controlled defects

    International Nuclear Information System (INIS)

    Girard, E.; Chaix, J.M.; Carry, C.; Valdivieso, F.; Goeuriot, P.; Lechelle, J.

    2005-01-01

    UO 2 powder containing 5% of almost spherical defects of controlled size have been sintered. The defects were prepared with the same powder by pre-sintering either the natural powder aggregates or partially milled pressed powder. Systematic image analysis was performed to get the local microstructure features inside the defects and in the matrix outside the defects. The set of results is used here as a sintering database with three identified sintering 'constraint' parameters (compaction level C 0 , radial distance r to the defect edge, and sintering 'history' H) and three microstructure 'responses' (pore volume fraction V V P , pore mean diameter D P , and grain mean diameter D G ). Data analysis in the 3D responses space shows that these variables are not independent but define a unique surface, on which each point corresponds to a set of constraints (C 0 ,r,H). (authors)

  13. Features of the use of charge-coupled devices in emission spectroscopic analysis

    International Nuclear Information System (INIS)

    Livshits, A.M.; Peleznev, A.V.

    1993-01-01

    Multielement radiation receivers based on linear charge-coupled photodiode devices have become more aand more widely used recently in spectroscopic analysis. The main feature of such receivers is their ability to record not only the intensity of the incident light flux, but also its spatial distribution. This article considers the advantages and disadvantages of charge-coupled devices when used in emission spectroscopic analysis. The main methods nd devices employed for this purpose and discussed here can be divided into four types: photographic photometry, visual styloscopy, quantometry, and successive analysis. 4 refs., 1 fig

  14. Image-analysis techniques for investigation localized corrosion processes

    International Nuclear Information System (INIS)

    Quinn, M.J.; Bailey, M.G.; Ikeda, B.M.; Shoesmith, D.W.

    1993-12-01

    We have developed a procedure for determining the mode and depth of penetration of localized corrosion by combining metallography and image analysis of corroded coupons. Two techniques, involving either a face-profiling or an edge-profiling procedure, have been developed. In the face-profiling procedure, successive surface grindings and image analyses were performed until corrosion was no longer visible. In this manner, the distribution of corroded sites on the surface and the total area of the surface corroded were determined as a function of depth into the specimen. In the edge-profiling procedure, surface grinding exposed successive cross sections of the corroded region. Image analysis of the cross section quantified the distribution of depths across the corroded section, and a three-dimensional distribution of penetration depths was obtained. To develop these procedures, we used artificially creviced Grade-2 titanium specimens that were corroded in saline solutions containing various amounts of chloride maintained at various fixed temperatures (105 to 150 degrees C) using a previously developed galvanic-coupling technique. We discuss some results from these experiments to illustrate how the procedures developed can be applied to a real corroded system. (author). 6 refs., 4 tabs., 21 figs

  15. A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

    Directory of Open Access Journals (Sweden)

    Reinders Marcel JT

    2009-11-01

    Full Text Available Abstract Background Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear. Results We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight diferent datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical

  16. A Novel Approach in Quantifying the Effect of Urban Design Features on Local-Scale Air Pollution in Central Urban Areas.

    Science.gov (United States)

    Miskell, Georgia; Salmond, Jennifer; Longley, Ian; Dirks, Kim N

    2015-08-04

    Differences in urban design features may affect emission and dispersion patterns of air pollution at local-scales within cities. However, the complexity of urban forms, interdependence of variables, and temporal and spatial variability of processes make it difficult to quantify determinants of local-scale air pollution. This paper uses a combination of dense measurements and a novel approach to land-use regression (LUR) modeling to identify key controls on concentrations of ambient nitrogen dioxide (NO2) at a local-scale within a central business district (CBD). Sixty-two locations were measured over 44 days in Auckland, New Zealand at high density (study area 0.15 km(2)). A local-scale LUR model was developed, with seven variables identified as determinants based on standard model criteria. A novel method for improving standard LUR design was developed using two independent data sets (at local and "city" scales) to generate improved accuracy in predictions and greater confidence in results. This revised multiscale LUR model identified three urban design variables (intersection, proximity to a bus stop, and street width) as having the more significant determination on local-scale air quality, and had improved adaptability between data sets.

  17. Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

    Directory of Open Access Journals (Sweden)

    Shengwen Guo

    2017-05-01

    Full Text Available Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI. Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI, the converted MCI (cMCI, and the normal control (NC groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM. An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and

  18. Optimal Feature Space Selection in Detecting Epileptic Seizure based on Recurrent Quantification Analysis and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Saleh LAshkari

    2016-06-01

    Full Text Available Selecting optimal features based on nature of the phenomenon and high discriminant ability is very important in the data classification problems. Since it doesn't require any assumption about stationary condition and size of the signal and the noise in Recurrent Quantification Analysis (RQA, it may be useful for epileptic seizure Detection. In this study, RQA was used to discriminate ictal EEG from the normal EEG where optimal features selected by combination of algorithm genetic and Bayesian Classifier. Recurrence plots of hundred samples in each two categories were obtained with five distance norms in this study: Euclidean, Maximum, Minimum, Normalized and Fixed Norm. In order to choose optimal threshold for each norm, ten threshold of ε was generated and then the best feature space was selected by genetic algorithm in combination with a bayesian classifier. The results shown that proposed method is capable of discriminating the ictal EEG from the normal EEG where for Minimum norm and 0.1˂ε˂1, accuracy was 100%. In addition, the sensitivity of proposed framework to the ε and the distance norm parameters was low. The optimal feature presented in this study is Trans which it was selected in most feature spaces with high accuracy.

  19. Which patellofemoral joint imaging features are associated with patellofemoral pain? Systematic review and meta-analysis.

    Science.gov (United States)

    Drew, B T; Redmond, A C; Smith, T O; Penny, F; Conaghan, P G

    2016-02-01

    To review the association between patellofemoral joint (PFJ) imaging features and patellofemoral pain (PFP). A systematic review of the literature from AMED, CiNAHL, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PEDro, EMBASE and SPORTDiscus was undertaken from their inception to September 2014. Studies were eligible if they used magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US) or X-ray (XR) to compare PFJ features between a PFP group and an asymptomatic control group in people patellofemoral contact area. Limited evidence was found to support the association of other imaging features with PFP. A sensitivity analysis showed an increase in the SMD for patella bisect offset at 0° knee flexion (1.91; 95% CI: 1.31, 2.52) and patella tilt at 0° knee flexion (0.99; 95% CI: 0.47, 1.52) under full weight bearing. Certain PFJ imaging features were associated with PFP. Future interventional strategies may be targeted at these features. CRD 42014009503. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  20. TU-C-12A-09: Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemo-Radiotherapy Using Quantitative PET/CT Features, Clinical Parameters and Demographics

    International Nuclear Information System (INIS)

    Zhang, H; Chen, W; Kligerman, S; D’Souza, W; Suntharalingam, M; Lu, W; Tan, S; Kim, G

    2014-01-01

    Purpose: To develop predictive models using quantitative PET/CT features for the evaluation of tumor response to neoadjuvant chemo-radiotherapy (CRT) in patients with locally advanced esophageal cancer. Methods: This study included 20 patients who underwent tri-modality therapy (CRT + surgery) and had 18 F-FDG PET/CT scans before initiation of CRT and 4-6 weeks after completion of CRT but prior to surgery. Four groups of tumor features were examined: (1) conventional PET/CT response measures (SUVmax, tumor diameter, etc.); (2) clinical parameters (TNM stage, histology, etc.) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, using cross-validations to avoid model over-fitting. Prediction accuracy was assessed via area under the receiver operating characteristic curve (AUC), and precision was evaluated via confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). Using spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications), significantly better than using conventional PET/CT measures or clinical parameters and demographics alone. For groups with a large number of tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than the LR model. Conclusion: The SVM model using all features including

  1. Semantic Feature Analysis Treatment for Anomia of Two Nonfluent Persian-Speaking Aphasic Patients

    Directory of Open Access Journals (Sweden)

    Mozhgan Asadi

    2014-09-01

    Full Text Available Objectives: Semantic Feature Analysis was designed to improve lexical retrieval of aphasic patients via activation of semantic networks of the words. In this approach, the anomic patients are cured with semantic information to assist oral naming. The purpose of this study was to examine the effects of Semantic Feature Analysis treatment on anomia of two nonfluent aphasic patients. Methods: A single-subject study with ABA design was applied to two Persian-speaking patients with chronic nonfluent aphasia. Assessment, baseline, ntervention and maintenance phases were carried out respectively during 6 weeks. A picture naming task which was made up of pictures with high name- agreement comprising 12 target, 18 non-treated control and 5 easy words was used for probes and intervention. Intervention was performed in 5 successive days, 60 minutes per session. Descriptive statistics, level, trend & slope analyses, C and d statistics were used for data analysis. Results: Both participants revealed statistically significant improvements in naming target words. Some generalizations to control words was also occured. A minimal decrease in naming of target words was observed in maintenance phase but the naming ability was still above the baseline. The therapy maintenance effect size for both patients were obtained as medium. Discussion: The findings of the current study seems to confirm Semantic Feature Analysis as an effective intervention for improving naming ability of Persian-speaking aphasic patients.

  2. Feature-space-based FMRI analysis using the optimal linear transformation.

    Science.gov (United States)

    Sun, Fengrong; Morris, Drew; Lee, Wayne; Taylor, Margot J; Mills, Travis; Babyn, Paul S

    2010-09-01

    The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.

  3. Validation of High-Risk Computed Tomography Features for Detection of Local Recurrence After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Peulen, Heike [Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Mantel, Frederick [Department of Radiation Oncology, University of Wuerzburg, Wuerzburg (Germany); Department of Radiation Oncology, University Hospital Zurich, Zurich (Switzerland); Guckenberger, Matthias [Department of Radiation Oncology, University of Wuerzburg, Wuerzburg (Germany); Belderbos, José [Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Werner-Wasik, Maria [Department of Radiation Oncology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania (United States); Hope, Andrew; Giuliani, Meredith [Department of Radiation Oncology University of Toronto and Princess Margaret Cancer Center, Toronto, Ontario (Canada); Grills, Inga [Department of Radiation Oncology Beaumont Hospital, Royal Oak, Michigan (United States); Sonke, Jan-Jakob, E-mail: j.sonke@nki.nl [Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands)

    2016-09-01

    Purpose: Fibrotic changes after stereotactic body radiation therapy (SBRT) for stage I non-small cell lung cancer (NSCLC) are difficult to distinguish from local recurrences (LR), hampering proper patient selection for salvage therapy. This study validates previously reported high-risk computed tomography (CT) features (HRFs) for detection of LR in an independent patient cohort. Methods and Materials: From a multicenter database, 13 patients with biopsy-proven LR were matched 1:2 to 26 non-LR control patients based on dose, planning target volume (PTV), follow-up time, and lung lobe. Tested HRFs were enlarging opacity, sequential enlarging opacity, enlarging opacity after 12 months, bulging margin, linear margin disappearance, loss of air bronchogram, and craniocaudal growth. Additionally, 2 new features were analyzed: the occurrence of new unilateral pleural effusion, and growth based on relative volume, assessed by manual delineation. Results: All HRFs were significantly associated with LR except for loss of air bronchogram. The best performing HRFs were bulging margin, linear margin disappearance, and craniocaudal growth. Receiver operating characteristic analysis of the number of HRFs to detect LR had an area under the curve (AUC) of 0.97 (95% confidence interval [CI] 0.9-1.0), which was identical to the performance described in the original report. The best compromise (closest to 100% sensitivity and specificity) was found at ≥4 HRFs, with a sensitivity of 92% and a specificity of 85%. A model consisting of only 2 HRFs, bulging margin and craniocaudal growth, resulted in a sensitivity of 85% and a specificity of 100%, with an AUC of 0.96 (95% CI 0.9-1.0) (HRFs ≥2). Pleural effusion and relative growth did not significantly improve the model. Conclusion: We successfully validated CT-based HRFs for detection of LR after SBRT for early-stage NSCLC. As an alternative to number of HRFs, we propose a simplified model with the combination of the 2 best HRFs

  4. MICROSTRUCTURAL FEATURES EVALUATION OF AGE-HARDENED A 226 CAST ALLOY BY IMAGE ANALYSIS

    Directory of Open Access Journals (Sweden)

    Lenka Kuchariková

    2018-01-01

    Full Text Available Age-hardening provides one of the most widely used mechanisms for the strengthening of aluminum alloys. The age-hardening involves three steps: solution treatment, quenching and aging. The temperature of solution treatment and aging is very important in order to reach desired properties of castings. The optimum temperature of solution treatment and aging led to formation microstructural features in form which does not lead to decreasing properties, but increasing ones. The major microstructural features in A 226 cast alloys which are responsible for increasing properties are: eutectic Si particles, Cu-rich phases, Fe-rich phases and porosity. The increase of properties depends on morphology, size and volume of microstructural features. In order to assess age-hardening influence on microstructural features in A226 cast alloys were used as possibilities of evaluation by means of image analysis. Quantitative analysis decelerate changes in microstructure includes the spheroidization and coarsening of eutectic silicon, gradual disintegration, shortening and thinning of Fe-rich intermetallic phases, the dissolution of precipitates and the precipitation of finer hardening phase (Al2Cu further increase in the hardness and tensile strength in the alloy. Changes of mechanical properties were measured in line with STN EN ISO.

  5. On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis.

    Science.gov (United States)

    Yamazaki, Keisuke

    2012-07-01

    Parametric models for sequential data, such as hidden Markov models, stochastic context-free grammars, and linear dynamical systems, are widely used in time-series analysis and structural data analysis. Computation of the likelihood function is one of primary considerations in many learning methods. Iterative calculation of the likelihood such as the model selection is still time-consuming though there are effective algorithms based on dynamic programming. The present paper studies parameter learning in a simplified feature space to reduce the computational cost. Simplifying data is a common technique seen in feature selection and dimension reduction though an oversimplified space causes adverse learning results. Therefore, we mathematically investigate a condition of the feature map to have an asymptotically equivalent convergence point of estimated parameters, referred to as the vicarious map. As a demonstration to find vicarious maps, we consider the feature space, which limits the length of data, and derive a necessary length for parameter learning in hidden Markov models. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. Epidemiological surveillance of tegumentary leishmaniasis: local territorial analysis.

    Science.gov (United States)

    Soares, Valdenir Bandeira; Almeida, Andréa Sobral de; Sabroza, Paulo Chagastelles; Vargas, Waldemir Paixão

    2017-06-26

    To propose a new operational unit in the locality scale capable of subsidizing the construction of an information system to control the transmission of tegumentary leishmaniasis at this scale, in a region of high endemicity of the Atlantic Forest. We examined the adequacy of data and instruments in an area of high endemicity in the Atlantic Forest located in the South of the State of Rio de Janeiro from 1990 to 2012. The study proposed an operational unit called Local Surveillance Unit to make all used databases compatible by adjusting census sectors. This enabled the overlap and comparison of information in different periods. The spreading process of the transmission of tegumentary leishmaniasis in the Baía da Ilha Grande region does not depend on great population movements, and can occur in areas with population growth or decrease. The data information system allowed the adequate identification and characterization of the place of residence. We identified relevant characteristics of the place of transmission, such as self-limited in time and not associated with recent deforestation. The results also highlight the lack of synchronicity in the case production in territorial units involved in the endemic-epidemic process, noting that this process is in constant motion. The transmission process seems more connected to the presence and movement of rodents that move continuously in the region than to the local density of vectors or the permanence of infected dogs at home. New control strategies targeted at the foci of transmission must be considered. The construction of a new operational unit, called Local Surveillance Unit, was instrumental in the endemic-epidemic process analysis. Propor uma nova unidade operacional na escala de localidade capaz de subsidiar a construção de um sistema de informação orientado para o controle da transmissão da leishmaniose tegumentar nesse nível. Uma região de alta endemicidade da Mata Atlântica no sul do estado do Rio de

  7. Clinical features of type 1 autoimmune pancreatitis: an analysis of 13 cases

    Directory of Open Access Journals (Sweden)

    MO Xue

    2018-03-01

    Full Text Available ObjectiveTo investigate the clinical features of type 1 autoimmune pancreatitis (AIP, and to deepen the understanding of this disease, reduce false positive rate, and enhance people′s awareness of this disease. MethodsA retrospective analysis was performed for the clinical data of 13 patients with type 1 AIP who were admitted to The First Hospital of Jilin University from January 2012 to December 2016, including general status, clinical manifestations, laboratory serological examination, imaging findings, histopathological findings, treatment, and prognosis. ResultsOf all 13 patients, there were 9 male and 4 female patients with a mean age of 60.08±9.47 years. Major clinical manifestations included jaundice (69.2%, abdominal pain (61.5%, and weight loss (61.5%. The most common organ involved was bile duct (462%, and 30.8% of the patients had sclerosing cholangitis. Of all patients, 23.1% had diabetes. As for serological markers, 92.30% patients had more than 2 times increase in IgG4, and 7.69% had 1-2 times increase in IgG4, 53.85% patients had an increase in CA19-9, 69.23% patients had an increase in total bilirubin, more than two thirds of the patients had an increase in aminotransferases or gamma-glutamyl transpeptidase. As for imaging findings, 53.8% patients had diffuse enlargement of the pancreas on CT, 46.2% had focal enlargement of the pancreas, and 46.2% patients had low-density cyst-like shadow in pancreatic lesions. Pathological examination showed fibrous connective tissue proliferation with infiltration of lymphocytes and plasma cells. All patients were given standard glucocorticoid therapy (initial dose of prednisone: 30-40 mg/d and the remission rate of glucocorticoid therapy was 100%. The follow-up time was 12 months, and one patient experienced multiple recurrences in the course of the disease. ConclusionType 1 AIP is the local manifestation of IgG4-associated disease in the pancreas, which often occurs in middle-aged and

  8. Graph matching using position coordinates and local features for image analysis

    OpenAIRE

    Sanromà Güell, Gerard

    2012-01-01

    Trobar les correspondències entre dues imatges és un problema crucial en el camp de la visió per ordinador i el reconeixement de patrons. És rellevant per un ampli ventall de propòsits des d’aplicacions de reconeixement d’objectes en les àrees de biometria, anàlisi de documents i anàlisi de formes fins aplicacions relacionades amb geometria des de múltiples punts de vista tals com recuperació de pose, estructura des del moviment i localització i mapeig. La majoria de les tècniques existents ...

  9. Strong Light Localization and a Peculiar Feature of Light Leakage in the Negative Curvature Hollow Core Fibers

    Directory of Open Access Journals (Sweden)

    Andrey D. Pryamikov

    2017-11-01

    Full Text Available In this paper we would like to continue a discussion started in our previous work and devoted to the mechanism of light localization in hollow core microstructured fibers with a noncircular core-cladding boundary. It has been shown in many works that, for waveguide microstructures with different types of core-cladding boundary shape, the positions of the transmission bands’ edges can be predicted by applying the well-known anti–resonant reflecting optical waveguide (ARROW model. At the same time, the ARROW model cannot explain the strong light localization and guiding at high material loss inside the transmission bands which are observed in negative curvature hollow core fibers, for example. In this paper we want to clarify our previous findings and consider the light localization process from another point of view, namely, by comparing the light leakage process in waveguide microstructures with different shapes of the core-cladding boundary. The results are discussed based on the ARROW model and a new approach associated with the consideration of spatial dispersion occurring under the interaction of the air core mode with the core-cladding boundary.

  10. Analysis of Local Government Performance and Leadership in Nigeria

    Directory of Open Access Journals (Sweden)

    Ada Uche

    2014-12-01

    Full Text Available This paper examines the quality of local government leaderships in Nigeria. It explores how local governments’ inefficiency and poor leadership have been a major challenge facing the development process in Nigeria. The paper has two objectives. The first is to identify the professionalism of a sample of Nigerian local government chairpersons. The second is to examine whether there are systematic correlations between local government chairpersons’ professionalism, political partisanship, local characteristics, and performance. The paper argues that the quality of local government chairpersons has significant policy implications because of their vital role in policy making and implementation. The concluding section provides some policy recommendations on how local government leaders could improve performance.

  11. NEW PERMAFROST FEATURE – DEP CRATER IN CENTRAL YAMAL (WEST SIBERIA, RUSIA AS A RESPONSE TO LOCAL CLIMATE FLUCTUATIONS

    Directory of Open Access Journals (Sweden)

    Marina O. Leibman

    2014-01-01

    Full Text Available This paper is based on field data obtained during short visits to a newly formed permafrost feature in a form of relatively narrow, deep crater. Excluding impossible and improbable versions of the crater’s development, the authors conclude that it originated from warmerground temperatures and an increase in unfrozen water content, leading to an increase in pressure from gas emissions from permafrost and ground ice. This conclusion is also supported by known processes in the palaeo-geography of Yamal lakes and recent studies of gas-hydrate behavior and subsea processes in gas-bearing provinces.

  12. Analysis of the Clinicopathologic Features and Prognosis in Triple-Negative Breast Cancer

    Institute of Scientific and Technical Information of China (English)

    Dehong Yang; Hong Liu; Jing Zhao

    2008-01-01

    OBJECTIVE To investigate the clinical and pathological features,as well as prognosis in triple-negative breast cancer patients.METHODS A total of 509 cases of operable breast cancer from January,2002 to June,2002 treated in the Cancer Hospital of Tianjin Medical University were analyzed.The Her-2,ER and PR status was determined using immunohistochemistry.Of the total cases,one group was identified as triple negative breast cancer,ie defined as ER,PR and Her-2 negative.The other group was nontriple-negative breast cancer.Clinicopathologic features of the groups were compared and 5-year disease-free survival (DFS)analyzed by the Kaplan-Meier method.RESULTS Of the total cases,21.4% (109/509) of cases were found to be triple- negative while 78.6% (400/509) were non-triplenegative.The triple negative group had higher incidence rates than the non-triple-negative group of the medullary type and Grade Ⅲ tumors (P < 0.05).There was no other difference in the clinicopathologic features between the 2 groups.From follow-up to June,2007,21.1% (23/109) of the triple-negative group and 12.7%(51/400) of the non-triple negative group had a local recurrence or distant metastasis,resulting in a significant difference (P < 0.05).In the triple-negative group and non-triple-negative group,5-year DFS were 78.9% and 87.3% respectively.There was a statistically significant difference between the 2 groups (P = 0.031).CONCLUSION Compared with non-triple-negative breast cancer,triple-negative breast cancer patients have an increased likehood of a local recurrence or distant metastasis and a poorer prognosis.

  13. PENENTUAN FITUR WEBSITE BIDANG PARIWISATA DAN KEBUDAYAAN DENGAN METODE FEATURE-ORIENTED DOMAIN ANALYSIS (FODA

    Directory of Open Access Journals (Sweden)

    Muhammad Iqbal

    2016-10-01

    Penentuan fitur dalam membuat website bidang pariwisata dan kebudayaan dibutuhkan untuk mengetahui fitur yang bisa diimplementasikan. Untuk membantu menentukan fitur tersebut, digunakan analisis domain dengan metode Feature-Oriented Domain Analysis (FODA. Metode tersebut mempunyai tahapan dimulai dari tinjauan aplikasi terhadap ketiga website sebagai sampel untuk mengambil fitur. Selanjutnya tahapan analisis konteks yang mendapatkan diagram struktur dan diagram konteks. Berikutnya tahapan pemodelan domain yang dibagi dua langkah yaitu analisis fitur untuk mendapatkan fitur-fitur pada aplikasi web melalui diagram fitur dengan penjelasan melalui kamus terminologi domain. Langkah berikutnya adalah pemodelan entity-relationship dengan membuat diagram entity-relationship untuk pembuatan database. Terakhir, pemodelan arsitektur dengan membuat arsitektur domain untuk pengembangan aplikasi yang hanya fokus pada fitur.  Hasil dari analisis fitur adalah didapatkan sebanyak 38 fitur mandatory yang berarti fitur tersebut wajib diimplementasikan dalam aplikasi web untuk pariwisata dan kebudayaan.  Kata kunci: Pariwisata, Kebudayaan, Website, Fitur, Feature-Oriented Domain Analysis

  14. Perineural inflammation in morphea (localized scleroderma): systematic characterization of a poorly recognized but potentially useful histopathological feature.

    Science.gov (United States)

    Dhaliwal, Catharine A; MacKenzie, Andrew I; Biswas, Asok

    2014-01-01

    The association between morphea and perineural inflammation has been reported sporadically but never studied systematically. To assess the prevalence and nature of perineural inflammation in various clinicopathologic stages of morphea and a cohort of other inflammatory dermatoses, 80 morphea and 36 control skin biopsies were studied using hematoxylin/eosin and S100 stains. Perineural inflammation was semiquantitatively analyzed (scored), which along with the pattern (concentric vs. marginal) and cellular composition was compared in the two groups. Perineural inflammation was identified in 84% and 61% of morphea and control cases, respectively. Examination of only routinely stained sections could still detect this feature in 58% of morphea and 33% of control biopsies. Mean perineural inflammation score in morphea (0.65) was significantly higher than in the control group (0.23) (p morphea cases. Although perineural inflammation is common in morphea, it is not unusual to find this feature in other inflammatory conditions. Nevertheless, perineural inflammation can serve as an important diagnostic adjunct in difficult cases of morphea if one considers its greater intensity, predominantly concentric pattern and the tendency to show plasma cell neurotropism. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  15. Applied behavior analysis as intervention for autism: definition, features and philosophical concepts

    Directory of Open Access Journals (Sweden)

    Síglia Pimentel Höher Camargo

    2013-11-01

    Full Text Available Autism spectrum disorder (ASD is a lifelong pervasive developmental disorder with no known causes and cure. However, educational and behavioral interventions with a foundation in applied behavior analysis (ABA have been shown to improve a variety of skill areas such as communication, social, academic, and adaptive behaviors of individuals with ASD. The goal of this work is to present the definition, features and philosophical concepts that underlie ABA and make this science an effective intervention method for people with autism.

  16. Absurdity:Analysis of Features of the Style of Harold Pinter's Plays

    Institute of Scientific and Technical Information of China (English)

    南华; 王佳佳

    2016-01-01

    Absurdity is the most distinctive feature of the early works of Harold Pinter. The nature of absurdity is the purposeless-ness of life without the balance and harmony between men and environment. This paper analyzes the distinctive style of Pinter to mainly analyze the application and reflection of the absurd style of Pinter in his plays, and highlight its expressing forms through the analysis on the plot, characters and languages.

  17. An empirical analysis of the impact of renewable energy deployment on local sustainability

    Energy Technology Data Exchange (ETDEWEB)

    Del Rio, Pablo [Institute for Public Goods and Policies (IPP), Centro de Ciencias Humanas y Sociales, Consejo Superior de Investigaciones Cientificas (CSIC), C/Albasanz 26-28, 28037 Madrid (Spain); Burguillo, Mercedes [Facultad de Ciencias Economicas y Empresariales, Universidad de Alcala, Pza. de la Victoria 3, 28802 Alcala de Henares, Madrid (Spain)

    2009-08-15

    It is usually mentioned that renewable energy sources (RES) have a large potential to contribute to the sustainable development of specific territories by providing them with a wide variety of socioeconomic benefits, including diversification of energy supply, enhanced regional and rural development opportunities, creation of a domestic industry and employment opportunities. The analysis of these benefits has usually been too general (i.e., mostly at the national level) and a focus on the regional and especially the local level has been lacking. This paper empirically analyses those benefits, by applying a conceptual and methodological framework previously developed by the authors to three renewable energy technologies in three different places in Spain. With the help of case studies, the paper shows that the contribution of RES to the economic and social dimensions of sustainable development might be significant. Particularly important is employment creation in these areas. Although, in absolute terms, the number of jobs created may not be high, it may be so with respect to the existing jobs in the areas considered. Socioeconomic benefits depend on several factors, and not only on the type of renewable energy, as has usually been mentioned. The specific socioeconomic features of the territories, including the productive structure of the area, the relationships between the stakeholders and the involvement of the local actors in the renewable energy project may play a relevant role in this regard. Furthermore, other local (socioeconomic) sustainability aspects beyond employment creation should be considered. (author)

  18. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening

    Science.gov (United States)

    Tu, Shu-Ju; Wang, Chih-Wei; Pan, Kuang-Tse; Wu, Yi-Cheng; Wu, Chen-Te

    2018-03-01

    Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining

  19. Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

    Science.gov (United States)

    Stewart, C M; Newlands, S D; Perachio, A A

    2004-12-01

    Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

  20. Personal recognition using finger knuckle shape oriented features and texture analysis

    Directory of Open Access Journals (Sweden)

    K. Usha

    2016-10-01

    Full Text Available Finger knuckle print is considered as one of the emerging hand biometric traits due to its potentiality toward the identification of individuals. This paper contributes a new method for personal recognition using finger knuckle print based on two approaches namely, geometric and texture analyses. In the first approach, the shape oriented features of the finger knuckle print are extracted by means of angular geometric analysis and then integrated to achieve better precision rate. Whereas, the knuckle texture feature analysis is carried out by means of multi-resolution transform known as Curvelet transform. This Curvelet transform has the ability to approximate curved singularities with minimum number of Curvelet coefficients. Since, finger knuckle patterns mainly consist of lines and curves, Curvelet transform is highly suitable for its representation. Further, the Curvelet transform decomposes the finger knuckle image into Curvelet sub-bands which are termed as ‘Curvelet knuckle’. Finally, principle component analysis is applied on each Curvelet knuckle for extracting its feature vector through the covariance matrix derived from their Curvelet coefficients. Extensive experiments were conducted using PolyU database and IIT finger knuckle database. The experimental results confirm that, our proposed method shows a high recognition rate of 98.72% with lower false acceptance rate of 0.06%.

  1. Analysis of feature stability for laser-based determination of tissue thickness

    Science.gov (United States)

    Ernst, Floris; Schweikard, Achim; Stüber, Patrick; Bruder, Ralf; Wagner, Benjamin; Wissel, Tobias

    2015-03-01

    Localisation of the cranium is necessary for accurate stereotactic radiotherapy of malign lesions in the brain. This is achieved by immobilizing the patient's head (typically by using thermoplastic masks, bite blocks or combinations thereof) and x-ray imaging to determine the actual position of the patient with respect to the treatment device. In previous work we have developed a novel method for marker-less and non-invasive tracking of the skull using a combination of laser-based surface triangulation and the analysis of backscattered feature patterns of a tightly collimated NIR laser beam scanned over the patient's forehead. An HDR camera is coupled into the beam path of the laser scanning system to acquire one image per projected laser point. We have demonstrated that this setup is capable of accurately determining the tissue thickness for each triangulation point and consequently allows detecting the surface of the cranial bone with sub-millimetre accuracy. Typical clinical settings (treatment times of 15-90 min) require feature stability over time, since the determination of tissue thickness is achieved by machine learning methods trained on initial feature scans. We have collected initial scans of the forehead as well as long-term backscatter data (20 images per seconds over 30 min) from five subjects and extracted the relevant tissue features from the image streams. Based on the knowledge of the relationship between the tissue feature values and the tissue thickness, the analysis of the long-term data showed that the noise level is low enough to allow robust discrimination of tissue thicknesses of 0.5 mm.

  2. An explorative childhood pneumonia analysis based on ultrasonic imaging texture features

    Science.gov (United States)

    Zenteno, Omar; Diaz, Kristians; Lavarello, Roberto; Zimic, Mirko; Correa, Malena; Mayta, Holger; Anticona, Cynthia; Pajuelo, Monica; Oberhelman, Richard; Checkley, William; Gilman, Robert H.; Figueroa, Dante; Castañeda, Benjamín.

    2015-12-01

    According to World Health Organization, pneumonia is the respiratory disease with the highest pediatric mortality rate accounting for 15% of all deaths of children under 5 years old worldwide. The diagnosis of pneumonia is commonly made by clinical criteria with support from ancillary studies and also laboratory findings. Chest imaging is commonly done with chest X-rays and occasionally with a chest CT scan. Lung ultrasound is a promising alternative for chest imaging; however, interpretation is subjective and requires adequate training. In the present work, a two-class classification algorithm based on four Gray-level co-occurrence matrix texture features (i.e., Contrast, Correlation, Energy and Homogeneity) extracted from lung ultrasound images from children aged between six months and five years is presented. Ultrasound data was collected using a L14-5/38 linear transducer. The data consisted of 22 positive- and 68 negative-diagnosed B-mode cine-loops selected by a medical expert and captured in the facilities of the Instituto Nacional de Salud del Niño (Lima, Peru), for a total number of 90 videos obtained from twelve children diagnosed with pneumonia. The classification capacity of each feature was explored independently and the optimal threshold was selected by a receiver operator characteristic (ROC) curve analysis. In addition, a principal component analysis was performed to evaluate the combined performance of all the features. Contrast and correlation resulted the two more significant features. The classification performance of these two features by principal components was evaluated. The results revealed 82% sensitivity, 76% specificity, 78% accuracy and 0.85 area under the ROC.

  3. Local Guided Wavefield Analysis for Characterization of Delaminations in Composites

    Science.gov (United States)

    Rogge, Matthew D.; Campbell Leckey, Cara A.

    2012-01-01

    Delaminations in composite laminates resulting from impact events may be accompanied by minimal indication of damage at the surface. As such, inspection techniques are required to ensure defects are within allowable limits. Conventional ultrasonic scanning techniques have been shown to effectively characterize the size and depth of delaminations but require physical contact with the structure. Alternatively, a noncontact scanning laser vibrometer may be used to measure guided wave propagation in the laminate structure. A local Fourier domain analysis method is presented for processing guided wavefield data to estimate spatially-dependent wavenumber values, which can be used to determine delamination depth. The technique is applied to simulated wavefields and results are analyzed to determine limitations of the technique with regards to determining defect size and depth. Finally, experimental wavefield data obtained in quasi-isotropic carbon fiber reinforced polymer (CFRP) laminates with impact damage is analyzed and wavenumber is measured to an accuracy of 8.5% in the region of shallow delaminations. Keywords: Ultrasonic wavefield imaging, Windowed Fourier transforms, Guided waves, Structural health monitoring, Nondestructive evaluation

  4. Risk analysis for a local gas distribution network

    International Nuclear Information System (INIS)

    Peters, J.W.

    1991-01-01

    Cost control and service reliability are popular topics when discussing strategic issues facing local distribution companies (LDCs) in the 1990s. The ability to provide secure and uninterrupted gas service is crucial for growth and company image, both with the public and regulatory agencies. At the same time, the industry is facing unprecedented competition from alternate fuels, and cost control is essential for maintaining a competitive edge in the market. On the surface, it would appear that cost control and service reliability are contradictory terms. Improvement in service reliability should cost something, or does it? Risk analysis can provide the answer from a distribution design perspective. From a gas distribution engineer's perspective, projects such as loops, backfeeds and even valve placement are designed to reduce, minimize and/or eliminate potential customer outages. These projects improve service reliability by acting as backups should a failure occur on a component of the distribution network. These contingency projects are cost-effective but their longterm benefit or true value is under question. Their purpose is to maintain supply to an area in the distribution network in the event of a failure somewhere else. Two phrases, potential customer outages and in the event of failure, identify uncertainty

  5. Adenocarcinoma of the uncinate process of the pancreas: MDCT patterns of local invasion and clinical features at presentation

    Energy Technology Data Exchange (ETDEWEB)

    Padilla-Thornton, Amie E.; Willmann, Juergen K.; Jeffrey, R.B. [Stanford University School of Medicine, Department of Radiology, Stanford, CA (United States)

    2012-05-15

    To compare the multidetector CT (MDCT) patterns of local invasion and clinical findings at presentation in patients with adenocarcinoma of the uncinate process of the pancreas to patients with adenocarcinomas in the non-uncinate head of the pancreas. We evaluated the two cohorts for common duct and pancreatic duct dilatation, mesenteric vascular encasement, root of mesentery invasion, perineural invasion and duodenal invasion. In addition, we compared the clinical findings at presentation in both groups. Common duct (P < 0.001) and pancreatic duct dilatation (P = 0.001) were significantly less common in uncinate process adenocarcinomas than in the non-uncinate head of the pancreas. Clinical findings of jaundice (P = 0.01) and pruritis (P = 0.004) were significantly more common in patients with lesions in the non-uncinate head of the pancreas. Superior mesenteric artery encasement (P = 0.02) and perineural invasion (P = 0.001) were significantly more common with uncinate process adenocarcinomas. Owing to its unique anatomic location, adenocarcinomas within the uncinate process of the pancreas have significantly different patterns of both local invasion and clinical presentation compared to patients with carcinomas in the non-uncinate head of the pancreas. (orig.)

  6. Adenocarcinoma of the uncinate process of the pancreas: MDCT patterns of local invasion and clinical features at presentation

    International Nuclear Information System (INIS)

    Padilla-Thornton, Amie E.; Willmann, Juergen K.; Jeffrey, R.B.

    2012-01-01

    To compare the multidetector CT (MDCT) patterns of local invasion and clinical findings at presentation in patients with adenocarcinoma of the uncinate process of the pancreas to patients with adenocarcinomas in the non-uncinate head of the pancreas. We evaluated the two cohorts for common duct and pancreatic duct dilatation, mesenteric vascular encasement, root of mesentery invasion, perineural invasion and duodenal invasion. In addition, we compared the clinical findings at presentation in both groups. Common duct (P < 0.001) and pancreatic duct dilatation (P = 0.001) were significantly less common in uncinate process adenocarcinomas than in the non-uncinate head of the pancreas. Clinical findings of jaundice (P = 0.01) and pruritis (P = 0.004) were significantly more common in patients with lesions in the non-uncinate head of the pancreas. Superior mesenteric artery encasement (P = 0.02) and perineural invasion (P = 0.001) were significantly more common with uncinate process adenocarcinomas. Owing to its unique anatomic location, adenocarcinomas within the uncinate process of the pancreas have significantly different patterns of both local invasion and clinical presentation compared to patients with carcinomas in the non-uncinate head of the pancreas. (orig.)

  7. Non--Local Approach to the Analysis of the Stress Distribution in Granular Systems.

    Science.gov (United States)

    Scott, J. E.; Kenkre, V. M.; Hurd, A. J.

    1998-03-01

    A continuum mechanical theory of the stress distribution in granular materials is presented, where the transformation of the vertical spatial coordinate into a formal time variable converts the study of the static stress distribution into a generally non--Markoffian, i.e., memory-possessing (non-local) propagation analysis. Previous treatments (J. -P). Bouchaud, M. E. Cates, and P. Claudin, J. Phys. I France 5, 639 (1995). (C. -h). Liu, S. R. Nagel, D. A. Schecter, S. N. Coppersmith, S. Majumdar, O. Narayan, and T. A. Witten, Science 269, 513 (1995). are shown to be particular cases of our theory corresponding to, respectively, wave-like and dif fusive limits of the general evolution. Calculations are presented for the example of ceramic or metal powder compaction in dies, with emphasis on the understanding of previously unexplained features as seen in experimental data found in the literature o ver the past 50 years. Specific proposals for new experimental investigations are presented.

  8. Global/local methods research using a common structural analysis framework

    Science.gov (United States)

    Knight, Norman F., Jr.; Ransom, Jonathan B.; Griffin, O. H., Jr.; Thompson, Danniella M.

    1991-01-01

    Methodologies for global/local stress analysis are described including both two- and three-dimensional analysis methods. These methods are being developed within a common structural analysis framework. Representative structural analysis problems are presented to demonstrate the global/local methodologies being developed.

  9. Feature-Based Analysis of Plasma-Based Particle Acceleration Data

    Energy Technology Data Exchange (ETDEWEB)

    Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Geddes, Cameron G. R. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chen, Min [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Cormier-Michel, Estelle [Tech-X Corp., Boulder, CO (United States); Bethel, E. Wes [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2014-02-01

    Plasma-based particle accelerators can produce and sustain thousands of times stronger acceleration fields than conventional particle accelerators, providing a potential solution to the problem of the growing size and cost of conventional particle accelerators. To facilitate scientific knowledge discovery from the ever growing collections of accelerator simulation data generated by accelerator physicists to investigate next-generation plasma-based particle accelerator designs, we describe a novel approach for automatic detection and classification of particle beams and beam substructures due to temporal differences in the acceleration process, here called acceleration features. The automatic feature detection in combination with a novel visualization tool for fast, intuitive, query-based exploration of acceleration features enables an effective top-down data exploration process, starting from a high-level, feature-based view down to the level of individual particles. We describe the application of our analysis in practice to analyze simulations of single pulse and dual and triple colliding pulse accelerator designs, and to study the formation and evolution of particle beams, to compare substructures of a beam and to investigate transverse particle loss.

  10. Arrows as anchors: An analysis of the material features of electric field vector arrows

    Science.gov (United States)

    Gire, Elizabeth; Price, Edward

    2014-12-01

    Representations in physics possess both physical and conceptual aspects that are fundamentally intertwined and can interact to support or hinder sense making and computation. We use distributed cognition and the theory of conceptual blending with material anchors to interpret the roles of conceptual and material features of representations in students' use of representations for computation. We focus on the vector-arrows representation of electric fields and describe this representation as a conceptual blend of electric field concepts, physical space, and the material features of the representation (i.e., the physical writing and the surface upon which it is drawn). In this representation, spatial extent (e.g., distance on paper) is used to represent both distances in coordinate space and magnitudes of electric field vectors. In conceptual blending theory, this conflation is described as a clash between the input spaces in the blend. We explore the benefits and drawbacks of this clash, as well as other features of this representation. This analysis is illustrated with examples from clinical problem-solving interviews with upper-division physics majors. We see that while these intermediate physics students make a variety of errors using this representation, they also use the geometric features of the representation to add electric field contributions and to organize the problem situation productively.

  11. Arrows as anchors: An analysis of the material features of electric field vector arrows

    Directory of Open Access Journals (Sweden)

    Elizabeth Gire

    2014-08-01

    Full Text Available Representations in physics possess both physical and conceptual aspects that are fundamentally intertwined and can interact to support or hinder sense making and computation. We use distributed cognition and the theory of conceptual blending with material anchors to interpret the roles of conceptual and material features of representations in students’ use of representations for computation. We focus on the vector-arrows representation of electric fields and describe this representation as a conceptual blend of electric field concepts, physical space, and the material features of the representation (i.e., the physical writing and the surface upon which it is drawn. In this representation, spatial extent (e.g., distance on paper is used to represent both distances in coordinate space and magnitudes of electric field vectors. In conceptual blending theory, this conflation is described as a clash between the input spaces in the blend. We explore the benefits and drawbacks of this clash, as well as other features of this representation. This analysis is illustrated with examples from clinical problem-solving interviews with upper-division physics majors. We see that while these intermediate physics students make a variety of errors using this representation, they also use the geometric features of the representation to add electric field contributions and to organize the problem situation productively.

  12. A Meta-Analysis of Local Climate Change Adaptation Actions

    Science.gov (United States)

    Local governments are beginning to take steps to address the consequences of climate change, such as sea level rise and heat events. However, we do not have a clear understanding of what local governments are doing -- the extent to which they expect climate change to affect their...

  13. Strong Localization in Disordered Media: Analysis of the Backscattering Cone

    KAUST Repository

    Delgado, Edgar

    2012-06-01

    A very interesting effect in light propagation through a disordered system is Anderson localization of light, this phenomenon emerges as the result of multiple scattering of waves by electric inhomogeneities like spatial variations of index of refraction; as the amount of scattering is increased, light propagation is converted from quasi-diffusive to exponentially localized, with photons confined in a limited spatial region characterized by a fundamental quantity known as localization length. Light localization is strongly related to another interference phenomenon emerged from the multiple scattering effect: the coherent backscattering effect. In multiple scattering of waves, in fact, coherence is preserved in the backscattering direction and produces a reinforcement of the field flux originating an observable peak in the backscattered intensity, known as backscattering cone. The study of this peak provide quantitative information about the transport properties of light in the material. In this thesis we report a complete FDTD ab-initio study of light localization and coherent backscattering. In particular, we consider a supercontinuum pulse impinging on a sample composed of randomly positioned scatterers. We study coherent backscattering by averaging over several realizations of the sample properties. We study then the coherent backscattering cone properties as the relative permittivity of the sample is changed, relating the latter with the light localization inside the sample. We demonstrate important relationships between the width of the backscattering cone and the localization length, which shows a linear proportionality in the strong localization regime.

  14. Analysis of Acoustic Features in Speakers with Cognitive Disorders and Speech Impairments

    Science.gov (United States)

    Saz, Oscar; Simón, Javier; Rodríguez, W. Ricardo; Lleida, Eduardo; Vaquero, Carlos

    2009-12-01

    This work presents the results in the analysis of the acoustic features (formants and the three suprasegmental features: tone, intensity and duration) of the vowel production in a group of 14 young speakers suffering different kinds of speech impairments due to physical and cognitive disorders. A corpus with unimpaired children's speech is used to determine the reference values for these features in speakers without any kind of speech impairment within the same domain of the impaired speakers; this is 57 isolated words. The signal processing to extract the formant and pitch values is based on a Linear Prediction Coefficients (LPCs) analysis of the segments considered as vowels in a Hidden Markov Model (HMM) based Viterbi forced alignment. Intensity and duration are also based in the outcome of the automated segmentation. As main conclusion of the work, it is shown that intelligibility of the vowel production is lowered in impaired speakers even when the vowel is perceived as correct by human labelers. The decrease in intelligibility is due to a 30% of increase in confusability in the formants map, a reduction of 50% in the discriminative power in energy between stressed and unstressed vowels and to a 50% increase of the standard deviation in the length of the vowels. On the other hand, impaired speakers keep good control of tone in the production of stressed and unstressed vowels.

  15. Local cell metrics: a novel method for analysis of cell-cell interactions

    Directory of Open Access Journals (Sweden)

    Chen Chien-Chiang

    2009-10-01

    Full Text Available Abstract Background The regulation of many cell functions is inherently linked to cell-cell contact interactions. However, effects of contact interactions among adherent cells can be difficult to detect with global summary statistics due to the localized nature and noise inherent to cell-cell interactions. The lack of informatics approaches specific for detecting cell-cell interactions is a limitation in the analysis of large sets of cell image data, including traditional and combinatorial or high-throughput studies. Here we introduce a novel histogram-based data analysis strategy, termed local cell metrics (LCMs, which addresses this shortcoming. Results The new LCM method is demonstrated via a study of contact inhibition of proliferation of MC3T3-E1 osteoblasts. We describe how LCMs can be used to quantify the local environment of cells and how LCMs are decomposed mathematically into metrics specific to each cell type in a culture, e.g., differently-labelled cells in fluorescence imaging. Using this approach, a quantitative, probabilistic description of the contact inhibition effects in MC3T3-E1 cultures has been achieved. We also show how LCMs are related to the naïve Bayes model. Namely, LCMs are Bayes class-conditional probability functions, suggesting their use for data mining and classification. Conclusion LCMs are successful in robust detection of cell contact inhibition in situations where conventional global statistics fail to do so. The noise due to the random features of cell behavior was suppressed significantly as a result of the focus on local distances, providing sensitive detection of cell-cell contact effects. The methodology can be extended to any quantifiable feature that can be obtained from imaging of cell cultures or tissue samples, including optical, fluorescent, and confocal microscopy. This approach may prove useful in interpreting culture and histological data in fields where cell-cell interactions play a critical

  16. Improved local control with neoadjuvant chemotherapy for locally advanced rectal carcinoma: Long-term analysis

    International Nuclear Information System (INIS)

    Nakfoor, Bruce M.; Willett, Christopher G.; Kaufman, S. Donald; Shellito, Paul C.; Daly, William J.

    1996-01-01

    Objective: Since 1979, our institution has treated locally advanced rectal cancer with preoperative irradiation followed by resection with or without intraoperative radiation therapy (IORT). In 1986, our preoperative treatment policy was changed to include bolus 5-FU chemotherapy concurrent with irradiation in hopes of improving resectability, downstaging and/or local control rates. We report the long-term results with the addition of 5-FU chemotherapy to preoperative irradiation. Materials and Methods: From 1979 - 1994, 200 patients with locally advanced rectal carcinoma (primary or recurrent) received preoperative irradiation, resection and IORT if indicated. Bolus 5-FU (500mg/m 2 /day) chemotherapy was administered for three days during weeks one and five of irradiation. The change in treatment policy was limited to the addition of 5-FU chemotherapy: the radiation techniques (four-field), doses (50.4 Gy), and indications for intraoperative radiation (microscopic residual, gross residual, tumor adherence) remained constant. The median follow-up for the entire group of patients was 33 months (.95 months - 199 months), and the minimum follow-up was 1.5 years. Tabular results are 5-year actuarial calculations. Results: One hundred and five patients received preoperative 5-FU chemotherapy and irradiation whereas 95 patients underwent preoperative irradiation alone. Sixty-five percent of the patients were able to undergo complete resections, and 53% had transmural disease upon pathologic examination. The addition of chemotherapy did not affect the rates of resectability or tumor downstaging. However, the 10-year local control rate was significantly improved for those patients who received preoperative chemotherapy: 77% vs. 44% (p<0.01) (see figure). When stratified by extent of resection and stage, those patients who underwent complete resections or had transmural disease had significantly improved local control rates when compared to the non-chemotherapy group: No

  17. Data driven analysis of rain events: feature extraction, clustering, microphysical /macro physical relationship

    Science.gov (United States)

    Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

    2017-04-01

    The study of rain time series records is mainly carried out using rainfall rate or rain accumulation parameters estimated on a fixed integration time (typically 1 min, 1 hour or 1 day). In this study we used the concept of rain event. In fact, the discrete and intermittent natures of rain processes make the definition of some features inadequate when defined on a fixed duration. Long integration times (hour, day) lead to mix rainy and clear air periods in the same sample. Small integration time (seconds, minutes) will lead to noisy data with a great sensibility to detector characteristics. The analysis on the whole rain event instead of individual short duration samples of a fixed duration allows to clarify relationships between features, in particular between macro physical and microphysical ones. This approach allows suppressing the intra-event variability partly due to measurement uncertainties and allows focusing on physical processes. An algorithm based on Genetic Algorithm (GA) and Self Organising Maps (SOM) is developed to obtain a parsimonious characterisation of rain events using a minimal set of variables. The use of self-organizing map (SOM) is justified by the fact that it allows to map a high dimensional data space in a two-dimensional space while preserving as much as possible the initial space topology in an unsupervised way. The obtained SOM allows providing the dependencies between variables and consequently removing redundant variables leading to a minimal subset of only five features (the event duration, the rain rate peak, the rain event depth, the event rain rate standard deviation and the absolute rain rate variation of order 0.5). To confirm relevance of the five selected features the corresponding SOM is analyzed. This analysis shows clearly the existence of relationships between features. It also shows the independence of the inter-event time (IETp) feature or the weak dependence of the Dry percentage in event (Dd%e) feature. This confirms

  18. Local changes of work function near rough features on Cu surfaces operated under high external electric field

    Energy Technology Data Exchange (ETDEWEB)

    Djurabekova, Flyura, E-mail: flyura.djurabekova@helsinki.fi; Ruzibaev, Avaz; Parviainen, Stefan [Helsinki Institute of Physics and Department of Physics, University of Helsinki, P.O. Box 43, FI-00014 Helsinki (Finland); Holmström, Eero [Department of Physics, University of Helsinki, P.O. Box 64, FIN-00014 Helsinki (Finland); Department of Earth Sciences, Faculty of Maths and Physical Sciences, UCL Earth Sciences, Gower Street, London WC1E 6BT (United Kingdom); Hakala, Mikko [Department of Physics, University of Helsinki, P.O. Box 64, FIN-00014 Helsinki (Finland)

    2013-12-28

    Metal surfaces operated under high electric fields produce sparks even if they are held in ultra high vacuum. In spite of extensive research on the topic of vacuum arcs, the mystery of vacuum arc origin still remains unresolved. The indications that the sparking rates depend on the material motivate the research on surface response to extremely high external electric fields. In this work by means of density-functional theory calculations we analyze the redistribution of electron density on (100) Cu surfaces due to self-adatoms and in presence of high electric fields from −1 V/nm up to −2 V/nm (−1 to −2 GV/m, respectively). We also calculate the partial charge induced by the external field on a single adatom and a cluster of two adatoms in order to obtain reliable information on charge redistribution on surface atoms, which can serve as a benchmarking quantity for the assessment of the electric field effects on metal surfaces by means of molecular dynamics simulations. Furthermore, we investigate the modifications of work function around rough surface features, such as step edges and self-adatoms.

  19. A Comparative Study of Feature Selection Methods for the Discriminative Analysis of Temporal Lobe Epilepsy

    Directory of Open Access Journals (Sweden)

    Chunren Lai

    2017-12-01

    Full Text Available It is crucial to differentiate patients with temporal lobe epilepsy (TLE from the healthy population and determine abnormal brain regions in TLE. The cortical features and changes can reveal the unique anatomical patterns of brain regions from structural magnetic resonance (MR images. In this study, structural MR images from 41 patients with left TLE, 34 patients with right TLE, and 58 normal controls (NC were acquired, and four kinds of cortical measures, namely cortical thickness, cortical surface area, gray matter volume (GMV, and mean curvature, were explored for discriminative analysis. Three feature selection methods including the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM, and the support vector machine-recursive feature elimination (SVM-RFE were investigated to extract dominant features among the compared groups for classification using the support vector machine (SVM classifier. The results showed that the SVM-RFE achieved the highest performance (most classifications with more than 84% accuracy, followed by the SCDRM, and the t-test. Especially, the surface area and GMV exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical measures were combined. Additionally, the dominant regions with higher classification weights were mainly located in the temporal and the frontal lobe, including the entorhinal cortex, rostral middle frontal, parahippocampal cortex, superior frontal, insula, and cuneus. This study concluded that the cortical features provided effective information for the recognition of abnormal anatomical patterns and the proposed methods had the potential to improve the clinical diagnosis of TLE.

  20. Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

    Directory of Open Access Journals (Sweden)

    Saleem Riaz

    2017-02-01

    Full Text Available Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, developing effective bearing fault diagnostic method using different fault features at different steps becomes more attractive. Bearings are widely used in medical applications, food processing industries, semi-conductor industries, paper making industries and aircraft components. This paper review has demonstrated that the latest reviews applied to rotating machinery on the available a variety of vibration feature extraction. Generally literature is classified into two main groups: frequency domain, time frequency analysis. However, fault detection and diagnosis of rotating machine vibration signal processing methods to present their own limitations. In practice, most healthy ingredients faulty vibration signal from background noise and mechanical vibration signals are buried. This paper also reviews that how the advanced signal processing methods, empirical mode decomposition and interference cancellation algorithm has been investigated and developed. The condition for rotating machines based rehabilitation, prevent failures increase the availability and reduce the cost of maintenance is becoming necessary too. Rotating machine fault detection and diagnostics in developing algorithms signal processing based on a key problem is the fault feature extraction or quantification. Currently, vibration signal, fault detection and diagnosis of rotating machinery based techniques most widely used techniques. Furthermore, the researchers are widely interested to make automatic

  1. Feature analysis for correlation studies of simultaneous EEG-fMRI data: A proof of concept for neurofeedback approaches.

    Science.gov (United States)

    Simoes, Simões; Lima, João; Direito, Bruno; Castelhano, João; Ferreira, Carlos; Carvalho, Paulo; Castelo-Branco, Miguel

    2015-01-01

    The identification and interpretation of facial expressions is an important feature of social cognition. This characteristic is often impaired in various neurodevelopmental disorders. Recent therapeutic approaches to intervene in social communication impairments include neurofeedback (NF). In this study, we present a NF real-time functional Magnetic Resonance Imaging (rt-fMRI), combined with electroencephalography (EEG) to train social communication skills. In this sense, we defined the right Superior Temporal Sulcus as our target region-of-interest. To analyze the correlation between the fMRI regions of interest and the EEG data, we transposed the sources located at the nearest cortical location to the target region. We extracted a set of 75 features from EEG segments and performed a correlation analysis with the brain activations extracted from rt-fMRI in the right pSTS region. The finding of significant correlations of simultaneously measured signals in distinct modalities (EEG and fMRI) is promising. Future studies should address whether the observed correlation levels between local brain activity and scalp measures are sufficient to implement NF approaches.

  2. A gateway for phylogenetic analysis powered by grid computing featuring GARLI 2.0.

    Science.gov (United States)

    Bazinet, Adam L; Zwickl, Derrick J; Cummings, Michael P

    2014-09-01

    We introduce molecularevolution.org, a publicly available gateway for high-throughput, maximum-likelihood phylogenetic analysis powered by grid computing. The gateway features a garli 2.0 web service that enables a user to quickly and easily submit thousands of maximum likelihood tree searches or bootstrap searches that are executed in parallel on distributed computing resources. The garli web service allows one to easily specify partitioned substitution models using a graphical interface, and it performs sophisticated post-processing of phylogenetic results. Although the garli web service has been used by the research community for over three years, here we formally announce the availability of the service, describe its capabilities, highlight new features and recent improvements, and provide details about how the grid system efficiently delivers high-quality phylogenetic results. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

  3. Discrete subgroups of adolescents diagnosed with borderline personality disorder: a latent class analysis of personality features.

    Science.gov (United States)

    Ramos, Vera; Canta, Guilherme; de Castro, Filipa; Leal, Isabel

    2014-08-01

    Research suggests that borderline personality disorder (BPD) can be diagnosed in adolescents and is marked by considerable heterogeneity. This study aimed to identify personality features characterizing adolescents with BPD and possible meaningful patterns of heterogeneity that could lead to personality subgroups. The authors analyzed data on 60 adolescents, ages 15 to 18 years, who met DSM criteria for a BPD diagnosis. The authors used latent class analysis (LCA) to identify subgroups based on the personality pattern scales from the Millon Adolescent Clinical Inventory (MACI). LCA indicated that the best-fitting solution was a two-class model, identifying two discrete subgroups of BPD adolescents that were described as internalizing and externalizing. The subgroups were then compared on clinical and sociodemographic variables, measures of personality dimensions, DSM BPD criteria, and perception of attachment styles. Adolescents with a BPD diagnosis constitute a heterogeneous group and vary meaningfully on personality features that can have clinical implications for treatment.

  4. [Induction and analysis for NIR features of frequently-used mineral traditional Chinese medicines].

    Science.gov (United States)

    Chen, Long; Yuan, Ming-Yang; Chen, Ke-Li

    2016-10-01

    In order to provide theoretical basis for the rapid identification of mineral traditional Chinese medicines(TCM) with near infrared (NIR)diffuse reflectance spectroscopy, Characteristic NIR spectra of 51 kinds of mineral TCMs were generalized and compared on the basis of the previous research, and the characteristic spectral bands were determined and analyzed by referring to mineralogical and geological literatures. It turned out that the NIR features of mineral TCMs were mainly at 8 000-4 000 cm ⁻¹ wavebands, which can be assigned as the absorption of water, -OH and[CO3 ²⁻] and so on. Absorption peaks of water has regularity as follows, the structure water and -OH had a combined peak which was strong and keen-edged around 7 000 cm ⁻¹, the crystal water had two strong peak around 7 000 cm ⁻¹ and 5 100 cm ⁻¹, and water only has a broad peak around 5 100 cm ⁻¹. Due to the differences in the crystal form and the contents of water in mineral TCMs, NIR features of water in mineral TCMs which could be used for identification were different. Mineral TCMs containing sulfate are rich in crystal water, mineral TCMs containing silicate generally had structure water, and mineral TCMs containing carbonate merely had a little of water, so it was reasonable for the use of NIR spectroscopy to classify mineral TCMs with anionic type. In addition, because of the differences in cationic type, impurities, crystal form and crystallinity, mineral TCMs have exclusive NIR features at 4 600-4 000 cm ⁻¹, which can be assigned as Al-OH, Mg-OH, Fe-OH, Si-OH,[CO3 ²⁻] and so on. Calcined mineral TCMs are often associated with water and main composition changes, also changes of the NIR features, which could be used for the monitoring of the processing, and to provide references for the quality control of mineral TCMs. The adaptability and limitation of NIR analysis for mineral TCMs were also discussed:the majority of mineral TCMs had noteworthy NIR features which could be

  5. Analysis and classification of commercial ham slice images using directional fractal dimension features.

    Science.gov (United States)

    Mendoza, Fernando; Valous, Nektarios A; Allen, Paul; Kenny, Tony A; Ward, Paddy; Sun, Da-Wen

    2009-02-01

    This paper presents a novel and non-destructive approach to the appearance characterization and classification of commercial pork, turkey and chicken ham slices. Ham slice images were modelled using directional fractal (DF(0°;45°;90°;135°)) dimensions and a minimum distance classifier was adopted to perform the classification task. Also, the role of different colour spaces and the resolution level of the images on DF analysis were investigated. This approach was applied to 480 wafer thin ham slices from four types of hams (120 slices per type): i.e., pork (cooked and smoked), turkey (smoked) and chicken (roasted). DF features were extracted from digitalized intensity images in greyscale, and R, G, B, L(∗), a(∗), b(∗), H, S, and V colour components for three image resolution levels (100%, 50%, and 25%). Simulation results show that in spite of the complexity and high variability in colour and texture appearance, the modelling of ham slice images with DF dimensions allows the capture of differentiating textural features between the four commercial ham types. Independent DF features entail better discrimination than that using the average of four directions. However, DF dimensions reveal a high sensitivity to colour channel, orientation and image resolution for the fractal analysis. The classification accuracy using six DF dimension features (a(90°)(∗),a(135°)(∗),H(0°),H(45°),S(0°),H(90°)) was 93.9% for training data and 82.2% for testing data.

  6. Analysis of Human's Motions Based on Local Mean Decomposition in Through-wall Radar Detection

    Science.gov (United States)

    Lu, Qi; Liu, Cai; Zeng, Zhaofa; Li, Jing; Zhang, Xuebing

    2016-04-01

    Observation of human motions through a wall is an important issue in security applications and search-and rescue. Radar has advantages in looking through walls where other sensors give low performance or cannot be used at all. Ultrawideband (UWB) radar has high spatial resolution as a result of employment of ultranarrow pulses. It has abilities to distinguish the closely positioned targets and provide time-lapse information of targets. Moreover, the UWB radar shows good performance in wall penetration when the inherently short pulses spread their energy over a broad frequency range. Human's motions show periodic features including respiration, swing arms and legs, fluctuations of the torso. Detection of human targets is based on the fact that there is always periodic motion due to breathing or other body movements like walking. The radar can gain the reflections from each human body parts and add the reflections at each time sample. The periodic movements will cause micro-Doppler modulation in the reflected radar signals. Time-frequency analysis methods are consider as the effective tools to analysis and extract micro-Doppler effects caused by the periodic movements in the reflected radar signal, such as short-time Fourier transform (STFT), wavelet transform (WT), and Hilbert-Huang transform (HHT).The local mean decomposition (LMD), initially developed by Smith (2005), is to decomposed amplitude and frequency modulated signals into a small set of product functions (PFs), each of which is the product of an envelope signal and a frequency modulated signal from which a time-vary instantaneous phase and instantaneous frequency can be derived. As bypassing the Hilbert transform, the LMD has no demodulation error coming from window effect and involves no negative frequency without physical sense. Also, the instantaneous attributes obtained by LMD are more stable and precise than those obtained by the empirical mode decomposition (EMD) because LMD uses smoothed local

  7. The gravity of pollination: integrating at-site features into spatial analysis of contemporary pollen movement.

    Science.gov (United States)

    DiLeo, Michelle F; Siu, Jenna C; Rhodes, Matthew K; López-Villalobos, Adriana; Redwine, Angela; Ksiazek, Kelly; Dyer, Rodney J

    2014-08-01

    Pollen-mediated gene flow is a major driver of spatial genetic structure in plant populations. Both individual plant characteristics and site-specific features of the landscape can modify the perceived attractiveness of plants to their pollinators and thus play an important role in shaping spatial genetic variation. Most studies of landscape-level genetic connectivity in plants have focused on the effects of interindividual distance using spatial and increasingly ecological separation, yet have not incorporated individual plant characteristics or other at-site ecological variables. Using spatially explicit simulations, we first tested the extent to which the inclusion of at-site variables influencing local pollination success improved the statistical characterization of genetic connectivity based upon examination of pollen pool genetic structure. The addition of at-site characteristics provided better models than those that only considered interindividual spatial distance (e.g. IBD). Models parameterized using conditional genetic covariance (e.g. population graphs) also outperformed those assuming panmixia. In a natural population of Cornus florida L. (Cornaceae), we showed that the addition of at-site characteristics (clumping of primary canopy opening above each maternal tree and maternal tree floral output) provided significantly better models describing gene flow than models including only between-site spatial (IBD) and ecological (isolation by resistance) variables. Overall, our results show that including interindividual and local ecological variation greatly aids in characterizing landscape-level measures of contemporary gene flow. © 2014 John Wiley & Sons Ltd.

  8. Screening Analysis of Criticality Features, Events, and Processes for License Application

    International Nuclear Information System (INIS)

    J.A. McClure

    2004-01-01

    This report documents the screening analysis of postclosure criticality features, events, and processes. It addresses the probability of criticality events resulting from degradation processes as well as disruptive events (i.e., seismic, rock fall, and igneous). Probability evaluations are performed utilizing the configuration generator described in ''Configuration Generator Model'', a component of the methodology from ''Disposal Criticality Analysis Methodology Topical Report''. The total probability per package of criticality is compared against the regulatory probability criterion for inclusion of events established in 10 CFR 63.114(d) (consider only events that have at least one chance in 10,000 of occurring over 10,000 years). The total probability of criticality accounts for the evaluation of identified potential critical configurations of all baselined commercial and U.S. Department of Energy spent nuclear fuel waste form and waste package combinations, both internal and external to the waste packages. This criticality screening analysis utilizes available information for the 21-Pressurized Water Reactor Absorber Plate, 12-Pressurized Water Reactor Absorber Plate, 44-Boiling Water Reactor Absorber Plate, 24-Boiling Water Reactor Absorber Plate, and the 5-Defense High-Level Radioactive Waste/U.S. Department of Energy Short waste package types. Where defensible, assumptions have been made for the evaluation of the following waste package types in order to perform a complete criticality screening analysis: 21-Pressurized Water Reactor Control Rod, 5-Defense High-Level Radioactive Waste/U.S. Department of Energy Long, and 2-Multi-Canister Overpack/2-Defense High-Level Radioactive Waste package types. The inputs used to establish probabilities for this analysis report are based on information and data generated for the Total System Performance Assessment for the License Application, where available. This analysis report determines whether criticality is to be

  9. L1-norm kernel discriminant analysis via Bayes error bound optimization for robust feature extraction.

    Science.gov (United States)

    Zheng, Wenming; Lin, Zhouchen; Wang, Haixian

    2014-04-01

    A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.

  10. Semiclassical analysis of quantum localization of the periodically kicked Rydberg atom

    International Nuclear Information System (INIS)

    Yoshida, S.; Persson, E.; Burgdoerfer, J.; Grossmann, F.

    2004-01-01

    The periodically kicked Rydberg atom displays quantum localization, features of which depend on the orientation and strength of the unidirectional kicks. They include scarring of the wave function, localization by cantori, and exponential localization in the regime of strong perturbation resembling dynamical localization. Using the semiclassical Herman-Kluk propagator we investigate the degree to which semiclassical dynamics can mimic quantum localization. While the semiclassical approximation has difficulties to reproduce the scarred wave functions, the exponential tail which is a typical signature of the dynamical localization is well represented in the case of strong classical diffusion. Also the localization by broken tori is observed in the semiclassical recurrence probability for short times but the deviation from the corresponding quantum dynamics becomes more pronounced for the long-time evolution

  11. Performance Evaluation of State-of-the-Art Local Feature Detectors and Descriptors in the Context of Longitudinal Registration of Retinal Images.

    Science.gov (United States)

    Saha, Sajib K; Xiao, Di; Frost, Shaun; Kanagasingam, Yogesan

    2018-02-17

    In this paper we systematically evaluate the performance of several state-of-the-art local feature detectors and descriptors in the context of longitudinal registration of retinal images. Longitudinal (temporal) registration facilitates to track the changes in the retina that has happened over time. A wide number of local feature detectors and descriptors exist and many of them have already applied for retinal image registration, however, no comparative evaluation has been made so far to analyse their respective performance. In this manuscript we evaluate the performance of the widely known and commonly used detectors such as Harris, SIFT, SURF, BRISK, and bifurcation and cross-over points. As of descriptors SIFT, SURF, ALOHA, BRIEF, BRISK and PIIFD are used. Longitudinal retinal image datasets containing a total of 244 images are used for the experiment. The evaluation reveals some potential findings including more robustness of SURF and SIFT keypoints than the commonly used bifurcation and cross-over points, when detected on the vessels. SIFT keypoints can be detected with a reliability of 59% for without pathology images and 45% for with pathology images. For SURF keypoints these values are respectively 58% and 47%. ALOHA descriptor is best suited to describe SURF keypoints, which ensures an overall matching accuracy, distinguishability of 83%, 93% and 78%, 83% for without pathology and with pathology images respectively.

  12. Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation

    Directory of Open Access Journals (Sweden)

    Tetsuyou Watanabe

    2014-01-01

    Full Text Available Intermittent claudication is a walking symptom. Patients with intermittent claudication experience lower limb pain after walking for a short time. However, rest relieves the pain and allows the patient to walk again. Unfortunately, this symptom predominantly arises from not 1 but 2 different diseases: LSS (lumber spinal canal stenosis and PAD (peripheral arterial disease. Patients with LSS can be subdivided by the affected vertebra into 2 main groups: L4 and L5. It is clinically very important to determine whether patients with intermittent claudication suffer from PAD, L4, or L5. This paper presents a novel SVM- (support vector machine- based methodology for such discrimination/differentiation using minimally required data, simple walking motion data in the sagittal plane. We constructed a simple walking measurement system that is easy to set up and calibrate and suitable for use by nonspecialists in small spaces. We analyzed the obtained gait patterns and derived input parameters for SVM that are also visually detectable and medically meaningful/consistent differentiation features. We present a differentiation methodology utilizing an SVM classifier. Leave-one-out cross-validation of differentiation/classification by this method yielded a total accuracy of 83%.

  13. Physical analysis of some features of the gauge theories with Higgs sectors

    International Nuclear Information System (INIS)

    Beshtoev, Kh.M.

    1995-01-01

    A physical analysis of some features of the gauge theories with Higgs sectors is made. It is shown that we should assume gauge transformations in the fermion and Higgs sectors to be different (i.e., to have different charges) in order to remove contradictions arising in gauge theories with Higgs sectors. Then, the Higgs mechanism can be interpreted as some mechanism of gauge field shielding. In such a mechanism fermions remain without masses. The conclusion is made that in the standard theory of the development of the Universe, monopoles cannot survive at low temperatures. 15 refs

  14. Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

    Science.gov (United States)

    Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen

    2017-09-01

    Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

  15. Standard practice for extreme value analysis of nonmetallic inclusions in steel and other microstructural features

    CERN Document Server

    American Society for Testing and Materials. Philadelphia

    2008-01-01

    1.1 This practice describes a methodology to statistically characterize the distribution of the largest indigenous nonmetallic inclusions in steel specimens based upon quantitative metallographic measurements. The practice is not suitable for assessing exogenous inclusions. 1.2 Based upon the statistical analysis, the nonmetallic content of different lots of steels can be compared. 1.3 This practice deals only with the recommended test methods and nothing in it should be construed as defining or establishing limits of acceptability. 1.4 The measured values are stated in SI units. For measurements obtained from light microscopy, linear feature parameters shall be reported as micrometers, and feature areas shall be reported as micrometers. 1.5 The methodology can be extended to other materials and to other microstructural features. 1.6 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish app...

  16. Analysis of muscle fatigue conditions using time-frequency images and GLCM features

    Directory of Open Access Journals (Sweden)

    Karthick P.A.

    2016-09-01

    Full Text Available In this work, an attempt has been made to differentiate muscle non-fatigue and fatigue conditions using sEMG signals and texture representation of the time-frequency images. The sEMG signals are recorded from the biceps brachii muscle of 25 healthy adult volunteers during dynamic fatiguing contraction. The first and last curls of these signals are considered as the non-fatigue and fatigue zones, respectively. These signals are preprocessed and the time-frequency spectrum is computed using short time fourier transform (STFT. Gray-Level Co-occurrence Matrix (GLCM is extracted from low (15–45 Hz, medium (46–95 Hz and high (96–150 Hz frequency bands of the time-frequency images. Further, the features such as contrast, correlation, energy and homogeneity are calculated from the resultant matrices. The results show that the high frequency band based features are able to differentiate non-fatigue and fatigue conditions. The features such as correlation, contrast and homogeneity extracted at angles 0°, 45°, 90°, and 135° are found to be distinct with high statistical significance (p < 0.0001. Hence, this framework can be used for analysis of neuromuscular disorders.

  17. Analysis of local budgets revenues in the Republic of Moldova: level, structure and dynamics for 2008-2014 years

    Directory of Open Access Journals (Sweden)

    Andrei PETROIA

    2015-12-01

    Full Text Available This paper consist largely of analysis of the local budgets revenues of the Republic of Moldova for the period of 2008-2014. The analysis can not offer many information in dynamics due to the few number of years, but we tried to make an analyze of the situation. Informational support of the work are the laws and regulations of the country; data provided by the Ministry of Finance, National Bureau of Statistics and the literature. The purpose of this paper is to approach each category of income and part of it, the time evolution of local budget revenues over several consecutive years of budget income distribution in the directions of the Republic of Moldova, and analysis of each component of the local budget. In this paper, we have analyzed in dynamics structure in total revenues of local budgets, in national public budget, in state budget and in GDP. The main components analyzed were: tax and non-tax revenues, revenues without grants and transfers, grants and transfers from the state budget. Also we have attracted special attention to direct and indirect taxes, as well as their components.The necessity of this study is to present a clear and concise form of the administrative-territorial units' budget feature, conceptual notions, policies which are reflected on local revenues in Moldova. This paper aims possibility of revising the budget system structure, improve financial relations within it and its evolution, but it does not reflect fully all matters related to the budget system.

  18. Local level epidemiological analysis of TB in people from a high incidence country of birth

    OpenAIRE

    Massey Peter D; Durrheim David N; Stephens Nicola; Christensen Amanda

    2013-01-01

    Abstract Background The setting for this analysis is the low tuberculosis (TB) incidence state of New South Wales (NSW), Australia. Local level analysis of TB epidemiology in people from high incidence countries-of-birth (HIC) in a low incidence setting has not been conducted in Australia and has not been widely reported. Local level analysis could inform measures such as active case finding and targeted earlier diagnosis. The aim of this study was to use a novel approach to identify local ar...

  19. Prediction of protein modification sites of pyrrolidone carboxylic acid using mRMR feature selection and analysis.

    Directory of Open Access Journals (Sweden)

    Lu-Lu Zheng

    Full Text Available Pyrrolidone carboxylic acid (PCA is formed during a common post-translational modification (PTM of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR and incremental feature selection (IFS. We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations.

  20. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network

    Science.gov (United States)

    Zhang, Kai; Long, Erping; Cui, Jiangtao; Zhu, Mingmin; An, Yingying; Zhang, Jia; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni

    2017-01-01

    Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model. PMID:28306716

  1. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network.

    Directory of Open Access Journals (Sweden)

    Xiyang Liu

    Full Text Available Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI and employing a deep learning convolutional neural network (CNN. First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83% and a three-degree grading area (89.02%, 86.63%, and 90.75%, density (92.68%, 91.05%, and 93.94% and location (89.28%, 82.70%, and 93.08%. Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.

  2. Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

    Science.gov (United States)

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-08-01

    The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793  ±  0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

  3. Critical Analysis of 2012 Local Elections in Bosnia-Herzegovina

    Directory of Open Access Journals (Sweden)

    Mirsad Karic

    2013-03-01

    Full Text Available This paper provides a critical analyzes of 2012 local elections in Bosnia-Herzegovina. Since 1995 the local elections and its political and electoral system have been based on the Dayton Peace Agreement (DPA. According to DPA Bosnia-Herzegovina has the multiparty system and regular and free elections. These local elections were held amidst continuously renewed political turmoil at the cantonal, entity and state levels. 2012 local elections results have shown that the HDZ and SDA continued to dominate politics at the local level in the Federation of BiH while in the RS, position of SNSD has been strongly shaken by very good performance of SDS. The SDA won majority of votes in Bosniak majority areas while SDS and HDZ secured their votes in the Serb and Croat majority areas respectively. In the Federation of BiH, SDP and SBB suffered dramatic fall in votes comparing to the last general elections while in the RS, SNSD, which has dominated politics since 2006 lost significant number of votes, mayoral posts and municipality seats to SDS and some other political parties such as PDP, SP and DNS.

  4. Local Chromatin Features Including PU.1 and IKAROS Binding and H3K4 Methylation Shape the Repertoire of Immunoglobulin Kappa Genes Chosen for V(DJ Recombination

    Directory of Open Access Journals (Sweden)

    Louise S. Matheson

    2017-11-01

    Full Text Available V(DJ recombination is essential for the generation of diverse antigen receptor (AgR repertoires. In B cells, immunoglobulin kappa (Igκ light chain recombination follows immunoglobulin heavy chain (Igh recombination. We recently developed the DNA-based VDJ-seq assay for the unbiased quantitation of Igh VH and DH repertoires. Integration of VDJ-seq data with genome-wide datasets revealed that two chromatin states at the recombination signal sequence (RSS of VH genes are highly predictive of recombination in mouse pro-B cells. It is unknown whether local chromatin states contribute to Vκ gene choice during Igκ recombination. Here we adapt VDJ-seq to profile the Igκ VκJκ repertoire and present a comprehensive readout in mouse pre-B cells, revealing highly variable Vκ gene usage. Integration with genome-wide datasets for histone modifications, DNase hypersensitivity, transcription factor binding and germline transcription identified PU.1 binding at the RSS, which was unimportant for Igh, as highly predictive of whether a Vκ gene will recombine or not, suggesting that it plays a binary, all-or-nothing role, priming genes for recombination. Thereafter, the frequency with which these genes recombine was shaped both by the presence and level of enrichment of several other chromatin features, including H3K4 methylation and IKAROS binding. Moreover, in contrast to the Igh locus, the chromatin landscape of the promoter, as well as of the RSS, contributes to Vκ gene recombination. Thus, multiple facets of local chromatin features explain much of the variation in Vκ gene usage. Together, these findings reveal shared and divergent roles for epigenetic features and transcription factors in AgR V(DJ recombination and provide avenues for further investigation of chromatin signatures that may underpin V(DJ-mediated chromosomal translocations.

  5. New features of MadAnalysis 5 for analysis design and reinterpretation

    CERN Document Server

    Conte, Eric; Fuks, Benjamin; Schmitt, Thibaut

    2015-01-01

    We present MadAnalysis 5, an analysis package dedicated to phenomenological studies of simulated collisions occurring in high-energy physics experiments. Within this framework, users are invited, through a user-friendly Python interpreter, to implement physics analyses in a very simple manner. A C++ code is then automatically generated, compiled and executed. Very recently, the expert mode of the program has been extended so that analyses with multiple signal/control regions can be handled. Additional observables have also been included, and an interface to several fast detector simulation packages has been developed, one of them being a tune of the Delphes 3 software. As a result, a recasting of existing ATLAS and CMS analyses can be achieved straightforwardly.

  6. Local control of Ewing's sarcoma: an analysis of 67 patients

    International Nuclear Information System (INIS)

    Brown, A.P.; Fixsen, J.A.; Plowman, P.N.

    1987-01-01

    Local control of Ewing's sarcoma was analysed in a series of 67 patients treated by surgery and/or radiotherapy as well as combination chemotherapy. Radiotherapy was employed with or without surgery in 60 patients and produced an overall local control rate of 55%; complete excision of the primary lesion seemed to be beneficial. There was a marked variation in control rates depending on the site of the primary lesion: limb 85%, rib 53%, pelvis 31% and other sites 33%.Primary tumours greater than 10 cm in diameter were significantly less likely to be controlled. Using daily fractions of approximately 180 cGy, total doses in excess of 6000 cGy seem more likely to produce serious late morbidity amd may not increase the local control rate. No cases of second malignancy arising in irradiated tissue have been observed to date, but one patient developed acute lymphoblastic leukaemia. (author)

  7. Extracting intrinsic functional networks with feature-based group independent component analysis.

    Science.gov (United States)

    Calhoun, Vince D; Allen, Elena

    2013-04-01

    There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro

  8. Tracking Water, C, N, and P by Linking Local Scale Soil Hydrologic and Biogeochemical Features to Watershed Scale

    Science.gov (United States)

    Sedaghatdoost, A.; Mohanty, B.; Huang, Y.

    2017-12-01

    The biogeochemical cycles of carbon (C), nitrogen (N), and phosphorus (P) have many contemporary significance due to their critical roles in determining the structure and function of ecosystems. The objectives of our study is to find out temporal dynamics and spatial distribution of soil physical, chemical, and biological properties and their interaction with C, N, and P cycles in the soil for different land covers and weather conditions. The study is being conducted at three locations within Texas Water Observatory (TWO), including Riesel (USDA-ARS experimental watersheds), Texas A&M Agrilife Research Farm, and Danciger forest in Texas. Soil physical, hydraulic, chemical (total C, total N, total P, pH, EC, redox potential, N-NO3-, N-NH4+, PO42-, K, Ca, Mg, Na, Mn, and Alox and Feox), and microbiological (Microbial biomass C, N, and P, PLFA analysis, enzymatic activity) properties are being measured in the top 30 cm of the soil profile. Our preliminary data shows that biogeochemical processes would be more profound in the areas with higher temperature and precipitation as these factors stimulate microbial activity and thus influence C, N, and P cycles. Also concentrations of C and N are greater in woodlands relative to remnant grasslands as a consequence of the greater above- and below-ground productivity of woodlands relative to remnant grasslands. We hypothesize that finer soil textures have more organic matter, microbial population, and reactive surfaces for chemicals than coarse soils, as described in some recent literature. However, the microbial activity may not be active in fine textured soils as organic materials may be sorbed to clay surfaces or protected from decomposing organisms. We also expect reduced condition in saturated soils which will decrease carbon mineralization while increase denitrification and alkalinity in the soil. Spatio-temporal data with initial evaluation of biogeochemical factors/processes for different land covers will be presented.

  9. Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

    Directory of Open Access Journals (Sweden)

    Margarita Gamarra

    2017-08-01

    Full Text Available Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

  10. Semiautomated analysis of embryoscope images: Using localized variance of image intensity to detect embryo developmental stages.

    Science.gov (United States)

    Mölder, Anna; Drury, Sarah; Costen, Nicholas; Hartshorne, Geraldine M; Czanner, Silvester

    2015-02-01

    Embryo selection in in vitro fertilization (IVF) treatment has traditionally been done manually using microscopy at intermittent time points during embryo development. Novel technique has made it possible to monitor embryos using time lapse for long periods of time and together with the reduced cost of data storage, this has opened the door to long-term time-lapse monitoring, and large amounts of image material is now routinely gathered. However, the analysis is still to a large extent performed manually, and images are mostly used as qualitative reference. To make full use of the increased amount of microscopic image material, (semi)automated computer-aided tools are needed. An additional benefit of automation is the establishment of standardization tools for embryo selection and transfer, making decisions more transparent and less subjective. Another is the possibility to gather and analyze data in a high-throughput manner, gathering data from multiple clinics and increasing our knowledge of early human embryo development. In this study, the extraction of data to automatically select and track spatio-temporal events and features from sets of embryo images has been achieved using localized variance based on the distribution of image grey scale levels. A retrospective cohort study was performed using time-lapse imaging data derived from 39 human embryos from seven couples, covering the time from fertilization up to 6.3 days. The profile of localized variance has been used to characterize syngamy, mitotic division and stages of cleavage, compaction, and blastocoel formation. Prior to analysis, focal plane and embryo location were automatically detected, limiting precomputational user interaction to a calibration step and usable for automatic detection of region of interest (ROI) regardless of the method of analysis. The results were validated against the opinion of clinical experts. © 2015 International Society for Advancement of Cytometry. © 2015 International

  11. Estimation of local spectrum content of cervical cancer-related features via two dimensional method of geometric restriction in frequency domain

    International Nuclear Information System (INIS)

    Van Raad, V.

    2004-01-01

    Digital colposcopy is an emerging new technology, which can be used as adjunct to the conventional Pap test for staging of cervical cancer and it can improve the diagnostic accuracy of the test. Computer aided diagnosis (CAD) in digital colposcopy has as a goal to segment and outline abnormal areas on the cervix, one of which is an important anatomical landmark on the ectocervix - the transformation zone (TZ). In this paper we proposed a new method for estimation of the local spectrum features of cervical cancer in vivo. We used a 2D method to estimate the energy of the local frequency bands, using a geometric restriction (GR). In the current work we reported up to 12 dB difference between the local power spectral density content of the region of interest (ROI) and (ROI) C for the mid-frequency band. We devised a method to present pseudo-color visual maps of the cervical images, useful for CAD and successful ROI segmentation. (author)

  12. Efficient moving target analysis for inverse synthetic aperture radar images via joint speeded-up robust features and regular moment

    Science.gov (United States)

    Yang, Hongxin; Su, Fulin

    2018-01-01

    We propose a moving target analysis algorithm using speeded-up robust features (SURF) and regular moment in inverse synthetic aperture radar (ISAR) image sequences. In our study, we first extract interest points from ISAR image sequences by SURF. Different from traditional feature point extraction methods, SURF-based feature points are invariant to scattering intensity, target rotation, and image size. Then, we employ a bilateral feature registering model to match these feature points. The feature registering scheme can not only search the isotropic feature points to link the image sequences but also reduce the error matching pairs. After that, the target centroid is detected by regular moment. Consequently, a cost function based on correlation coefficient is adopted to analyze the motion information. Experimental results based on simulated and real data validate the effectiveness and practicability of the proposed method.

  13. Comparative analysis of features of Polish and Lithuanian Day-ahead electricity market prices

    International Nuclear Information System (INIS)

    Bobinaite, Viktorija; Juozapaviciene, Aldona; Staniewski, Marcin; Szczepankowski, Piotr

    2013-01-01

    The goal of this article is to better understand the processes of electricity market price formation in Poland and Lithuania through an analysis of the features (volatility and spikes) of Lithuanian and Polish day-ahead electricity market prices and to assess how acquired electricity price features could affect the achievement of the main goals of the national energy policy. The following indicators have been calculated to determine electricity market price volatility: the oscillation coefficient, the coefficient of variation, an adjusted coefficient of variation, the standard deviation indicator, the daily velocity indicator (based on the overall average price) and the daily velocity indicator (based on the daily average price). Critical values for electricity market price have been calculated to evaluate price spikes. This analysis reveals that electricity market-price volatility is moderate in Poland and high in Lithuania. Electricity price spikes have been an observable phenomenon both in Lithuanian and in Polish day-ahead electricity markets, but they are more common in Lithuania, encompassing 3.15% of the time period analysed in Poland and 4.68% of the time period analysed in Lithuania. Volatile, spiking and increasing electricity prices in day-ahead electricity markets in Lithuania and Poland create preconditions and substantiate the relevance of implementation of the national energy policies and measures. - Highlights: • Moderate and seasonal volatility. • spiking market price and. • stable average price

  14. Recognizing stationary and locomotion activities using combinational of spectral analysis with statistical descriptors features

    Science.gov (United States)

    Zainudin, M. N. Shah; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

    2017-10-01

    Prior knowledge in pervasive computing recently garnered a lot of attention due to its high demand in various application domains. Human activity recognition (HAR) considered as the applications that are widely explored by the expertise that provides valuable information to the human. Accelerometer sensor-based approach is utilized as devices to undergo the research in HAR since their small in size and this sensor already build-in in the various type of smartphones. However, the existence of high inter-class similarities among the class tends to degrade the recognition performance. Hence, this work presents the method for activity recognition using our proposed features from combinational of spectral analysis with statistical descriptors that able to tackle the issue of differentiating stationary and locomotion activities. The noise signal is filtered using Fourier Transform before it will be extracted using two different groups of features, spectral frequency analysis, and statistical descriptors. Extracted signal later will be classified using random forest ensemble classifier models. The recognition results show the good accuracy performance for stationary and locomotion activities based on USC HAD datasets.

  15. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

    Directory of Open Access Journals (Sweden)

    Mauricio Holguín-Londoño

    2016-01-01

    Full Text Available Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM. This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

  16. Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography

    International Nuclear Information System (INIS)

    Naeppi, Janne; Yoshida, Hiroyuki

    2003-01-01

    We evaluated the effect of our novel technique of feature-guided analysis of polyps on the reduction of false-positive (FP) findings generated by our computer-aided diagnosis (CAD) scheme for the detection of polyps from computed tomography colonographic data sets. The detection performance obtained by use of feature-guided analysis in the segmentation and feature analysis of polyp candidates was compared with that obtained by use of our previously employed fuzzy clustering technique. We also evaluated the effect of a feature called modified gradient concentration (MGC) on the detection performance. A total of 144 data sets, representing prone and supine views of 72 patients that included 14 patients with 21 colorectal polyps 5-25 mm in diameter, were used in the evaluation. At a 100% by-patient (95% by-polyp) detection sensitivity, the FP rate of our CAD scheme with feature-guided analysis based on round-robin evaluation was 1.3 (1.5) FP detections per patient. This corresponds to a 70-75 % reduction in the number of FPs obtained by use of fuzzy clustering at the same sensitivity levels. Application of the MGC feature instead of our previously used gradient concentration feature did not improve the detection result. The results indicate that feature-guided analysis is useful for achieving high sensitivity and a low FP rate in our CAD scheme

  17. Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer.

    Science.gov (United States)

    Garcia-Vicente, Ana María; Molina, David; Pérez-Beteta, Julián; Amo-Salas, Mariano; Martínez-González, Alicia; Bueno, Gloria; Tello-Galán, María Jesús; Soriano-Castrejón, Ángel

    2017-12-01

    To study the influence of dual time point 18F-FDG PET/CT in textural features and SUV-based variables and their relation among them. Fifty-six patients with locally advanced breast cancer (LABC) were prospectively included. All of them underwent a standard 18F-FDG PET/CT (PET-1) and a delayed acquisition (PET-2). After segmentation, SUV variables (SUVmax, SUVmean, and SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained. Eighteen three-dimensional (3D) textural measures were computed including: run-length matrices (RLM) features, co-occurrence matrices (CM) features, and energies. Differences between all PET-derived variables obtained in PET-1 and PET-2 were studied. Significant differences were found between the SUV-based parameters and MTV obtained in the dual time point PET/CT, with higher values of SUV-based variables and lower MTV in the PET-2 with respect to the PET-1. In relation with the textural parameters obtained in dual time point acquisition, significant differences were found for the short run emphasis, low gray-level run emphasis, short run high gray-level emphasis, run percentage, long run emphasis, gray-level non-uniformity, homogeneity, and dissimilarity. Textural variables showed relations with MTV and TLG. Significant differences of textural features were found in dual time point 18F-FDG PET/CT. Thus, a dynamic behavior of metabolic characteristics should be expected, with higher heterogeneity in delayed PET acquisition compared with the standard PET. A greater heterogeneity was found in bigger tumors.

  18. Profitability analysis of catfish farming in Suleja local government ...

    African Journals Online (AJOL)

    The problem of profitability and scale of production of catfish has not been properly addressed. This study was conducted in Suleja Local Government Area of Niger State to assess the profitability of catfish production. Forty (40) catfish farmers were selected from the study area using simple random sampling techniques.

  19. Motion and time study analysis of wooden locally manufactured ...

    African Journals Online (AJOL)

    Studies were carried out on time-and-motion-economy of wooden locally manufactured duplicating machines. Two versions of the machine were used for the study, viz: standard version and semi-mechanized version. Working with both auxiliary and routine operations, the standard duplicator produced printed paper at an ...

  20. Local perception and proximate analysis of some edible forest plants

    African Journals Online (AJOL)

    User

    The Focus Group. Discussion (FGD) technique was employed to obtain information from local residents in four ... 0 and 0.13 ± 0.07), fibre and ash content were highest in Cissus populnea (29.37 ± 0.41 and ..... Agricultural Systems. Pp 1-14.

  1. Analysis of experimental data sets for local scour depth around ...

    African Journals Online (AJOL)

    The performance of soft computing techniques to analyse and interpret the experimental data of local scour depth around bridge abutment, measured at different laboratory conditions and environment, is presented. The scour around bridge piers and abutments is, in the majority of cases, the main reason for bridge failures.

  2. EBSD Analysis of Relationship Between Microstructural Features and Toughness of a Medium-Carbon Quenching and Partitioning Bainitic Steel

    Science.gov (United States)

    Li, Qiangguo; Huang, Xuefei; Huang, Weigang

    2017-12-01

    A multiphase microstructure of bainite, martensite and retained austenite in a 0.3C bainitic steel was obtained by a novel bainite isothermal transformation plus quenching and partitioning (B-QP) process. The correlations between microstructural features and toughness were investigated by electron backscatter diffraction (EBSD), and the results showed that the multiphase microstructure containing approximately 50% bainite exhibits higher strength (1617 MPa), greater elongation (18.6%) and greater impact toughness (103 J) than the full martensite. The EBSD analysis indicated that the multiphase microstructure with a smaller average local misorientation (1.22°) has a lower inner stress concentration possibility and that the first formed bainitic ferrite plates in the multiphase microstructure can refine subsequently generated packets and blocks. The corresponding packet and block average size decrease from 11.9 and 2.3 to 8.4 and 1.6 μm, respectively. A boundary misorientation analysis indicated that the multiphase microstructure has a higher percentage of high-angle boundaries (67.1%) than the full martensite (57.9%) because of the larger numbers and smaller sizes of packets and blocks. The packet boundary obstructs crack propagation more effectively than the block boundary.

  3. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

    International Nuclear Information System (INIS)

    Harmon, S; Wendelberger, B; Jeraj, R

    2014-01-01

    Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [ 18 F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI mean = 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI range : 0.2301–1). Conclusion: Using commonly-used clustering algorithms, we found

  4. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Harmon, S; Wendelberger, B [University of Wisconsin-Madison, Madison, WI (United States); Jeraj, R [University of Wisconsin-Madison, Madison, WI (United States); University of Ljubljana (Slovenia)

    2014-06-01

    Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [{sup 18}F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI{sub mean}= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI{sub range}: 0.2301–1). Conclusion: Using commonly-used clustering algorithms

  5. Automatic analysis and characterization of the hummingbird wings motion using dense optical flow features

    International Nuclear Information System (INIS)

    Martínez, Fabio; Romero, Eduardo; Manzanera, Antoine

    2015-01-01

    A new method for automatic analysis and characterization of recorded hummingbird wing motion is proposed. The method starts by computing a multiscale dense optical flow field, which is used to segment the wings, i.e., pixels with larger velocities. Then, the kinematic and deformation of the wings were characterized as a temporal set of global and local measures: a global angular acceleration as a time function of each wing and a local acceleration profile that approximates the dynamics of the different wing segments. Additionally, the variance of the apparent velocity orientation estimates those wing foci with larger deformation. Finally a local measure of the orientation highlights those regions with maximal deformation. The approach was evaluated in a total of 91 flight cycles, captured using three different setups. The proposed measures follow the yaw turn hummingbird flight dynamics, with a strong correlation of all computed paths, reporting a standard deviation of 0.31 rad/frame 2 and 1.9 (rad/frame) 2 for the global angular acceleration and the global wing deformation respectively. (paper)

  6. Bayesian Multiscale Analysis of X-Ray Jet Features in High Redshift Quasars

    Science.gov (United States)

    McKeough, Kathryn; Siemiginowska, A.; Kashyap, V.; Stein, N.

    2014-01-01

    X-ray emission of powerful quasar jets may be a result of the inverse Compton (IC) process in which the Cosmic Microwave Background (CMB) photons gain energy by interactions with the jet’s relativistic electrons. However, there is no definite evidence that IC/CMB process is responsible for the observed X-ray emission of large scale jets. A step toward understanding the X-ray emission process is to study the Radio and X-ray morphologies of the jet. We implement a sophisticated Bayesian image analysis program, Low-count Image Reconstruction and Analysis (LIRA) (Esch et al. 2004; Conners & van Dyk 2007), to analyze jet features in 11 Chandra images of high redshift quasars (z ~ 2 - 4.8). Out of the 36 regions where knots are visible in the radio jets, nine showed detectable X-ray emission. We measured the ratios of the X-ray and radio luminosities of the detected features and found that they are consistent with the CMB radiation relationship. We derived a range of the bulk lorentz factor (Γ) for detected jet features under the CMB jet emission model. There is no discernible trend of Γ with redshift within the sample. The efficiency of the X-ray emission between the detected jet feature and the corresponding quasar also shows no correlation with redshift. This work is supported in part by the National Science Foundation REU and the Department of Defense ASSURE programs under NSF Grant no.1262851 and by the Smithsonian Institution, and by NASA Contract NAS8-39073 to the Chandra X-ray Center (CXC). This research has made use of data obtained from the Chandra Data Archive and Chandra Source Catalog, and software provided by the CXC in the application packages CIAO, ChIPS, and Sherpa. We thank Teddy Cheung for providing the VLA radio images. Connors, A., & van Dyk, D. A. 2007, Statistical Challenges in Modern Astronomy IV, 371, 101 Esch, D. N., Connors, A., Karovska, M., & van Dyk, D. A. 2004, ApJ, 610, 1213

  7. Cite Globally, Analyze Locally: Citation Analysis from a Local Latin American Studies Perspective

    Science.gov (United States)

    Schadl, Suzanne M.; Todeschini, Marina

    2015-01-01

    This citation analysis examines the use of Spanish- and Portuguese-language books and articles in PhD dissertations on Latin America at the University of New Mexico between 2000 and 2009. Two sets of data are presented: The first identifies the use of Spanish- and Portuguese-language books and articles across 17 academic departments; and the…

  8. Multi-resolution analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification

    Science.gov (United States)

    Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree; Sadhu, Anup; Arif, Wasim

    2018-02-01

    In this paper, Curvelet based local attributes, Curvelet-Local configuration pattern (C-LCP), is introduced for the characterization of mammographic masses as benign or malignant. Amid different anomalies such as micro- calcification, bilateral asymmetry, architectural distortion, and masses, the reason for targeting the mass lesions is due to their variation in shape, size, and margin which makes the diagnosis a challenging task. Being efficient in classification, multi-resolution property of the Curvelet transform is exploited and local information is extracted from the coefficients of each subband using Local configuration pattern (LCP). The microscopic measures in concatenation with the local textural information provide more discriminating capability than individual. The measures embody the magnitude information along with the pixel-wise relationships among the neighboring pixels. The performance analysis is conducted with 200 mammograms of the DDSM database containing 100 mass cases of each benign and malignant. The optimal set of features is acquired via stepwise logistic regression method and the classification is carried out with Fisher linear discriminant analysis. The best area under the receiver operating characteristic curve and accuracy of 0.95 and 87.55% are achieved with the proposed method, which is further compared with some of the state-of-the-art competing methods.

  9. Cost-assessment Analysis of Local Vehicle Scrapping Facility

    Science.gov (United States)

    Grabowski, Lukasz; Gliniak, Maciej; Polek, Daria; Gruca, Maria

    2017-12-01

    The purpose of the paper was to analyse the costs of recycling vehicles at local vehicle scrapping facility. The article contains regulations concerning vehicle decommissioning, describes the types of recovery, vehicles recycling networks, analyses the structure of a disassembly station, as well as the financial and institutional system in charge of dealing with the recycling of vehicles in Poland. The authors present the number of scrapped vehicles at local recycling company and the level of achieved recovery and recycling. The research presented in the article shows financial situation of the vehicle scrapping industry. In addition, it has been observed that the number of subsidies are directly proportional to the number of scrapped vehicles, and achieved levels of recycling and recovery depends on the percentage of incomplete vehicles.

  10. Modification of computational auditory scene analysis (CASA) for noise-robust acoustic feature

    Science.gov (United States)

    Kwon, Minseok

    While there have been many attempts to mitigate interferences of background noise, the performance of automatic speech recognition (ASR) still can be deteriorated by various factors with ease. However, normal hearing listeners can accurately perceive sounds of their interests, which is believed to be a result of Auditory Scene Analysis (ASA). As a first attempt, the simulation of the human auditory processing, called computational auditory scene analysis (CASA), was fulfilled through physiological and psychological investigations of ASA. CASA comprised of Zilany-Bruce auditory model, followed by tracking fundamental frequency for voice segmentation and detecting pairs of onset/offset at each characteristic frequency (CF) for unvoiced segmentation. The resulting Time-Frequency (T-F) representation of acoustic stimulation was converted into acoustic feature, gammachirp-tone frequency cepstral coefficients (GFCC). 11 keywords with various environmental conditions are used and the robustness of GFCC was evaluated by spectral distance (SD) and dynamic time warping distance (DTW). In "clean" and "noisy" conditions, the application of CASA generally improved noise robustness of the acoustic feature compared to a conventional method with or without noise suppression using MMSE estimator. The intial study, however, not only showed the noise-type dependency at low SNR, but also called the evaluation methods in question. Some modifications were made to capture better spectral continuity from an acoustic feature matrix, to obtain faster processing speed, and to describe the human auditory system more precisely. The proposed framework includes: 1) multi-scale integration to capture more accurate continuity in feature extraction, 2) contrast enhancement (CE) of each CF by competition with neighboring frequency bands, and 3) auditory model modifications. The model modifications contain the introduction of higher Q factor, middle ear filter more analogous to human auditory system

  11. Local regularity analysis of strata heterogeneities from sonic logs

    Directory of Open Access Journals (Sweden)

    S. Gaci

    2010-09-01

    Full Text Available Borehole logs provide geological information about the rocks crossed by the wells. Several properties of rocks can be interpreted in terms of lithology, type and quantity of the fluid filling the pores and fractures.

    Here, the logs are assumed to be nonhomogeneous Brownian motions (nhBms which are generalized fractional Brownian motions (fBms indexed by depth-dependent Hurst parameters H(z. Three techniques, the local wavelet approach (LWA, the average-local wavelet approach (ALWA, and Peltier Algorithm (PA, are suggested to estimate the Hurst functions (or the regularity profiles from the logs.

    First, two synthetic sonic logs with different parameters, shaped by the successive random additions (SRA algorithm, are used to demonstrate the potential of the proposed methods. The obtained Hurst functions are close to the theoretical Hurst functions. Besides, the transitions between the modeled layers are marked by Hurst values discontinuities. It is also shown that PA leads to the best Hurst value estimations.

    Second, we investigate the multifractional property of sonic logs data recorded at two scientific deep boreholes: the pilot hole VB and the ultra deep main hole HB, drilled for the German Continental Deep Drilling Program (KTB. All the regularity profiles independently obtained for the logs provide a clear correlation with lithology, and from each regularity profile, we derive a similar segmentation in terms of lithological units. The lithological discontinuities (strata' bounds and faults contacts are located at the local extrema of the Hurst functions. Moreover, the regularity profiles are compared with the KTB estimated porosity logs, showing a significant relation between the local extrema of the Hurst functions and the fluid-filled fractures. The Hurst function may then constitute a tool to characterize underground heterogeneities.

  12. Potential safety features and safety analysis aspects for high performance light water reactor (HPLWR)

    International Nuclear Information System (INIS)

    Aksan, N.; Schulenberg, T.; Squarer, D.

    2003-01-01

    Research Activities are ongoing worldwide to develop advanced nuclear power plants with high thermal efficiency for the purpose to improve their economical competitiveness. Within the 5th Framework Programme of the European Commission, a project has been launched with the main objective to assess the technical and economical feasibility of a high efficiency LWR operating at super critical pressure conditions. Several European research institutions, industrial partners and the University of Tokyo participated and worked in this common research project. Within the aims of the development of the HPLWR is to use both passive and active safety systems for performing safety related functions in the event of transients or accidents. Consequently substantial effort has been invested in order to define the safety features of the plant in a European environment, as well as to incorporate passive safety features into the design. Throughout this process, the European Utility Requirements (EUR) and requirements known from Generation IV initiative were considered as a guideline in general terms in order to include further advanced ideas. The HPLWR general features were compared to both requirements, indicating a potential to meet these. Since, the supercritical HPLWR represents a challenge for best-estimate safety codes like RELAP5, CATHARE and TRAB due to the fact that these codes were developed for two-phase or single-phase coolant at pressures far below critical point, work on the preliminary assessment of the appropriateness of these codes have been performed for selected relevant phenomena, and application of the codes to the selected transients on the basis of defined 'reference design'. An overview on their successful upgrade to supercritical pressures and application to some plant safety analysis are provided in the paper. Further elaborations in relation to future needs are also discussed. (author)

  13. Alagille syndrome in a Vietnamese cohort: mutation analysis and assessment of facial features.

    Science.gov (United States)

    Lin, Henry C; Le Hoang, Phuc; Hutchinson, Anne; Chao, Grace; Gerfen, Jennifer; Loomes, Kathleen M; Krantz, Ian; Kamath, Binita M; Spinner, Nancy B

    2012-05-01

    Alagille syndrome (ALGS, OMIM #118450) is an autosomal dominant disorder that affects multiple organ systems including the liver, heart, eyes, vertebrae, and face. ALGS is caused by mutations in one of two genes in the Notch Signaling Pathway, Jagged1 (JAG1) or NOTCH2. In this study, analysis of 21 Vietnamese ALGS individuals led to the identification of 19 different mutations (18 JAG1 and 1 NOTCH2), 17 of which are novel, including the third reported NOTCH2 mutation in Alagille Syndrome. The spectrum of JAG1 mutations in the Vietnamese patients is similar to that previously reported, including nine frameshift, three missense, two splice site, one nonsense, two whole gene, and one partial gene deletion. The missense mutations are all likely to be disease causing, as two are loss of cysteines (C22R and C78G) and the third creates a cryptic splice site in exon 9 (G386R). No correlation between genotype and phenotype was observed. Assessment of clinical phenotype revealed that skeletal manifestations occur with a higher frequency than in previously reported Alagille cohorts. Facial features were difficult to assess and a Vietnamese pediatric gastroenterologist was only able to identify the facial phenotype in 61% of the cohort. To assess the agreement among North American dysmorphologists at detecting the presence of ALGS facial features in the Vietnamese patients, 37 clinical dysmorphologists evaluated a photographic panel of 20 Vietnamese children with and without ALGS. The dysmorphologists were unable to identify the individuals with ALGS in the majority of cases, suggesting that evaluation of facial features should not be used in the diagnosis of ALGS in this population. This is the first report of mutations and phenotypic spectrum of ALGS in a Vietnamese population. Copyright © 2012 Wiley Periodicals, Inc.

  14. Isolated familial somatotropinomas: clinical features and analysis of the MEN1 gene.

    Science.gov (United States)

    De Menis, Ernesto; Prezant, Toni R

    2002-01-01

    Isolated familial somatotropinomas (IFS) rarely occurs in the absence of multiple endocrine neoplasia type I (MEN1) or the Carney complex. In the present study we report two Italian siblings affected by GH-secreting adenomas. There was no history of parental consanguinity. The sister presented at 18 years of age with secondary amenorrhea and acromegalic features and one of her two brothers presented with gigantism at the same age. Endocrinological investigations confirmed GH hypersecretion in both cases. Although a pituitary microadenoma was detected in both patients, transsphenoidal surgery was not successful. The sister received conventional radiotherapy and acromegaly is now considered controlled; the brother is being treated with octreotide LAR 30 mg monthly and the disease is considered clinically active. Patients, their parents and the unaffected brother underwent extensive evaluation, and no features of MEN1 or Carney complex were found. Analysis of polymorphic microsatellite markers from chromosome 11q13 (D11S599, D11S4945, D11S4939, D11S4938 and D11S987) showed that the acromegalic siblings had inherited different maternal chromosomes and shared the paternal chromosome. No pathogenic MEN1 sequence changes were detected by sequencing or dideoxy fingerprinting of the coding sequence (exons 2-10) and exon/intron junctions. Although mutations in the promoter, introns or untranslated regions of the MEN1 gene cannot be excluded, germline mutations within the coding region of this gene do not appear responsible for IFS in this family.

  15. Clinical and ultrasonographic features of male breast tumors: A retrospective analysis.

    Science.gov (United States)

    Yuan, Wei-Hsin; Li, Anna Fen-Yau; Chou, Yi-Hong; Hsu, Hui-Chen; Chen, Ying-Yuan

    2018-01-01

    The purpose of this study was to determine clinical and ultrasonographic characteristics of male breast tumors. The medical records of male patients with breast lesions were retrieved from an electronic medical record database and a pathology database and retrospectively reviewed. A total of 112 men (125 breast masses) with preoperative breast ultrasonography (US) were included (median age, 59.50 years; age range, 15-96 years). Data extracted included patient age, if the lesions were bilateral, palpable, and tender, and the presence of nipple discharge. Breast lesion features on static US images were reviewed by three experienced radiologists without knowledge of physical examination or pathology results, original breast US image interpretations, or surgical outcomes. The US features were documented according to the BI-RADS (Breast Imaging-Reporting and Data System) US lexicons. A forth radiologist compiled the data for analysis. Of the 125 breast masses, palpable tender lumps and bilateral synchronous masses were more likely to be benign than malignant (both, 100% vs 0%, P nipples were common in malignant lesions (P nipple, irregular shape, the presence of an echogenic halo, predominantly internal vascularity, and rich color flow signal on color Doppler ultrasound were significantly related to malignancy (all, P < 0.05). An echogenic halo and the presence of rich color flow signal were independent predictors of malignancy. Specific clinical and US characteristics of male breast tumors may help guide treatment, and determine if surgery or conservative treatment is preferable.

  16. A Hybrid Feature Subset Selection Algorithm for Analysis of High Correlation Proteomic Data

    Science.gov (United States)

    Kordy, Hussain Montazery; Baygi, Mohammad Hossein Miran; Moradi, Mohammad Hassan

    2012-01-01

    Pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine. The surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF MS) has been used to generate proteomic profiles from biological fluids. Mass spectrometry yields redundant noisy data that the most data points are irrelevant features for differentiating between cancer and normal cases. In this paper, we have proposed a hybrid feature subset selection algorithm based on maximum-discrimination and minimum-correlation coupled with peak scoring criteria. Our algorithm has been applied to two independent SELDI-TOF MS datasets of ovarian cancer obtained from the NCI-FDA clinical proteomics databank. The proposed algorithm has used to extract a set of proteins as potential biomarkers in each dataset. We applied the linear discriminate analysis to identify the important biomarkers. The selected biomarkers have been able to successfully diagnose the ovarian cancer patients from the noncancer control group with an accuracy of 100%, a sensitivity of 100%, and a specificity of 100% in the two datasets. The hybrid algorithm has the advantage that increases reproducibility of selected biomarkers and able to find a small set of proteins with high discrimination power. PMID:23717808

  17. Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications

    Directory of Open Access Journals (Sweden)

    Francesco Nex

    2009-05-01

    Full Text Available In the photogrammetry field, interest in region detectors, which are widely used in Computer Vision, is quickly increasing due to the availability of new techniques. Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions. Feature extraction and matching techniques, which are traditionally used in photogrammetry, are usually inefficient for these applications as they are unable to provide reliable results under extreme geometrical conditions (convergent taking geometry, strong affine transformations, etc. and for bad-textured images. A performance analysis of the SIFT technique in aerial and close-range photogrammetric applications is presented in this paper. The goal is to establish the suitability of the SIFT technique for automatic tie point extraction and approximate DSM (Digital Surface Model generation. First, the performances of the SIFT operator have been compared with those provided by feature extraction and matching techniques used in photogrammetry. All these techniques have been implemented by the authors and validated on aerial and terrestrial images. Moreover, an auto-adaptive version of the SIFT operator has been developed, in order to improve the performances of the SIFT detector in relation to the texture of the images. The Auto-Adaptive SIFT operator (A2 SIFT has been validated on several aerial images, with particular attention to large scale aerial images acquired using mini-UAV systems.

  18. Singular value decomposition based feature extraction technique for physiological signal analysis.

    Science.gov (United States)

    Chang, Cheng-Ding; Wang, Chien-Chih; Jiang, Bernard C

    2012-06-01

    Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.

  19. Construction of local boundary conditions for an eigenvalue problem using micro-local analysis: application to optical waveguide problems

    International Nuclear Information System (INIS)

    Barucq, Helene; Bekkey, Chokri; Djellouli, Rabia

    2004-01-01

    We present a general procedure based on the pseudo-differential calculus for deriving artificial boundary conditions for an eigenvalue problem that characterizes the propagation of guided modes in optical waveguides. This new approach allows the construction of local conditions that (a) are independent of the frequency regime, (b) preserve the sparsity pattern of the finite element discretization, and (c) are applicable to arbitrarily shaped convex artificial boundaries. The last feature has the potential for reducing the size of the computational domain. Numerical results are presented to highlight the potential of conditions of order 1/2 and 1, for improving significantly the computational efficiency of finite element methods for the solution of optical waveguide problems

  20. The Features of Definition and Analysis of the Labor Productivity in Education

    Directory of Open Access Journals (Sweden)

    Kryvusha Svitlana H.

    2018-03-01

    Full Text Available The article is aimed at analyzing the features of definition and the tendencies of labor productivity changes in the education sector at the macro level. The need to study the problem of labor productivity in the sphere of education is substantiated. Several contemporary researches on the economic nature of non-market educational services are provided. A comparative analysis of the dynamics of labor productivity in the sphere of education, market and non-market services, and the sphere of production of goods was carried out. The tendencies in the ratio of productivity and increase of wages are analyzed. Emphasis has been given to increasing the role of education, which has a significant impact on the overall indicator of productivity growth at the macro level.

  1. Chromatic analysis and possible local chromatic correction in RHIC

    International Nuclear Information System (INIS)

    Luo, Y.; Fischer, W.; Gu, X.; Trbojevic, D.

    2011-01-01

    In this article we will answer the following questions for the RHIC polarized proton (p-p) and Au-Au run lattices: (1) what are the sources of second order chromaticities? (2) what is the dependence of second order chromaticity on the on-momentum β-beat? (3) what is the dependence of second order chromaticity on β* at IP6 and IP8? To answer these questions, we use the perturbation theory to numerically calculate the contributions of each quadrupole and sextupole to the first, second, and third order chromaticities. Possible local methods to reduce chromatic effects in RHIC ring are shortly discussed.

  2. A comparative analysis between France and Japan on local governments' involvement in nuclear safety governance

    International Nuclear Information System (INIS)

    Sugawara, Shin-etsu; Shiroyama, Hideaki

    2011-01-01

    This paper shows a comparative analysis between France and Japan on the way of the local governments' involvement in nuclear safety governance through some interviews. In France, a law came into force that requires related local governments to establish 'Commision Locale d'Information' (CLI), which means the local governments officially involve in nuclear regulatory activity. Meanwhile, in Japan, related local governments substantially involve in the operation of nuclear facilities through the 'safety agreements' in spite of the lack of legal authority. As a result of comparative analysis, we can point out some institutional input from French cases as follows: to clarify the local governments' roles in the nuclear regulation system, to establish the official channels of communication among nuclear utilities, national regulatory authorities and local governments, and to stipulate explicitly the transparency as a purpose of safety regulation. (author)

  3. New features and improved uncertainty analysis in the NEA nuclear data sensitivity tool (NDaST)

    Science.gov (United States)

    Dyrda, J.; Soppera, N.; Hill, I.; Bossant, M.; Gulliford, J.

    2017-09-01

    Following the release and initial testing period of the NEA's Nuclear Data Sensitivity Tool [1], new features have been designed and implemented in order to expand its uncertainty analysis capabilities. The aim is to provide a fre