Sample records for undertake spectral classifications

  1. Stellar Spectral Classification with Locality Preserving Projections ...

    Indian Academy of Sciences (India)

    With the help of computer tools and algorithms, automatic stellar spectral classification has become an area of current interest. The process of stellar spectral classification mainly includes two steps: dimension reduction and classification. As a popular dimensionality reduction technique, Principal Component Analysis (PCA) ...

  2. Automated spectral classification and the GAIA project (United States)

    Lasala, Jerry; Kurtz, Michael J.


    Two dimensional spectral types for each of the stars observed in the global astrometric interferometer for astrophysics (GAIA) mission would provide additional information for the galactic structure and stellar evolution studies, as well as helping in the identification of unusual objects and populations. The classification of the large quantity generated spectra requires that automated techniques are implemented. Approaches for the automatic classification are reviewed, and a metric-distance method is discussed. In tests, the metric-distance method produced spectral types with mean errors comparable to those of human classifiers working at similar resolution. Data and equipment requirements for an automated classification survey, are discussed. A program of auxiliary observations is proposed to yield spectral types and radial velocities for the GAIA-observed stars.

  3. Stellar Spectral Classification with Locality Preserving Projections ...

    Indian Academy of Sciences (India)

    type and 3 subclasses of F-type spectra from. Sloan Digital Sky Survey (SDSS). Lastly, the performance of LPP+SVM is compared with that of PCA+SVM in stellar spectral classification, and we found that LPP does better than PCA. Key words.

  4. Spectral Classification of Asteroids by Random Forest (United States)

    Huang, C.; Ma, Y. H.; Zhao, H. B.; Lu, X. P.


    With the increasing asteroid spectral and photometric data, a variety of classification methods for asteroids have been proposed. This paper classifies asteroids based on the observations of Sloan Digital Sky Survey (SDSS) Moving Object Catalogue (MOC) by using the random forest algorithm. With the training data derived from the taxonomies of Tholen, Bus, Lazzaro, DeMeo, and Principal Component Analysis, we classify 48642 asteroids according to g, r, i, and z SDSS magnitudes. In this way, asteroids are divided into 8 spectral classes (C, X, S, B, D, K, L, and V).

  5. Spectral Classification of Asteroids by Random Forest (United States)

    Huang, Chao; Ma, Yue-hua; Zhao, Hai-bin; Lu, Xiao-ping


    With the increasing spectral and photometric data of asteroids, a variety of classification methods for asteroids have been proposed. This paper classifies asteroids based on the observations in the Sloan Digital Sky Survey (SDSS) Moving Object Catalogue (MOC) by using the random forest algorithm. In combination with the present taxonomies of Tholen, Bus, Lazzaro, and DeMeo, and the principal component analysis, we have classified 48642 asteroids according to their SDSS magnitudes at the g, r, i, and z wavebands. In this way, these asteroids are divided into 8 (C, X, S, B, D, K, L, and V) classes.

  6. Spectral feature classification and spatial pattern recognition (United States)

    Sivertson, W. E., Jr.; Wilson, R. G.


    This paper introduces a spatial pattern recognition processing concept involving the use of spectral feature classification technology and coherent optical correlation. The concept defines a hybrid image processing system incorporating both digital and optical technology. The hybrid instrument provides simplified pseudopattern images as functions of pixel classification from information embedded within a real-scene image. These pseudoimages become simplified inputs to an optical correlator for use in a subsequent pattern identification decision useful in executing landmark pointing, tracking, or navigating functions. Real-time classification is proposed as a research tool for exploring ways to enhance input signal-to-noise ratio as an aid in improving optical correlation. The approach can be explored with developing technology, including a current NASA Langley Research Center technology plan that involves a series of related Shuttle-borne experiments. A first-planned experiment, Feature Identification and Location Experiment (FILE), is undergoing final ground testing, and is scheduled for flight on the NASA Shuttle (STS2/flight OSTA-1) in 1980. FILE will evaluate a technique for autonomously classifying earth features into the four categories: bare land; water; vegetation; and clouds, snow, or ice.

  7. Multiple Spectral-Spatial Classification Approach for Hyperspectral Data (United States)

    Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.


    A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

  8. Spatial-spectral blood cell classification with microscopic hyperspectral imagery (United States)

    Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng


    Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

  9. Stellar Spectral Classification with Minimum Within-Class and ...

    Indian Academy of Sciences (India)

    Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified SVM-named Minimum within-class and ...

  10. Stellar Spectral Classification with Minimum Within-Class and ...

    Indian Academy of Sciences (India)

    Abstract. Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified. SVM-named Minimum within-class ...

  11. Spectral band selection for classification of soil organic matter content (United States)

    Henderson, Tracey L.; Szilagyi, Andrea; Baumgardner, Marion F.; Chen, Chih-Chien Thomas; Landgrebe, David A.


    This paper describes the spectral-band-selection (SBS) algorithm of Chen and Landgrebe (1987, 1988, and 1989) and uses the algorithm to classify the organic matter content in the earth's surface soil. The effectiveness of the algorithm was evaluated comparing the results of classification of the soil organic matter using SBS bands with those obtained using Landsat MSS bands and TM bands, showing that the algorithm was successful in finding important spectral bands for classification of organic matter content. Using the calculated bands, the probabilities of correct classification for climate-stratified data were found to range from 0.910 to 0.980.

  12. Soil classification basing on the spectral characteristics of topsoil samples (United States)

    Liu, Huanjun; Zhang, Xiaokang; Zhang, Xinle


    Soil taxonomy plays an important role in soil utility and management, but China has only course soil map created based on 1980s data. New technology, e.g. spectroscopy, could simplify soil classification. The study try to classify soils basing on the spectral characteristics of topsoil samples. 148 topsoil samples of typical soils, including Black soil, Chernozem, Blown soil and Meadow soil, were collected from Songnen plain, Northeast China, and the room spectral reflectance in the visible and near infrared region (400-2500 nm) were processed with weighted moving average, resampling technique, and continuum removal. Spectral indices were extracted from soil spectral characteristics, including the second absorption positions of spectral curve, the first absorption vale's area, and slope of spectral curve at 500-600 nm and 1340-1360 nm. Then K-means clustering and decision tree were used respectively to build soil classification model. The results indicated that 1) the second absorption positions of Black soil and Chernozem were located at 610 nm and 650 nm respectively; 2) the spectral curve of the meadow is similar to its adjacent soil, which could be due to soil erosion; 3) decision tree model showed higher classification accuracy, and accuracy of Black soil, Chernozem, Blown soil and Meadow are 100%, 88%, 97%, 50% respectively, and the accuracy of Blown soil could be increased to 100% by adding one more spectral index (the first two vole's area) to the model, which showed that the model could be used for soil classification and soil map in near future.

  13. Spectral-spatial classification of hyperspectral imagery with cooperative game (United States)

    Zhao, Ji; Zhong, Yanfei; Jia, Tianyi; Wang, Xinyu; Xu, Yao; Shu, Hong; Zhang, Liangpei


    Spectral-spatial classification is known to be an effective way to improve classification performance by integrating spectral information and spatial cues for hyperspectral imagery. In this paper, a game-theoretic spectral-spatial classification algorithm (GTA) using a conditional random field (CRF) model is presented, in which CRF is used to model the image considering the spatial contextual information, and a cooperative game is designed to obtain the labels. The algorithm establishes a one-to-one correspondence between image classification and game theory. The pixels of the image are considered as the players, and the labels are considered as the strategies in a game. Similar to the idea of soft classification, the uncertainty is considered to build the expected energy model in the first step. The local expected energy can be quickly calculated, based on a mixed strategy for the pixels, to establish the foundation for a cooperative game. Coalitions can then be formed by the designed merge rule based on the local expected energy, so that a majority game can be performed to make a coalition decision to obtain the label of each pixel. The experimental results on three hyperspectral data sets demonstrate the effectiveness of the proposed classification algorithm.

  14. Exploratory Item Classification Via Spectral Graph Clustering. (United States)

    Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang


    Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster analysis. The method is computationally efficient, effective for data with missing or incomplete responses, easy to implement, and often outperforms traditional clustering algorithms in the context of high dimensionality. The spectral clustering algorithm is based on graph theory, a branch of mathematics that studies the properties of graphs. The algorithm first constructs a graph of items, characterizing the similarity structure among items. It then extracts item clusters based on the graphical structure, grouping similar items together. The proposed method is evaluated through simulations and an application to the revised Eysenck Personality Questionnaire.

  15. Advances in Spectral-Spatial Classification of Hyperspectral Images (United States)

    Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.


    Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

  16. Vulnerable land ecosystems classification using spatial context and spectral indices (United States)

    Ibarrola-Ulzurrun, Edurne; Gonzalo-Martín, Consuelo; Marcello, Javier


    Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.

  17. On the relevance of spectral features for instrument classification

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Sigurdsson, Sigurdur; Hansen, Lars Kai


    Automatic knowledge extraction from music signals is a key component for most music organization and music information retrieval systems. In this paper, we consider the problem of instrument modelling and instrument classification from the rough audio data. Existing systems for automatic instrument...... classification operate normally on a relatively large number of features, from which those related to the spectrum of the audio signal are particularly relevant. In this paper, we confront two different models about the spectral characterization of musical instruments. The first assumes a constant envelope...... on a large database of real instrument recordings show that the first model offers a more satisfactory characterization, and therefore MFCCs should be preferred to HR for instrument modelling/classification....

  18. Fluorescence spectral classification of iron deficiency anemia and thalassemia. (United States)

    Devanesan, Sandhanasamy; Mohamad Saleh, AlSalhi; Ravikumar, Mani; Perinbam, Kantharaj; Prasad, Saradh; Abbas, H Al-Saeed; Palled, Siddanna R; Jeyaprakash, Karuppaiah; Masilamani, Vadivel


    Thalassemia (Thal), sickle cell anemia, and iron deficiency anemia (IDA) are the most common blood disorders in many parts of the world, particularly in developing countries like India and Bangladesh. The well-established diagnostic procedure for them is the complete blood count (CBC); however, there is substantial confusion in discrimination between Thal and IDA blood samples based on such CBC. We propose a new spectral technique for reliable classification between the above two anemias. This is based on the identification and quantification of a certain set of fluorescent metabolites found in the blood samples of patients of Thal and IDA.

  19. Spectral classification and composites of galaxies in LAMOST DR4 (United States)

    Wang, Li-Li; Luo, A.-Li; Shen, Shi-Yin; Hou, Wen; Kong, Xiao; Song, Yi-Han; Zhang, Jian-Nan; Wu, Hong; Cao, Zi-Huang; Hou, Yong-Hui; Wang, Yue-Fei; Zhang, Yong; Zhao, Yong-Heng


    We study the classification and composite spectra of galaxies in the fourth data release (DR4) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST). We select 40 182 spectra of galaxies from LAMOST DR4, which have photometric information but no spectroscopic observations in the Sloan Digital Sky Survey (SDSS). These newly observed spectra are recalibrated and classified into six classes - passive, Hα-weak, star-forming, composite, LINER and Seyfert - using the line intensity (Hβ, [O III]λ5007, Hα and [N II]λ6585). We also study the correlation between spectral class and morphological type through three parameters: concentration index, (u - r) colour and D4000n index. We calculate composite spectra of high signal-to-noise ratio (S/N) for six spectral classes and, using these composites, we pick out some features that can differentiate the classes effectively, including Hβ, Fe5015, HγA, HK and the Mg2 band. In addition, we compare our composite spectra with the SDSS ones and analyse their differences. A galaxy catalogue of 40 182 newly observed spectra (36 601 targets) and the composite spectra of the six classes are available online.

  20. Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification. (United States)

    Haoliang Yuan; Yuan Yan Tang


    Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

  1. Aerosol Classification from High Spectral Resolution Lidar Measurements (United States)

    Burton, S. P.; Hair, J. W.; Ferrare, R. A.; Hostetler, C. A.; Kahnert, M.; Vaughan, M. A.; Cook, A. L.; Harper, D. B.; Berkoff, T.; Seaman, S. T.; Collins, J. E., Jr.; Fenn, M. A.; Rogers, R. R.


    The NASA Langley airborne High Spectral Resolution Lidars, HSRL-1 and HSRL-2, have acquired large datasets of vertically resolved aerosol extinction, backscatter, and depolarization during >30 airborne field missions since 2006. The lidar measurements of aerosol intensive parameters like lidar ratio and color ratio embed information about intrinsic aerosol properties, and are combined to qualitatively classify HSRL aerosol measurements into aerosol types. Knowledge of aerosol type is important for assessing aerosol radiative forcing, and can provide useful information for source attribution studies. However, atmospheric aerosol is frequently not a single pure type, but instead is a mixture, which affects the optical and radiative properties of the aerosol. We show that aerosol intensive parameters measured by lidar can be understood using mixing rules for cases of external mixing. Beyond coarse classification and mixing between classes, variations in the lidar aerosol intensive parameters provide additional insight into aerosol processes and composition. This is illustrated by depolarization measurements at three wavelengths, 355 nm, 532 nm, and 1064 nm, made by HSRL-2. Particle depolarization ratio is an indicator of non-spherical particles. Three cases each have a significantly different spectral dependence of the depolarization ratio, related to the size of the depolarizing particles. For two dust cases, large non-spherical particles account for the depolarization of the lidar light. The spectral dependence reflects the size distribution of these particles and reveals differences in the transport histories of the two plumes. For a smoke case, the depolarization is inferred to be due to the presence of small coated soot aggregates. Interestingly, the depolarization at 355 nm is similar for this smoke case compared to the dust cases, having potential implications for the upcoming EarthCARE satellite, which will measure particle depolarization ratio only at 355 nm.

  2. A Spectral Signature Shape-Based Algorithm for Landsat Image Classification

    Directory of Open Access Journals (Sweden)

    Yuanyuan Chen


    Full Text Available Land-cover datasets are crucial for earth system modeling and human-nature interaction research at local, regional and global scales. They can be obtained from remotely sensed data using image classification methods. However, in processes of image classification, spectral values have received considerable attention for most classification methods, while the spectral curve shape has seldom been used because it is difficult to be quantified. This study presents a classification method based on the observation that the spectral curve is composed of segments and certain extreme values. The presented classification method quantifies the spectral curve shape and takes full use of the spectral shape differences among land covers to classify remotely sensed images. Using this method, classification maps from TM (Thematic mapper data were obtained with an overall accuracy of 0.834 and 0.854 for two respective test areas. The approach presented in this paper, which differs from previous image classification methods that were mostly concerned with spectral “value” similarity characteristics, emphasizes the "shape" similarity characteristics of the spectral curve. Moreover, this study will be helpful for classification research on hyperspectral and multi-temporal images.

  3. Central stars of planetary nebulae: New spectral classifications and catalogue (United States)

    Weidmann, W. A.; Gamen, R.


    Context. There are more than 3000 confirmed and probable known Galactic planetary nebulae (PNe), but central star spectroscopic information is available for only 13% of them. Aims: We undertook a spectroscopic survey of central stars of PNe at low resolution and compiled a large list of central stars for which information was dispersed in the literature. Methods: We observed 45 PNs using the 2.15 m telescope at Casleo, Argentina. Results: We present a catalogue of 492 confirmed and probable CSPN and provide a preliminary spectral classification for 45 central star of PNe. This revises previous values of the proportion of CSPN with atmospheres poor in hydrogen in at least 30% of cases and provide statistical information that allows us to infer the origin of H-poor stars. Based on data collected at the Complejo Astronómico El Leoncito (CASLEO), which is operated under agreement between the Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina y Universidades Nacionales de La Plata, Córdoba y San Juan, Argentina.

  4. Spectral Classification of Similar Materials using the Tetracorder Algorithm: The Calcite-Epidote-Chlorite Problem (United States)

    Dalton, J. Brad; Bove, Dana; Mladinich, Carol; Clark, Roger; Rockwell, Barnaby; Swayze, Gregg; King, Trude; Church, Stanley


    Recent work on automated spectral classification algorithms has sought to distinguish ever-more similar materials. From modest beginnings separating shade, soil, rock and vegetation to ambitious attempts to discriminate mineral types and specific plant species, the trend seems to be toward using increasingly subtle spectral differences to perform the classification. Rule-based expert systems exploiting the underlying physics of spectroscopy such as the US Geological Society Tetracorder system are now taking advantage of the high spectral resolution and dimensionality of current imaging spectrometer designs to discriminate spectrally similar materials. The current paper details recent efforts to discriminate three minerals having absorptions centered at the same wavelength, with encouraging results.

  5. [Interference hyperspectral data compression based on spectral classification and local DPCM]. (United States)

    Tu, Xiao-Long; Huang, Min; Lü, Qun-Bo; Wang, Jian-Wei; Pei, Lin-Lin


    In order to get a high compression ratio, according to the spatial dimension correlation and the interference spectral dimension correlation of interference hyperspectral image data, the present article provides a new compression algorithm that combines spectral classification with local DPCM. This algorithm requires spectral classification for the whole interference hyperspectral image to get a classification number matrix corresponding to the two-dimensional space and a spectral classification library corresponding to the interference spectra first, then local DPCM is performed for the spectral classification library to get a further compression. As the first step of the compression, the spectral classification is very important to the compression effect. This article analyzes the differences of compression effect with different standard and different accuracy of classification, the relative Euclidean distance standard is better than the angle standard and the interference RQE standard. Finally, this article chooses an appropriate standard of compression and achieves the combined compression algorithm with programming. Compared to JPEG2000, the compression effect of combined compression algorithm is better.


    Energy Technology Data Exchange (ETDEWEB)



    We consider the problem of pixel-by-pixel classification of a multi-spectral image using supervised learning. Conventional supervised classification techniques such as maximum likelihood classification and less conventional ones such as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see why the color of a pixel provides a nice, bounded, fixed dimensional space in which these classifiers work well. It is often the case however, that spectral information alone is not sufficient to correctly classify a pixel. Maybe spatial neighborhood information is required as well. Or may be the raw spectral components do not themselves make for easy classification, but some arithmetic combination of them would. In either of these cases we have the problem of selecting suitable spatial, spectral or spatio-spectral features that allow the classifier to do its job well. The number of all possible such features is extremely large. How can we select a suitable subset? We have developed GENIE, a hybrid learning system that combines a genetic algorithm that searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output a final classification. In this paper we show that the use of a hybrid GA provides significant advantages over using either a GA alone or more conventional classification methods alone. We present results using high-resolution IKONOS data, looking for regions of burned forest and for roads.

  7. A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching. (United States)

    Mei, Xiaoguang; Ma, Yong; Li, Chang; Fan, Fan; Huang, Jun; Ma, Jiayi


    The state-of-the-art ultra-spectral sensor technology brings new hope for high precision applications due to its high spectral resolution. However, it also comes with new challenges, such as the high data dimension and noise problems. In this paper, we propose a real-time method for infrared ultra-spectral signature classification via spatial pyramid matching (SPM), which includes two aspects. First, we introduce an infrared ultra-spectral signature similarity measure method via SPM, which is the foundation of the matching-based classification method. Second, we propose the classification method with reference spectral libraries, which utilizes the SPM-based similarity for the real-time infrared ultra-spectral signature classification with robustness performance. Specifically, instead of matching with each spectrum in the spectral library, our method is based on feature matching, which includes a feature library-generating phase. We calculate the SPM-based similarity between the feature of the spectrum and that of each spectrum of the reference feature library, then take the class index of the corresponding spectrum having the maximum similarity as the final result. Experimental comparisons on two publicly-available datasets demonstrate that the proposed method effectively improves the real-time classification performance and robustness to noise.

  8. Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach (United States)

    Paul, Subir; Nagesh Kumar, D.


    Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.

  9. Hyper-spectral frequency selection for the classification of vegetation diseases


    Dijkstra, Klaas; van de Loosdrecht, Jaap; Schomaker, Lambert; Wiering, Marco


    Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the...

  10. Unsupervised Classification of Mercury's Surface Spectral and Chemical Characteristics (United States)

    D'Amore, M.; Helbert, J.; Ferrari, S.; Maturilli, A.; Nittler, L. R.; Domingue, D. L.; Vilas, F.; Weider, S. Z.; Starr, R. D.; Crapster-Pregont, E. J.; Ebel, D. S.; Solomon, S. C.


    The spectral reflectance of Mercury's surface has been mapped in the 400-1145 nm wavelength range by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft. Under the hypothesis that surface compositional information can be efficiently derived from such spectral measurements with the use of statistical techniques, we have conducted unsupervised hierarchical clustering analyses to identify and characterize spectral units from MASCS observations. The results display a large-scale dichotomy, with two spectrally distinct units: polar and equatorial, possibly linked to differences in surface environment or composition. The spatial extent of the polar unit in the northern hemisphere correlates approximately with that of the northern volcanic plains. To explore possible relations between composition and spectral behavior, we have compared the spectral units with elemental abundance maps derived from MESSENGER's X-Ray Spectrometer (XRS). It is important to note that the mapping coverage for XRS differs from that of MASCS, particularly for the heavy elements. Nonetheless, by comparing the visible and near-infrared MASCS and XRS datasets and investigating the links between them, we seek further clues to the formation and evolution of Mercury's crust. Moreover, the methodology will permit automation of the production of new maps of the spectral and chemical signature of the surface.

  11. Multi Angle Imaging With Spectral Remote Sensing for Scene Classification

    National Research Council Canada - National Science Library

    Prasert, Sunyaruk


    .... QuickBird panchromatic (0.61 meter) and multispectral (2.44 meter) imagery collected in July 2003 are examined to determine the impact of adding multi-angles and filtered texture information to the standard MSI classification approaches...

  12. Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    YANG Zhaoxia


    Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

  13. JET Joint Undertaking

    International Nuclear Information System (INIS)

    Keen, B.E.


    The paper presents the progress report of the Joint European Torus (JET) Joint Undertaking, 1986. The report contains a survey of the scientific and technical achievements on JET during 1986; the more important articles referred to in this survey are reproduced as appendices to this Report. The last section discusses developments which might improve the overall performance of the machine. (U.K.)

  14. Multi-spectral Image Analysis for Astaxanthin Coating Classification

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht


    Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. In this study multi-spectral image analysis of pellets was performed using LDA, QDA, SNV and PCA on pixel level and mean value of pixels...

  15. Stellar Spectral Classification with Minimum Within-Class and ...

    Indian Academy of Sciences (India)

    to feature extraction, and then the traditional classifier SVM is used to classify the stellar spectra. The above classification method uses similar concepts with the pro- posed MMSVM method in this paper. However, there are huge differences between these two methods. In the method proposed by Liu & Song (2015), MDA is ...

  16. Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

    Directory of Open Access Journals (Sweden)

    Ming-Der Yang


    Full Text Available Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV imagery. In addition to spectral information, digital surface model (DSM and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%. The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment.

  17. Public Undertakings and Imputability

    DEFF Research Database (Denmark)

    Ølykke, Grith Skovgaard


    exercised by the State, imputability to the State, and the State’s fulfilment of the Market Economy Investor Principle. Furthermore, it is examined whether, in the absence of imputability, public undertakings’ market behaviour is subject to the Market Economy Investor Principle, and it is concluded...... that this is not the case. Lastly, it is discussed whether other legal instruments, namely competition law, public procurement law, or the Transparency Directive, regulate public undertakings’ market behaviour. It is found that those rules are not sufficient to mend the gap created by the imputability requirement. Legal......In this article, the issue of impuability to the State of public undertakings’ decision-making is analysed and discussed in the context of the DSBFirst case. DSBFirst is owned by the independent public undertaking DSB and the private undertaking FirstGroup plc and won the contracts in the 2008...


    Energy Technology Data Exchange (ETDEWEB)

    Gray, R. O. [Department of Physics and Astronomy, Appalachian State University, Boone, NC 28608 (United States); Corbally, C. J. [Vatican Observatory Research Group, Steward Observatory, Tucson, AZ 85721-0065 (United States); Cat, P. De [Royal Observatory of Belgium, Ringlaan 3, B-1180 Brussel (Belgium); Fu, J. N.; Ren, A. B. [Department of Astronomy, Beijing Normal University, 19 Avenue Xinjiekouwai, Beijing 100875 (China); Shi, J. R.; Luo, A. L.; Zhang, H. T.; Wu, Y.; Cao, Z.; Li, G. [Key Laboratory for Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 (China); Zhang, Y.; Hou, Y.; Wang, Y. [Nanjing Institute of Astronomical Optics and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Nanjing 210042 (China)


    The LAMOST-Kepler project was designed to obtain high-quality, low-resolution spectra of many of the stars in the Kepler field with the Large Sky Area Multi Object Fiber Spectroscopic Telescope (LAMOST) spectroscopic telescope. To date 101,086 spectra of 80,447 objects over the entire Kepler field have been acquired. Physical parameters, radial velocities, and rotational velocities of these stars will be reported in other papers. In this paper we present MK spectral classifications for these spectra determined with the automatic classification code MKCLASS. We discuss the quality and reliability of the spectral types and present histograms showing the frequency of the spectral types in the main table organized according to luminosity class. Finally, as examples of the use of this spectral database, we compute the proportion of A-type stars that are Am stars, and identify 32 new barium dwarf candidates.

  19. On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements. (United States)

    Li, Zhan; Schaefer, Michael; Strahler, Alan; Schaaf, Crystal; Jupp, David


    The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWEL's novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultaneous scanning at both 1064 nm and 1548 nm by DWEL instruments provides a new spectral dimension to TLS data that joins the 3D spatial dimension of lidar as an information source. Our point cloud classification algorithm explores the utilization of both spectral and spatial attributes of individual points from DWEL scans and highlights the strengths and weaknesses of each attribute domain. The spectral and spatial attributes for vegetation element classification each perform better in different parts of vegetation (canopy interior, fine branches, coarse trunks, etc.) and under different vegetation conditions (dead or live, leaf-on or leaf-off, water content, etc.). These environmental characteristics of vegetation, convolved with the lidar instrument specifications and lidar data quality, result in the actual capabilities of spectral and spatial attributes to classify vegetation elements in 3D space. The spectral and spatial information domains thus complement each other in the classification process. The joint use of both not only enhances the classification accuracy but also reduces its variance across the multiple vegetation types we have examined, highlighting the value of the DWEL as a new source of 3D spectral information. Wider deployment of the DWEL instruments is in practice currently held back by challenges in instrument development and the demands of data processing required by coaxial dual- or multi-wavelength scanning. But the simultaneous 3D acquisition of both spectral and spatial features, offered by new multispectral scanning instruments such as the DWEL, opens

  20. Spectral-spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder (United States)

    Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter


    Classification of hyperspectral remote sensing imagery is one of the most popular topics because of its intrinsic potential to gather spectral signatures of materials and provides distinct abilities to object detection and recognition. In the last decade, an enormous number of methods were suggested to classify hyperspectral remote sensing data using spectral features, though some are not using all information and lead to poor classification accuracy; on the other hand, the exploration of deep features is recently considered a lot and has turned into a research hot spot in the geoscience and remote sensing research community to enhance classification accuracy. A deep learning architecture is proposed to classify hyperspectral remote sensing imagery by joint utilization of spectral-spatial information. A stacked sparse autoencoder provides unsupervised feature learning to extract high-level feature representations of joint spectral-spatial information; then, a soft classifier is employed to train high-level features and to fine-tune the deep learning architecture. Comparative experiments are performed on two widely used hyperspectral remote sensing data (Salinas and PaviaU) and a coarse resolution hyperspectral data in the long-wave infrared range. The obtained results indicate the superiority of the proposed spectral-spatial deep learning architecture against the conventional classification methods.

  1. Three-Dimensional Spatial-Spectral Filtering Based Feature Extraction for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    AKYUREK, H. A.


    Full Text Available Hyperspectral pixels which have high spectral resolution are used to predict decomposition of material types on area of obtained image. Due to its multidimensional form, hyperspectral image classification is a challenging task. Hyperspectral images are also affected by radiometric noise. In order to improve the classification accuracy, many researchers are focusing on the improvement of filtering, feature extraction and classification methods. In the context of hyperspectral image classification, spatial information is as important as spectral information. In this study, a three-dimensional spatial-spectral filtering based feature extraction method is presented. It consists of three main steps. The first is a pre-processing step which include spatial-spectral information filtering in three-dimensional space. The second comprises extract functional features of filtered data. The last one is combining extracted features by serial feature fusion strategy and using to classify hyperspectral image pixels. Experiments were conducted on two popular public hyperspectral remote sensing image, 1%, 5%, 10% and 15% of samples of each classes used as training set, the remaining is used as test set. The proposed method compared with well-known methods. Experimental results show that the proposed method achieved outstanding performance than compared methods in hyperspectral image classification task.

  2. Semi-supervised Spatial-spectral Discriminant Analysis for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    HOU Banghuan


    Full Text Available In order to make full use of the spatial information embedded in the hyperspectral image to improve the classification accuracy, a semi-supervised spatial-spectral discriminant analysis (S3DA algorithm for hyperspectral image classification is proposed. According to the spatial consistency property of hyperspectral image, the intra-class scatter matrix infered from a little labeled samples preserves the spectral similarity of the same class pixels, while the spatial local pixel scatter matrix defined by the unlabeled spatial neighbors uncovers the spatial-domain local pixel neighborhood structures and the ground objects detailed distribution. The S3DA method not only maintains the spectral-domain separability of the data set, but also preserves the spatial-domain local pixel neighborhood structure, which promotes the compactness of the same class pixels or the spatial neighbor pixels in the projected subspace and enhances the classification performance. The overall classification accuracies respectively reach 81.50% and 71.77% on the PaviaU and Indian Pines data sets. Compared with the traditional spectral methods, the proposed method can effectively improve ground objects classification accuracy.

  3. Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL (United States)

    Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.


    Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

  4. Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data (United States)

    Liu, Wanjun; Liang, Xuejian; Qu, Haicheng


    Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.

  5. Spectral-spatial classification combined with diffusion theory based inverse modeling of hyperspectral images (United States)

    Paluchowski, Lukasz A.; Bjorgan, Asgeir; Nordgaard, Hâvard B.; Randeberg, Lise L.


    Hyperspectral imagery opens a new perspective for biomedical diagnostics and tissue characterization. High spectral resolution can give insight into optical properties of the skin tissue. However, at the same time the amount of collected data represents a challenge when it comes to decomposition into clusters and extraction of useful diagnostic information. In this study spectral-spatial classification and inverse diffusion modeling were employed to hyperspectral images obtained from a porcine burn model using a hyperspectral push-broom camera. The implemented method takes advantage of spatial and spectral information simultaneously, and provides information about the average optical properties within each cluster. The implemented algorithm allows mapping spectral and spatial heterogeneity of the burn injury as well as dynamic changes of spectral properties within the burn area. The combination of statistical and physics informed tools allowed for initial separation of different burn wounds and further detailed characterization of the injuries in short post-injury time.

  6. Dichotomous classification of black-colored metal using spectral analysis

    Directory of Open Access Journals (Sweden)

    Abramovich A.O.


    Full Text Available The task of detecting metal objects in different environments has always been important. To solve it metal detectors are used. They are designed to detect and identify objects that in their electric or magnetic properties different from the environment in which they are located. The most common among them are the metal detectors of the «detection of very low frequency» type (Very Low Frequency (VLF detectors. They use eddy current testing for detecting metal targets, which solves the problem of dichotomous distinction, that is a problem of splitting (or set into two parts (subsets: black or colored target. The target distinction is performed by a threshold level of the received signal. However, this approach does not allow to identify the type of target, if two samples of different metals are nearby. To overcome the above described limitations we propose another way of distinction based on the use of spectral analysis, which occurs in the metal detector antenna by Foucault current. We show that the problem of dichotomous distinction can be solved in just a measurement of width and area by the envelope of amplitude spectrum (hereinafter spectrum of the received signal. In this regard the laboratory model using eddy current metal detector will combat withdrawal from two samples – steel and copper, located along and calculate its range. The task of distinguishing between metal targets reduced to determining the hit spectra of reference samples obtained spectrum. The ratio between the areas is measured and reference spectra indicates the percentage of specific metals (e.g. two identical samples of different metals lying side by side. Signal processing is performed by specially designed program that compares two spectra along posted samples of black and colored metals with base.

  7. An expert computer program for classifying stars on the MK spectral classification system

    Energy Technology Data Exchange (ETDEWEB)

    Gray, R. O. [Department of Physics and Astronomy, Appalachian State University, Boone, NC 26808 (United States); Corbally, C. J. [Vatican Observatory Research Group, Tucson, AZ 85721-0065 (United States)


    This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

  8. An expert computer program for classifying stars on the MK spectral classification system

    International Nuclear Information System (INIS)

    Gray, R. O.; Corbally, C. J.


    This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

  9. Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique. (United States)

    Choi, Samjin; Jiang, Zhongwei


    In this paper, a novel cardiac sound spectral analysis method using the normalized autoregressive power spectral density (NAR-PSD) curve with the support vector machine (SVM) technique is proposed for classifying the cardiac sound murmurs. The 489 cardiac sound signals with 196 normal and 293 abnormal sound cases acquired from six healthy volunteers and 34 patients were tested. Normal sound signals were recorded by our self-produced wireless electric stethoscope system where the subjects are selected who have no the history of other heart complications. Abnormal sound signals were grouped into six heart valvular disorders such as the atrial fibrillation, aortic insufficiency, aortic stenosis, mitral regurgitation, mitral stenosis and split sounds. These abnormal subjects were also not included other coexistent heart valvular disorder. Considering the morphological characteristics of the power spectral density of the heart sounds in frequency domain, we propose two important diagnostic features Fmax and Fwidth, which describe the maximum peak of NAR-PSD curve and the frequency width between the crossed points of NAR-PSD curve on a selected threshold value (THV), respectively. Furthermore, a two-dimensional representation on (Fmax, Fwidth) is introduced. The proposed cardiac sound spectral envelope curve method is validated by some case studies. Then, the SVM technique is employed as a classification tool to identify the cardiac sounds by the extracted diagnostic features. To detect abnormality of heart sound and to discriminate the heart murmurs, the multi-SVM classifiers composed of six SVM modules are considered and designed. A data set was used to validate the classification performances of each multi-SVM module. As a result, the accuracies of six SVM modules used for detection of abnormality and classification of six heart disorders showed 71-98.9% for THVs=10-90% and 81.2-99.6% for THVs=10-50% with respect to each of SVM modules. With the proposed cardiac sound

  10. Linear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Images. (United States)

    Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Jiang, Mengying; Ling, Wing-Kuen


    As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are an improvement of linear ELM (LELM). However, based on lots of experiments and much analysis, it is found that the LELM is a better choice than nonlinear ELM for the spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learns such a distribution using the LBP. The proposed method not only maintains the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines, and Pavia University, demonstrate the good performance of the proposed method.

  11. Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging. (United States)

    Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei


    Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

  12. Spectral Lidar Analysis and Terrain Classification in a Semi-Urban Environment (United States)


    Netherlands. Fernandez-Diaz, Juan Carlos, William E. Carter , Craig Glennie, Ramesh L. Shrestha, Zhigang Pan, Nima Ekhtari, Abhinav Singhania, is accomplished using a K -means classifier for comparison. The campus is classified into 10 and 16 classes, compared to the four from...classification for the combined spatial/spectral data is accomplished using a K -means classifier for comparison. The campus is classified into 10 and 16

  13. Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data. (United States)

    Zhao, Weixiang; Davis, Cristina E


    This paper introduces the ant colony algorithm, a novel swarm intelligence based optimization method, to select appropriate wavelet coefficients from mass spectral data as a new feature selection method for ovarian cancer diagnostics. By determining the proper parameters for the ant colony algorithm (ACA) based searching algorithm, we perform the feature searching process for 100 times with the number of selected features fixed at 5. The results of this study show: (1) the classification accuracy based on the five selected wavelet coefficients can reach up to 100% for all the training, validating and independent testing sets; (2) the eight most popular selected wavelet coefficients of the 100 runs can provide 100% accuracy for the training set, 100% accuracy for the validating set, and 98.8% accuracy for the independent testing set, which suggests the robustness and accuracy of the proposed feature selection method; and (3) the mass spectral data corresponding to the eight popular wavelet coefficients can be located by reverse wavelet transformation and these located mass spectral data still maintain high classification accuracies (100% for the training set, 97.6% for the validating set, and 98.8% for the testing set) and also provide sufficient physical and medical meaning for future ovarian cancer mechanism studies. Furthermore, the corresponding mass spectral data (potential biomarkers) are in good agreement with other studies which have used the same sample set. Together these results suggest this feature extraction strategy will benefit the development of intelligent and real-time spectroscopy instrumentation based diagnosis and monitoring systems.

  14. Improving Spectral Image Classification through Band-Ratio Optimization and Pixel Clustering (United States)

    O'Neill, M.; Burt, C.; McKenna, I.; Kimblin, C.


    The Underground Nuclear Explosion Signatures Experiment (UNESE) seeks to characterize non-prompt observables from underground nuclear explosions (UNE). As part of this effort, we evaluated the ability of DigitalGlobe's WorldView-3 (WV3) to detect and map UNE signatures. WV3 is the current state-of-the-art, commercial, multispectral imaging satellite; however, it has relatively limited spectral and spatial resolutions. These limitations impede image classifiers from detecting targets that are spatially small and lack distinct spectral features. In order to improve classification results, we developed custom algorithms to reduce false positive rates while increasing true positive rates via a band-ratio optimization and pixel clustering front-end. The clusters resulting from these algorithms were processed with standard spectral image classifiers such as Mixture-Tuned Matched Filter (MTMF) and Adaptive Coherence Estimator (ACE). WV3 and AVIRIS data of Cuprite, Nevada, were used as a validation data set. These data were processed with a standard classification approach using MTMF and ACE algorithms. They were also processed using the custom front-end prior to the standard approach. A comparison of the results shows that the custom front-end significantly increases the true positive rate and decreases the false positive rate.This work was done by National Security Technologies, LLC, under Contract No. DE-AC52-06NA25946 with the U.S. Department of Energy. DOE/NV/25946-3283.

  15. Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Zhi He


    Full Text Available Recently, sparse representation has yielded successful results in hyperspectral image (HSI classification. In the sparse representation-based classifiers (SRCs, a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC.

  16. Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging (United States)

    Lu, Guolan; Halig, Luma; Wang, Dongsheng; Qin, Xulei; Chen, Zhuo Georgia; Fei, Baowei


    Abstract. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors. PMID:25277147

  17. Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing (United States)

    Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier


    The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

  18. Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhigao Zeng


    Full Text Available This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.


    Directory of Open Access Journals (Sweden)

    D. Akbari


    Full Text Available An effective approach based on the Minimum Spanning Forest (MSF, grown from automatically selected markers using Support Vector Machines (SVM, has been proposed for spectral-spatial classification of hyperspectral images by Tarabalka et al. This paper aims at improving this approach by using image segmentation to integrate the spatial information into marker selection process. In this study, the markers are extracted from the classification maps, obtained by both SVM and segmentation algorithms, and then are used to build the MSF. The segmentation algorithms are the watershed, expectation maximization (EM and hierarchical clustering. These algorithms are used in parallel and independently to segment the image. Moreover, the pixels of each class, with the largest population in the classification map, are kept for each region of the segmentation map. Lastly, the most reliable classified pixels are chosen from among the exiting pixels as markers. Two benchmark urban hyperspectral datasets are used for evaluation: Washington DC Mall and Berlin. The results of our experiments indicate that, compared to the original MSF approach, the marker selection using segmentation algorithms leads in more accurate classification maps.

  20. Aerosol classification using airborne High Spectral Resolution Lidar measurements – methodology and examples

    Directory of Open Access Journals (Sweden)

    S. P. Burton


    Full Text Available The NASA Langley Research Center (LaRC airborne High Spectral Resolution Lidar (HSRL on the NASA B200 aircraft has acquired extensive datasets of aerosol extinction (532 nm, aerosol optical depth (AOD (532 nm, backscatter (532 and 1064 nm, and depolarization (532 and 1064 nm profiles during 18 field missions that have been conducted over North America since 2006. The lidar measurements of aerosol intensive parameters (lidar ratio, depolarization, backscatter color ratio, and spectral depolarization ratio are shown to vary with location and aerosol type. A methodology based on observations of known aerosol types is used to qualitatively classify the extensive set of HSRL aerosol measurements into eight separate types. Several examples are presented showing how the aerosol intensive parameters vary with aerosol type and how these aerosols are classified according to this new methodology. The HSRL-based classification reveals vertical variability of aerosol types during the NASA ARCTAS field experiment conducted over Alaska and northwest Canada during 2008. In two examples derived from flights conducted during ARCTAS, the HSRL classification of biomass burning smoke is shown to be consistent with aerosol types derived from coincident airborne in situ measurements of particle size and composition. The HSRL retrievals of AOD and inferences of aerosol types are used to apportion AOD to aerosol type; results of this analysis are shown for several experiments.

  1. The Galah Survey: Classification and Diagnostics with t-SNE Reduction of Spectral Information

    Energy Technology Data Exchange (ETDEWEB)

    Traven, G.; Zwitter, T.; Žerjal, M. [Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, 1000 Ljubljana (Slovenia); Matijevič, G. [Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, D-14482 Potsdam (Germany); Kos, J.; Bland-Hawthorn, J.; De Silva, G.; Sharma, S. [Sydney Institute for Astronomy, School of Physics, A28, The University of Sydney, NSW 2006 (Australia); Asplund, M.; Freeman, K.; Lin, J.; Da Costa, G.; Duong, L. [Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611 (Australia); Casey, A. R. [Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA (United Kingdom); Martell, S. L. [School of Physics, University of New South Wales, Sydney, NSW 2052 (Australia); Schlesinger, K. J. [Research School of Astronomy and Astrophysics, Mount Stromlo Observatory, Cotter Road, Weston Creek, ACT 2611 (Australia); Simpson, J. D. [Australian Astronomical Observatory, North Ryde, NSW 2113 (Australia); Zucker, D. B. [Australian Astronomical Observatory, P.O. Box 915, North Ryde, NSW 1670 (Australia); Anguiano, B. [Department of Physics and Astronomy, Macquarie University, North Ryde, NSW 2109 (Australia); Horner, J., E-mail: [Computational Engineering and Science Research Centre, University of Southern Queensland, Towoomba QLD 4350 (Australia); and others


    Galah is an ongoing high-resolution spectroscopic survey with the goal of disentangling the formation history of the Milky Way using the fossil remnants of disrupted star formation sites that are now dispersed around the Galaxy. It is targeting a randomly selected magnitude-limited ( V ≤ 14) sample of stars, with the goal of observing one million objects. To date, 300,000 spectra have been obtained. Not all of them are correctly processed by parameter estimation pipelines, and we need to know about them. We present a semi-automated classification scheme that identifies different types of peculiar spectral morphologies in an effort to discover and flag potentially problematic spectra and thus help to preserve the integrity of the survey results. To this end, we employ the recently developed dimensionality reduction technique t-SNE ( t -distributed stochastic neighbor embedding), which enables us to represent the complex spectral morphology in a two-dimensional projection map while still preserving the properties of the local neighborhoods of spectra. We find that the majority (178,483) of the 209,533 Galah spectra considered in this study represents normal single stars, whereas 31,050 peculiar and problematic spectra with very diverse spectral features pertaining to 28,579 stars are distributed into 10 classification categories: hot stars, cool metal-poor giants, molecular absorption bands, binary stars, H α /H β emission, H α /H β emission superimposed on absorption, H α /H β P-Cygni, H α /H β inverted P-Cygni, lithium absorption, and problematic. Classified spectra with supplementary information are presented in the catalog, indicating candidates for follow-up observations and population studies of the short-lived phases of stellar evolution.

  2. Tensor subspace analysis for spatial-spectral classification of hyperspectral data (United States)

    Fan, Lei; Messinger, David W.


    Remotely sensed data fusion aims to integrate multi-source information generated from different perspectives, acquired with different sensors or captured at different times in order to produce fused data that contains more information than one individual data source. Recently, extended morphological attribute profiles (EMAPs) were proposed to embed contextual information, such as texture, shape, size and etc., into a high dimensional feature space as an alternative data source to hyperspectral image (HSI). Although EMAPs provide greater capabilities in modeling both spatial and spectral information, they lead to an increase in the dimensionality of the extracted features. Conventionally, a data point in high dimensional feature space is represented by a vector. For HSI, this data representation has one obvious shortcoming in that only spectral knowledge is utilized without contextual relationship being exploited. Tensors provide a natural representation for HSI data by incorporating both spatial neighborhood awareness and spectral information. Besides, tensors can be conveniently incorporated into a superpixel-based HSI image processing framework. In our paper, three tensor-based dimensionality reduction (DR) approaches were generalized for high dimensional image with promising results reported. Among the tensor-based DR approaches, the Tensor Locality Preserving Projection (TLPP) algorithm utilized graph Laplacian to model the pairwise relationship among the tensor data points. It also demonstrated excellent performance for both pixel-wise and superpixel-wise classification on Pavia University dataset.

  3. Classification of cancer cell death with spectral dimensionality reduction and generalized eigenvalues. (United States)

    Guarracino, Mario R; Xanthopoulos, Petros; Pyrgiotakis, Georgios; Tomaino, Vera; Moudgil, Brij M; Pardalos, Panos M


    Accurate cell death discrimination is a time consuming and expensive process that can only be performed in biological laboratories. Nevertheless, it is very useful and arises in many biological and medical applications. Raman spectra are collected for 84 samples of A549 cell line (human lung cancer epithelia cells) that has been exposed to toxins to simulate the necrotic and apoptotic death. The proposed data mining approach for the multiclass cell death discrimination problem uses a multiclass regularized generalized eigenvalue algorithm for classification (multiReGEC), together with a dimensionality reduction algorithm based on spectral clustering. The proposed algorithmic scheme can classify A549 lung cancer cells from three different classes (apoptotic death, necrotic death and control cells) with 97.78%± 0.047 accuracy versus 92.22 ± 0.095 without the proposed feature selection preprocessing. The spectrum areas depicted by the algorithm corresponds to the 〉C O bond from the lipids and the lipid bilayer. This chemical structure undergoes different change of state based on cell death type. Further evidence of the validity of the technique is obtained through the successful classification of 7 cell spectra that undergo hyperthermic treatment. In this study we propose a fast and automated way of processing Raman spectra for cell death discrimination, using a feature selection algorithm that not only enhances the classification accuracy, but also gives more insight in the undergoing cell death process. Copyright © 2011 Elsevier B.V. All rights reserved.

  4. Novel event classification based on spectral analysis of scintillation waveforms in Double Chooz (United States)

    Abrahão, T.; Almazan, H.; dos Anjos, J. C.; Appel, S.; Bekman, I.; Bezerra, T. J. C.; Bezrukov, L.; Blucher, E.; Brugière, T.; Buck, C.; Busenitz, J.; Cabrera, A.; Camilleri, L.; Cerrada, M.; Chauveau, E.; Chimenti, P.; Corpace, O.; Crespo-Anadón, J. I.; Dawson, J. V.; Djurcic, Z.; Etenko, A.; Fallot, M.; Franco, D.; Furuta, H.; Gil-Botella, I.; Givaudan, A.; Gómez, H.; Gonzalez, L. F. G.; Goodman, M.; Hara, T.; Haser, J.; Hellwig, D.; Hourlier, A.; Ishitsuka, M.; Jochum, J.; Jollet, C.; Kale, K.; Kampmann, P.; Kaneda, M.; Kawasaki, T.; Kemp, E.; de Kerret, H.; Kryn, D.; Kuze, M.; Lachenmaier, T.; Lane, C.; Lasserre, T.; Lastoria, C.; Lhuillier, D.; Lima, H.; Lindner, M.; López-Castaño, J. M.; LoSecco, J. M.; Lubsandorzhiev, B.; Maeda, J.; Mariani, C.; Maricic, J.; Matsubara, T.; Mention, G.; Meregaglia, A.; Miletic, T.; Milincic, R.; Minotti, A.; Navas-Nicolás, D.; Novella, P.; Oberauer, L.; Obolensky, M.; Onillon, A.; Oralbaev, A.; Palomares, C.; Pepe, I.; Pronost, G.; Reinhold, B.; Santorelli, R.; Schönert, S.; Schoppmann, S.; Settimo, M.; Sharankova, R.; Sibille, V.; Sinev, V.; Skorokhvatov, M.; Soldin, P.; Stahl, A.; Stancu, I.; Stokes, L. F. F.; Suekane, F.; Sukhotin, S.; Sumiyoshi, T.; Sun, Y.; Tonazzo, A.; Veyssiere, C.; Viaud, B.; Vivier, M.; Wagner, S.; Wiebusch, C.; Yang, G.; Yermia, F.


    Liquid scintillators are a common choice for neutrino physics experiments, but their capabilities to perform background rejection by scintillation pulse shape discrimination is generally limited in large detectors. This paper describes a novel approach for a pulse shape based event classification developed in the context of the Double Chooz reactor antineutrino experiment. Unlike previous implementations, this method uses the Fourier power spectra of the scintillation pulse shapes to obtain event-wise information. A classification variable built from spectral information was able to achieve an unprecedented performance, despite the lack of optimization at the detector design level. Several examples of event classification are provided, ranging from differentiation between the detector volumes and an efficient rejection of instrumental light noise, to some sensitivity to the particle type, such as stopping muons, ortho-positronium formation, alpha particles as well as electrons and positrons. In combination with other techniques the method is expected to allow for a versatile and more efficient background rejection in the future, especially if detector optimization is taken into account at the design level.


    Directory of Open Access Journals (Sweden)

    B. Kumar


    Full Text Available Extended morphological profile (EMP is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectralspatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.


    Directory of Open Access Journals (Sweden)

    X. Han


    Full Text Available Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE. Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI datasets – Pavia University dataset and the Kennedy Space Centre (KSC dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

  7. VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2009-2012) (United States)

    Skiff, B. A.


    This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. These papers of course should be cited in publication, not this compilation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000 (and epoch 2000 if possible), and these serve as a secondary identifier. To the extent possible with current astrometric sources, the components of double stars and stars with composite spectra are shown as separate entries. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or spectral energy distributions. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. System-defining primary MK standard stars are included from the last lists by Morgan and Keenan, and are flagged by a + sign in column 83. The early-type standards comprise the 1973 "dagger standards" (1973ARA&A..11...29M) and stars from the Morgan, Abt, and Tapscott atlas ( Standards from Table I of the

  8. VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2009-2013) (United States)

    Skiff, B. A.


    This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. These papers of course should be cited in publication, not this compilation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000 (and epoch 2000 if possible), and these serve as a secondary identifier. To the extent possible with current astrometric sources, the components of double stars and stars with composite spectra are shown as separate entries. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or spectral energy distributions. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. System-defining primary MK standard stars are included from the last lists by Morgan and Keenan, and are flagged by a + sign in column 83. The early-type standards comprise the 1973 "dagger standards" (1973ARA&A..11...29M) and stars from the Morgan, Abt, and Tapscott atlas ( Standards from Table I of the

  9. VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2009-2016) (United States)

    Skiff, B. A.


    This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. These papers of course should be cited in publication, not this compilation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000 (and epoch 2000 if possible), and these serve as a secondary identifier. To the extent possible with current astrometric sources, the components of double stars and stars with composite spectra are shown as separate entries. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or spectral energy distributions. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. System-defining primary MK standard stars are included from the last lists by Morgan and Keenan, and are flagged by a + sign in column 83. The early-type standards comprise the 1973 "dagger standards" (1973ARA&A..11...29M) and stars from the Morgan, Abt, and Tapscott atlas ( Standards from Table I of the

  10. VizieR Online Data Catalog: Catalogue of Stellar Spectral Classifications (Skiff, 2009-2014) (United States)

    Skiff, B. A.


    This file contains spectral classifications for stars collected from the literature, serving as a continuation of the compilations produced by the Jascheks, by Kennedy, and by Buscombe. The source of each spectral type is indicated by a standard 19-digit bibcode citation. These papers of course should be cited in publication, not this compilation. The stars are identified either by the name used in each publication or by a valid SIMBAD identifier. Some effort has been made to determine accurate (~1" or better) coordinates for equinox J2000 (and epoch 2000 if possible), and these serve as a secondary identifier. To the extent possible with current astrometric sources, the components of double stars and stars with composite spectra are shown as separate entries. Magnitudes are provided as an indication of brightness, but these data are not necessarily accurate, as they often derive from photographic photometry or rough estimates. The file includes only spectral types determined from spectra (viz. line and band strengths or ratios), omitting those determined from photometry (e.g. DDO, Vilnius) or inferred from broadband colors or spectral energy distributions. The classifications include MK types as well as types not strictly on the MK system (white dwarfs, Wolf-Rayet, etc), and in addition simple HD-style temperature types. Luminosity classes in the early Mount Wilson style (e.g. 'd' for dwarf, 'g' for giant) and other similar schemes have been converted to modern notation. Since a citation is provided for each entry, the source paper should be consulted for details about classification schemes, spectral dispersion, and instrumentation used. System-defining primary MK standard stars are included from the last lists by Morgan and Keenan, and are flagged by a + sign in column 83. The early-type standards comprise the 1973 "dagger standards" (1973ARA&A..11...29M) and stars from the Morgan, Abt, and Tapscott atlas ( Standards from Table I of the

  11. Data preprocessing methods of FT-NIR spectral data for the classification cooking oil (United States)

    Ruah, Mas Ezatul Nadia Mohd; Rasaruddin, Nor Fazila; Fong, Sim Siong; Jaafar, Mohd Zuli


    This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cooking oil and its quality parameters with chemometrics method. Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modelling. Hence, this work is dedicated to investigate the utility and effectiveness of pre-processing algorithms namely row scaling, column scaling and single scaling process with Standard Normal Variate (SNV). The combinations of these scaling methods have impact on exploratory analysis and classification via Principle Component Analysis plot (PCA). The samples were divided into palm oil and non-palm cooking oil. The classification model was build using FT-NIR cooking oil spectra datasets in absorbance mode at the range of 4000cm-1-14000cm-1. Savitzky Golay derivative was applied before developing the classification model. Then, the data was separated into two sets which were training set and test set by using Duplex method. The number of each class was kept equal to 2/3 of the class that has the minimum number of sample. Then, the sample was employed t-statistic as variable selection method in order to select which variable is significant towards the classification models. The evaluation of data pre-processing were looking at value of modified silhouette width (mSW), PCA and also Percentage Correctly Classified (%CC). The results show that different data processing strategies resulting to substantial amount of model performances quality. The effects of several data pre-processing i.e. row scaling, column standardisation and single scaling process with Standard Normal Variate indicated by mSW and %CC. At two PCs model, all five classifier gave high %CC except Quadratic Distance Analysis.

  12. Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations (United States)

    Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto


    Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising


    Energy Technology Data Exchange (ETDEWEB)

    Arias, Julia I.; Barbá, Rodolfo H.; Sabín-Sanjulián, Carolina [Departamento de Física y Astronomía, Universidad de La Serena, Av. Cisternas 1200 Norte, La Serena (Chile); Walborn, Nolan R. [Space Telescope Science Institute, 3700 San Martin Drive, MD 21218, Baltimore (United States); Díaz, Sergio Simón [Instituto de Astrofísica de Canarias, E-38200, Departamento de Astrofísica, Universidad de La Laguna, E-38205, La Laguna, Tenerife (Spain); Apellániz, Jesús Maíz [Centro de Astrobiología, CSIC-INTA, campus ESAC, Camino Bajo del Castillo s/n, E-28 692 Madrid (Spain); Gamen, Roberto C. [Instituto de Astrofísica de La Plata (CONICET, UNLP), Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, Paseo del Bosque s/n, 1900 La Plata (Argentina); Morrell, Nidia I. [Las Campanas Observatory, Carnegie Observatories, Casilla 601, La Serena (Chile); Sota, Alfredo [Instituto de Astrofísica de Andalucía-CSIC, Glorieta de la Astronomía s/n, E-18 008 Granada (Spain); Marco, Amparo; Negueruela, Ignacio, E-mail: [Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal, Escuela Politécnica Superior, Universidad de Alicante, Carretera San Vicente del Raspeig s/n, E03690, San Vicente del Raspeig (Spain); and others


    On the basis of the Galactic O Star Spectroscopic Survey (GOSSS), we present a detailed systematic investigation of the O Vz stars. The currently used spectral classification criteria are rediscussed, and the Vz phenomenon is recalibrated through the addition of a quantitative criterion based on the equivalent widths of the He i λ 4471, He ii λ 4542, and He ii λ 4686 spectral lines. The GOSSS O Vz and O V populations resulting from the newly adopted spectral classification criteria are comparatively analyzed. The locations of the O Vz stars are probed, showing a concentration of the most extreme cases toward the youngest star-forming regions. The occurrence of the Vz spectral peculiarity in a solar-metallicity environment, as predicted by the fastwind code, is also investigated, confirming the importance of taking into account several processes for the correct interpretation of the phenomenon.

  14. [Spectral analysis and LDB based classification of heart sounds with mechanical prosthetic heart valves]. (United States)

    Zhang, Di; Wu, Yuequan; Yao, Jianping; Yang, Song; Du, Minghui


    Auscultation, the act of listening for heart sounds to aid in the diagnosis of various heart diseases, is a widely used efficient technique by cardiologists. Since the mechanical prosthetic heart valves are widely used today, it is important to develop a simple and efficient method to detect abnormal mechanical valves. The study on five different mechanical valves showed that only the case of perivalvular leakage could be detected by spectral estimation. Though it is possible to classify different mechanical valves by using time-frequency components of the signal directly, the recognition rate is merely 84%. However, with the improved local discriminant bases (LDB) algorithm to extract features from heart sounds, the recognition rate is 97.3%. Experimental results demonstrated that the improved LDB algorithm could improve classification rate and reduce computational complexity in comparison with original LDB algorithm.

  15. Small Galactic H II regions. I. Spectral classifications of massive stars

    Energy Technology Data Exchange (ETDEWEB)

    Hunter, D.A.; Massey, P. (Lowell Observatory, Flagstaff, AZ (USA) Kitt Peak National Observatory, Tucson, AZ (USA))


    By studying the stellar content of star-forming regions with different characteristics, such as gas cloud size, one can determine factors that affect the star-formation process. This paper is part of a study of the stellar content and natal cloud characteristics of a sample of relatively small Galactic star-forming regions. Spectral classifications based on moderate dispersion spectra of the optically visible stars in the regions are presented. The H-alpha, radio, and far-infrared luminosities of the nebulas are used as a check for additional embedded or unidentitied hot stars. A histogram of the most massive star per star-forming unit shows that there is a range in upper mass limits for the sample and that one is statistically sampling a mass function intermediate between that of Selpeter and that of Miller-Scalo. 68 refs.

  16. a New Spectral-Spatial Framework for Classification of Hyperspectral Data (United States)

    Akbari, D.


    In this paper, an innovative framework, based on both spectral and spatial information, is proposed. The objective is to improve the classification of hyperspectral images for high resolution land cover mapping. The spatial information is obtained by a marker-based Minimum Spanning Forest (MSF) algorithm. A pixel-based SVM algorithm is first used to classify the image. Then, the marker-based MSF spectral-spatial algorithm is applied to improve the accuracy for classes with low accuracy. The marker-based MSF algorithm is used as a binary classifier. These two classes are the low accuracy class and the remaining classes. Finally, the SVM algorithm is trained for classes with acceptable accuracy. To evaluate the proposed approach, the Berlin hyperspectral dataset is tested. Experimental results demonstrate the superiority of the proposed method compared to the original MSF-based approach. It achieves approximately 5 % higher rates in kappa coefficients of agreement, in comparison to the original MSF-based method.

  17. The vegetation classification in coal mine overburden dump using canopy spectral reflectance

    Energy Technology Data Exchange (ETDEWEB)

    Sun, H.; Li, M.Z.; Li, D.L. [China Agricultural University, Beijing (China)


    The canopy spectral characteristics of typical plants in the overburden of the Fuxin coal mine dump were measured and analyzed. The reflectance of Leymus chinensis was affected by the soil, with a slight shift from green (550 nm) to the near infrared (NIR) region. Changes in chlorophyll and water absorption were not significant in the red (670 nm) and NIR bands, respectively. The reflectance curve trend for Artemisia lavandulaefolia was similar to those of Sophora japonica and Ulmus pumila, while the reflectance of S. japonica and U. pumila fluctuated in the NIR region (760-1200 nm). In contrast, the reflectance of A. lavandulaefolia fluctuated slightly around 930 nm and a significant peak appeared at 1127 nm. In addition, the spectral reflectance of S. japonica was lower than for the other species in the visible band (400-700 nm). However, it was higher than for L. chinensis in the NIR region (780-1200 nm). Three classifiers, the self-organizing map (SOM), learning-vector quantization (LVQ), and a probabilistic neural network (PNN), were used to classify the vegetation and the results of all classifiers were compared based on total spectral reflectance data from 400 to 1200 nm. The PNN was the best classifier in terms of training and testing accuracy. The first difference reflectance was calculated, and the red edge parameter was able to classify the herbs (L. chinensis and A. lavandulaefolia) and the arbores (S. japonica and U. pumila) with an accuracy of 77 and 84%, respectively, although it did not perform as well for detail species. A mixing parameter matrix was built based on the sensitive wavelengths (550, 674, 810, 935, and 1125 nm), the vegetation indices (SAVI and NDGI), and the water absorption slope. High classification accuracy was obtained by applying the mixing parameter matrix. This method could be used for revegetation monitoring and in decision making.

  18. Spectral classification indicators of emission-line galaxies from the Sloan Digital Sky Survey (United States)

    Shi, Fei; Liu, Yu-Yan; Li, Pei-Yu; Yu, Ming; Lei, Yu-Ming; Wang, Jian


    To find efficient spectral classification diagrams to classify emission-line galaxies, especially in large surveys and huge data bases, an artificial neural network (ANN) supervised learning algorithms is applied to a sample of emission-line galaxies from the Sloan Digital Sky Survey data release 9 provided by the Max Planck Institute and the Johns Hopkins University (MPA/JHU) ( A two-step approach is adopted. (i) The ANN network must be trained with a subset of objects that are known to be active galactic nuclei (AGNs) hosts, composites or star-forming galaxies, treating the strong emission-line flux measurements as input feature vectors in n-dimensional space, where n is the number of strong emission-line flux ratios. (ii) After the network is trained on a sample of galaxies, the remaining galaxies are classified in the automatic test analysis as AGN hosts, composites or star-forming galaxies. We show that the classification diagrams based on the [N II]/Hα versus other emission-line ratio, such as [O III]/Hβ, [Ne III]/[O II], ([O III]λ4959 + [O III]λ5007)/[O III]λ4363, [O II]/Hβ, [Ar III]/[O III], [S II]/Hα, and [O I]/Hα, plus colour, allows us to separate unambiguously AGN hosts, composites or star-forming galaxies. Among them, the diagram of [N II]/Hα versus [O III]/Hβ achieved an accuracy of 98 per cent for classification of AGN hosts, composites or star-forming galaxies. The other diagrams above except the diagram of [N II]/Hα versus [O III]/Hβ give an accuracy of ˜90 per cent. The code in the paper is available on the web (

  19. Feature driven classification of Raman spectra for real-time spectral brain tumour diagnosis using sound. (United States)

    Stables, Ryan; Clemens, Graeme; Butler, Holly J; Ashton, Katherine M; Brodbelt, Andrew; Dawson, Timothy P; Fullwood, Leanne M; Jenkinson, Michael D; Baker, Matthew J


    Spectroscopic diagnostics have been shown to be an effective tool for the analysis and discrimination of disease states from human tissue. Furthermore, Raman spectroscopic probes are of particular interest as they allow for in vivo spectroscopic diagnostics, for tasks such as the identification of tumour margins during surgery. In this study, we investigate a feature-driven approach to the classification of metastatic brain cancer, glioblastoma (GB) and non-cancer from tissue samples, and we provide a real-time feedback method for endoscopic diagnostics using sound. To do this, we first evaluate the sensitivity and specificity of three classifiers (SVM, KNN and LDA), when trained with both sub-band spectral features and principal components taken directly from Raman spectra. We demonstrate that the feature extraction approach provides an increase in classification accuracy of 26.25% for SVM and 25% for KNN. We then discuss the molecular assignment of the most salient sub-bands in the dataset. The most salient sub-band features are mapped to parameters of a frequency modulation (FM) synthesizer in order to generate audio clips from each tissue sample. Based on the properties of the sub-band features, the synthesizer was able to maintain similar sound timbres within the disease classes and provide different timbres between disease classes. This was reinforced via listening tests, in which participants were able to discriminate between classes with mean classification accuracy of 71.1%. Providing intuitive feedback via sound frees the surgeons' visual attention to remain on the patient, allowing for greater control over diagnostic and surgical tools during surgery, and thus promoting clinical translation of spectroscopic diagnostics.

  20. Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images. (United States)

    Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo


    Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples

  1. Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

    Directory of Open Access Journals (Sweden)

    Eva Husson


    Full Text Available Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a Growth forms including the classes of water, nymphaeid, and helophyte; and (b dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%–21% for growth-forms and 3%–30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities

  2. Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

    Directory of Open Access Journals (Sweden)

    Hongrui Zheng


    Full Text Available Land Use and Land Cover (LULC classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS, nearest neighbor diffusion (NNDiffusion, PANSHARP algorithm proposed by Y. Zhang, wavelet transformation fusion (WTF and high-pass filter fusion (HPF. Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM and extended attribute profiles (EAPs, are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet

  3. Gravitational self-organizing map-based seismic image classification with an adaptive spectral-textural descriptor (United States)

    Hao, Yanling; Sun, Genyun


    Seismic image classification is of vital importance for extracting damage information and evaluating disaster losses. With the increasing availability of high resolution remote sensing images, automatic image classification offers a unique opportunity to accommodate the rapid damage mapping requirements. However, the diversity of disaster types and the lack of uniform statistical characteristics in seismic images increase the complexity of automated image classification. This paper presents a novel automatic seismic image classification approach by integrating an adaptive spectral-textural descriptor into gravitational self-organizing map (gSOM). In this approach, seismic image is first segmented into several objects based on mean shift (MS) method. These objects are then characterized explicitly by spectral and textural feature quantization histograms. To objectify the image object delineation adapt to various disaster types, an adaptive spectral-textural descriptor is developed by integrating the histograms automatically. Subsequently, these objects as classification units are represented by neurons in a self-organizing map and clustered by adjacency gravitation. By moving the neurons around the gravitational space and merging them according to the gravitation, the object-based gSOM is able to find arbitrary shape and determine the class number automatically. Taking advantage of the diversity of gSOM results, consensus function is then conducted to discover the most suitable classification result. To confirm the validity of the presented approach, three aerial seismic images in Wenchuan covering several disaster types are utilized. The obtained quantitative and qualitative experimental results demonstrated the feasibility and accuracy of the proposed seismic image classification method.

  4. Jet Joint Undertaking. Vol. 2

    International Nuclear Information System (INIS)


    The scientific, technical, experimental and theoretical investigations related to JET tokamak are presented. The JET Joint Undertaking, Volume 2, includes papers presented at: the 15th European Conference on controlled fusion and plasma heating, the 15th Symposium on fusion technology, the 12th IAEA Conference on plasma physics and controlled nuclear fusion research, the 8th Topical Meeting on technology of fusion. Moreover, the following topics, concerning JET, are discussed: experience with wall materials, plasma performance, high power ion cyclotron resonance heating, plasma boundary, results and prospects for fusion, preparation for D-T operation, active gas handling system and remote handling equipment

  5. Combining spectral mixture analysis and object-based classification for fire severity mapping

    Energy Technology Data Exchange (ETDEWEB)

    Fernadez-Manso, O.; Quintano, C.; Quintano, C.; Fernandez-Manso, A.


    This study shows an accurate and fast methodology in order to evaluate fire severity classes of large forest fires. A single Landsat Enhanced Thematic Mapper multispectral image was utilized with the aim of mapping fire severity classes (high, moderate and low) using a combined-approach based in a spectral mixing model and object-based image analysis. A large wildfire in the Northwest of Spain was used to test the model. Fraction images obtained by Landsat unmixing were used as input data in the object-based image analysis. A multilevel segmentation and a classification were carried out by using membership functions. This method was compared with other simpler in order to evaluate the suitability to distinguish between the three fire severity classes above mentioned. McNemar's test was used to evaluate the statistical significance of the difference between approaches tested in this study. The combined approach achieved the highest accuracy reaching 97.32% and kappa index of agreement of 95.96% and improving accuracy of individual classes. (Author) 89 refs.

  6. Spectral Pattern Classification in Lidar Data for Rock Identification in Outcrops

    Directory of Open Access Journals (Sweden)

    Leonardo Campos Inocencio


    Full Text Available The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from terrestrial laser scanner in order to identify the presence of different rocks in outcrops and to generate a digital outcrop model. To achieve this objective, a software based on cluster analysis was created, named K-Clouds. This software was developed through a partnership between UNISINOS and the company V3D. This tool was designed to begin with an analysis and interpretation of a histogram from a point cloud of the outcrop and subsequently indication of a number of classes provided by the user, to process the intensity return values. This classified information can then be interpreted by geologists, to provide a better understanding and identification from the existing rocks in the outcrop. Beyond the detection of different rocks, this work was able to detect small changes in the physical-chemical characteristics of the rocks, as they were caused by weathering or compositional changes.

  7. Assessing the Impact of Spectral Resolution on Classification of Lowland Native Grassland Communities Based on Field Spectroscopy in Tasmania, Australia

    Directory of Open Access Journals (Sweden)

    Bethany Melville


    Full Text Available This paper presents a case study for the analysis of endangered lowland native grassland communities in the Tasmanian Midlands region using field spectroscopy and spectral convolution techniques. The aim of the study was to determine whether there was significant improvement in classification accuracy for lowland native grasslands and other vegetation communities based on hyperspectral resolution datasets over multispectral equivalents. A spectral dataset was collected using an ASD Handheld-2 spectroradiometer at Tunbridge Township Lagoon. The study then employed a k-fold cross-validation approach for repeated classification of a full hyperspectral dataset, a reduced hyperspectral dataset, and two convoluted multispectral datasets. Classification was performed on each of the four datasets a total of 30 times, based on two different class configurations. The classes analysed were Themeda triandra grassland, Danthonia/Poa grassland, Wilsonia rotundifolia/Selliera radicans, saltpan, and a simplified C3 vegetation class. The results of the classifications were then tested for statistically significant differences using ANOVA and Tukey’s post-hoc comparisons. The results of the study indicated that hyperspectral resolution provides small but statistically significant increases in classification accuracy for Themeda and Danthonia grasslands. For other classes, differences in classification accuracy for all datasets were not statistically significant. The results obtained here indicate that there is some potential for enhanced detection of major lowland native grassland community types using hyperspectral resolution datasets, and that future analysis should prioritise good performance in these classes over others. This study presents a method for identification of optimal spectral resolution across multiple datasets, and constitutes an important case study for lowland native grassland mapping in Tasmania.

  8. Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

    Directory of Open Access Journals (Sweden)

    Marilusa P. C. Lacerda


    Full Text Available The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400–2500 nm. The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA to select relevant information on the spectra. Partial least squares regression (PLSR was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can

  9. Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging (United States)

    Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan


    Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

  10. Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information

    Directory of Open Access Journals (Sweden)

    Yi Wang


    Full Text Available In this paper, we introduce a novel classification framework for hyperspectral images (HSIs by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through each pixel’s vector value in the original HSI, and the spatial kernel is modeled by using the extended morphological profile method due to its simplicity and effectiveness. To accurately characterize hierarchical structure features, the techniques of Fish-Markov selector (FMS, marker-based hierarchical segmentation (MHSEG and algebraic multigrid (AMG are combined. First, the FMS algorithm is used on the original HSI for feature selection to produce its spectral subset. Then, the multigrid structure of this subset is constructed using the AMG method. Subsequently, the MHSEG algorithm is exploited to obtain a hierarchy consist of a series of segmentation maps. Finally, the hierarchical structure information is represented by using these segmentation maps. The main contributions of this work is to present an effective composite kernel for HSI classification by utilizing spatial structure information in multiple scales. Experiments were conducted on two hyperspectral remote sensing images to validate that the proposed framework can achieve better classification results than several popular kernel-based classification methods in terms of both qualitative and quantitative analysis. Specifically, the proposed classification framework can achieve 13.46–15.61% in average higher than the standard SVM classifier under different training sets in the terms of overall accuracy.

  11. Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features (United States)

    Wan, Xiaoqing; Zhao, Chunhui; Wang, Yanchun; Liu, Wu


    This paper proposes a novel classification paradigm for hyperspectral image (HSI) using feature-level fusion and deep learning-based methodologies. Operation is carried out in three main steps. First, during a pre-processing stage, wave atoms are introduced into bilateral filter to smooth HSI, and this strategy can effectively attenuate noise and restore texture information. Meanwhile, high quality spectral-spatial features can be extracted from HSI by taking geometric closeness and photometric similarity among pixels into consideration simultaneously. Second, higher order statistics techniques are firstly introduced into hyperspectral data classification to characterize the phase correlations of spectral curves. Third, multifractal spectrum features are extracted to characterize the singularities and self-similarities of spectra shapes. To this end, a feature-level fusion is applied to the extracted spectral-spatial features along with higher order statistics and multifractal spectrum features. Finally, stacked sparse autoencoder is utilized to learn more abstract and invariant high-level features from the multiple feature sets, and then random forest classifier is employed to perform supervised fine-tuning and classification. Experimental results on two real hyperspectral data sets demonstrate that the proposed method outperforms some traditional alternatives.

  12. Spectral characterization of tissues in high spectral and spatial resolution MR images: Implications for a classification-based synthetic CT algorithm. (United States)

    Wood, Abbie M; Shea, Steven M; Medved, Milica; Karczmar, Gregory S; Surucu, Murat; Gros, Sebastien; Small, William; Roeske, John


    To characterize the spectral parameters of tissues with high spectral and spatial resolution magnetic resonance images to be used as a foundation for a classification-based synthetic CT algorithm. A phantom was constructed consisting of a section of fresh beef leg with bone embedded in 1% agarose gel. The high spectral and spatial (HiSS) resolution MR imaging sequence used had 1.0 mm in-plane resolution and 11.1 Hz spectral resolution. This sequence was used to image the phantom and one patient. Post-processing was performed off-line with IDL and included Fourier transformation of the time-domain data, labeling of fat and water peaks, and fitting the magnitude spectra with Lorentzian functions. Images of the peak height and peak integral of both the water and fat resonances were generated and analyzed. Several regions-of-interest (ROIs) were identified in phantom: bone marrow, cortical bone, adipose tissue, muscle, agar gel, and air; in the patient, no agar gel was present but an ROI of saline in the bladder was analyzed. All spectra were normalized by the noise within each voxel; thus, all parameters are reported in terms of signal-to-noise (SNR). The distributions of tissue spectral parameters were analyzed and scatterplots generated. Water peak height in cortical bone was compared to air using a nonparametric t-test. Composition of the various ROIs in terms of water, fat, or fat and water was also reported. In phantom, the scatterplot of peak height (water versus fat) showed good separation of bone marrow and adipose tissue. Water versus fat integral scatterplot showed better separation of muscle and cortical bone than the peak height scatterplot. In the patient data, the distributions of water and fat peak heights were similar to that in phantom, with more overlap of bone marrow and cortical bone than observed in phantom. The relationship between bone marrow and cortical bone for peak integral was better separated than those of peak heights in the patient data

  13. A Spectral-Spatial Classification of Hyperspectral Images Based on the Algebraic Multigrid Method and Hierarchical Segmentation Algorithm

    Directory of Open Access Journals (Sweden)

    Haiwei Song


    Full Text Available The algebraic multigrid (AMG method is used to solve linear systems of equations on a series of progressively coarser grids and has recently attracted significant attention for image segmentation due to its high efficiency and robustness. In this paper, a novel spectral-spatial classification method for hyperspectral images based on the AMG method and hierarchical segmentation (HSEG algorithm is proposed. Our method consists of the following steps. First, the AMG method is applied to hyperspectral imagery to construct a multigrid structure of fine-to-coarse grids based on the anisotropic diffusion partial differential equation (PDE. The vertices in the multigrid structure are then considered as the initial seeds (markers for growing regions and are clustered to obtain a sequence of segmentation results. In the next step, a maximum vote decision rule is employed to combine the pixel-wise classification map and the segmentation maps. Finally, a final classification map is produced by choosing the optimal grid level to extract representative spectra. Experiments based on three different types of real hyperspectral datasets with different resolutions and contexts demonstrate that our method can obtain 3.84%–13.81% higher overall accuracies than the SVM classifier. The performance of our method was further compared to several marker-based spectral-spatial classification methods using objective quantitative measures and a visual qualitative evaluation.


    Qu, Jinfeng; Velaga, Swetha Bindu; Hariri, Amir H; Nittala, Muneeswar Gupta; Sadda, Srinivas


    The junctional zone at the border of areas of geographic atrophy (GA) in eyes with nonneovascular age-related macular degeneration is an important target region for future therapeutic strategies. The goal of this study was to perform a detailed classification and quantitative characterization of the junctional zone using spectral domain optical coherence tomography. Spectral domain optical coherence tomography volume cube scans (Spectralis OCT, 1024 × 37, Automatic Real Time > 9) were obtained from 15 eyes of 11 patients with GA because of nonneovascular age-related macular degeneration. Volume optical coherence tomography data were imported into previously described validated grading software (3D-OCTOR), and manual segmentation of the retinal pigment epithelium (RPE) and photoreceptor layers was performed on all B-scans (total of 555). Retinal pigment epithelium and photoreceptor defect maps were produced for each case. The borders of the photoreceptor defect area and RPE defect area were delineated individually on separate annotation layers. The two outlines were then superimposed to compare the areas of overlap and nonoverlap. The perimeter of the RPE defect area was calculated by the software in pixels. The superimposed outline of the photoreceptor defect area and the RPE defect area was scrutinized to classify the overlap configuration of the junctional zone into one of three categories: Type 0, exact correspondence between the edge of the RPE defect and photoreceptor defect; Type 1, loss of photoreceptors outside and beyond the edge of the RPE defect; Type 2, preservation of photoreceptors beyond the edge of the RPE defect. The relative proportion of the various border configurations was expressed as a percentage of the perimeter of the RPE defect. Each configuration was then classified into four subgroups according to irregularity of the RPE band and the presence of debris. Fifteen eyes of 11 patients (mean age: 79.3 ± 4.3 years; range: 79-94 years) were

  15. Application of spectral and spatial indices for specific class identification in Airborne Prism EXperiment (APEX) imaging spectrometer data for improved land cover classification (United States)

    Kallepalli, Akhil; Kumar, Anil; Khoshelham, Kourosh; James, David B.


    Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c-Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and

  16. Infrared Spectral Classification with Artificial Neural Networks and Classical Pattern Recognition

    National Research Council Canada - National Science Library

    Mayfield, Howard


    .... Computer-assisted classification tools, including pattern recognition and artificial neural network techniques, have been applied to a collection of infrared spectra of organophosphorus compounds...

  17. Hyper-spectral frequency selection for the classification of vegetation diseases

    NARCIS (Netherlands)

    Dijkstra, Klaas; van de Loosdrecht, Jaap; Schomaker, Lambert; Wiering, Marco


    Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduction of spectral

  18. Using naive Bayes classifier for classification of convective rainfall intensities based on spectral characteristics retrieved from SEVIRI (United States)

    Hameg, Slimane; Lazri, Mourad; Ameur, Soltane


    This paper presents a new algorithm to classify convective clouds and determine their intensity, based on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The convective rainfall events at 15 min, 4 × 5 km spatial resolution from 2006 to 2012 are analysed over northern Algeria. The convective rain classification methodology makes use of the relationship between cloud spectral characteristics and cloud physical properties such as cloud water path (CWP), cloud phase (CP) and cloud top height (CTH). For this classification, a statistical method based on `naive Bayes classifier' is applied. This is a simple probabilistic classifier based on applying `Bayes' theorem with strong (naive) independent assumptions. For a 9-month period, the ability of SEVIRI to classify the rainfall intensity in the convective clouds is evaluated using weather radar over the northern Algeria. The results indicate an encouraging performance of the new algorithm for intensity differentiation of convective clouds using SEVIRI data.


    Directory of Open Access Journals (Sweden)

    K. Roychowdhury


    Full Text Available Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July and winter (December months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC data of the region while ground range detected (GRD data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70% was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI and Normalized Difference Vegetation Index (NDVI obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

  20. Manifold learning based feature extraction for classification of hyper-spectral data

    CSIR Research Space (South Africa)

    Lunga, D


    Full Text Available Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. Hyperspectral image data...

  1. When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

    Directory of Open Access Journals (Sweden)

    Cong Wang


    Full Text Available When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the shallow feature learning model, as well as the insufficient robustness of the classifier which only depends on the supervision of labelled samples. To address these two problems simultaneously, we present an effective low-rank representation-based classification framework for hyperspectral imagery. In particular, a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. With the extracted features, a low-rank representation based robust classifier is then developed which takes advantage of both the supervision provided by labelled samples and unsupervised correlation (e.g., intra-class similarity and inter-class dissimilarity, etc. among those unlabelled samples. Both the deep unsupervised feature learning and the robust classifier benefit, improving the classification accuracy with limited labelled samples. Extensive experiments on hyperspectral imagery classification demonstrate the effectiveness of the proposed framework.

  2. An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images (United States)

    Bhardwaj, Kaushal; Patra, Swarnajyoti


    Inclusion of spatial information along with spectral features play a significant role in classification of remote sensing images. Attribute profiles have already proved their ability to represent spatial information. In order to incorporate proper spatial information, multiple attributes are required and for each attribute large profiles need to be constructed by varying the filter parameter values within a wide range. Thus, the constructed profiles that represent spectral-spatial information of an hyperspectral image have huge dimension which leads to Hughes phenomenon and increases computational burden. To mitigate these problems, this work presents an unsupervised feature selection technique that selects a subset of filtered image from the constructed high dimensional multi-attribute profile which are sufficiently informative to discriminate well among classes. In this regard the proposed technique exploits genetic algorithms (GAs). The fitness function of GAs are defined in an unsupervised way with the help of mutual information. The effectiveness of the proposed technique is assessed using one-against-all support vector machine classifier. The experiments conducted on three hyperspectral data sets show the robustness of the proposed method in terms of computation time and classification accuracy.

  3. Classification of Edible Oils Based on ATR-FTIR Spectral Information During a Long Heating Treatment. (United States)

    Mahboubifar, Marjan; Hemmateenejad, Bahram; Yousefinejad, Saeed


    Identification of oil type and its QC are important concerns in food control laboratories. Classifying edible oils that have not been used (i.e., unheated) with the aid of vibrational spectroscopy has previously been reported. However, the classification of used (i.e., heat-treated) oils needs special attention. The effect of long heating times on the classification of four kinds of edible oils (canola, corn, frying, and sunflower) based on attenuated total reflectance (ATR)-FTIR spectra was surveyed. The sampling was done on the oils during a 36 h heating process (at 170°C). The ATR-FTIR spectra of the samples were collected in the range of 4000-550 cm-1. Interval extended canonical variates analysis (ECVA), as a variable selection and classification tool, was used to determine the best intervals during the heating procedure for classification. Principal component analysis discriminate analysis, partial least-squares discriminate analysis, and ECVA were performed on the selected intervals and on the total heating time. The effect of autoscaling and mean-centering, as data preprocessing methods, was also investigated. The ECVA method resulted in the best performances for classification, with a 94% cross-validated nonerror rate (one misclassification) for the heating process times of 24-27 and 33-36 h.

  4. Comparison of Aerosol Classification From Airborne High Spectral Resolution Lidar and the CALIPSO Vertical Feature Mask (United States)

    Burton, Sharon P.; Ferrare, Rich A.; Omar, Ali H.; Vaughan, Mark A.; Rogers, Raymond R.; Hostetler, Chris a.; Hair, Johnathan W.; Obland, Michael D.; Butler, Carolyn F.; Cook, Anthony L.; hide


    Knowledge of aerosol composition and vertical distribution is crucial for assessing the impact of aerosols on climate. In addition, aerosol classification is a key input to CALIOP aerosol retrievals, since CALIOP requires an inference of the lidar ratio in order to estimate the effects of aerosol extinction and backscattering. In contrast, the NASA airborne HSRL-1 directly measures both aerosol extinction and backscatter, and therefore the lidar ratio (extinction-to-backscatter ratio). Four aerosol intensive properties from HSRL-1 are combined to infer aerosol type. Aerosol classification results from HSRL-1 are used here to validate the CALIOP aerosol type inferences.

  5. Using LIDAR data and airborne spectral images for urban land cover classification based on fuzzy set method (United States)

    Lai, Zulong; Shen, Shaohong; Chen, Xingyi; Liang, Xinmei; Zhang, Jie


    In this paper, we propose an analysis on the combinative effect of high-resolution airborne image and light detection and ranging (LIDAR) data for the classification of complex urban areas. In greater detail, the proposed system is composed of three models briefly. Model one includes an advanced kernelized fuzzy c-means classification method for high-resolution airborne image. The characteristics of LIDAR point cloud are introduced in model two, membership degree function of buildings, vegetations and naked land have been built. In model three, high-resolution image and elevation data form LIDAR point cloud are jointed. Experiment carried out on a complex urban area provide interesting conclusions on the effectiveness and protentialities of the joint use of high-resolution image and LIDAR data. In particular, the elevation data was very effective for the separation of species with similar spectral signatures but different elevation information. Experimental results approve that elevation data can improve classification accuracy in building occupied area obviously.

  6. Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm. (United States)

    Al Ajmi, Eiman; Forghani, Behzad; Reinhold, Caroline; Bayat, Maryam; Forghani, Reza


    There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

  7. VizieR Online Data Catalog: Spectral classification of O Vz stars from GOSSS (Arias+, 2016) (United States)

    Arias, J. I.; Walborn, N. R.; Diaz, S. S.; Barba, R. H.; Apellaniz, J. M.; Sabin-Sanjulian, C.; Gamen, R. C.; Morrell, N. I.; Sota, A.; Marco, A.; Negueruela, I.; Leao, J. R. S.; Herrero, A.; Alfaro, E. J.


    All of the observations used in this work come from the Galactic O Star Spectroscopic Survey (GOSSS). Details on the data and analysis procedures are fully discussed in the three papers from the project (Sota et al. 2011, 2014, Cat. III/274; Maiz Apellaniz et al. 2016ApJS..224....4M). GOSSS is a long-term systematic survey of all of the Galactic stars ever classified as O. This project provides moderate resolution (R~2500) spectroscopy in the blue-violet region (approximately 3900-5000Å) with a high signal-to-noise ratio, typically S/N~200-300. The spectral types are available through the latest version of the Galactic O Star Catalog (GOSC; Maiz Apellaniz et al. 2004, Cat. V/116). In this paper, we include 226 O stars from both hemispheres pertaining to the three published GOSSS installments. The categories and numbers that characterize our sample objects are the following: (1) objects that are single lined in the GOSSS spectra and for which no evidence of binarity is known (132 stars listed in Table1); (2) objects that are single lined in the GOSSS spectra but are known to be spectroscopic binaries (SBs) from high-resolution data (45 binaries, Table2); (3) objects that are double lined in the GOSSS spectra (explicit SB2) for which the line separation is sufficiently large to allow measurements of the CDs and EWs of the individual components by the use of deblending methods (23 binaries providing 32 components with spectral types earlier than O9); and (4) explicit SB2 whose spectral components are not sufficiently separated to be measured individually (15 binaries). Binaries belonging to groups (3) and (4) are listed in Table3. (3 data files).

  8. Classification of Clean and Dirty Pighouse Surfaces Based on Spectral Reflectance

    DEFF Research Database (Denmark)

    Blanke, Mogens; Braithwaite, Ian David; Zhang, Guo-Qiang


    Current pig house cleaning procedures are hazardous to the health of farm workers, and yet necessary if the spread of disease between batches of animals is to be satisfactorily controlled. Autonomous cleaning using robot technology offers salient benefits. This report addresses the feasibility...... of designing a vision based system to locate dirty areas and subsequently direct a cleaning robot to remove dirt. Novel results include the characterisation of the spectral reflectance of real surfaces and dirt in a pig house and the design of illumination to obtain discrimination of clean from dirty areas...

  9. Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes (United States)

    Morozov, Yu. V.; Spektor, A. A.


    A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

  10. Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification

    Directory of Open Access Journals (Sweden)

    Wei Gong


    Full Text Available The abilities of multispectral LiDAR (MSL as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%–39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image.

  11. OCT Glaucoma Staging System: a new method for retinal nerve fiber layer damage classification using spectral-domain OCT. (United States)

    Brusini, P


    PurposeTo describe a new method, the Optical Coherence Tomography (OCT) Glaucoma Staging System, for classifying retinal nerve fiber layer (RNFL) damage assessed with OCT.Patients and methodsThe OCT Glaucoma Staging System was created based on data obtained from Nidek RS 3000 spectral-domain (sd)-OCT. This system uses the superior and inferior quadrant RNFL thickness values, plotted on an x-y diagram for staging structural damage severity in glaucoma. A non-linear equation and two regression lines describe the boundary lines which separate the different sectors of the diagram. These mathematical formulas have been used to create a software, which provides a quick classification of the RNFL damage. Sensitivity and specificity of the system were assessed in a different cohort including 64 patients with early OAG, and 62 normal subjects.ResultsThree hundred and two OCT tests from 98 healthy controls and 284 patients affected by either ocular hypertension or chronic open-angle glaucoma were considered in order to design the new classification system. The OCT Glaucoma Staging System classifies RNFL defects into 6 stages of increasing severity ranging from borderline to stage 5, and 3 groups according to defect localization (superior, inferior, or diffuse). Sensitivity and specificity in discriminating between healthy and glaucomatous eyes were 95.2 and 91.9%, respectively, considering borderline results as abnormal.ConclusionsThe OCT Glaucoma Staging System appears to provide a standardized and objective classification of glaucomatous RNFL damage. It can be used in day-to-day clinical practice for an easy and fast interpretation of RNFL measurements obtained with OCT.

  12. Spectral classification of medium-scale high-latitude F region plasma density irregularities

    International Nuclear Information System (INIS)

    Singh, M.; Rodriguez, P.; Szuszczewicz, E.P.; Sachs Freeman Associates, Bowie, MD)


    The high-latitude ionosphere represents a highly structured plasma. Rodriguez and Szuszczewicz (1984) reported a wide range of plasma density irregularities (150 km to 75 m) at high latitudes near 200 km. They have shown that the small-scale irregularities (7.5 km to 75 m) populated the dayside oval more often than the other phenomenological regions. It was suggested that in the lower F region the chemical recombination is fast enough to remove small-scale irregularities before convection can transport them large distances, leaving structured particle precipitation as the dominant source term for irregularities. The present paper provides the results of spectral analyses of pulsed plasma probe data collected in situ aboard the STP/S3-4 satellite during the period March-September 1978. A quantitative description of irregularity spectra in the high-latitude lower F region plasma density is given. 22 references

  13. Application of higher order spectral features and support vector machines for bearing faults classification. (United States)

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat


    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. Copyright © 2014 ISA

  14. Use of feature extraction techniques for the texture and context information in ERTS imagery: Spectral and textural processing of ERTS imagery. [classification of Kansas land use (United States)

    Haralick, R. H. (Principal Investigator); Bosley, R. J.


    The author has identified the following significant results. A procedure was developed to extract cross-band textural features from ERTS MSS imagery. Evolving from a single image texture extraction procedure which uses spatial dependence matrices to measure relative co-occurrence of nearest neighbor grey tones, the cross-band texture procedure uses the distribution of neighboring grey tone N-tuple differences to measure the spatial interrelationships, or co-occurrences, of the grey tone N-tuples present in a texture pattern. In both procedures, texture is characterized in such a way as to be invariant under linear grey tone transformations. However, the cross-band procedure complements the single image procedure by extracting texture information and spectral information contained in ERTS multi-images. Classification experiments show that when used alone, without spectral processing, the cross-band texture procedure extracts more information than the single image texture analysis. Results show an improvement in average correct classification from 86.2% to 88.8% for ERTS image no. 1021-16333 with the cross-band texture procedure. However, when used together with spectral features, the single image texture plus spectral features perform better than the cross-band texture plus spectral features, with an average correct classification of 93.8% and 91.6%, respectively.

  15. Classification (United States)

    Clary, Renee; Wandersee, James


    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  16. Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification

    Directory of Open Access Journals (Sweden)

    Zhiyong Lv


    Full Text Available Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

  17. Ontology-based classification of remote sensing images using spectral rules (United States)

    Andrés, Samuel; Arvor, Damien; Mougenot, Isabelle; Libourel, Thérèse; Durieux, Laurent


    Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for "domain" experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.

  18. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

    Directory of Open Access Journals (Sweden)

    Himmelreich Uwe


    Full Text Available Abstract Background Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space. Results We propose to combine the best of both approaches, and evaluated the joint use of a feature selection based on a recursive feature elimination using the Gini importance of random forests' together with regularized classification methods on spectral data sets from medical diagnostics, chemotaxonomy, biomedical analytics, food science, and synthetically modified spectral data. Here, a feature selection using the Gini feature importance with a regularized classification by discriminant partial least squares regression performed as well as or better than a filtering according to different univariate statistical tests, or using regression coefficients in a backward feature elimination. It outperformed the direct application of the random forest classifier, or the direct application of the regularized classifiers on the full set of features. Conclusion The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data, but – on an optimal subset of features – the regularized classifiers might be preferable over the random forest classifier, in spite of their limitation to model linear dependencies only. A feature selection based on Gini importance, however, may precede a regularized linear classification to identify this optimal subset of features, and to earn a double benefit of both dimensionality reduction and the elimination of noise from the classification task.

  19. Accounting Systems for New Public Sector Undertakings ...

    African Journals Online (AJOL)

    Accounting Systems for New Public Sector Undertakings Management: A Case Study. ... African Journal of Finance and Management ... It concludes that the current accounting systems such as financial accounting, cost accounting and reporting systems are not suitable for the effective and efficient management.

  20. Classification

    DEFF Research Database (Denmark)

    Hjørland, Birger


    This article presents and discusses definitions of the term “classification” and the related concepts “Concept/conceptualization,”“categorization,” “ordering,” “taxonomy” and “typology.” It further presents and discusses theories of classification including the influences of Aristotle...... and Wittgenstein. It presents different views on forming classes, including logical division, numerical taxonomy, historical classification, hermeneutical and pragmatic/critical views. Finally, issues related to artificial versus natural classification and taxonomic monism versus taxonomic pluralism are briefly...


    Directory of Open Access Journals (Sweden)

    D. Nguyen Dinh


    Full Text Available Recently USGS released provisional Landsat 8 Surface Reflectance product, which allows conducting land cover mapping over large composed of number of image scenes without necessity of atmospheric correction. In this study, the authors present a new concept for automated classification of land cover. This concept is based on spectral patterns analysis of reflected bands and can be automated using predefined classification rule set constituted of spectral pattern shape, total reflected radiance index (TRRI and ratios of spectral bands. Given a pixel vector B6 = {b1,b2,b3,b4,b5,b6} where b1, b2,...,b6 denote bands 2, 3, ...,7 of OLI sensor respectively. By using the pixel vector B6 we can construct spectral reflectance curve. Each spectral curve is featured by a shape, which can be described in simplified form of an analogue pattern, which is consisted of 15 digits of 0, 1 and 2 showing mutual relative position of spectral vertices. Value of comparison between band i and j is 2 if bj > bi, 1 if bj = bi and 0 if bj i. Simplified spectral pattern is defined by 15 digits as m1,2m1,3m1,4m1,5m1,6m2,3m2,4m2,5m2,6m3,4m3,5m3,6m4,5m4,6m5,6 where mi,j is result of comparison of reflectance between bi and bj and has values of 0, 1 and 2. After construction of SSP for each pixel in the input image, the original image will be decomposed to component images, which contain pixels with the same SRCS pattern. The decomposition can be written analytically by equation A = Σnk=1Ck where A stands for original image with 6 spectral bands, n is number of component images decomposed from A and Ck is component image. For this study, we use Landsat 8 OLI reflectance image LC81270452013352LGN00 and LC81270452015182LGN00. For the decomposition, we use only six reflective bands. Each land cover class is defined by SSP code, threshold values for TRRI and band ratios. Automated classification of land cover was realized with 8 classes: forest, shrub, grass, water, wetland

  2. Improving the classification accuracy for IR spectroscopic diagnosis of stomach and colon malignancy using non-linear spectral feature extraction methods. (United States)

    Lee, Sanguk; Kim, Kyoungok; Lee, Hyeseon; Jun, Chi-Hyuck; Chung, Hoeil; Park, Jong-Jae


    Non-linear feature extraction methods, neighborhood preserving embedding (NPE) and supervised NPE (SNPE), were employed to effectively represent the IR spectral features of stomach and colon biopsy tissues for classification, and improve the classification accuracy for diagnosis of malignancy. The motivation was to utilize the NPE and SNPE's capability of capturing non-linear spectral behaviors by simultaneously preserving local relationships in order that minute spectral differences among classes would be effectively recognized. NPE and SNPE derive an optimal embedding feature such that the local neighborhood structure can be preserved in reduced spaces (variables). The IR spectra collected from stomach and colon tissues were represented by several new variables through NPE and SNPE, and also by using the principal component analysis (PCA). Then, the feature-extracted variables were subsequently classified into normal, adenoma and cancer tissues by using both k-nearest neighbor (k-NN) and support vector machine (SVM), and the resulting accuracies were compared with each other. In both cases, the combination of SNPE-SVM provided the best classification performance, and the accuracy was substantially improved compared to when PCA-SVM was used. Overall results demonstrate that NPE and SNPE could be potential feature-representation strategies useful in biomedical diagnosis based on vibrational spectroscopy where effective recognition of minute spectral differences is critical.

  3. Benefits of Red-Edge Spectral Band and Texture Features for the Object-based Classification using RapidEye sSatellite Image data (United States)

    Kim, H. O.; Yeom, J. M.


    Space-based remote sensing in agriculture is particularly relevant to issues such as global climate change, food security, and precision agriculture. Recent satellite missions have opened up new perspectives by offering high spatial resolution, various spectral properties, and fast revisit rates to the same regions. Here, we examine the utility of broadband red-edge spectral information in multispectral satellite image data for classifying paddy rice crops in South Korea. Additionally, we examine how object-based spectral features affect the classification of paddy rice growth stages. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the crop condition in heterogeneous paddy rice crop environments, particularly when single-season image data were used. This positive effect appeared to be offset by the multi-temporal image data. Additional texture information brought only a minor improvement or a slight decline, although it is well known to be advantageous for object-based classification in general. We conclude that broadband red-edge information derived from conventional multispectral satellite data has the potential to improve space-based crop monitoring. Because the positive or negative effects of texture features for object-based crop classification could barely be interpreted, the relationships between the textual properties and paddy rice crop parameters at the field scale should be further examined in depth.

  4. Land Cover Classification in an Ecuadorian Mountain Geosystem Using a Random Forest Classifier, Spectral Vegetation Indices, and Ancillary Geographic Data

    Directory of Open Access Journals (Sweden)

    Johanna E. Ayala-Izurieta


    Full Text Available We presented a methodology to accurately classify mountainous regions in the tropics. These landscapes are complex in terms of their geology, ecosystems, climate and land use. Obtaining accurate maps to assess land cover change is essential. The objectives of this study were to (1 map vegetation using the Random Forest Classifier (RFC, spectral vegetation index (SVI, and ancillar geographic data (2 identify important variables that help differentiate vegetation cover, and (3 assess the accuracy of the vegetation cover classification in hard-to-reach Ecuadorian mountain region. We used Landsat 7 ETM+ satellite images of the entire scene, a RFC algorithm, and stratified random sampling. The altitude and the two band enhanced vegetation index (EVI2 provide more information on vegetation cover than the traditional and often use normalized difference vegetation index (NDVI in other settings. We classified the vegetation cover of mountainous areas within the 1016 km2 area of study, at 30 m spatial resolution, using RFC that yielded a land cover map with an overall accuracy of 95%. The user´s accuracy and the half-width of the confidence interval for 95% of the basic map units, forest (FOR, páramo (PAR, crop (CRO and pasture (PAS were 95.85% ± 2.86%, 97.64% ± 1.24%, 91.53% ± 3.35% and 82.82% ± 7.74%, respectively. The overall disagreement was 4.47%, which results from adding 0.43% of quantity disagreement and 4.04% of allocation disagreement. The methodological framework presented in this paper and the combined use of SVIs, ancillary geographic data, and the RFC allowed the accurate mapping of hard-to-reach mountain landscapes as well as uncovering the underlying factors that help differentiate vegetation cover in the Ecuadorian mountain geosystem.

  5. JET joint undertaking. Annual report 1978

    International Nuclear Information System (INIS)


    This document is intended for information only and should not be used as a technical reference. After an introductive part on the controlled nuclear fusion research and an historical survey of the JET project, are presented: the JET joint undertaking (members of council and committee...) with its administration (finance, personnel, external relations), and the scientific and technical department with its divisions for systems (experimental, magnet, plasma, assembly, power supplies, control and data acquisition, and site and building). In appendix is described the Euratom fusion research programme

  6. Classification (United States)

    Oza, Nikunj C.


    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

  7. An Integrated Spatial and Spectral Approach to the Classification of Mediterranean Land Cover Types: the SSC Method.

    NARCIS (Netherlands)

    Jong, de S.M.; Hornstra, T.; Maas, H.G.


    Classification of remotely sensed images is often based on assigning classes on a pixel by pixel basis. Such a classification ignores often useful reflectance information in neighbouring pixels. Open types of natural land cover such as maquis and garrigue ecosystems as found in the Mediterranean



    Muhammad Kamal


    The HyMap hyper-spectral data was used to classify photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and exposed soils in a semiarid savannah environment of McKinlay, northern Queensland, and Australia. This study aimed to understandhow effective the sub-pixel classificationapproach applied on hyper-spectral data to distinguish the vegetation and soil features in semi-arid environment. In contrast to the per-pixel approach this approach treats the pixel va...



    Kamal, Muhammad


    The HyMap hyper-spectral data was used to classify photosyntheticvegetation (PV), non-photosynthetic vegetation (NPV), and exposed soils in a semiaridsavannah environment of McKinlay, northern Queensland, and Australia. Thisstudy aimed to understandhow effective the sub-pixel classificationapproach appliedon hyper-spectral data to distinguish the vegetation and soil features in semi-aridenvironment. In contrast to the per-pixel approach this approach treats the pixelvalue as reflectance sum o...

  10. Classification of local and regional events in central Europe based on estimates of S-wave spectral variance (United States)

    Koch, Karl


    The Vogtland region, in the border region of Germany and the Czech Republic, is of special interest for the identification of seismic events on a local and regional scale, since both earthquakes and explosions occur frequently in the same area, and thus are relevant for discrimination research for verification of the Comprehensive Nuclear Test Ban Treaty. Previous research on event discrimination using spectral decay and variance from data recorded by the GERESS array indicated that spectral variance determined for the S phase for the seismic events in the Vogtland region seems to be the most promising parameter for event discrimination, because this parameter provides for almost complete separation of the earthquake and explosion populations. Almost the entire set of Vogtland events used in this research and more than 3000 local events detected in Germany in 1998 and 1999 were analysed to determine spectral slopes and variance for the P- and S-wave windows from stacked spectra of recordings at the GERESS array. The results suggest that small values for the spectral variance are associated not only with earthquakes in the Vogtland region, but also with earthquakes in other parts of Germany and neighbouring countries. While mining blasts show larger spectral variance values, mining-induced events yield a wide range of values, for example, in the Lubin area. A threshold-based identification scheme was applied; almost all events classified as earthquakes are found in seismically active regions. While the earthquakes are uniformly distributed throughout the day, events classified as explosions correlate with normal working hours, which is when blasting is done in Germany. In this study spectral variance provides good event discrimination for events in other parts of Germany, not only for the Vogtland region, showing that this identification parameter may be transported to other geological regions.

  11. Detection and classification of salmonella serotypes using spectral signatures collected by fourier transform infrared (FT-IR) spectroscopy (United States)

    Spectral signatures of Salmonella serotypes namely Salmonella Typhimurium, Salmonella Enteritidis, Salmonella Infantis, Salmonella Heidelberg and Salmonella Kentucky were collected using Fourier transform infrared spectroscopy (FT-IR). About 5-10 µL of Salmonella suspensions with concentrations of 1...

  12. Continuous Field Vegetation Classification of a Sagebrush (Artemesia spp.) Dominated Ecosystem Using High Spatial-Resolution Multi-spectral Satellite Imagery (United States)

    Buchert, M. P.; White, M.


    Global change scientists have grown increasingly interested over the past decade in continuous field vegetation mapping, whereby remotely sensed imagery is processed to yield a data product whose pixel values represent the percent of ground element land area covered by vegetative canopy. The continuous field approach offers distinct benefits over older discrete classification approaches, but current methods developed for production of global tree cover data sets are biased in favor of taller, denser vegetation and misrepresent percent cover of woody shrubs, the dominant vegetation throughout large reaches of North America's Intermountain West. The underperformance of current methods in accurately representing shrub cover is of significant interest to conservation biologists, as shrub-steppe ecosystems dominated by sagebrush (Artemesia spp.) are among the most threatened habitats in North America. We report on our efforts to generate percent-cover vegetation classifications for shrub, herbaceous, and bare land cover classes for study sites in northeastern Nevada and northeastern Utah, using 4m multi-spectral satellite imagery, a regression-tree classification method, and ground-collected reference data. Continuous field vegetation cover data are typically used as inputs to carbon cycling models, but we anticipate that at the fine grain of our dataset, such data will also be useful in ecological and conservation biological applications. In this vein, we demonstrate the applicability of high resolution shrub percent-cover data to habitat modeling efforts for threatened sage-obligate species.

  13. Modality-specific spectral dynamics in response to visual and tactile sequential shape information processing tasks: An MEG study using multivariate pattern classification analysis. (United States)

    Gohel, Bakul; Lee, Peter; Jeong, Yong


    Brain regions that respond to more than one sensory modality are characterized as multisensory regions. Studies on the processing of shape or object information have revealed recruitment of the lateral occipital cortex, posterior parietal cortex, and other regions regardless of input sensory modalities. However, it remains unknown whether such regions show similar (modality-invariant) or different (modality-specific) neural oscillatory dynamics, as recorded using magnetoencephalography (MEG), in response to identical shape information processing tasks delivered to different sensory modalities. Modality-invariant or modality-specific neural oscillatory dynamics indirectly suggest modality-independent or modality-dependent participation of particular brain regions, respectively. Therefore, this study investigated the modality-specificity of neural oscillatory dynamics in the form of spectral power modulation patterns in response to visual and tactile sequential shape-processing tasks that are well-matched in terms of speed and content between the sensory modalities. Task-related changes in spectral power modulation and differences in spectral power modulation between sensory modalities were investigated at source-space (voxel) level, using a multivariate pattern classification (MVPC) approach. Additionally, whole analyses were extended from the voxel level to the independent-component level to take account of signal leakage effects caused by inverse solution. The modality-specific spectral dynamics in multisensory and higher-order brain regions, such as the lateral occipital cortex, posterior parietal cortex, inferior temporal cortex, and other brain regions, showed task-related modulation in response to both sensory modalities. This suggests modality-dependency of such brain regions on the input sensory modality for sequential shape-information processing. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. Spectral Classification of Galaxies at 0.5 <= z <= 1 in the CDFS: The Artificial Neural Network Approach (United States)

    Teimoorinia, H.


    The aim of this work is to combine spectral energy distribution (SED) fitting with artificial neural network techniques to assign spectral characteristics to a sample of galaxies at 0.5 MUSIC catalog covering bands between ~0.4 and 24 μm in 10-13 filters. We use the CIGALE code to fit photometric data to Maraston's synthesis spectra to derive mass, specific star formation rate, and age, as well as the best SED of the galaxies. We use the spectral models presented by Kinney et al. as targets in the wavelength interval ~1200-7500 Å. Then a series of neural networks are trained, with average performance ~90%, to classify the best SED in a supervised manner. We consider the effects of the prominent features of the best SED on the performance of the trained networks and also test networks on the galaxy spectra of Coleman et al., which have a lower resolution than the target models. In this way, we conclude that the trained networks take into account all the features of the spectra simultaneously. Using the method, 105 out of 142 galaxies of the sample are classified with high significance. The locus of the classified galaxies in the three graphs of the physical parameters of mass, age, and specific star formation rate appears consistent with the morphological characteristics of the galaxies.

  15. Dimensionality reduction of hyperspectral imagery based on spectral analysis of homogeneous segments: distortion measurements and classification scores (United States)

    Alparone, Luciano; Argenti, Fabrizio; Dionisio, Michele; Santurri, Leonardo


    In this work, a new strategy for the analysis of hyperspectral image data is described and assessed. Firstly, the image is segmented into areas based on a spatial homogeneity criterion of pixel spectra. Then, a reduced data set (RDS) is produced by applying the projection pursuit (PP) algorithm to each of the segments in which the original hyperspectral image has been partitioned. Few significant spectral pixels are extracted from each segment. This operation allows the size of the data set to be dramatically reduced; nevertheless, most of the spectral information relative to the whole image is retained by RDS. In fact, RDS constitutes a good approximation of the most representative elements that would be found for the whole image, as the spectral features of RDS are very similar to the features of the original hyperspectral data. Therefore, the elements of a basis, either orthogonal or nonorthogonal, that best represents RDS, are searched for. Algorithms that can be used for this task are principal component analysis (PCA), independent component analysis (ICA), PP, or matching pursuit (MP). Once the basis has been calculated from RDS, the whole hyperspectral data set is decomposed on such a basis to yield a sequence of components, or features, whose (statistical) significance decreases with the index. Hence, minor components may be discarded without compromising the results of application tasks. Experiments carried out on AVIRIS data, whose ground truth was available, show that PCA based on RDS, even if suboptimal in the MMSE sense with respect to standard PCA, increases the separability of thematic classes, which is favored when pixel vectors in the transformed domain are homogeneously spread around their class centers.


    International Nuclear Information System (INIS)

    Teimoorinia, H.


    The aim of this work is to combine spectral energy distribution (SED) fitting with artificial neural network techniques to assign spectral characteristics to a sample of galaxies at 0.5 < z < 1. The sample is selected from the spectroscopic campaign of the ESO/GOODS-South field, with 142 sources having photometric data from the GOODS-MUSIC catalog covering bands between ∼0.4 and 24 μm in 10-13 filters. We use the CIGALE code to fit photometric data to Maraston's synthesis spectra to derive mass, specific star formation rate, and age, as well as the best SED of the galaxies. We use the spectral models presented by Kinney et al. as targets in the wavelength interval ∼1200-7500 Å. Then a series of neural networks are trained, with average performance ∼90%, to classify the best SED in a supervised manner. We consider the effects of the prominent features of the best SED on the performance of the trained networks and also test networks on the galaxy spectra of Coleman et al., which have a lower resolution than the target models. In this way, we conclude that the trained networks take into account all the features of the spectra simultaneously. Using the method, 105 out of 142 galaxies of the sample are classified with high significance. The locus of the classified galaxies in the three graphs of the physical parameters of mass, age, and specific star formation rate appears consistent with the morphological characteristics of the galaxies.

  17. EXPORT: Spectral classification and projected rotational velocities of Vega-type and pre-main sequence stars (United States)

    Mora, A.; Merín, B.; Solano, E.; Montesinos, B.; de Winter, D.; Eiroa, C.; Ferlet, R.; Grady, C. A.; Davies, J. K.; Miranda, L. F.; Oudmaijer, R. D.; Palacios, J.; Quirrenbach, A.; Harris, A. W.; Rauer, H.; Collier Cameron, A.; Deeg, H. J.; Garzón, F.; Penny, A.; Schneider, J.; Tsapras, Y.; Wesselius, P. R.


    In this paper we present the first comprehensive results extracted from the spectroscopic campaigns carried out by the EXPORT (EXoPlanetary Observational Research Team) consortium. During 1998-1999, EXPORT carried out an intensive observational effort in the framework of the origin and evolution of protoplanetary systems in order to obtain clues on the evolutionary path from the early stages of the pre-main sequence to stars with planets already formed. The spectral types of 70 stars, and the projected rotational velocities, v sin i, of 45 stars, mainly Vega-type and pre-main sequence, have been determined from intermediate- and high-resolution spectroscopy, respectively. The first part of the work is of fundamental importance in order to accurately place the stars in the HR diagram and determine the evolutionary sequences; the second part provides information on the kinematics and dynamics of the stars and the evolution of their angular momentum. The advantage of using the same observational configuration and methodology for all the stars is the homogeneity of the set of parameters obtained. Results from previous work are revised, leading in some cases to completely new determinations of spectral types and projected rotational velocities; for some stars no previous studies were available. Tables 1 and 2 are only, and Table 6 also, available in electronic form at the CDS via anonymous ftp to or via Based on observations made with the Isaac Newton and the William Herschel telescopes operated on the island of La Palma by the Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias.

  18. Classification of real farm conditions Iberian pigs according to the feeding regime with multivariate models developed by using fatty acids composition or NIR spectral data

    Directory of Open Access Journals (Sweden)

    De Pedro, Emiliano


    Full Text Available Multivariate Classification models to classify real farm conditions Iberian pigs, according to the feeding regime were developed by using fatty acids composition or NIR spectral data of liquid fat samples. A total of 121 subcutaneous fat samples were taken from Iberian pigs carcasses belonging to 5 batches reared under different feeding systems. Once the liquid sample was extracted from each subcutaneous fat sample, it was determined the percentage of 11 fatty acids (C14:0, C16:0, C16:1, C17:0, C17:1, C18:0, C18:1, C18:2, C18:3, C20:0 and C20:1. At the same time, Near Infrared (NIR spectrum of each liquid sample was obtained. Linear Discriminant Analysis (LDA was considered as pattern recognition method to develop the multivariate models. Classification errors of the LDA models generated by using NIR spectral data were 0.0% and 1.7% for the model generated by using fatty acids composition. Results confirm the possibility to discriminate Iberian pig liquid samples from animals reared under different feeding regimes on real farm conditions by using NIR spectral data or fatty acids composition. Classification error obtained using models generated from NIR spectral data were lower than those obtained in models based on fatty acids composition.Se han desarrollado modelos multivariantes, generados a partir de la composición en ácidos grasos o datos espectrales NIR, para clasificar según el régimen alimenticio cerdos Ibéricos producidos bajo condiciones no experimentales. Se han empleado 121 muestras de grasa líquida procedentes de grasa subcutánea de canales de cerdos Ibéricos pertenecientes a 5 partidas con regímenes alimenticios diferentes. A dichas muestras líquidas se les determinó el contenido en 11 ácidos grasos (C14:0, C16:0, C16:1, C17:0, C17:1, C18:0, C18:1, C18:2, C18:3, C20:0 and C20:1 y se obtuvo su espectro NIR. Los modelos de clasificación multivariantes se desarrollaron mediante Análisis Discriminante Lineal. Dichos

  19. Students Collaborating to Undertake Tracking Efforts for Sturgeon(SCUTES) (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Students Collaborating to Undertake Tracking Efforts for Sturgeon (SCUTES) is a collaboration between NOAA Fisheries, sturgeon researchers, and teachers/educators in...

  20. High throughput assessment of cells and tissues: Bayesian classification of spectral metrics from infrared vibrational spectroscopic imaging data. (United States)

    Bhargava, Rohit; Fernandez, Daniel C; Hewitt, Stephen M; Levin, Ira W


    Vibrational spectroscopy allows a visualization of tissue constituents based on intrinsic chemical composition and provides a potential route to obtaining diagnostic markers of diseases. Characterizations utilizing infrared vibrational spectroscopy, in particular, are conventionally low throughput in data acquisition, generally lacking in spatial resolution with the resulting data requiring intensive numerical computations to extract information. These factors impair the ability of infrared spectroscopic measurements to represent accurately the spatial heterogeneity in tissue, to incorporate robustly the diversity introduced by patient cohorts or preparative artifacts and to validate developed protocols in large population studies. In this manuscript, we demonstrate a combination of Fourier transform infrared (FTIR) spectroscopic imaging, tissue microarrays (TMAs) and fast numerical analysis as a paradigm for the rapid analysis, development and validation of high throughput spectroscopic characterization protocols. We provide an extended description of the data treatment algorithm and a discussion of various factors that may influence decision-making using this approach. Finally, a number of prostate tissue biopsies, arranged in an array modality, are employed to examine the efficacy of this approach in histologic recognition of epithelial cell polarization in patients displaying a variety of normal, malignant and hyperplastic conditions. An index of epithelial cell polarization, derived from a combined spectral and morphological analysis, is determined to be a potentially useful diagnostic marker.


    Directory of Open Access Journals (Sweden)



    Full Text Available Cet article propose une nouvelle méthodologie pour la réalisation d’un classifieur automatique de blocs d’images multimodales. Cette méthode fait appel à un système de décision basé sur l’analyse et la caractérisation d’images multimodales en fonction de leurs propriétés locales. Ces propriétés sont modélisées par un ensemble de six familles de paramètres. Les blocs d’images sont classés par une méthode de classification non supervisée qui prend en compte les paramètres les plus discriminants. Une comparaison des classifieurs automatiques obtenus, en fonction de la taille des blocs, montre l'intérêt à adapter cette dernière au degré d'hétérogéneité de l'image. Enfin, l’efficacité de ces classifieurs est évaluée dans le cas d’images bruitées.

  2. Effects of Subsetting by Carbon Content, Soil Order, and Spectral Classification on Prediction of Soil Total Carbon with Diffuse Reflectance Spectroscopy

    Directory of Open Access Journals (Sweden)

    Meryl L. McDowell


    Full Text Available Subsetting of samples is a promising avenue of research for the continued improvement of prediction models for soil properties with diffuse reflectance spectroscopy. This study examined the effects of subsetting by soil total carbon (Ct content, soil order, and spectral classification with k-means cluster analysis on visible/near-infrared and mid-infrared partial least squares models for Ct prediction. Our sample set was composed of various Hawaiian soils from primarily agricultural lands with Ct contents from <1% to 56%. Slight improvements in the coefficient of determination (R2 and other standard model quality parameters were observed in the models for the subset of the high activity clay soil orders compared to the models of the full sample set. The other subset models explored did not exhibit improvement across all parameters. Models created from subsets consisting of only low Ct samples (e.g., Ct < 10% showed improvement in the root mean squared error (RMSE and percent error of prediction for low Ct soil samples. These results provide a basis for future study of practical subsetting strategies for soil Ct prediction.

  3. Non-financial reporting, CSR frameworks and groups of undertakings

    DEFF Research Database (Denmark)

    Szabó, Dániel Gergely; Sørensen, Karsten Engsig


    The recently adopted Directive on non-financial reporting (Directive 2014/95/EU) and several CSR frameworks are based on the assumption that groups of undertakings adopt, report and implement one group policy. This is a very important but also rather unique approach to groups. This article first...

  4. Using an undertaker's data to assess changing patterns of mortality ...

    African Journals Online (AJOL)

    Key informant interviews were done to support the undertaker's data and determine how families bear the burden of burying deceased relatives. Despite a disproportionate increase in deaths in certain age categories and evidence of worsening poverty, funerals remain large and elaborate affairs. Keywords: AIDS, burial ...

  5. Original article Personality determinants of motivation to undertake vocational training

    Directory of Open Access Journals (Sweden)

    Dorota Godlewska-Werner


    Full Text Available Background Recently, at a time of frequent changes in the economic and socio-economic circumstances, knowledge acquired in the course of formal education is insufficient. Especially, the education system is still criticized for a lack of flexibility and strong resistance to change. Therefore, regular participation in various forms of training is required. Employee education and training are becoming an optimal answer to complex business challenges. The aim of this study was to determine which personality traits are responsible for the strength of motivation to undertake vocational training and other educational forms. Participants and procedure The study included 104 staff members of Polish companies (60 women and 44 men. The study used Cattell’s 16 PF Questionnaire and the scales of readiness to undertake training and further education as a measure of the strength of motivation (Kawecka, Łaguna & Tabor, 2010. Results The study showed that openness to change and tension (primary traits had the greatest impact on the intention and planning to take vocational training. Additionally, the intention and planning to take vocational training were found to be associated with mindedness, independence, self-control, and anxiety (secondary traits. Such traits as rule-consciousness [G], social-boldness [H], abstractedness [M], and apprehension [O] (primary traits, were important in some aspects, which could constitute a background for further research and discussion of the results. Conclusions The obtained results lead to the conclusion that some of the individual differences in personality determine the motivation to undertake vocational training.

  6. Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data (United States)

    Teluguntla, Pardhasaradhi G.; Thenkabail, Prasad S.; Xiong, Jun N.; Gumma, Murali Krishna; Congalton, Russell G.; Oliphant, Adam; Poehnelt, Justin; Yadav, Kamini; Rao, Mahesh N.; Massey, Richard


    Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa = 0.814) with six classes: (a) producer’s accuracies varying

  7. Changes in the functions of undertakings in electricity supply

    International Nuclear Information System (INIS)

    Oberlack, H.W.


    For the electricity supply industry also it is necessary, by means of more intensive publicity work, to achieve the general realisation that neither new laws nor intervention of the state are required for dealing in the interests of the consumer with the problems arising, from great changes in all fields of business enterprise. It is more important for the electricity supply undertakings (EVU), by means of executive power and the administration of justice, to be put a position to carry out in the most efficient manner the functions entrusted to them by the Federal Government under the Power Supply Law and the energy programme. (orig.) [de

  8. An undertaking planning game for the electricity supply industry

    International Nuclear Information System (INIS)

    Troescher, H.


    Planning games have been found satisfactory in many field in political and economic life. In particular the more convenient access to electronic calculators has made a contrinution to their wider use. It is therefore surprising that the first planning game which has become known for the electricity supply industry was first published in the year 1975. This is the planning game for the Bernischen Kraftwerke AG, which is based on a simplified model of a small electricity supply undertaking (EVU). This planning game was adapted in the RWE to the conditions in larger EVU and a few additional model components were added. Besides the general points of view on planning games for EVU the author deals with the extended planning game which is termed in the article PEW. (orig.) [de

  9. The exclusion of 'public undertakings' from the re-use of public sector information regime


    Ricolfi, M.; Drexl, J.; van Eechoud, M.; Salmeron, M.; Sappa, C.; Tziavos, P.; Valero, J.; Pavoni, F.; Patrito, P.


    Should public undertakings be covered by the PSI Directive? The definitions of public sector bodies and bodies governed by public law, to which the PSI Directive applies, are currently taken from the public procurement Directives and public undertakings are not covered by these definitions. Should public undertakings be considered as public sector bodies in the meaning of the Directive? Are there public undertakings holding "interesting" PSI? Are there different definitions of national legisl...

  10. 42 CFR 137.165 - Are Self-Governance Tribes required to undertake annual audits? (United States)


    ... Operational Provisions Audits and Cost Principles § 137.165 Are Self-Governance Tribes required to undertake...-Governance Tribes must undertake annual audits pursuant to the Single Audit Act, 31 U.S.C. 7501 et seq. ... 42 Public Health 1 2010-10-01 2010-10-01 false Are Self-Governance Tribes required to undertake...

  11. A multi-tier higher order Conditional Random Field for land cover classification of multi-temporal multi-spectral Landsat imagery

    CSIR Research Space (South Africa)

    Salmon, BP


    Full Text Available In this paper the authors present a 2-tier higher order Conditional Random Field which is used for land cover classification. The Conditional Random Field is based on probabilistic messages being passed along a graph to compute efficiently...

  12. 12 CFR 980.2 - Limitation on Bank authority to undertake new business activities. (United States)


    ... business activities. 980.2 Section 980.2 Banks and Banking FEDERAL HOUSING FINANCE BOARD NEW FEDERAL HOME LOAN BANK ACTIVITIES NEW BUSINESS ACTIVITIES § 980.2 Limitation on Bank authority to undertake new business activities. No Bank shall undertake any new business activity except in accordance with the...

  13. The exclusion of 'public undertakings' from the re-use of public sector information regime

    NARCIS (Netherlands)

    Ricolfi, M.; Drexl, J.; van Eechoud, M.; Salmeron, M.; Sappa, C.; Tziavos, P.; Valero, J.; Pavoni, F.; Patrito, P.


    Should public undertakings be covered by the PSI Directive? The definitions of public sector bodies and bodies governed by public law, to which the PSI Directive applies, are currently taken from the public procurement Directives and public undertakings are not covered by these definitions. Should

  14. Stellar Spectral Classification with Locality Preserving Projections ...

    Indian Academy of Sciences (India)

    non-linearly assembling learning machine (Liu et al. 2016) and the method based on. Fisher criterion and manifold learning (Liu & Song 2015). To our knowledge, Locality Preserving ..... an active area of research even in the Machine Learning community and we attempt to overcome the above problems in our future work.

  15. Classifying Classifications

    DEFF Research Database (Denmark)

    Debus, Michael S.


    al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors......This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... into the topic of game classifications....

  16. Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery


    Moran, Emilio Federico.


    High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervi...


    Directory of Open Access Journals (Sweden)

    Vlad – Teodor Florea


    Full Text Available This study concerns the general rules on competition between undertakings in the EU. The author paid attention primarly to matters on the prohibition of agreements that aim to distort or impair competition on the internal market. Moreover, he examined in detail the matter concerning the regulation and interdiction of the abuse of a dominant position. The work also reviews doctrinal opinions, as well as the jurisprudential solutions in the area. The author’s concern to summarize and develop the conditions for the implementation of each of the two legal mechanisms is worth noting: the prohibition of agreements between undertakings and the abuse of a dominant position. The essential considerations taken into account by the Court of Justice of the European Union in settling a case whose subject consisted of assessing the manner in which an undertaking reflected on competition on the internal market were selected at the end of the work.

  18. SCAT Classifications of 5 Supernovae with the UH88/SNIFS (United States)

    Tucker, Michael A.; Huber, Mark; Shappee, Benjamin J.; Dong, Subo; Bose, S.; Chen, Ping


    We present the first classifications from the newly formed Spectral Classification of Astronomical Transients (SCAT) survey. SCAT is a transient identification survey utilizing the SuperNova Integral Field Spectrograph (SNIFS) on the University of Hawaii (UH) 88-inch telescope.

  19. Who Assists the Faculty? The Need for Mentorship Programs for Faculty Undertaking Global Education Initiatives (United States)

    Dean, Yasmin; London, Chad; Carston, Cathy; Salyers, Vincent


    This study explored the expectations, motivations, and experiences of Canadian faculty members undertaking development and implementation of global education initiatives (GEI) for students in the form of exchange and study abroad programs, supervised practical coursework, and experiential learning in international settings. Findings revealed that…

  20. The Complexities of Supporting Asian International Pre-Service Teachers as They Undertake Practicum (United States)

    Spooner-Lane, Rebecca; Tangen, Donna; Campbell, Marilyn


    Increasing numbers of Asian international students are choosing to undertake their tertiary studies in English-speaking countries. For universities, international students are an important source of revenue. However, Asian international students face multiple challenges in adapting to a foreign culture, understanding the expectations of their…

  1. 31 CFR 248.5 - Exception to requirement of undertaking of indemnity Form 2244. (United States)


    ... 31 Money and Finance: Treasury 2 2010-07-01 2010-07-01 false Exception to requirement of... POSSESSIONS Action to Be Taken by Claimants § 248.5 Exception to requirement of undertaking of indemnity Form... and agents, of and from any and all liability, loss, expense, claim, and demand whatsoever, arising in...

  2. Assisted reproductive technologies in Ghana : Transnational undertakings, local practices and ‘more affordable’ IVF

    NARCIS (Netherlands)

    Gerrits, T.

    The article sketches the origins and development of IVF in Ghana as a highly transnational undertaking. Movements are from and to Africa, involving human beings (providers and users), and also refer to other entities such as technologies, skills and knowledge. None of these movements are paid for

  3. Best practice in undertaking and reporting health technology assessments : Working Group 4 report


    Busse, R.; Orvain, J.; Velasco, M.; Perleth, M.; Drummond, M.; Gurtner, F.; Jorgensen, T.; Jovell, A.; Malone, J.; Ruther, A; Wild, C.


    [Executive Summary] The aim of Working Group 4 has been to develop and disseminate best practice in undertaking and reporting assessments, and to identify needs for methodologic development. Health technology assessment (HTA) is a multidisciplinary activity that systematically examines the technical performance, safety, clinical efficacy, and effectiveness, cost, costeffectiveness, organizational implications, social consequences, legal, and ethical considerations of the application of a heal...

  4. Spectral Pollution


    Davies, E B; Plum, M


    We discuss the problems arising when computing eigenvalues of self-adjoint operators which lie in a gap between two parts of the essential spectrum. Spectral pollution, i.e. the apparent existence of eigenvalues in numerical computations, when no such eigenvalues actually exist, is commonplace in problems arising in applied mathematics. We describe a geometrically inspired method which avoids this difficulty, and show that it yields the same results as an algorithm of Zimmermann and Mertins.

  5. Metallicity and the spectral energy distribution and spectral types of dwarf O-stars

    NARCIS (Netherlands)

    Mokiem, M.R.; Martín-Hernández, N.L.; Lenorzer, A.; de Koter, A.; Tielens, A.G.G.M.


    We present a systematic study of the effect of metallicity on the stellar spectral energy distribution (SED) of O main sequence (dwarf) stars, focussing on the hydrogen and helium ionizing continua, and on the optical and near-IR lines used for spectral classification. The spectra are based on

  6. Be my guest! Challenges and practical solutions of undertaking interviews with children in the home setting. (United States)

    Coad, Jane; Gibson, Faith; Horstman, Maire; Milnes, Linda; Randall, Duncan; Carter, Bernie


    This article aims to share critical debate on undertaking interviews with children in the home setting and draws on the authors' extensive research fieldwork. The article focuses on three key processes: planning entry to the child's home, conducting the interviews and exiting the field. In planning entry, we include children's engagement and issues of researcher gender. In conducting the interviews, we consider issues such as the balance of power, the importance of building a rapport, the voluntary nature of consent and the need for a flexible interview structure. Finally, we address exiting from the child's home with sensitivity at the end of the interview and/or research study. Undertaking research in the child's home provides a known and familiar territory for the child, but it means that the researcher faces a number of challenges that require solutions whilst they are a guest in a child's home. © The Author(s) 2014.

  7. [Environmental licensing of major undertakings: possible connection between health and environment]. (United States)

    Silveira, Missifany; Araújo Neto, Mário Diniz de


    The prospect of multidisciplinary assessment that considers the environmental impacts on the health of the population during the implementation of potentially polluting projects is incipient in Brazil. Considering the scenario of major undertakings in the country, broadening the outlook on the health and environment relationship based on social and economic development processes striving for environmentally sustainable projects is a key strategy. This article examines the debate on the relationship between the current development model, the risks, the environment and health and discusses the importance of the participation of the health sector in the environmental licensing procedures, which is the instrument of the Environmental Impact Assessment (EIA). Seeking to create more environmentally and socially sustainable territories, the health sector has been looking for opportunities to participate in the licensing processes of major undertakings from the EIA standpoint. Results of research conducted by the Ministry of Health have demonstrated the form of participation in these processes, highlighting the strengths and weaknesses that favor or hinder the increase of preventive actions in public health in the implementation of major undertakings in Brazil.

  8. Researcher or nurse? Difficulties of undertaking semi-structured interviews on sensitive topics. (United States)

    Ashton, Susan


    To reflect on the author's personal and professional journey when undertaking semi-structured interviews on sensitive topics with potentially vulnerable people. When discussing care at the end of life, researchers must accept that some participants may become distressed or emotional, depending on their previous experiences. Interviews that involve sensitive topics require careful planning. The semi-structured interviews were conducted as part of the author's PhD study examining the experiences of advance care planning among family caregivers of people with advanced dementia. A reflection on my personal and professional journey when undertaking semi-structured interviews on sensitive topics with potentially vulnerable people. The frustration and tragedy of dementia, as experienced by the family caregivers, were powerful and required the author to exert self-control to avoid being overly sympathetic and offering words of reassurance, agreement and comfort. This blurring of roles between researcher and nurse has implications for all nurse researchers who undertake qualitative interviews, particularly when an intense emotional response is likely. Nurse researchers should plan and prepare for potential blurring of roles during emotional interviews and should never automatically assume that they are sufficiently prepared as a result of their previous experience and nurse training.

  9. Text classification


    Deveikis, Karolis


    This paper investigates the problem of text classification. The task of text classification is to assign a piece of text to one of several categories based on its content. Text classification is one of the tasks of natural language processing. Like the others, it is often solved using machine learning algorithms. There are many algorithms suitable for text classification. As a result, a problem of choice arises. In an effort to solve this problem, this paper analyzes various feature extractio...

  10. Accuracy assessment between different image classification ...

    African Journals Online (AJOL)

    What image classification does is to assign pixel to a particular land cover and land use type that has the most similar spectral signature. However, there are possibilities that different methods or algorithms of image classification of the same data set could produce appreciable variant results in the sizes, shapes and areas of ...

  11. Classification of Pansharpened Urban Satellite Images

    DEFF Research Database (Denmark)

    Palsson, Frosti; Sveinsson, Johannes R.; Benediktsson, Jon Atli


    The classification of high resolution urban remote sensing imagery is addressed with the focus on classification of imagery that has been pansharpened by a number of different pansharpening methods. The pansharpening process introduces some spectral and spatial distortions in the resulting fused ...

  12. Who should be undertaking population-based surveys in humanitarian emergencies?

    Directory of Open Access Journals (Sweden)

    Spiegel Paul B


    Full Text Available Abstract Background Timely and accurate data are necessary to prioritise and effectively respond to humanitarian emergencies. 30-by-30 cluster surveys are commonly used in humanitarian emergencies because of their purported simplicity and reasonable validity and precision. Agencies have increasingly used 30-by-30 cluster surveys to undertake measurements beyond immunisation coverage and nutritional status. Methodological errors in cluster surveys have likely occurred for decades in humanitarian emergencies, often with unknown or unevaluated consequences. Discussion Most surveys in humanitarian emergencies are done by non-governmental organisations (NGOs. Some undertake good quality surveys while others have an already overburdened staff with limited epidemiological skills. Manuals explaining cluster survey methodology are available and in use. However, it is debatable as to whether using standardised, 'cookbook' survey methodologies are appropriate. Coordination of surveys is often lacking. If a coordinating body is established, as recommended, it is questionable whether it should have sole authority to release surveys due to insufficient independence. Donors should provide sufficient funding for personnel, training, and survey implementation, and not solely for direct programme implementation. Summary A dedicated corps of trained epidemiologists needs to be identified and made available to undertake surveys in humanitarian emergencies. NGOs in the field may need to form an alliance with certain specialised agencies or pool technically capable personnel. If NGOs continue to do surveys by themselves, a simple training manual with sample survey questionnaires, methodology, standardised files for data entry and analysis, and manual for interpretation should be developed and modified locally for each situation. At the beginning of an emergency, a central coordinating body should be established that has sufficient authority to set survey standards

  13. Undertaking and writing research that is important, targeted, and the best you can do. (United States)

    McLeod, Sharynne


    Conducting and writing research is a privilege. It is a privilege because researchers can change lives through their findings and can influence public knowledge and debate. It is also a privilege because researchers are reliant on the time and goodwill of participants (and colleagues), and research is often underpinned by funding raised by the public, either through taxes or philanthropic donations. This privilege comes with responsibility. Researchers have a responsibility to undertake research that is important, targeted, and of high quality. This editorial aims to inspire, challenge, and bolster the research efforts of individuals and teams.

  14. Training staff to empower people with long-term conditions to undertake self care activities. (United States)

    Bowler, Mandy

    Self care can help people with long-term conditions take control of their lives. However, their interest and ability to engage with it may fluctuate over the course of an illness and many need support to undertake self care activities. A team of community matrons in NHS South of Tyne and Wear helped to develop and pilot an e-learning tool for staff, to remind them of the importance of self care and give advice on ways to support patients. The tool has since been rolled out to all staff groups.

  15. Benefits and barriers for registered nurses undertaking post-graduate diplomas in paediatric nursing. (United States)

    Johnson, Anne; Copnell, Beverley


    This paper presents one aspect of a larger study identifying key influences on curriculum redesign and development of a post-graduate diploma in advanced clinical nursing. The focus is on paediatric intensive care and general paediatric streams. Data presented here relate to registered nurses' perceptions of benefits and barriers when undertaking this post-graduate diploma. As well as interviews and focus group discussions with a number of nurses, data were collected through a self-administered questionnaire, A total of 885 surveys were distributed to nurses working in paediatric areas in five hospitals in Victoria, Australia. Of these, 391 were completed (response rate 44%). One hundred and thirty (33%) had post-registration or post-graduate paediatric qualifications. Perceived benefits of undertaking the post-graduate diploma mainly related to an increase in knowledge and experience and improvement of employment opportunities. Perceived barriers mainly related to financial and professional issues such as cost of the course, loss of salary, the lack of direct remuneration on completion of the course and a lack of promotional opportunities. It was of concern that several nurses expressed a belief that paediatric qualifications were unnecessary and that many believed their employers did not value the qualification. Several recommendations are suggested to address the main barriers. These include more flexibility in the provision of such courses and opportunities for financial assistance. Copyright 2002, Elsevier Science Ltd. All rights reserved.

  16. Potential of Spectral Reflectance as Postharvest Classification Tool for Flower Development of Calla Lily (Zantedeschia aethiopica (L. Spreng. Potencial de la Reflectancia Espectral como Herramienta para la Clasificación Poscosecha del Desarrollo Floral en Cala (Zantedeschia aethiopica (L. Spreng.

    Directory of Open Access Journals (Sweden)

    Antonio J . Steidle Neto


    Full Text Available Unsuitable postharvest management is one of the most serious problems that floriculture has to face. An option for reducing postharvest losses is to use automatic systems for flower sorting and classification, which yield consistent results, reduce costs and speed up these tasks. The objective of this work was show the potential of spectral reflectance to distinguish different postharvest development stages of calla lily flowers, Zantedeschia aethiopica (L. Spreng., aiming the use of this technology within automatic systems for flower classification. The measuring equipment was a spectrometer connected to a portable computer and configured for reflectance data acquisition in the 400 to 1000 nm range. Based on the results, it was verified a differentiation between the spectral reflectance curves of calla lily flowers, with gradual decreases on the measured values according to the increase of the senescence stages. Thus, the spectral reflectance has potential to be used in the development of automatic systems for postharvest classification of calla lily flowers.El manejo poscosecha inadecuado es uno de los problemas más serios que la floricultura tiene que enfrentar. Una opción para reducir las pérdidas poscosecha es emplear sistemas automáticos para ordenar y clasificar las flores, los cuales permiten resultados consistentes, reducen gastos y aceleran estas tareas. El objetivo de este trabajo fue demostrar el potencial de la reflectancia espectral para discriminar las diferentes fases de desarrollo poscosecha de flores de cala (Zantedeschia aethiopica [L.] Spreng., visando el uso de esta tecnología en sistemas automáticos para la clasificación de flores. El equipo de medición fue un espectrómetro conectado a un computador portátil y configurado para la adquisición de datos de reflectancia comprendidos en la región espectral de 400 a 1000 nm. Con base en los resultados, se constató una diferencia entre las curvas de reflectancia

  17. Spectral Decomposition Algorithm (SDA) (United States)

    National Aeronautics and Space Administration — Spectral Decomposition Algorithm (SDA) is an unsupervised feature extraction technique similar to PCA that was developed to better distinguish spectral features in...

  18. Spectral Imaging by Upconversion

    DEFF Research Database (Denmark)

    Dam, Jeppe Seidelin; Pedersen, Christian; Tidemand-Lichtenberg, Peter


    We present a method to obtain spectrally resolved images using upconversion. By this method an image is spectrally shifted from one spectral region to another wavelength. Since the process is spectrally sensitive it allows for a tailored spectral response. We believe this will allow standard sili...... silicon based cameras designed for visible/near infrared radiation to be used for spectral images in the mid infrared. This can lead to much lower costs for such imaging devices, and a better performance.......We present a method to obtain spectrally resolved images using upconversion. By this method an image is spectrally shifted from one spectral region to another wavelength. Since the process is spectrally sensitive it allows for a tailored spectral response. We believe this will allow standard...

  19. [Automatic classification method of star spectrum data based on classification pattern tree]. (United States)

    Zhao, Xu-Jun; Cai, Jiang-Hui; Zhang, Ji-Fu; Yang, Hai-Feng; Ma, Yang


    Frequent pattern, frequently appearing in the data set, plays an important role in data mining. For the stellar spectrum classification tasks, a classification rule mining method based on classification pattern tree is presented on the basis of frequent pattern. The procedures can be shown as follows. Firstly, a new tree structure, i. e., classification pattern tree, is introduced based on the different frequencies of stellar spectral attributes in data base and its different importance used for classification. The related concepts and the construction method of classification pattern tree are also described in this paper. Then, the characteristics of the stellar spectrum are mapped to the classification pattern tree. Two modes of top-to-down and bottom-to-up are used to traverse the classification pattern tree and extract the classification rules. Meanwhile, the concept of pattern capability is introduced to adjust the number of classification rules and improve the construction efficiency of the classification pattern tree. Finally, the SDSS (the Sloan Digital Sky Survey) stellar spectral data provided by the National Astronomical Observatory are used to verify the accuracy of the method. The results show that a higher classification accuracy has been got.

  20. Undertaking cause-specific mortality measurement in an unregistered population: an example from Tigray Region, Ethiopia. (United States)

    Godefay, Hagos; Abrha, Atakelti; Kinsman, John; Myléus, Anna; Byass, Peter


    The lack of adequate documentation of deaths, and particularly their cause, is often noted in African and Asian settings, but practical solutions for addressing the problem are not always clear. Verbal autopsy methods (interviewing witnesses after a death) have developed rapidly, but there remains a lack of clarity as to how these methods can be effectively applied to large unregistered populations. This paper sets out practical details for undertaking a representative survey of cause-specific mortality in a population of several million, taking Tigray Region in Ethiopia as a prototype. Sampling was designed around an expected level of maternal mortality ratio of 400 per 100,000 live births, which needed measuring within a 95% confidence interval of approximately ±100. Taking a stratified cluster sample within the region at the district level for logistic reasons, and allowing for a design effect of 2, this required a population of around 900,000 people, equating to six typical districts. Since the region is administered in six geographic zones, one district per zone was randomly selected. The survey was implemented as a two-stage process: first, to trace deaths that occurred in the sampled districts within the preceding year, and second to follow them up with verbal autopsy interviews. The field work for both stages was undertaken by health extension workers, working in their normally assigned areas. Most of the work was associated with tracing the deaths, rather than undertaking the verbal autopsy interviews. This approach to measuring cause-specific mortality in an unregistered Ethiopian population proved to be feasible and effective. Although it falls short of the ideal situation of continuous civil registration and vital statistics, a survey-based strategy of this kind may prove to be a useful intermediate step on the road towards full civil registration and vital statistics implementation.

  1. Undertaking cause-specific mortality measurement in an unregistered population: an example from Tigray Region, Ethiopia

    Directory of Open Access Journals (Sweden)

    Hagos Godefay


    Full Text Available Background: The lack of adequate documentation of deaths, and particularly their cause, is often noted in African and Asian settings, but practical solutions for addressing the problem are not always clear. Verbal autopsy methods (interviewing witnesses after a death have developed rapidly, but there remains a lack of clarity as to how these methods can be effectively applied to large unregistered populations. This paper sets out practical details for undertaking a representative survey of cause-specific mortality in a population of several million, taking Tigray Region in Ethiopia as a prototype. Sampling: Sampling was designed around an expected level of maternal mortality ratio of 400 per 100,000 live births, which needed measuring within a 95% confidence interval of approximately ±100. Taking a stratified cluster sample within the region at the district level for logistic reasons, and allowing for a design effect of 2, this required a population of around 900,000 people, equating to six typical districts. Since the region is administered in six geographic zones, one district per zone was randomly selected. Implementation: The survey was implemented as a two-stage process: first, to trace deaths that occurred in the sampled districts within the preceding year, and second to follow them up with verbal autopsy interviews. The field work for both stages was undertaken by health extension workers, working in their normally assigned areas. Most of the work was associated with tracing the deaths, rather than undertaking the verbal autopsy interviews. Discussion: This approach to measuring cause-specific mortality in an unregistered Ethiopian population proved to be feasible and effective. Although it falls short of the ideal situation of continuous civil registration and vital statistics, a survey-based strategy of this kind may prove to be a useful intermediate step on the road towards full civil registration and vital statistics implementation.

  2. Spectrally based mapping of riverbed composition (United States)

    Legleiter, Carl; Stegman, Tobin K.; Overstreet, Brandon T.


    Remote sensing methods provide an efficient means of characterizing fluvial systems. This study evaluated the potential to map riverbed composition based on in situ and/or remote measurements of reflectance. Field spectra and substrate photos from the Snake River, Wyoming, USA, were used to identify different sediment facies and degrees of algal development and to quantify their optical characteristics. We hypothesized that accounting for the effects of depth and water column attenuation to isolate the reflectance of the streambed would enhance distinctions among bottom types and facilitate substrate classification. A bottom reflectance retrieval algorithm adapted from coastal research yielded realistic spectra for the 450 to 700 nm range; but bottom reflectance-based substrate classifications, generated using a random forest technique, were no more accurate than classifications derived from above-water field spectra. Additional hypothesis testing indicated that a combination of reflectance magnitude (brightness) and indices of spectral shape provided the most accurate riverbed classifications. Convolving field spectra to the response functions of a multispectral satellite and a hyperspectral imaging system did not reduce classification accuracies, implying that high spectral resolution was not essential. Supervised classifications of algal density produced from hyperspectral data and an inferred bottom reflectance image were not highly accurate, but unsupervised classification of the bottom reflectance image revealed distinct spectrally based clusters, suggesting that such an image could provide additional river information. We attribute the failure of bottom reflectance retrieval to yield more reliable substrate maps to a latent correlation between depth and bottom type. Accounting for the effects of depth might have eliminated a key distinction among substrates and thus reduced discriminatory power. Although further, more systematic study across a broader

  3. Aspiring to Spectral Ignorance in Earth Observation (United States)

    Oliver, S. A.


    Enabling robust, defensible and integrated decision making in the Era of Big Earth Data requires the fusion of data from multiple and diverse sensor platforms and networks. While the application of standardised global grid systems provides a common spatial analytics framework that facilitates the computationally efficient and statistically valid integration and analysis of these various data sources across multiple scales, there remains the challenge of sensor equivalency; particularly when combining data from different earth observation satellite sensors (e.g. combining Landsat and Sentinel-2 observations). To realise the vision of a sensor ignorant analytics platform for earth observation we require automation of spectral matching across the available sensors. Ultimately, the aim is to remove the requirement for the user to possess any sensor knowledge in order to undertake analysis. This paper introduces the concept of spectral equivalence and proposes a methodology through which equivalent bands may be sourced from a set of potential target sensors through application of equivalence metrics and thresholds. A number of parameters can be used to determine whether a pair of spectra are equivalent for the purposes of analysis. A baseline set of thresholds for these parameters and how to apply them systematically to enable relation of spectral bands amongst numerous different sensors is proposed. The base unit for comparison in this work is the relative spectral response. From this input, determination of a what may constitute equivalence can be related by a user, based on their own conceptualisation of equivalence.

  4. Does undertaking an intercalated BSc influence first clinical year exam results at a London medical school? (United States)

    Howman, Mary; Jones, Melvyn


    Intercalated BScs (iBScs) are an optional part of the medical school curriculum in many Universities. Does undertaking an iBSc influence subsequent student performance? Previous studies addressing this question have been flawed by iBSc students being highly selected. This study looks at data from medical students where there is a compulsory iBSc for non-graduates. Our aim was to see whether there was any difference in performance between students who took an iBSc before or after their third year (first clinical year) exams. A multivariable analysis was performed to compare the third year results of students at one London medical school who had or had not completed their iBSc by the start of this year (n = 276). A general linear model was applied to adjust for differences between the two groups in terms of potential confounders (age, sex, nationality and baseline performance). The results of third year summative exams for 276 students were analysed (184 students with an iBSc and 92 without). Unadjusted analysis showed students who took an iBSc before their third year achieved significantly higher end of year marks than those who did not with a mean score difference of 4.4 (0.9 to 7.9 95% CI, p = 0.01). (overall mean score 238.4 "completed iBSc" students versus 234.0 "not completed", range 145.2 - 272.3 out of 300). There was however a significant difference between the two groups in their prior second year exam marks with those choosing to intercalate before their third year having higher marks. Adjusting for this, the difference in overall exam scores was no longer significant with a mean score difference of 1.4 (-4.9 to +7.7 95% CI, p = 0.66). (overall mean score 238.0 " completed iBSc" students versus 236.5 "not completed"). Once possible confounders are controlled for (age, sex, previous academic performance) undertaking an iBSc does not influence third year exam results. One explanation for this confounding in unadjusted results is that students who do better

  5. Gaia-ESO Survey: Empirical classification of VLT/Giraffe stellar spectra in the wavelength range 6440-6810 Å in the γ Velorum cluster, and calibration of spectral indices (United States)

    Damiani, F.; Prisinzano, L.; Micela, G.; Randich, S.; Gilmore, G.; Drew, J. E.; Jeffries, R. D.; Frémat, Y.; Alfaro, E. J.; Bensby, T.; Bragaglia, A.; Flaccomio, E.; Lanzafame, A. C.; Pancino, E.; Recio-Blanco, A.; Sacco, G. G.; Smiljanic, R.; Jackson, R. J.; de Laverny, P.; Morbidelli, L.; Worley, C. C.; Hourihane, A.; Costado, M. T.; Jofré, P.; Lind, K.; Maiorca, E.


    We present a study of spectral diagnostics available from optical spectra with R = 17 000 obtained with the VLT/Giraffe HR15n setup, using observations from the Gaia-ESO Survey, on the γ Vel young cluster, with the purpose of classifying these stars and finding their fundamental parameters. We define several spectroscopic indices, sampling the amplitude of TiO bands, the Hα line core and wings, and temperature- and gravity-sensitive sets of lines, each useful as a Teff or log g indicator over a limited range of stellar spectral types. Hα line indices are also useful as chromospheric activity or accretion indicators. Furthermore, we use all indices to define additional global Teff- and log g-sensitive indices τ and γ, valid for the entire range of types in the observed sample. We find a clear difference between gravity indices of main-sequence and pre-main-sequence stars, as well as a much larger difference between these and giant stars. The potentially great usefulness of the (γ,τ) diagram as a distance-independent age measurement tool for young clusters is discussed. We discuss the effect on the defined indices of classical T Tauri star veiling, which is however detected in only a few stars in the present sample. Then, we present tests and calibrations of these indices, on the basis of both photometry and literature reference spectra, from the UVES Paranal Observatory Projectand the ELODIE 3.1 Library. The known properties of these stars, spanning a wide range of stellar parameters, enable us to obtain a good understanding of the performances of our new spectral indices. For non-peculiar stars with known temperature, gravity, and metallicity, we are able to calibrate quantitatively our indices, and derive stellar parameters for a wide range of stellar types. To this aim, a new composite index is defined, providing a good metallicity indicator. The ability of our indices to select peculiar, or otherwise rare classes of stars is also established. For pre

  6. Decision tree approach for classification of remotely sensed satellite ...

    Indian Academy of Sciences (India)

    DTC) algorithm for classification of remotely sensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using. WEKA, open source ...

  7. Decision tree approach for classification of remotely sensed satellite

    Indian Academy of Sciences (India)

    DTC) algorithm for classification of remotely sensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source ...

  8. Xenolog classification. (United States)

    Darby, Charlotte A; Stolzer, Maureen; Ropp, Patrick J; Barker, Daniel; Durand, Dannie


    Orthology analysis is a fundamental tool in comparative genomics. Sophisticated methods have been developed to distinguish between orthologs and paralogs and to classify paralogs into subtypes depending on the duplication mechanism and timing, relative to speciation. However, no comparable framework exists for xenologs: gene pairs whose history, since their divergence, includes a horizontal transfer. Further, the diversity of gene pairs that meet this broad definition calls for classification of xenologs with similar properties into subtypes. We present a xenolog classification that uses phylogenetic reconciliation to assign each pair of genes to a class based on the event responsible for their divergence and the historical association between genes and species. Our classes distinguish between genes related through transfer alone and genes related through duplication and transfer. Further, they separate closely-related genes in distantly-related species from distantly-related genes in closely-related species. We present formal rules that assign gene pairs to specific xenolog classes, given a reconciled gene tree with an arbitrary number of duplications and transfers. These xenology classification rules have been implemented in software and tested on a collection of ∼13 000 prokaryotic gene families. In addition, we present a case study demonstrating the connection between xenolog classification and gene function prediction. The xenolog classification rules have been implemented in N otung 2.9, a freely available phylogenetic reconciliation software package. . Gene trees are available at . Supplementary data are available at Bioinformatics online.

  9. Spectral signature selection for mapping unvegetated soils (United States)

    May, G. A.; Petersen, G. W.


    Airborne multispectral scanner data covering the wavelength interval from 0.40-2.60 microns were collected at an altitude of 1000 m above the terrain in southeastern Pennsylvania. Uniform training areas were selected within three sites from this flightline. Soil samples were collected from each site and a procedure developed to allow assignment of scan line and element number from the multispectral scanner data to each sampling location. These soil samples were analyzed on a spectrophotometer and laboratory spectral signatures were derived. After correcting for solar radiation and atmospheric attenuation, the laboratory signatures were compared to the spectral signatures derived from these same soils using multispectral scanner data. Both signatures were used in supervised and unsupervised classification routines. Computer-generated maps using the laboratory and multispectral scanner derived signatures resulted in maps that were similar to maps resulting from field surveys. Approximately 90% agreement was obtained between classification maps produced using multispectral scanner derived signatures and laboratory derived signatures.

  10. The capabilities and scope-of-practice requirements of advanced life support practitioners undertaking critical care transfers: A Delphi study

    Directory of Open Access Journals (Sweden)

    Monique Venter


    Full Text Available Background. Critical care transfers (CCT refer to the high level of care given during transport (via ambulance, helicopter or fixed-wing aircraft of patients who are of high acuity. In South Africa (SA, advanced life support (ALS paramedics undertake CCTs. The scope of ALS in SA has no extended protocol regarding procedures or medications in terms of dealing with these CCTs. Aim. The aim of this study was to obtain the opinions of several experts in fields pertaining to critical care and transport and to gain consensus on the skills and scope-of-practice requirements of paramedics undertaking CCTs in the SA setting. Methods. A modified Delphi study consisting of three rounds was undertaken using an online survey platform. A heterogeneous sample (n=7, consisting of specialists in the fields of anaesthesiology, emergency medicine, internal medicine, critical care, critical care transport and paediatrics, was asked to indicate whether, in their opinion, selected procedures and medications were needed within the scope of practice of paramedics undertaking CCTs. Results. After three rounds, consensus was obtained in 70% (57/81 of procedures and medications. Many of these items are not currently within the scope of paramedics’ training. The panel felt that paramedics undertaking these transfers should have additional postgraduate training that is specific to critical care. Conclusion. Major discrepancies exist between the current scope of paramedic practice and the suggested required scope of practice for CCTs. An extended scope of practice and additional training should be considered for these practitioners.

  11. Technology transfer in the hydropower industry: An analysis of Chinese dam developers’ undertakings in Europe and Latin America

    NARCIS (Netherlands)

    Kirchherr, Julian; Matthews, Nathanial


    Technology transfer is essential for transitioning to a low carbon economy which can include hydropower. Chinese dam developers allegedly dominate the global hydropower industry. Studies have been carried out on technology transfer in their undertakings in Africa and Asia. However, such work is

  12. Transporter Classification Database (TCDB) (United States)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  13. Multispectral Image classification using the theories of neural networks

    International Nuclear Information System (INIS)

    Ardisasmita, M.S.; Subki, M.I.R.


    Image classification is the one of the important part of digital image analysis. the objective of image classification is to identify and regroup the features occurring in an image into one or several classes in terms of the object. basic to the understanding of multispectral classification is the concept of the spectral response of an object as a function of the electromagnetic radiation and the wavelength of the spectrum. new approaches to classification has been developed to improve the result of analysis, these state-of-the-art classifiers are based upon the theories of neural networks. Neural network classifiers are algorithmes which mimic the computational abilities of the human brain. Artificial neurons are simple emulation's of biological neurons; they take in information from sensors or other artificial neurons, perform very simple operations on this data, and pass the result to other recognize the spectral signature of each image pixel. Neural network image classification has been divided into supervised and unsupervised training procedures. In the supervised approach, examples of each cover type can be located and the computer can compute spectral signatures to categorize all pixels in a digital image into several land cover classes. In supervised classification, spectral signatures are generated by mathematically grouping and it does not require analyst-specified training data. Thus, in the supervised approach we define useful information categories and then examine their spectral reparability; in the unsupervised approach the computer determines spectrally sapable classes and then we define thei information value

  14. Tissue Classification

    DEFF Research Database (Denmark)

    Van Leemput, Koen; Puonti, Oula


    Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are now...... well established. In their simplest form, these methods classify voxels independently based on their intensity alone, although much more sophisticated models are typically used in practice. This article aims to give an overview of often-used computational techniques for brain tissue classification....... Although other methods exist, we concentrate on Bayesian modeling approaches, in which generative image models are constructed and subsequently ‘inverted’ to obtain automated segmentations. This general framework encompasses a large number of segmentation methods, including those implemented in widely used...

  15. Importance of spatial and spectral data reduction in the detection of internal defects in food products. (United States)

    Zhang, Xuechen; Nansen, Christian; Aryamanesh, Nader; Yan, Guijun; Boussaid, Farid


    Despite the importance of data reduction as part of the processing of reflection-based classifications, this study represents one of the first in which the effects of both spatial and spectral data reductions on classification accuracies are quantified. Furthermore, the effects of approaches to data reduction were quantified for two separate classification methods, linear discriminant analysis (LDA) and support vector machine (SVM). As the model dataset, reflection data were acquired using a hyperspectral camera in 230 spectral channels from 401 to 879 nm (spectral resolution of 2.1 nm) from field pea (Pisum sativum) samples with and without internal pea weevil (Bruchus pisorum) infestation. We deployed five levels of spatial data reduction (binning) and eight levels of spectral data reduction (40 datasets). Forward stepwise LDA was used to select and include only spectral channels contributing the most to the separation of pixels from non-infested and infested field peas. Classification accuracies obtained with LDA and SVM were based on the classification of independent validation datasets. Overall, SVMs had significantly higher classification accuracies than LDAs (P food products with internal defects, and it highlights that spatial and spectral data reductions can (1) improve classification accuracies, (2) vastly decrease computer constraints, and (3) reduce analytical concerns associated with classifications of large and high-dimensional datasets.

  16. Weighted Chebyshev distance classification method for hyperspectral imaging (United States)

    Demirci, S.; Erer, I.; Ersoy, O.


    The main objective of classification is to partition the surface materials into non-overlapping regions by using some decision rules. For supervised classification, the hyperspectral imagery (HSI) is compared with the reflectance spectra of the material containing similar spectral characteristic. As being a spectral similarity based classification method, prediction of different level of upper and lower spectral boundaries of all classes spectral signatures across spectral bands constitutes the basic principles of the Multi-Scale Vector Tunnel Algorithm (MS-VTA) classification algorithm. The vector tunnel (VT) scaling parameters obtained from means and standard deviations of the class references are used. In this study, MS-VT method is improved and a spectral similarity based technique referred to as Weighted Chebyshev Distance (WCD) method for the supervised classification of HSI is introduced. This is also shown to be equivalent to the use of the WCD in which the weights are chosen as an inverse power of the standard deviation per spectral band. The use of WCD measures in terms of the inverse power of standard deviations and optimization of power parameter constitute the most important side of the study. The algorithms are trained with the same kinds of training sets, and their performances are calculated for the power of the standard deviation. During these studies, various levels of the power parameters are evaluated based on the efficiency of the algorithms for choosing the best values of the weights.

  17. Classification System of Pathological Voices Using Correntropy

    Directory of Open Access Journals (Sweden)

    Aluisio I. R. Fontes


    Full Text Available This paper proposes the use of a similarity measure based on information theory called correntropy for the automatic classification of pathological voices. By using correntropy, it is possible to obtain descriptors that aggregate distinct spectral characteristics for healthy and pathological voices. Experiments using computational simulation demonstrate that such descriptors are very efficient in the characterization of vocal dysfunctions, leading to a success rate of 97% in the classification. With this new architecture, the classification process of vocal pathologies becomes much more simple and efficient.

  18. Knowledge base image classification using P-trees (United States)

    Seetha, M.; Ravi, G.


    Image Classification is the process of assigning classes to the pixels in remote sensed images and important for GIS applications, since the classified image is much easier to incorporate than the original unclassified image. To resolve misclassification in traditional parametric classifier like Maximum Likelihood Classifier, the neural network classifier is implemented using back propagation algorithm. The extra spectral and spatial knowledge acquired from the ancillary information is required to improve the accuracy and remove the spectral confusion. To build knowledge base automatically, this paper explores a non-parametric decision tree classifier to extract knowledge from the spatial data in the form of classification rules. A new method is proposed using a data structure called Peano Count Tree (P-tree) for decision tree classification. The Peano Count Tree is a spatial data organization that provides a lossless compressed representation of a spatial data set and facilitates efficient classification than other data mining techniques. The accuracy is assessed using the parameters overall accuracy, User's accuracy and Producer's accuracy for image classification methods of Maximum Likelihood Classification, neural network classification using back propagation, Knowledge Base Classification, Post classification and P-tree Classifier. The results reveal that the knowledge extracted from decision tree classifier and P-tree data structure from proposed approach remove the problem of spectral confusion to a greater extent. It is ascertained that the P-tree classifier surpasses the other classification techniques.

  19. Non proliferation regimes undertakings: Benefits and limits of synergies in verification technologies and procedures

    International Nuclear Information System (INIS)

    Richard, M.


    Full text: Thirty years ago the NPT was entering into force. Therewith, when a State became party to the NPT, it had, in accordance with article III.1 of the Treaty, an undertaking to conclude a Comprehensive Safeguards agreement with the IAEA and accept safeguards verification on source or special fissionable material in all peaceful nuclear activities within its territories in order to verify that such material is not diverted. This multilateral instrument was the foundation stone of the non-proliferation regime and marked the actual birth of internationally accepted measures to verily compliance with politically stringent agreements. Since that time several important multilateral or bilateral instruments on non-proliferation and disarmament have been negotiated and adopted to curb the development and the acquisition of Weapons of Mass Destruction (WMD) most of them since the middle of the eighties and the collapse of the Soviet Union. Amongst the multilateral instruments are the Convention on the Prohibition of Bacteriological Weapon and Toxin Weapons (1972), the Convention on the Prohibition of Chemical Weapons (1993), the Comprehensive Test Ban Treaty (1996), the Strengthening of the IAEA Safeguards and the Additional Protocol (1997), with some still in negotiation like the Protocol of the Convention on the Prohibition of Bacteriological and Toxin Weapons, and some on which negotiation is still a wish like the Fissile Material Cut-off Treaty. Bilateral disarmament agreements between the United States of America and the Russian Federation such as the INF Treaty, START I and II, the agreements on the elimination of excess defence nuclear material as well as the Trilateral Initiative with the IAEA pave the way to nuclear disarmament with the reduction of both the number of nuclear weapons arsenal and the fissile material inventories. The politically stringent undertakings of States that have become parties to those agreements would not be possible without the

  20. Adaptive Spectral Doppler Estimation

    DEFF Research Database (Denmark)

    Gran, Fredrik; Jakobsson, Andreas; Jensen, Jørgen Arendt


    . The methods can also provide better quality of the estimated power spectral density (PSD) of the blood signal. Adaptive spectral estimation techniques are known to pro- vide good spectral resolution and contrast even when the ob- servation window is very short. The 2 adaptive techniques are tested......In this paper, 2 adaptive spectral estimation techniques are analyzed for spectral Doppler ultrasound. The purpose is to minimize the observation window needed to estimate the spectrogram to provide a better temporal resolution and gain more flexibility when designing the data acquisition sequence...... and compared with the averaged periodogram (Welch’s method). The blood power spectral capon (BPC) method is based on a standard minimum variance technique adapted to account for both averaging over slow-time and depth. The blood amplitude and phase estimation technique (BAPES) is based on finding a set...

  1. Spectral properties of 441 radio pulsars (United States)

    Jankowski, F.; van Straten, W.; Keane, E. F.; Bailes, M.; Barr, E. D.; Johnston, S.; Kerr, M.


    We present a study of the spectral properties of 441 pulsars observed with the Parkes radio telescope near the centre frequencies of 728, 1382 and 3100 MHz. The observations at 728 and 3100 MHz were conducted simultaneously using the dual-band 10-50 cm receiver. These high-sensitivity, multifrequency observations provide a systematic and uniform sample of pulsar flux densities. We combine our measurements with spectral data from the literature in order to derive the spectral properties of these pulsars. Using techniques from robust regression and information theory, we classify the observed spectra in an objective, robust and unbiased way into five morphological classes: simple or broken power law, power law with either low- or high-frequency cut-off and log-parabolic spectrum. While about 79 per cent of the pulsars that could be classified have simple power-law spectra, we find significant deviations in 73 pulsars, 35 of which have curved spectra, 25 with a spectral break and 10 with a low-frequency turn-over. We identify 11 gigahertz-peaked spectrum (GPS) pulsars, with 3 newly identified in this work and 8 confirmations of known GPS pulsars; 3 others show tentative evidence of GPS, but require further low-frequency measurements to support this classification. The weighted mean spectral index of all pulsars with simple power-law spectra is -1.60 ± 0.03. The observed spectral indices are well described by a shifted log-normal distribution. The strongest correlations of spectral index are with spin-down luminosity, magnetic field at the light-cylinder and spin-down rate. We also investigate the physical origin of the observed spectral features and determine emission altitudes for three pulsars.

  2. A method to incorporate uncertainty in the classification of remote sensing images


    Gonçalves, Luísa M. S.; Fonte, Cidália C.; Júlio, Eduardo N. B. S.; Caetano, Mario


    The aim of this paper is to investigate if the incorporation of the uncertainty associated with the classification of surface elements into the classification of landscape units (LUs) increases the results accuracy. To this end, a hybrid classification method is developed, including uncertainty information in the classification of very high spatial resolution multi-spectral satellite images, to obtain a map of LUs. The developed classification methodology includes the following...

  3. Classification of Urinary Calculi using Feed-Forward Neural Networks

    African Journals Online (AJOL)

    In this work the results of classification of these types of calculi (using their infrared spectra in the region 1450–450 cm–1) by feed-forward neural networks are presented. Genetic algorithms were used for optimization of neural networks and for selection of the spectral regions most suitable for classification purposes.

  4. Classification of composite damage from FBG load monitoring signals

    NARCIS (Netherlands)

    Rajabzadehdizaji, Aydin; Hendriks, R.C.; Heusdens, R.; Groves, R.M.


    This paper describes a new method for the classification and identification of two major types of defects in composites, namely delamination and matrix cracks, by classification of the spectral features of fibre Bragg grating (FBG) signals. In aeronautical applications of composites, after a

  5. Classification and mapping of rangeland vegetation physiognomic ...

    African Journals Online (AJOL)

    Plot vegetation species growth form, cover and height data were collected from 450 sampling sites based on eight spectral strata generated using unsupervised image classification. Field data were grouped at four levels of seven, six, three and two vegetation physiognomic classes which were subjected to both ML and ...

  6. Verifying compliance with nuclear non-proliferation undertakings: IAEA safeguards agreements and additional protocols

    International Nuclear Information System (INIS)


    This report provides background information on safeguards and explains procedures for States to conclude Additional Protocols to comprehensive Safeguards Agreements with the IAEA. Since the IAEA was founded in 1957, its safeguards system has been an indispensable component of the nuclear non-proliferation regime and has facilitated peaceful nuclear cooperation. In recognition of this, the Treaty on the Non-Proliferation of Nuclear Weapons (NPT) makes it mandatory for all non-nuclear-weapon States (NNWS) party to the Treaty to conclude comprehensive safeguards agreements with the IAEA, and thus allow for the application of safeguards to all their nuclear material. Under Article III of the NPT, all NNWS undertake to accept safeguards, as set forth in agreements to be negotiated and concluded with the IAEA, for the exclusive purpose of verification of the fulfilment of the States' obligations under the NPT. In May 1997, the IAEA Board of Governors approved the Model Additional Protocol to Safeguards Agreements (reproduced in INFCIRC/540(Corr.)) which provided for an additional legal authority. In States that have both a comprehensive safeguards agreement and an additional protocol in force, the IAEA is able to optimize the implementation of all safeguards measures available. In order to simplify certain procedures under comprehensive safeguards agreements for States with little or no nuclear material and no nuclear material in a facility, the IAEA began making available, in 1971, a 'small quantities protocol' (SQP), which held in abeyance the implementation of most of the detailed provisions of comprehensive safeguards agreements for so long as the State concerned satisfied these criteria. The safeguards system aims at detecting and deterring the diversion of nuclear material. Such material includes enriched uranium, plutonium and uranium-233, which could be used directly in nuclear weapons. It also includes natural uranium and depleted uranium, the latter of which is

  7. Classification of Roof Materials Using Hyperspectral Data (United States)

    Chisense, C.


    Mapping of surface materials in urban areas using aerial imagery is a challenging task. This is because there are numerous materials present in relatively small regions. Hyperspectral data features a fine spectral resolution and thus has a significant capability for automatic identification and mapping of urban surface materials. In this study an approach for identification of roof surface materials using hyperspectral data is presented. The study is based on an urban area in Ludwigsburg, Germany, using a HyMap data set recorded during the HyMap campaign in August, 2010. Automatisierte Liegenschaftskarte (ALK) vector data with a building layer is combined with the HyMap data to limit the analysis to roofs. A spectral library for roofs is compiled based on field and image measurements. In the roof material identification process, supervised classification methods, namely spectral angle mapper and spectral information divergence and the object oriented ECHO (extraction and classification of homogeneous objects) approach are compared. In addition to the overall shape of spectral curves, position and strength of absorptions features are used to enhance material identification. The discriminant analysis feature extraction method is applied to the HyMap data in order to identify features (band combinations) suitable for discriminating between the target classes. The identified optimal features are used to create a new data set which is later classified using the ECHO classifier. The classification results with respect to material types of roofs are presented in this study. The most important results are evaluated using orthophotos, probability maps and field visits.

  8. Classification of independent components of EEG into multiple artifact classes

    DEFF Research Database (Denmark)

    Frølich, Laura; Andersen, Tobias; Mørup, Morten


    In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified...... neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We...

  9. Farmers Prone to Drought Risk: Why Some Farmers Undertake Farm-Level Risk-Reduction Measures While Others Not? (United States)

    Gebrehiwot, Tagel; van der Veen, Anne


    This research investigates farmers' cognitive perceptions of risk and the behavioral intentions to undertake farm-level risk-reduction measures. It has been observed that people who are susceptible to natural hazards often fail to act, or do very little, to protect their assets or lives. To answer the question of why some people show adaptive behavior while others do not, a socio-psychological model of precautionary adaptation based on protection motivation theory and trans-theoretical stage model has been applied for the first time to areas of drought risk in the developing countries cultural context. The applicability of the integrated model is explored by means of a representative sample survey of smallholder farmers in northern Ethiopia. The result of the study showed that there is a statistically significant association between farmer's behavioral intention to undertake farm-level risk-reduction measures and the main important protection motivation model variables. High perceived vulnerability, severity of consequences, self-efficacy, and response efficacy lead to higher levels of behavioral intentions to undertake farm-level risk-reduction measures. For farmers in the action stage, self-efficacy and response efficacy were the main motivators of behavioral intention. For farmers in the contemplative stage, self-efficacy and cost appear to be the main motivators for them to act upon risk reduction, while perceived severity of consequences and cost of response actions were found to be important for farmers in the pre-contemplative stage.

  10. Biomedical Applications of the Information-efficient Spectral Imaging Sensor (ISIS)

    Energy Technology Data Exchange (ETDEWEB)

    Gentry, S.M.; Levenson, R.


    The Information-efficient Spectral Imaging Sensor (ISIS) approach to spectral imaging seeks to bridge the gap between tuned multispectral and fixed hyperspectral imaging sensors. By allowing the definition of completely general spectral filter functions, truly optimal measurements can be made for a given task. These optimal measurements significantly improve signal-to-noise ratio (SNR) and speed, minimize data volume and data rate, while preserving classification accuracy. The following paper investigates the application of the ISIS sensing approach in two sample biomedical applications: prostate and colon cancer screening. It is shown that in these applications, two to three optimal measurements are sufficient to capture the majority of classification information for critical sample constituents. In the prostate cancer example, the optimal measurements allow 8% relative improvement in classification accuracy of critical cell constituents over a red, green, blue (RGB) sensor. In the colon cancer example, use of optimal measurements boost the classification accuracy of critical cell constituents by 28% relative to the RGB sensor. In both cases, optimal measurements match the performance achieved by the entire hyperspectral data set. The paper concludes that an ISIS style spectral imager can acquire these optimal spectral images directly, allowing improved classification accuracy over an RGB sensor. Compared to a hyperspectral sensor, the ISIS approach can achieve similar classification accuracy using a significantly lower number of spectral samples, thus minimizing overall sample classification time and cost.

  11. Schrodinger Eigenmaps for spectral target detection (United States)

    Dorado-Munoz, Leidy P.; Messinger, David W.


    Spectral imagery such as multispectral and hyperspectral data could be seen as a set of panchromatic images stacked as a 3d cube, with two spatial dimensions and one spectral. For hyperspectral imagery, the spectral dimension is highly sampled, which implies redundant information and a high spectral dimensionality. Therefore, it is necessary to use transformations on the data not only to reduce processing costs, but also to reveal some features or characteristics of the data that were hidden in the original space. Schrodinger Eigenmaps (SE) is a novel mathematical method for non-linear representation of a data set that attempts to preserve the local structure while the spectral dimension is reduced. SE could be seen as an extension of Laplacian Eigenmaps (LE), where the diffusion process could be steered in certain directions determined by a potential term. SE was initially introduced as a semi supervised classification technique and most recently, it has been applied to target detection showing promising performance. In target detection, only the barrier potential has been used, so different forms to define barrier potentials and its influence on the data embedding are studied here. In this way, an experiment to assess the target detection vs. how strong the influence of potentials is and how many eigenmaps are used in the detection, is proposed. The target detection is performed using a hyperspectral data set, where several targets with different complexity are presented in the same scene.

  12. Hyperspectral image classification based on volumetric texture and dimensionality reduction (United States)

    Su, Hongjun; Sheng, Yehua; Du, Peijun; Chen, Chen; Liu, Kui


    A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural features were extracted by volumetric gray-level co-occurrence matrices (VGLCM). The spectral features were extracted by minimum estimated abundance covariance (MEAC) and linear prediction (LP)-based band selection, and a semi-supervised k-means (SKM) clustering method with deleting the worst cluster (SKMd) bandclustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classification by using spectral and textural features. It has been proven that the proposed method using VGLCM outperforms the gray-level co-occurrence matrices (GLCM) method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.


    Directory of Open Access Journals (Sweden)

    A. Makarau


    Full Text Available Accurate classification of hyperspectral data is still a competitive task and new classification methods are developed to achieve desired tasks of hyperspectral data use. The objective of this paper is to develop a new method for hyperspectral data classification ensuring the classification model properties like transferability, generalization, probabilistic interpretation, etc. While factor graphs (undirected graphical models are unfortunately not widely employed in remote sensing tasks, these models possess important properties such as representation of complex systems to model estimation/decision making tasks. In this paper we present a new method for hyperspectral data classification using factor graphs. Factor graph (a bipartite graph consisting of variables and factor vertices allows factorization of a more complex function leading to definition of variables (employed to store input data, latent variables (allow to bridge abstract class to data, and factors (defining prior probabilities for spectral features and abstract classes; input data mapping to spectral features mixture and further bridging of the mixture to an abstract class. Latent variables play an important role by defining two-level mapping of the input spectral features to a class. Configuration (learning on training data of the model allows calculating a parameter set for the model to bridge the input data to a class. The classification algorithm is as follows. Spectral bands are separately pre-processed (unsupervised clustering is used to be defined on a finite domain (alphabet leading to a representation of the data on multinomial distribution. The represented hyperspectral data is used as input evidence (evidence vector is selected pixelwise in a configured factor graph and an inference is run resulting in the posterior probability. Variational inference (Mean field allows to obtain plausible results with a low calculation time. Calculating the posterior probability for

  14. Virtual Satellite Construction and Application for Image Classification

    International Nuclear Information System (INIS)

    Su, W G; Su, F Z; Zhou, C H


    Nowadays, most remote sensing image classification uses single satellite remote sensing data, so the number of bands and band spectral width is consistent. In addition, observed phenomenon such as land cover have the same spectral signature, which causes the classification accuracy to decrease as different data have unique characteristic. Therefore, this paper analyzes different optical remote sensing satellites, comparing the spectral differences and proposes the ideas and methods to build a virtual satellite. This article illustrates the research on the TM, HJ-1 and MODIS data. We obtained the virtual band X 0 through these satellites' bands combined it with the 4 bands of a TM image to build a virtual satellite with five bands. Based on this, we used these data for image classification. The experimental results showed that the virtual satellite classification results of building land and water information were superior to the HJ-1 and TM data respectively

  15. Superfícies de resposta espectro-temporal de imagens do sensor MODIS para classificação de área de soja no Estado do Rio Grande do Sul Spectral-temporal response surface of MODIS sensor images for soybean area classification in Rio Grande do Sul State

    Directory of Open Access Journals (Sweden)

    Conrado de Moraes Rudorff


    Full Text Available Este trabalho objetivou avaliar o potencial e as limitações das imagens MODIS para classificação e estimativa de área de soja por meio do método de superfície de resposta espectro-temporal (Spectral-Temporal Response Surface - STRS. Um mapa temático das áreas com soja, oriundo da classificação de imagens Landsat do Estado do Rio Grande do Sul, foi utilizado como referência para auxiliar na orientação da amostragem dos pixels de treinamento e para a comparação dos resultados. Seis imagens compostas do sensor MODIS foram utilizadas para a classificação supervisionada da área de soja por meio do algoritmo de máxima verossimilhança (MAXVER adaptado ao método STRS. Os resultados foram avaliados pelo coeficiente Kappa para a totalidade da área em estudo e também para uma região de latifúndios e outra de minifúndios. O método STRS subestimou em 6,6% a área de soja para toda a região estudada, sendo que a estatística Kappa foi de 0,503. Para as regiões de latifúndios e minifúndios, a área de soja foi superestimada em 8% (Kappa=0,424 e subestimada em 43,4% (Kappa=0,358, respectivamente. As imagens MODIS, por meio do método STRS, demonstraram ter potencial para classificar a área de soja, principalmente em regiões de latifúndios. Em regiões de minifúndios, a correta identificação e classificação das áreas de soja mostrou-se pouco eficiente em razão da baixa resolução espacial das imagens MODIS.This paper was aimed at evaluating the potential and the limitations of MODIS images for soybean classification and area estimation through a Spectral-Temporal Response Surface (STRS method. A soybean thematic map from Rio Grande do Sul State, Brazil, derived from Landsat images was used as reference data to assist both sample training and results comparison. Six 16-day composite MODIS images were classified through a supervised maximum likelihood algorithm (MAXVER adapted to the STRS method. The results were

  16. Classification in context

    DEFF Research Database (Denmark)

    Mai, Jens Erik


    This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  17. Spectral radius of graphs

    CERN Document Server

    Stevanovic, Dragan


    Spectral Radius of Graphs provides a thorough overview of important results on the spectral radius of adjacency matrix of graphs that have appeared in the literature in the preceding ten years, most of them with proofs, and including some previously unpublished results of the author. The primer begins with a brief classical review, in order to provide the reader with a foundation for the subsequent chapters. Topics covered include spectral decomposition, the Perron-Frobenius theorem, the Rayleigh quotient, the Weyl inequalities, and the Interlacing theorem. From this introduction, the

  18. Fuzzy logic merger of spectral and ecological information for improved montane forest mapping. (United States)

    White, Joseph D.; Running, Steven W.; Ryan, Kevin C.; Key, Carl H.


    Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.

  19. Road Classification and Condition Determination Using Hyperspectral Imagery (United States)

    Mohammadi, M.


    Hyperspectral data has remarkable capabilities for automatic identification and mapping of urban surface materials because of its high spectral resolution. It includes a wealth of information which facilitates an understanding of the ground material properties. For identification of road surface materials, information about their relation to hyperspectral sensor measurements is needed. In this study an approach for classification of road surface materials using hyperspectral data is developed. The condition of the road surface materials, in particular asphalt is also investigated. Hyperspectral data with 4m spatial resolution of the city of Ludwigsburg, Germany consisting of 125 bands (wavelength range of 0.4542μm to 2.4846 μm) is used. Different supervised classification methods such as spectral angle mapper are applied based on a spectral library established from field measurements and in-situ inspection. It is observed that using the spectral angle mapper approach with regions of interest is helpful for road surface material identification. Additionally, spectral features are tested using their spectral functions in order to achieve better classification results. Spectral functions such as mean and standard deviation are suitable for discriminating asphalt, concrete and gravel. Different asphalt conditions (good, intermediate and bad) are distinguished using the spectral functions such as mean and image ratio. The mean function gives reliable results. Automatisierte Liegenschaftskarte (ALK) vector data for roads is integrated in order to confine the analysis to roads. Reliable reference spectra are useful in evaluation of classification results for spectrally similar road surface materials. The classification results are assessed using orthophotos and field visits information.


    Directory of Open Access Journals (Sweden)

    M. Mohammadi


    Full Text Available Hyperspectral data has remarkable capabilities for automatic identification and mapping of urban surface materials because of its high spectral resolution. It includes a wealth of information which facilitates an understanding of the ground material properties. For identification of road surface materials, information about their relation to hyperspectral sensor measurements is needed. In this study an approach for classification of road surface materials using hyperspectral data is developed. The condition of the road surface materials, in particular asphalt is also investigated. Hyperspectral data with 4m spatial resolution of the city of Ludwigsburg, Germany consisting of 125 bands (wavelength range of 0.4542μm to 2.4846 μm is used. Different supervised classification methods such as spectral angle mapper are applied based on a spectral library established from field measurements and in-situ inspection. It is observed that using the spectral angle mapper approach with regions of interest is helpful for road surface material identification. Additionally, spectral features are tested using their spectral functions in order to achieve better classification results. Spectral functions such as mean and standard deviation are suitable for discriminating asphalt, concrete and gravel. Different asphalt conditions (good, intermediate and bad are distinguished using the spectral functions such as mean and image ratio. The mean function gives reliable results. Automatisierte Liegenschaftskarte (ALK vector data for roads is integrated in order to confine the analysis to roads. Reliable reference spectra are useful in evaluation of classification results for spectrally similar road surface materials. The classification results are assessed using orthophotos and field visits information.

  1. Unmixing of spectrally similar minerals

    CSIR Research Space (South Africa)

    Debba, Pravesh


    Full Text Available techniques is complicated when considering very similar spectral signatures. Iron-bearing oxide/hydroxide/sulfate minerals have similar spectral signatures. The study focuses on how could estimates of abundances of spectrally similar iron-bearing oxide...

  2. Spectral Biomimetic Technique for Wood Classification Inspired by Human Echolocation

    Directory of Open Access Journals (Sweden)

    Juan Antonio Martínez Rojas


    Full Text Available Palatal clicks are most interesting for human echolocation. Moreover, these sounds are suitable for other acoustic applications due to their regular mathematical properties and reproducibility. Simple and nondestructive techniques, bioinspired by synthetized pulses whose form reproduces the best features of palatal clicks, can be developed. The use of synthetic palatal pulses also allows detailed studies of the real possibilities of acoustic human echolocation without the problems associated with subjective individual differences. These techniques are being applied to the study of wood. As an example, a comparison of the performance of both natural and synthetic human echolocation to identify three different species of wood is presented. The results show that human echolocation has a vast potential.

  3. Ultracool Dwarf Stars: Surveys, Properties, and Spectral Classification (United States)

    Steele, Iain A.; Jones, Hugh R. A.


    Conference was held in Manchester, England, United Kingdom, in 2000 August. The Proceedings will be edited by H. R. A. Jones and I. A. Steele and published in the Lecture Notes in Physics Series by Springer-Verlag.

  4. Dimensionality reduction, classification, and spectral mixture analysis using nonnegative underapproximation (United States)

    Gillis, Nicolas; Plemmons, Robert J.


    Nonnegative Matrix Factorization (NMF) and its variants have recently been successfully used as dimensionality reduction techniques for identification of the materials present in hyperspectral images. In this paper, we present a new variant of NMF called Nonnegative Matrix Underapproximation (NMU): it is based on the introduction of underapproximation constraints which enables one to extract features in a recursive way, like PCA, but preserving nonnegativity. Moreover, we explain why these additional constraints make NMU particularly wellsuited to achieve a parts-based and sparse representation of the data, enabling it to recover the constitutive elements in hyperspectral data. We experimentally show the efficiency of this new strategy on hyperspectral images associated with space object material identification, and on HYDICE and related remote sensing images.

  5. Stellar spectral classification with locality preserving projections and ...

    Indian Academy of Sciences (India)


    and 3 subclasses of F-type spectra from Sloan Digital Sky Survey (SDSS); At last, the performance of LPP+SVM is compared ... (RAVE) and the Sloan Digital Sky Survey (SDSS) (Re Fiorentin et al, 2008). LLE as a nonlinear ... x in Rn, find a transformation matrix A that maps these N points to a set of points 1. 2. , ,..., N. y y.

  6. Vowel Inherent Spectral Change

    CERN Document Server

    Assmann, Peter


    It has been traditional in phonetic research to characterize monophthongs using a set of static formant frequencies, i.e., formant frequencies taken from a single time-point in the vowel or averaged over the time-course of the vowel. However, over the last twenty years a growing body of research has demonstrated that, at least for a number of dialects of North American English, vowels which are traditionally described as monophthongs often have substantial spectral change. Vowel Inherent Spectral Change has been observed in speakers’ productions, and has also been found to have a substantial effect on listeners’ perception. In terms of acoustics, the traditional categorical distinction between monophthongs and diphthongs can be replaced by a gradient description of dynamic spectral patterns. This book includes chapters addressing various aspects of vowel inherent spectral change (VISC), including theoretical and experimental studies of the perceptually relevant aspects of VISC, the relationship between ar...

  7. Identifying potential academic leaders: Predictors of willingness to undertake leadership roles in an academic department of family medicine. (United States)

    White, David; Krueger, Paul; Meaney, Christopher; Antao, Viola; Kim, Florence; Kwong, Jeffrey C


    To identify variables associated with willingness to undertake leadership roles among academic family medicine faculty. Web-based survey. Bivariate and multivariable analyses (logistic regression) were used to identify variables associated with willingness to undertake leadership roles. Department of Family and Community Medicine at the University of Toronto in Ontario. A total of 687 faculty members. Variables related to respondents' willingness to take on various academic leadership roles. Of all 1029 faculty members invited to participate in the survey, 687 (66.8%) members responded. Of the respondents, 596 (86.8%) indicated their level of willingness to take on various academic leadership roles. Multivariable analysis revealed that the predictors associated with willingness to take on leadership roles were as follows: pursuit of professional development opportunities (odds ratio [OR] 3.79, 95% CI 2.29 to 6.27); currently holding at least 1 leadership role (OR 5.37, 95% CI 3.38 to 8.53); a history of leadership training (OR 1.86, 95% CI 1.25 to 2.78); the perception that mentorship is important for one's current role (OR 2.25, 95% CI 1.40 to 3.60); and younger age (OR 0.97, 95% CI 0.95 to 0.99). Willingness to undertake new or additional leadership roles was associated with 2 variables related to leadership experiences, 2 variables related to perceptions of mentorship and professional development, and 1 demographic variable (younger age). Interventions that support opportunities in these areas might expand the pool and strengthen the academic leadership potential of faculty members.

  8. Hazard classification methodology

    International Nuclear Information System (INIS)

    Brereton, S.J.


    This document outlines the hazard classification methodology used to determine the hazard classification of the NIF LTAB, OAB, and the support facilities on the basis of radionuclides and chemicals. The hazard classification determines the safety analysis requirements for a facility

  9. The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae

    Directory of Open Access Journals (Sweden)

    René Hans-Jürgen Heim


    Full Text Available Epidermal structures (ES of leaves are known to affect the functional properties and spectral responses. Spectral studies focused mostly on the effect of hairs or wax layers only. We studied a wider range of different ES and their impact on spectral properties. Additionally, we identified spectral regions that allow distinguishing different ES. We used a field spectrometer to measure ex situ leaf spectral responses from 350 nm–2500 nm. A spectral library for 25 species of the succulent family Aizoaceae was assembled. Five functional types were defined based on ES: flat epidermal cell surface, convex to papillary epidermal cell surface, bladder cells, hairs and wax cover. We tested the separability of ES using partial least squares discriminant analysis (PLS-DA based on the spectral data. Subsequently, variable importance (VIP was calculated to identify spectral regions relevant for discriminating our functional types (classes. Classification performance was high, with a kappa value of 0.9 indicating well-separable spectral classes. VIP calculations identified six spectral regions of increased importance for the classification. We confirmed and extended previous findings regarding the visible-near-infrared spectral region. Our experiments also confirmed that epidermal leaf traits can be classified due to clearly distinguishable spectral signatures across species and genera within the Aizoaceae.

  10. High Resolution Spectral Analysis (United States)


    liable methods for high resolution spectral analysis of multivariable processes, as well as to distance measures for quantitative assessment of...called "modern nonlinear spectral analysis methods " [27]. An alternative way to reconstruct /„(#), based on Tn, is the periodogram/correlogram f{6...eie). A homotopy method was proposed in [8, 9] leading to a differential equation for A(T) in a homotopy variable r. If the statistics are consistent

  11. Sequence Classification Using Third-Order Moments

    DEFF Research Database (Denmark)

    Troelsgaard, Rasmus; Hansen, Lars Kai


    . The proposed method provides lower computational complexity at classification time than the usual likelihood-based methods. In order to demonstrate the properties of the proposed method, we perform classification of both simulated data and empirical data from a human activity recognition study.......Model-based classification of sequence data using a set of hidden Markov models is a well-known technique. The involved score function, which is often based on the class-conditional likelihood, can, however, be computationally demanding, especially for long data sequences. Inspired by recent...... theoretical advances in spectral learning of hidden Markov models, we propose a score function based on third-order moments. In particular, we propose to use the Kullback-Leibler divergence between theoretical and empirical third-order moments for classification of sequence data with discrete observations...

  12. Decree No. 67/77 of 6 May establishing a National Uranium Undertaking as a public body

    International Nuclear Information System (INIS)


    This Decree, promulgated on 29 March 1977, sets up a National Uranium Undertaking (ENU). The ENU Statute which is attached to the Decree lays down that its main purpose is to prospect for and inventory uranium deposits, to explore known deposits, to set up facilities for recovery and treatment of uranium ores, and finally, to market the products obtained. The ENU has taken over the work which, until now, had been carried out in that field by the Junta de Energia Nuclear and it is placed under the authority of the Minister of Industry and Technology. (NEA) [fr

  13. Spectrally selective glazings

    Energy Technology Data Exchange (ETDEWEB)



    Spectrally selective glazing is window glass that permits some portions of the solar spectrum to enter a building while blocking others. This high-performance glazing admits as much daylight as possible while preventing transmission of as much solar heat as possible. By controlling solar heat gains in summer, preventing loss of interior heat in winter, and allowing occupants to reduce electric lighting use by making maximum use of daylight, spectrally selective glazing significantly reduces building energy consumption and peak demand. Because new spectrally selective glazings can have a virtually clear appearance, they admit more daylight and permit much brighter, more open views to the outside while still providing the solar control of the dark, reflective energy-efficient glass of the past. This Federal Technology Alert provides detailed information and procedures for Federal energy managers to consider spectrally selective glazings. The principle of spectrally selective glazings is explained. Benefits related to energy efficiency and other architectural criteria are delineated. Guidelines are provided for appropriate application of spectrally selective glazing, and step-by-step instructions are given for estimating energy savings. Case studies are also presented to illustrate actual costs and energy savings. Current manufacturers, technology users, and references for further reading are included for users who have questions not fully addressed here.


    Directory of Open Access Journals (Sweden)

    A. Makarau


    Full Text Available The practical use of very high resolution visible and near-infrared (VNIR data is still growing (IKONOS, Quickbird, GeoEye-1, etc. but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission, unsupervised clustering (data representation on a finite domain and dimensionality reduction, and data aggregation (Bayesian or neural network. This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set, and Digital Surface Model (DSM data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands is provided and the numerical evaluation of the method in

  15. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery

    Directory of Open Access Journals (Sweden)

    Congcong Li


    Full Text Available Although a large number of new image classification algorithms have been developed, they are rarely tested with the same classification task. In this research, with the same Landsat Thematic Mapper (TM data set and the same classification scheme over Guangzhou City, China, we tested two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms that became popular in remote sensing during the past 20 years. Our analysis focused primarily on the spectral information provided by the TM data. We assessed all algorithms in a per-pixel classification decision experiment and all supervised algorithms in a segment-based experiment. We found that when sufficiently representative training samples were used, most algorithms performed reasonably well. Lack of training samples led to greater classification accuracy discrepancies than classification algorithms themselves. Some algorithms were more tolerable to insufficient (less representative training samples than others. Many algorithms improved the overall accuracy marginally with per-segment decision making.

  16. Nonparametric Collective Spectral Density Estimation and Clustering

    KAUST Repository

    Maadooliat, Mehdi


    In this paper, we develop a method for the simultaneous estimation of spectral density functions (SDFs) for a collection of stationary time series that share some common features. Due to the similarities among the SDFs, the log-SDF can be represented using a common set of basis functions. The basis shared by the collection of the log-SDFs is estimated as a low-dimensional manifold of a large space spanned by a pre-specified rich basis. A collective estimation approach pools information and borrows strength across the SDFs to achieve better estimation efficiency. Also, each estimated spectral density has a concise representation using the coefficients of the basis expansion, and these coefficients can be used for visualization, clustering, and classification purposes. The Whittle pseudo-maximum likelihood approach is used to fit the model and an alternating blockwise Newton-type algorithm is developed for the computation. A web-based shiny App found at

  17. Spectral imaging of multi-color chromogenic dyes in pathological specimens.

    NARCIS (Netherlands)

    Macville, M.V.E.; Laak, J.A.W.M. van der; Speel, E.J.; Katzir, N.; Garini, Y.; Soenksen, D.; McNamara, G.; Wilde, P.C.M. de; Hanselaar, A.G.J.M.; Hopman, A.H.N.; Ried, T.


    We have investigated the use of spectral imaging for multi-color analysis of permanent cytochemical dyes and enzyme precipitates on cytopathological specimens. Spectral imaging is based on Fourier-transform spectroscopy and digital imaging. A pixel-by-pixel spectrum-based color classification is

  18. Selection/extraction of spectral regions for autofluorescence spectra measured in the oral cavity

    NARCIS (Netherlands)

    Skurichina, M; Paclik, P; Duin, RPW; de Veld, D; Sterenborg, HJCM; Witjes, MJH; Roodenburg, JLN; Fred, A; Caelli, T; Duin, RPW; Campilho, A; DeRidder, D


    Recently a number of successful algorithms to select/extract discriminative spectral regions was introduced. These methods may be more beneficial than the standard feature selection/extraction methods for spectral classification. In this paper, on the example of autofluorescence spectra measured in

  19. Multiple Kernel Spectral Regression for Dimensionality Reduction

    Directory of Open Access Journals (Sweden)

    Bing Liu


    Full Text Available Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL into SR for dimensionality reduction. The proposed approach (termed MKL-SR seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.

  20. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik


    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...

  1. CRISS power spectral density

    International Nuclear Information System (INIS)

    Vaeth, W.


    The correlation of signal components at different frequencies like higher harmonics cannot be detected by a normal power spectral density measurement, since this technique correlates only components at the same frequency. This paper describes a special method for measuring the correlation of two signal components at different frequencies: the CRISS power spectral density. From this new function in frequency analysis, the correlation of two components can be determined quantitatively either they stem from one signal or from two diverse signals. The principle of the method, suitable for the higher harmonics of a signal as well as for any other frequency combinations is shown for the digital frequency analysis technique. Two examples of CRISS power spectral densities demonstrates the operation of the new method. (orig.) [de

  2. Parametric Explosion Spectral Model

    Energy Technology Data Exchange (ETDEWEB)

    Ford, S R; Walter, W R


    Small underground nuclear explosions need to be confidently detected, identified, and characterized in regions of the world where they have never before occurred. We develop a parametric model of the nuclear explosion seismic source spectrum derived from regional phases that is compatible with earthquake-based geometrical spreading and attenuation. Earthquake spectra are fit with a generalized version of the Brune spectrum, which is a three-parameter model that describes the long-period level, corner-frequency, and spectral slope at high-frequencies. Explosion spectra can be fit with similar spectral models whose parameters are then correlated with near-source geology and containment conditions. We observe a correlation of high gas-porosity (low-strength) with increased spectral slope. The relationship between the parametric equations and the geologic and containment conditions will assist in our physical understanding of the nuclear explosion source.

  3. Spectral analysis by correlation

    International Nuclear Information System (INIS)

    Fauque, J.M.; Berthier, D.; Max, J.; Bonnet, G.


    The spectral density of a signal, which represents its power distribution along the frequency axis, is a function which is of great importance, finding many uses in all fields concerned with the processing of the signal (process identification, vibrational analysis, etc...). Amongst all the possible methods for calculating this function, the correlation method (correlation function calculation + Fourier transformation) is the most promising, mainly because of its simplicity and of the results it yields. The study carried out here will lead to the construction of an apparatus which, coupled with a correlator, will constitute a set of equipment for spectral analysis in real time covering the frequency range 0 to 5 MHz. (author) [fr

  4. What factors influence community-dwelling older people’s intent to undertake multifactorial fall prevention programs?

    Directory of Open Access Journals (Sweden)

    Hill KD


    Full Text Available Keith D Hill,1,2 Lesley Day,3 Terry P Haines4,5 1School of Physiotherapy and Exercise Science, Faculty of Health Sciences, Curtin University, Perth, WA, Australia; 2National Ageing Research Institute, Royal Melbourne Hospital, Parkville, VIC, Australia; 3Falls Prevention Research Unit, Monash Injury Research Institute, Monash University, VIC, Australia; 4Allied Health Research Unit, Southern Health, Cheltenham, VIC, Australia; 5Physiotherapy Department, Faculty of Medicine, Nursing, and Health Sciences, Monash University, VIC, Australia Purpose: To investigate previous, current, or planned participation in, and perceptions toward, multifactorial fall prevention programs such as those delivered through a falls clinic in the community setting, and to identify factors influencing older people’s intent to undertake these interventions.Design and methods: Community-dwelling people aged >70 years completed a telephone survey. Participants were randomly selected from an electronic residential telephone listing, but purposeful sampling was used to include equal numbers with and without common chronic health conditions associated with fall-related hospitalization. The survey included scenarios for fall prevention interventions, including assessment/multifactorial interventions, such as those delivered through a falls clinic. Participants were asked about previous exposure to, or intent to participate in, the interventions. A path model analysis was used to identify factors associated with intent to participate in assessment/multifactorial interventions.Results: Thirty of 376 participants (8.0% reported exposure to a multifactorial falls clinic-type intervention in the past 5 years, and 16.0% expressed intention to undertake this intervention. Of the 132 participants who reported one or more falls in the past 12 months, over one-third were undecided or disagreed that a falls clinic type of intervention would be of benefit to them. Four elements

  5. Spectral identity mapping for enhanced chemical image analysis (United States)

    Turner, John F., II


    Advances in spectral imaging instrumentation during the last two decades has lead to higher image fidelity, tighter spatial resolution, narrower spectral resolution, and improved signal to noise ratios. An important sub-classification of spectral imaging is chemical imaging, in which the sought-after information from the sample is its chemical composition. Consequently, chemical imaging can be thought of as a two-step process, spectral image acquisition and the subsequent processing of the spectral image data to generate chemically relevant image contrast. While chemical imaging systems that provide turnkey data acquisition are increasingly widespread, better strategies to analyze the vast datasets they produce are needed. The Generation of chemically relevant image contrast from spectral image data requires multivariate processing algorithms that can categorize spectra according to shape. Conventional chemometric techniques like inverse least squares, classical least squares, multiple linear regression, principle component regression, and multivariate curve resolution are effective for predicting the chemical composition of samples having known constituents, but are less effective when a priori information about the sample is unavailable. To address these problems, we have developed a fully automated non-parametric technique called spectral identity mapping (SIMS) that reduces the dependence of spectral image analysis on training datasets. The qualitative SIMS method provides enhanced spectral shape specificity and improved chemical image contrast. We present SIMS results of infrared spectral image data acquired from polymer coated paper substrates used in the manufacture of pressure sensitive adhesive tapes. In addition, we compare the SIMS results to results from spectral angle mapping (SAM) and cosine correlation analysis (CCA), two closely related techniques.

  6. Research on intelligent fault diagnosis of gears using EMD, spectral features and data mining techniques (United States)

    Sagar, M.; Vivekkumar, G.; Reddy, Mallikarjuna; Devendiran, S.; Amarnath, M.


    In this present work aims to formulate an automated prediction model using vibration signals of various gear operating conditions by using EMD (empirical mode decomposition) and spectral features and different classification algorithms. In this present work empirical mode decomposition (EMD) is a signal processing technique used to extract more useful fault information from the vibration signals. The proposed method described in following parts gear test rig, data acquisition system, signal processing, feature extraction and classification algorithms and finally identification. Meanwhile, in order to remove the redundant and irrelevant spectral features and classification algorithms, data mining is implemented and it showed promising prediction results.

  7. Identification of Preferred Sources of Information for Undertaking Studies in the Faculty of Engineering Management at Poznan University of Technology

    Directory of Open Access Journals (Sweden)

    Magdalena Wyrwicka


    Full Text Available Since 2010 a survey has been conducted among first-year students about sources of information which influence the decision of undertaking field studies in Safety Engineering, Management Engineering and Logistics in the Faculty of Engineering Management at Poznan University of Technology. The goal of these analyses is both to assess the effectiveness of promotion and also show trends in the use of diverse channels of information transfer of studies. The results of the investigation show that internet promotion via university and faculty website plays the dominant role but also direct promotion, such as opinion of older friends, is crucial. Furthermore, from year to year the analyses indicate the significant increase of official media and reveal that the prospective students rely on a few sources of information simultaneously.

  8. Classification System of Pathological Voices Using Correntropy


    Aluisio I. R. Fontes; Pedro T. V. Souza; Adrião D. D. Neto; Allan de M. Martins; Luiz F. Q. Silveira


    This paper proposes the use of a similarity measure based on information theory called correntropy for the automatic classification of pathological voices. By using correntropy, it is possible to obtain descriptors that aggregate distinct spectral characteristics for healthy and pathological voices. Experiments using computational simulation demonstrate that such descriptors are very efficient in the characterization of vocal dysfunctions, leading to a success rate of 97% in the classificatio...

  9. SAW Classification Algorithm for Chinese Text Classification


    Xiaoli Guo; Huiyu Sun; Tiehua Zhou; Ling Wang; Zhaoyang Qu; Jiannan Zang


    Considering the explosive growth of data, the increased amount of text data’s effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words...

  10. Spectral Ensemble Kalman Filters

    Czech Academy of Sciences Publication Activity Database

    Mandel, Jan; Kasanický, Ivan; Vejmelka, Martin; Fuglík, Viktor; Turčičová, Marie; Eben, Kryštof; Resler, Jaroslav; Juruš, Pavel


    Roč. 11, - (2014), EMS2014-446 [EMS Annual Meeting /14./ & European Conference on Applied Climatology (ECAC) /10./. 06.10.2014-10.10.2014, Prague] R&D Projects: GA ČR GA13-34856S Grant - others:NSF DMS -1216481 Institutional support: RVO:67985807 Keywords : data assimilation * spectral filter Subject RIV: DG - Athmosphere Sciences, Meteorology

  11. Hungaria Asteroid Region Telescopic Spectral Survey (HARTSS) II: Spectral Homogeneity Among Hungaria Family Asteroids (United States)

    Lucas, Michael P.; Emery, Joshua; Pinilla-Alonso, Noemi; Lindsay, Sean S.; MacLennan, Eric M.; Cartwright, Richard; Reddy, Vishnu; Sanchez, Juan A.; Thomas, Cristina A.; Lorenzi, Vania


    Spectral observations of asteroid family members provide valuable information regarding parent body interiors, the source regions of near-Earth asteroids, and the link between meteorites and their parent bodies. Hungaria family asteroids constitute the closest samples to the Earth from a collisional family (~1.94 AU), permitting observations of smaller fragments than accessible for Main Belt families. We have carried out a ground-based observational campaign - Hungaria Asteroid Region Telescopic Spectral Survey (HARTSS) - to record reflectance spectra of these preserved samples from the inner-most primordial asteroid belt. During HARTSS phase one (Lucas et al. [2017]. Icarus 291, 268-287) we found that ~80% of the background population is comprised of stony S-complex asteroids that exhibit considerable spectral and mineralogical diversity. In HARTSS phase two, we turn our attention to family members and hypothesize that the Hungaria collisional family is homogeneous. We test this hypothesis through taxonomic classification, albedo estimates, and spectral properties.During phase two of HARTSS we acquired near-infrared (NIR) spectra of 50 new Hungarias (19 family; 31 background) with SpeX/IRTF and NICS/TNG. We analyzed X-type family spectra for NIR color indices (0.85-J J-K), and a subtle ~0.9 µm absorption feature that may be attributed to Fe-poor orthopyroxene. Surviving fragments of an asteroid collisional family typically exhibit similar taxonomies, albedos, and spectral properties. Spectral analysis of X-type Hungaria family members and independently calculated WISE albedo determinations for 428 Hungaria asteroids is consistent with this scenario. Furthermore, ~1/4 of the background population exhibit similar spectral properties and albedos to family X-types.Spectral observations of 92 Hungaria region asteroids acquired during both phases of HARTSS uncover a compositionally heterogeneous background and spectral homogeneity down to ~2 km for collisional family


    Directory of Open Access Journals (Sweden)

    C. Chisense


    Full Text Available Mapping of surface materials in urban areas using aerial imagery is a challenging task. This is because there are numerous materials present in relatively small regions. Hyperspectral data features a fine spectral resolution and thus has a significant capability for automatic identification and mapping of urban surface materials. In this study an approach for identification of roof surface materials using hyperspectral data is presented. The study is based on an urban area in Ludwigsburg, Germany, using a HyMap data set recorded during the HyMap campaign in August, 2010. Automatisierte Liegenschaftskarte (ALK vector data with a building layer is combined with the HyMap data to limit the analysis to roofs. A spectral library for roofs is compiled based on field and image measurements. In the roof material identification process, supervised classification methods, namely spectral angle mapper and spectral information divergence and the object oriented ECHO (extraction and classification of homogeneous objects approach are compared. In addition to the overall shape of spectral curves, position and strength of absorptions features are used to enhance material identification. The discriminant analysis feature extraction method is applied to the HyMap data in order to identify features (band combinations suitable for discriminating between the target classes. The identified optimal features are used to create a new data set which is later classified using the ECHO classifier. The classification results with respect to material types of roofs are presented in this study. The most important results are evaluated using orthophotos, probability maps and field visits.

  13. Topographic gradient based site characterization in India complemented by strong ground-motion spectral attributes

    KAUST Repository

    Nath, Sankar Kumar


    We appraise topographic-gradient approach for site classification that employs correlations between 30. m column averaged shear-wave velocity and topographic gradients. Assessments based on site classifications reported from cities across India indicate that the approach is reasonably viable at regional level. Additionally, we experiment three techniques for site classification based on strong ground-motion recordings, namely Horizontal-to-Vertical Spectral Ratio (HVSR), Response Spectra Shape (RSS), and Horizontal-to-Vertical Response Spectral Ratio (HVRSR) at the strong motion stations located across the Himalayas and northeast India. Statistical tests on the results indicate that these three techniques broadly differentiate soil and rock sites while RSS and HVRSR yield better signatures. The results also support the implemented site classification in the light of strong ground-motion spectral attributes observed in different parts of the globe. © 2013 Elsevier Ltd.

  14. Heart sound classification from unsegmented phonocardiograms. (United States)

    Langley, Philip; Murray, Alan


    Most algorithms for automated analysis of phonocardiograms (PCG) require segmentation of the signal into the characteristic heart sounds. The aim was to assess the feasibility for accurate classification of heart sounds on short, unsegmented recordings. PCG segments of 5 s duration from the PhysioNet/Computing in Cardiology Challenge database were analysed. Initially the 5 s segment at the start of each recording (seg 1) was analysed. Segments were zero-mean but otherwise had no pre-processing or segmentation. Normalised spectral amplitude was determined by fast Fourier transform and wavelet entropy by wavelet analysis. For each of these a simple single feature threshold-based classifier was implemented and the frequency/scale and thresholds for optimum classification accuracy determined. The analysis was then repeated using relatively noise free 5 s segments (seg 2) of each recording. Spectral amplitude and wavelet entropy features were then combined in a classification tree. There were significant differences between normal and abnormal recordings for both wavelet entropy and spectral amplitude across scales and frequency. In the wavelet domain the differences between groups were greatest at highest frequencies (wavelet scale 1, pseudo frequency 1 kHz) whereas in the frequency domain the differences were greatest at low frequencies (12 Hz). Abnormal recordings had significantly reduced high frequency wavelet entropy: (Median (interquartile range)) 6.63 (2.42) versus 8.36 (1.91), p  <  0.0001, suggesting the presence of discrete high frequency components in these recordings. Abnormal recordings exhibited significantly greater low frequency (12 Hz) spectral amplitude: 0.24 (0.22) versus 0.09 (0.15), p  <  0.0001. Classification accuracy (mean of specificity and sensitivity) was greatest for wavelet entropy: 76% (specificity 54%, sensitivity 98%) versus 70% (specificity 65%, sensitivity 75%) and was further improved by selecting the lowest

  15. A Simple Spectral Observer

    Directory of Open Access Journals (Sweden)

    Lizeth Torres


    Full Text Available The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier transform (FT of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1 the reconstruction of a simple periodical signal, (2 the approximation of both a square and a triangular signal, (3 the edge detection in signals by using the Fourier coefficients, (4 the fitting of the historical Bitcoin market data from 1 December 2014 to 8 January 2018 and (5 the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT, which is a well-known method for time-frequency analysis.

  16. [A novel spectral classifier based on coherence measure]. (United States)

    Li, Xiang-ru; Wu, Fu-chao; Hu, Zhan-yi; Luo, A-li


    Classification and discovery of new types of celestial bodies from voluminous celestial spectra are two important issues in astronomy, and these two issues are treated separately in the literature to our knowledge. In the present paper, a novel coherence measure is introduced which can effectively measure the coherence of a new spectrum of unknown type with the training sampleslocated within its neighbourhood, then a novel classifier is designed based on this coherence measure. The proposed classifier is capable of carrying out spectral classification and knowledge discovery simultaneously. In particular, it can effectively deal with the situation where different types of training spectra exist within the neighbourhood of a new spectrum, and the traditional k-nearest neighbour method usually fails to reach a correct classification. The satisfactory performance for classification and knowledge discovery has been obtained by the proposed novel classifier over active galactic nucleus (AGNs) and active galaxies (AGs) data.

  17. Classification in Astronomy: Past and Present (United States)

    Feigelson, Eric


    Astronomers have always classified celestial objects. The ancient Greeks distinguished between asteros, the fixed stars, and planetos, the roving stars. The latter were associated with the Gods and, starting with Plato in his dialog Timaeus, provided the first mathematical models of celestial phenomena. Giovanni Hodierna classified nebulous objects, seen with a Galilean refractor telescope in the mid-seventeenth century into three classes: "Luminosae," "Nebulosae," and "Occultae." A century later, Charles Messier compiled a larger list of nebulae, star clusters and galaxies, but did not attempt a classification. Classification of comets was a significant enterprise in the 19th century: Alexander (1850) considered two groups based on orbit sizes, Lardner (1853) proposed three groups of orbits, and Barnard (1891) divided them into two classes based on morphology. Aside from the segmentation of the bright stars into constellations, most stellar classifications were based on colors and spectral properties. During the 1860s, the pioneering spectroscopist Angelo Secchi classified stars into five classes: white, yellow, orange, carbon stars, and emission line stars. After many debates, the stellar spectral sequence was refined by the group at Harvard into the familiar OBAFGKM spectral types, later found to be a sequence on surface temperature (Cannon 1926). The spectral classification is still being extended with recent additions of O2 hot stars (Walborn et al. 2002) and L and T brown dwarfs (Kirkpatrick 2005). Townley (1913) reviews 30 years of variable star classification, emerging with six classes with five subclasses. The modern classification of variable stars has about 80 (sub)classes, and is still under debate (Samus 2009). Shortly after his confirmation that some nebulae are external galaxies, Edwin Hubble (1926) proposed his famous bifurcated classification of galaxy morphologies with three classes: ellipticals, spirals, and irregulars. These classes are still

  18. JET Joint Undertaking

    International Nuclear Information System (INIS)

    Keen, B.E.; Kupschus, P.


    The report is in sections, as follows. (1) Introduction and summary. (2) A brief description of the origins of the JET Project within the EURATOM fusion programme and the objectives and aims of the device. The basic JET design and the overall philosophy of operation are explained and the first six months of operation of the machine are summarised. The Project Team Structure adopted for the Operation Phase is set out. Finally, in order to set JET's progress in context, other large tokamaks throughout the world and their achievements are briefly described. (3) The activities and progress within the Operation and Development Department are set out; particularly relating to its responsibilities for the operation and maintenance of the tokamak and for developing the necessary engineering equipment to enhance the machine to full performance. (4) The activities and progress within the Scientific Department are described; particularly relating to the specification, procurement and operation of diagnostic equipment; definition and execution of the programme; and the interpretation of experimental results. (5) JET's programme plans for the immediate future and a broad outline of the JET Development Plan to 1990 are given. (author)

  19. JET Joint Undertaking

    International Nuclear Information System (INIS)

    Keen, B.E.


    The paper is a JET progress report 1987, and covers the fourth full year of JET's operation. The report contains an overview summary of the scientific and technical advances during the year, and is supplemented by appendices of detailed contributions of the more important JET articles published during 1987. The document is aimed at specialists and experts engaged in nuclear fusion and plasma physics, as well as the general scientific community. (U.K.)

  20. JET Joint Undertaking

    International Nuclear Information System (INIS)

    Keen, B.E.


    This is an overview summary of the scientific and technical advances at JET during the year 1985, supplemented by appendices of detailed contributions (in preprint form) of eight of the more important JET articles produced during that year. It is aimed not only at specialists and experts but also at a more general scientific community. Thus there is a brief summary of the background to the project, a description of the basic objectives of JET and the principle design features of the machine. The new structure of the Project Team is also explained. Developments and future plans are included. Improvements considered are those which are designed to overcome certain limitations encountered generally on Tokamaks, particularly those concerned with density limits, with plasma MHD behaviour, with impurities and with plasma transport. There is also a complete list of articles, reports and conference papers published in 1985 - there are 167 such items listed. (UK)

  1. JET joint undertaking

    International Nuclear Information System (INIS)


    JET began operations on 25 June 1983. This annual report contains administrative information and a general review of scientific and technical developments. Among them are vacuum systems, toroidal and poloidal field systems, power supplies, neutral beam heating, radiofrequency heating, remote handling, tritium handling, control and data acquisition systems and diagnostic systems

  2. Wavelength conversion based spectral imaging

    DEFF Research Database (Denmark)

    Dam, Jeppe Seidelin

    There has been a strong, application driven development of Si-based cameras and spectrometers for imaging and spectral analysis of light in the visible and near infrared spectral range. This has resulted in very efficient devices, with high quantum efficiency, good signal to noise ratio and high...... resolution for this spectral region. Today, an increasing number of applications exists outside the spectral region covered by Si-based devices, e.g. within cleantech, medical or food imaging. We present a technology based on wavelength conversion which will extend the spectral coverage of state of the art...... visible or near infrared cameras and spectrometers to include other spectral regions of interest....

  3. Classifications of objects on hyperspectral images

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey

    information about spatial relations of the pixels. This works well in general, especially for exploratory analysis or multivariate curve resolution, but for some specific tasks it is not beneficial at all. One of such tasks is classification or clustering of objects on hyperspectral images. An object here....... In the present work a classification method that combines classic image classification approach and MIA is proposed. The basic idea is to group all pixels and calculate spectral properties of the pixel group to be used further as a vector of predictors for calibration and class prediction. The grouping can......Hyperspectral imaging is a modern analytical technique combining benefits of digital imaging and vibrational spectroscopy. It allows to reveal and visualise spatial distribution of various chemical components. In a hyperspectral image every pixel is a spectrum (usually VNIR, SWIR or Raman...

  4. On the Use of Complementary Spectral Features for Speaker Recognition

    Directory of Open Access Journals (Sweden)

    Sridhar Krishnan


    Full Text Available The most popular features for speaker recognition are Mel frequency cepstral coefficients (MFCCs and linear prediction cepstral coefficients (LPCCs. These features are used extensively because they characterize the vocal tract configuration which is known to be highly speaker-dependent. In this work, several features are introduced that can characterize the vocal system in order to complement the traditional features and produce better speaker recognition models. The spectral centroid (SC, spectral bandwidth (SBW, spectral band energy (SBE, spectral crest factor (SCF, spectral flatness measure (SFM, Shannon entropy (SE, and Renyi entropy (RE were utilized for this purpose. This work demonstrates that these features are robust in noisy conditions by simulating some common distortions that are found in the speakers' environment and a typical telephone channel. Babble noise, additive white Gaussian noise (AWGN, and a bandpass channel with 1 dB of ripple were used to simulate these noisy conditions. The results show significant improvements in classification performance for all noise conditions when these features were used to complement the MFCC and ΔMFCC features. In particular, the SC and SCF improved performance in almost all noise conditions within the examined SNR range (10–40 dB. For example, in cases where there was only one source of distortion, classification improvements of up to 8% and 10% were achieved under babble noise and AWGN, respectively, using the SCF feature.

  5. River floodplain vegetation classification using multi-temporal high-resolution colour infrared UAV imagery.

    NARCIS (Netherlands)

    van Iersel, W.K.; Straatsma, M.W.; Addink, E.A.; Middelkoop, H.


    To evaluate floodplain functioning, monitoring of its vegetation is essential. Although airborne imagery is widely applied for this purpose, classification accuracy (CA) remains low for grassland (< 88%) and herbaceous vegetation (<57%) due to the spectral and structural similarity of these

  6. An Object-Oriented Classification Method on High Resolution Satellite Data

    National Research Council Canada - National Science Library

    Xiaoxia, Sun; Jixian, Zhang; Zhengjun, Liu


    .... Thereby only the spectral information is used for the classification. High spatial resolution sensors involves a general increase of spatial information and the accuracy of results may decrease on a per-pixel basis...

  7. Hybrid spectral CT reconstruction (United States)

    Clark, Darin P.


    Current photon counting x-ray detector (PCD) technology faces limitations associated with spectral fidelity and photon starvation. One strategy for addressing these limitations is to supplement PCD data with high-resolution, low-noise data acquired with an energy-integrating detector (EID). In this work, we propose an iterative, hybrid reconstruction technique which combines the spectral properties of PCD data with the resolution and signal-to-noise characteristics of EID data. Our hybrid reconstruction technique is based on an algebraic model of data fidelity which substitutes the EID data into the data fidelity term associated with the PCD reconstruction, resulting in a joint reconstruction problem. Within the split Bregman framework, these data fidelity constraints are minimized subject to additional constraints on spectral rank and on joint intensity-gradient sparsity measured between the reconstructions of the EID and PCD data. Following a derivation of the proposed technique, we apply it to the reconstruction of a digital phantom which contains realistic concentrations of iodine, barium, and calcium encountered in small-animal micro-CT. The results of this experiment suggest reliable separation and detection of iodine at concentrations ≥ 5 mg/ml and barium at concentrations ≥ 10 mg/ml in 2-mm features for EID and PCD data reconstructed with inherent spatial resolutions of 176 μm and 254 μm, respectively (point spread function, FWHM). Furthermore, hybrid reconstruction is demonstrated to enhance spatial resolution within material decomposition results and to improve low-contrast detectability by as much as 2.6 times relative to reconstruction with PCD data only. The parameters of the simulation experiment are based on an in vivo micro-CT experiment conducted in a mouse model of soft-tissue sarcoma. Material decomposition results produced from this in vivo data demonstrate the feasibility of distinguishing two K-edge contrast agents with a spectral

  8. Context Dependent Spectral Unmixing (United States)


    remote sensing [1–13]. It is also used in food safety [14–17], pharmaceutical process monitoring and quality control [18–22], as well as in biomedical...23,24], industrial [25], biometric [26] and forensic applications [27]. Hyperspectral sensors capture both the spatial and spectral information of a...imagery,” IEEE Signal Processing Magazine, vol. 19, no. 1, pp. 58–69, 2002. [12] A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile , L. Bruzzone, G

  9. Hybrid spectral CT reconstruction.

    Directory of Open Access Journals (Sweden)

    Darin P Clark

    Full Text Available Current photon counting x-ray detector (PCD technology faces limitations associated with spectral fidelity and photon starvation. One strategy for addressing these limitations is to supplement PCD data with high-resolution, low-noise data acquired with an energy-integrating detector (EID. In this work, we propose an iterative, hybrid reconstruction technique which combines the spectral properties of PCD data with the resolution and signal-to-noise characteristics of EID data. Our hybrid reconstruction technique is based on an algebraic model of data fidelity which substitutes the EID data into the data fidelity term associated with the PCD reconstruction, resulting in a joint reconstruction problem. Within the split Bregman framework, these data fidelity constraints are minimized subject to additional constraints on spectral rank and on joint intensity-gradient sparsity measured between the reconstructions of the EID and PCD data. Following a derivation of the proposed technique, we apply it to the reconstruction of a digital phantom which contains realistic concentrations of iodine, barium, and calcium encountered in small-animal micro-CT. The results of this experiment suggest reliable separation and detection of iodine at concentrations ≥ 5 mg/ml and barium at concentrations ≥ 10 mg/ml in 2-mm features for EID and PCD data reconstructed with inherent spatial resolutions of 176 μm and 254 μm, respectively (point spread function, FWHM. Furthermore, hybrid reconstruction is demonstrated to enhance spatial resolution within material decomposition results and to improve low-contrast detectability by as much as 2.6 times relative to reconstruction with PCD data only. The parameters of the simulation experiment are based on an in vivo micro-CT experiment conducted in a mouse model of soft-tissue sarcoma. Material decomposition results produced from this in vivo data demonstrate the feasibility of distinguishing two K-edge contrast agents with

  10. Spectral distributions and symmetries

    International Nuclear Information System (INIS)

    Quesne, C.


    As it is now well known, the spectral distribution method has both statistical and group theoretical aspects which make for great simplifications in many-Fermion system calculations with respect to more conventional ones. Although both aspects intertwine and are equally essential to understand what is going on, we are only going to discuss some of the group theoretical aspects, namely those connected with the propagation of information, in view of their fundamental importance for the actual calculations of spectral distributions. To be more precise, let us recall that the spectral distribution method may be applied in principle to many-Fermion spaces which have a direct-product structure, i.e., are obtained by distributing a certain number n of Fermions over N single-particle states (O less than or equal to n less than or equal to N), as it is the case for instance for the nuclear shell model spaces. For such systems, the operation of a central limit theorem is known to provide us with a simplifying principle which, when used in conjunction with exact or broken symmetries, enables us to make definite predictions in those cases which are not amendable to exact shell model diagonalizations. The distribution (in energy) of the states corresponding to a fixed symmetry is then defined by a small number of low-order energy moments. Since the Hamiltonian is defined in few-particle subspaces embedded in the n-particlespace, the low-order moments, we are interested in, can be expressed in terms of simpler quantities defined in those few-particle subspaces: the information is said to propagate from the simple subspaces to the more complicated ones. The possibility of actually calculating spectral distributions depends upon the finding of simple ways to propagate the information

  11. Spectral and Diffraction Tomography


    Lionheart, William


    We discuss several cases of what we call "Rich Tomography" problems in which more data is measured than a scalar for each ray. We give examples of infra red spectral tomography and Bragg edge neutron tomography in which the data is insufficient. For diffraction tomography of strain for polycrystaline materials we give an explicit reconstruction procedure. We go on to describe a way to find six independent rotation axes using Pascal's theorem of projective geometry

  12. Mechanical spectral shift reactor

    International Nuclear Information System (INIS)

    Wilson, J.F.; Sherwood, D.G.


    A mechanical spectral shift reactor comprises a reactive core having fuel assemblies accommodating both water displacer elements and neutron absorbing control rods for selectively changing the volume of water-moderator in the core. The fuel assemblies with displacer and control rods are arranged in alternating fashion so that one displacer element drive mechanism may move displacer elements in more than one fuel assembly without interfering with the movement of control rods of a corresponding control rod drive mechanisms. (author)


    Directory of Open Access Journals (Sweden)

    P. Kupidura


    Full Text Available In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture, which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the

  14. Classification of cultivated plants.

    NARCIS (Netherlands)

    Brandenburg, W.A.


    Agricultural practice demands principles for classification, starting from the basal entity in cultivated plants: the cultivar. In establishing biosystematic relationships between wild, weedy and cultivated plants, the species concept needs re-examination. Combining of botanic classification, based

  15. Accuracy in mineral identification: image spectral and spatial resolutions and mineral spectral properties

    Directory of Open Access Journals (Sweden)

    L. Pompilio


    Full Text Available Problems related to airborne hyperspectral image data are reviewed and the requirements for data analysis applied to mineralogical (rocks and soils interpretation are discussed. The variability of mineral spectral features, including absorption position, shape and depth is considered and interpreted as due to chemical composition, grain size effects and mineral association. It is also shown how this variability can be related to well defined geologic processes. The influence of sensor noise and diffuse atmospheric radiance in classification accuracy is also analyzed.

  16. Classification of Pemphigus

    Directory of Open Access Journals (Sweden)

    Ayşe Akman


    Full Text Available Clinical classification of pemphigus is not yet complete. The classic classification based on clinical and histologic features. Because of the progress in the pathogenesis of pemphigus, the current classifications based on accumulating analyses of antigen molecules and subclasses of immunoglobulins and etiologic aspects of pemphigus as weel as the clinical, histologic features. The aim of this paper is to review classification of pemphigus.

  17. Image Classification Workflow Using Machine Learning Methods (United States)

    Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.


    Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

  18. Behavioral state classification in epileptic brain using intracranial electrophysiology (United States)

    Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.


    Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

  19. Agricultural Land Use classification from Envisat MERIS (United States)

    Brodsky, L.; Kodesova, R.


    This study focuses on evaluation of a crop classification from middle-resolution images (Envisat MERIS) at national level. The main goal of such Land Use product is to provid spatial data for optimisation of monitoring of surface and groundwater pollution in the Czech Republic caused by pesticides use in agriculture. As there is a lack of spatial data on the pesticide use and their distribution, the localisation can be done according to the crop cover on arable land derived from the remote sensing images. Often high resolution data are used for agricultural Land Use classification but only at regional or local level. Envisat MERIS data, due to the wide satellite swath, can be used also at national level. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. Methodology of a pixel-based MERIS classification applying an artificial neural-network (ANN) technique was proposed and performed at a national level, the Czech Republic. Five crop groups were finally selected - winter crops, spring crops, summer crops and other crops to be classified. Classification models included a linear, radial basis function (RBF) and a multi-layer percepton (MLP) ANN with 50 networks tested in training. The training data set consisted of about 200 samples per class, on which bootstrap resampling was applied. Selection of a subset of independent variables (Meris spectral channels) was used in the procedure. The best selected ANN model (MLP: 3 in, 13 hidden, 3 out) resulted in very good performance (correct classification rate 0.974, error 0.103) applying three crop types data set. In the next step data set with five crop types was evaluated. The ANN model (MLP: 5 in, 12 hidden, 5 out) performance was also very good (correct classification rate 0.930, error 0.370). The study showed, that while accuracy of about 80 % was achieved at pixel level when classifying only three crops, accuracy of about 70 % was achieved for five crop

  20. The Level of Anxiety and Depression in Dialysis Patients Undertaking Regular Physical Exercise Training--a Preliminary Study. (United States)

    Dziubek, Wioletta; Kowalska, Joanna; Kusztal, Mariusz; Rogowski, Łukasz; Gołębiowski, Tomasz; Nikifur, Małgorzata; Szczepańska-Gieracha, Joanna; Zembroń-Łacny, Agnieszka; Klinger, Marian; Woźniewski, Marek


    The aim of the study was to evaluate the effects of a six-month physical training undertaken by haemodialysis (HD) patients, on the depression and anxiety. Patients with end stage renal disease (ESRD) were recruited from the dialysis station at the Department of Nephrology and Transplantation Medicine in Wroclaw. Physical training took place at the beginning of the first 4-hours of dialysis, three times a week for six months. A personal questionnaire, Beck Depression Inventory (BDI) and State-Trait Anxiety Inventory (STAI) were used in the study. A total of 28 patients completed the study: 20 were randomised to endurance training and 8 were randomised to resistance training. Statistical analysis of depression and anxiety at the initial (t1) and final examination (t2) indicated a significant reduction in depression and anxiety, particularly anxiety as a trait (X2) in the whole study group. The change in anxiety as a state correlated with the disease duration, duration of dialysis and the initial level of anxiety as a state (t1X1). The change in anxiety as a trait significantly correlated with age and the initial level of anxiety (t1X2). Undertaking physical training during dialysis by patients with ESRD is beneficial in reducing their levels of anxiety and depression. Both resistance and endurance training improves mood, but only endurance training additionally results in anxiety reduction. © 2016 S. Karger AG, Basel.

  1. Effects of Periodic Task-Specific Test Feedback on Physical Performance in Older Adults Undertaking Band-Based Resistance Exercise

    Directory of Open Access Journals (Sweden)

    Ryuichi Hasegawa


    Full Text Available The purpose of this study was to determine the effects of periodic task-specific test feedback on performance improvement in older adults undertaking community- and home-based resistance exercises (CHBRE. Fifty-two older adults (65–83 years were assigned to a muscular perfsormance feedback group (MPG, n=32 or a functional mobility feedback group (FMG, n=20. Both groups received exactly the same 9-week CHBRE program comprising one community-based and two home-based sessions per week. Muscle performance included arm curls and chair stands in 30 seconds, while functional mobility was determined by the timed up and go (TUG test. MPG received fortnightly test feedback only on muscle performance and FMG received feedback only on the TUG. Following training, there was a significant (P<0.05 interaction for all performance tests with MPG improving more for the arm curls (MPG 31.4%, FMG 15.9% and chair stands (MPG 33.7%, FMG 24.9% while FMG improved more for the TUG (MPG-3.5%, FMG-9.7%. Results from this nonrandomized study suggest that periodic test feedback during resistance training may enhance task-specific physical performance in older persons, thereby augmenting reserve capacity or potentially reducing the time required to recover functional abilities.

  2. Exploring the role of social interactions and supports in overcoming accessibility barriers while undertaking health tours in India. (United States)

    Jana, Arnab; Harata, Noboru; Kiyoshi, Takami; Ohmori, Nobuaki


    This article explores the phenomenon of companionship as an adaptation strategy to counter the existing barriers to health care access in developing nations. Companionship is argued to be an outcome of "inter" and "intra" household collaboration to offer diverse supports in addition to altruism. The analysis of the household survey conducted in West Bengal, India, exhibited different patterns of health care tours and the associated dependencies. In addition to support in terms of mobility while traveling and companionship while waiting for the opportunity, support in terms of refuge is also found to be essential, especially for the poor while they undertake regional tours. Causal models focusing on aggregated general health tours and specific regional tours were estimated separately to comprehend the implicit social interactions and their effects on the patient as well as the companions. The research demonstrated that accessibility barriers affect not only the ill, but also those associated with them and at times adversely. Segregation of regional tours illustrated the gaps, which instigated such tours and also might aid in health infrastructure planning as a whole.

  3. Nurses on the move: evaluation of a program to assist international students undertaking an accelerated Bachelor of Nursing program. (United States)

    Seibold, Carmel; Rolls, Colleen; Campbell, Michelle


    This paper reports on an evaluation of a Teaching and Learning Enhancement Scheme (TALES) program designed to meet the unique need of the 2005 cohort of international nursing students undertaking an accelerated Bachelor of Nursing (BN) program at the Victorian campus of Australian Catholic University (ACU) National. The program involved a team approach with three academic mentors and the international students working together to produce satisfactory learning outcomes through fortnightly meetings and provision of additional assistance including compiling a portfolio, reflective writing, English, including colloquial English and pronunciation, as well as familiarisation with handover and abbreviations common in the clinical field, general communication, assistance with preparing a resume and participation in simulated interviews. This relatively small group of international students (20) confirmed the findings of other studies from other countries of international nursing students' in terms of concerns in regard to studying in a foreign country, namely English proficiency, communication difficulties, cultural differences and unfamiliarity with the health care environment. The assistance provided by the program was identified by the completing students as invaluable in helping them settle into study and successfully complete the theoretical and clinical components of the course.

  4. Bio-based Industries Joint Undertaking: The catalyst for sustainable bio-based economic growth in Europe. (United States)

    Mengal, Philippe; Wubbolts, Marcel; Zika, Eleni; Ruiz, Ana; Brigitta, Dieter; Pieniadz, Agata; Black, Sarah


    This article discusses the preparation, structure and objectives of the Bio-based Industries Joint Undertaking (BBI JU). BBI JU is a public-private partnership (PPP) between the European Commission (EC) and the Bio-based Industries Consortium (BIC), the industry-led private not-for-profit organisation representing the private sectors across the bio-based industries. The model of the public-private partnership has been successful as a new approach to supporting research and innovation and de-risking investment in Europe. The BBI JU became a reality in 2014 and represents the largest industrial and economic cooperation endeavour financially ever undertaken in Europe in the area of industrial biotechnologies. It is considered to be one of the most forward-looking initiatives under Horizon 2020 and demonstrates the circular economy in action. The BBI JU will be the catalyst for this strategy to mobilise actors across Europe including large industry, small and medium-sized enterprises (SMEs), all types of research organisations, networks and universities. It will support regions and in doing so, the European Union Member States and associated countries in the implementation of their bioeconomy strategies. Copyright © 2017 Elsevier B.V. All rights reserved.


    Directory of Open Access Journals (Sweden)

    Przemysław Leń


    Full Text Available The object of the paper is to analyze the spatial structure of land and identification of the needs of consolidation works and exchange of land in the villages of the Sławno municipality, lying in the district of Opoczno, in the Łódzkie Voivodship. The authors use the method of zero unitarisation for the purposes of determining the order of undertaking consolidation works and exchange of land in the area of research. The basis for calculation is the database of 19 factors (x1–x19 characteristic for the listed five groups of issues, describing each of the following villages. The obtained results, in a form of synthetic meter for each village, allowed creating the hierarchy of the urgency of carrying out consolidation works. The problem of excessive fragmentation of farms, constituting the collections of a certain number of parcels, in a broader sense, is one of the elements that prevent the acceleration of reforms by conversion of the Land and Buildings Register (EGiB in a full valuable real estate cadastre in Poland. The importance of the problem is highlighted by the fact that there are ecological grounds in the study area, significant from the point of view of environmental protection.

  6. Chinese Anti-Cancer Association as a non-governmental organization undertakes systematic cancer prevention work in China (United States)


    Cancer has become the first leading cause of death in the world, particularly in low- and middle-income countries. Facing the increasing trend of cancer incidence and mortality, China issued and implemented “three-early (early prevention, early diagnosis and early treatment)” national cancer prevention plan. As the main body and dependence of social governance, non-governmental organizations (NGOs) take over the role of government in the field of cancer prevention and treatment. American Cancer Society (ACS) made a research on cancer NGOs and civil society in cancer control and found that cancer NGOs in developing countries mobilize civil society to work together and advocate governments in their countries to develop policies to address the growing cancer burden. Union for International Cancer Control (UICC), Cancer Council Australia (CCA), and Malaysian cancer NGOs are the representatives of cancer NGOs in promoting cancer control. Selecting Chinese Anti-Cancer Association (CACA) as an example in China, this article is to investigate how NGOs undertake systematic cancer prevention work in China. By conducting real case study, we found that, as a NGO, CACA plays a significant role in intensifying the leading role of government in cancer control, optimizing cancer outcomes, decreasing cancer incidence and mortality rates and improving public health. PMID:26361412

  7. On combining spectral and spatial information of hyperspectral image for camouflaged target detecting (United States)

    Hua, Wenshen; Liu, Xun; Yang, Jia


    Detecting enemy's targets and being undetectable play increasingly important roles in modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. As supervised classification requires prior knowledge which cannot be acquired easily, unsupervised classification usually is adopted to process hyperspectral images to detect camouflaged target. But one of its drawbacks—low detecting accuracy confines its application for camouflaged target detecting. Most research on the processing of hyperspectral image tends to focus exclusively on spectral domain and ignores spatial domain. However current hyperspectral image provides high spatial resolution which contains useful information for camouflaged target detecting. A new method combining spectral and spatial information is proposed to increase the detecting accuracy using unsupervised classification. The method has two steps. In the first step, a traditional unsupervised classifier (i.e. K-MEANS, ISODATA) is adopted to classify the hyperspectral image to acquire basic classifications or clusters. During the second step, a 3×3 model and spectral angle mapping are utilized to test the spatial character of the hyperspectral image. The spatial character is defined as spatial homogeneity and calculated by spectral angle mapping. Theory analysis and experiment shows the method is reasonable and efficient. Camouflaged targets are extracted from the background and different camouflaged targets are also recognized. And the proposed algorithm outperforms K-MEANS in terms of detecting accuracy, robustness and edge's distinction. This paper demonstrates the new method is meaningful to camouflaged targets detecting.

  8. Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China (United States)

    Wang, Yan-ying; Wang, Jin-liang; Wang, Ping; Hu, Wen-yin; Su, Shao-hua


    High accuracy remote sensed image classification technology is a long-term and continuous pursuit goal of remote sensing applications. In order to evaluate single classification algorithm accuracy, take Landsat TM image as data source, Northwest Yunnan as study area, seven types of land cover classification like Maximum Likelihood Classification has been tested, the results show that: (1)the overall classification accuracy of Maximum Likelihood Classification(MLC), Artificial Neural Network Classification(ANN), Minimum Distance Classification(MinDC) is higher, which is 82.81% and 82.26% and 66.41% respectively; the overall classification accuracy of Parallel Hexahedron Classification(Para), Spectral Information Divergence Classification(SID), Spectral Angle Classification(SAM) is low, which is 37.29%, 38.37, 53.73%, respectively. (2) from each category classification accuracy: although the overall accuracy of the Para is the lowest, it is much higher on grasslands, wetlands, forests, airport land, which is 89.59%, 94.14%, and 89.04%, respectively; the SAM, SID are good at forests classification with higher overall classification accuracy, which is 89.8% and 87.98%, respectively. Although the overall classification accuracy of ANN is very high, the classification accuracy of road, rural residential land and airport land is very low, which is 10.59%, 11% and 11.59% respectively. Other classification methods have their advantages and disadvantages. These results show that, under the same conditions, the same images with different classification methods to classify, there will be a classifier to some features has higher classification accuracy, a classifier to other objects has high classification accuracy, and therefore, we may select multi sub-classifier integration to improve the classification accuracy.

  9. Automated feature extraction and classification from image sources (United States)



    The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

  10. The Impact of the Spectral Band Number and Width on the Oil Pollution Diagnostics on Earth Surface by Laser Fluorescence Method

    Directory of Open Access Journals (Sweden)

    Yu. V. Fedotov


    Full Text Available Using the remote sensing methods is the most promising for day-to-day control of oil pollution. The laser-induced fluorescence method provides efficient detection and classification of oil pollutions. To monitor oil pollutions on the earth surface is more complicated than on the water one because of lower fluorescence intensity and interfering fluorescence of natural objects available on the earth surface.Properties of oil pollution classifiers depend largely on the number and positions of spectral bands of fluorescence registration. Reducing the number of spectral bands allows us to diminish computation complexity and cost of equipment. In some cases the reduction increases classification accuracy. The number of spectral bands can be reduced through increasing their width.The paper presents mathematical modeling of oil pollution detection and classification. The experimentally obtained fluorescence spectra of oil pollutions on different substrates were used as input data. The k-nearest neighbors algorithm was used to detect and classify oil pollutions. Cross validation was applied in mathematical modeling.The mathematical modeling results have shown that for oil pollutions detection using over 8 spectral bands (band width less than 50 nm a classification error rate does not depend on the further increasing number of the spectral bands.As to the type classification of oil pollutions (4 classes, an increasing width of the spectral bands up to 60 nm (the number of spectral bands reduced up 7 does not lead to a significantly decreasing overall classification accuracy.In the case of the sort classification of oil pollutions (8 classes a local maximum of the overall accuracy has been observed at 25-30 nm width of the spectral band (14-16 spectral bands. The spectral resolution improvement (increasing the number of bands does give an essentially increasing accuracy.The paper has shown that to detect and classify oil pollutions on the earth surface


    Directory of Open Access Journals (Sweden)

    R. Müller


    Full Text Available In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics – originally introduced by the physicist H. Haken – is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ecology, chemistry, cosmology, and thermodynamics up to sociology. Although this theory describes general rules for pattern formation it was linked also to pattern recognition. Pattern recognition algorithms based on synergetics theory have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analysed independently. The classification scheme based on synergetics introduces also methods for spatial regularization to get rid of "salt and pepper" classification results and for iterative parameter tuning to optimize class weights. The paper reports an experiment on a benchmark data set frequently used for method comparisons. This data set consists of a hyperspectral scene acquired by the Airborne Visible Infrared Imaging Spectrometer AVIRIS sensor of the Jet Propulsion Laboratory acquired over the Salinas Valley in CA, USA, with 15 vegetation classes. The results are compared to state-of-the-art methodologies like Support Vector Machines (SVM, Spectral Information Divergence (SID, Neural Networks, Logistic Regression, Factor Graphs or Spectral Angle Mapper (SAM. The outcomes are promising and often outperform state-of-the-art classification methodologies.

  12. Classification, disease, and diagnosis. (United States)

    Jutel, Annemarie


    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

  13. QCD spectral sum rules

    CERN Document Server

    Narison, Stéphan

    The aim of the book is to give an introduction to the method of QCD Spectral Sum Rules and to review its developments. After some general introductory remarks, Chiral Symmetry, the Historical Developments of the Sum Rules and the necessary materials for perturbative QCD including the MS regularization and renormalization schemes are discussed. The book also gives a critical review and some improvements of the wide uses of the QSSR in Hadron Physics and QSSR beyond the Standard Hadron Phenomenology. The author has participated actively in this field since 1978 just before the expanding success

  14. Spectral signatures of chirality

    DEFF Research Database (Denmark)

    Pedersen, Jesper Goor; Mortensen, Asger


    We present a new way of measuring chirality, via the spectral shift of photonic band gaps in one-dimensional structures. We derive an explicit mapping of the problem of oblique incidence of circularly polarized light on a chiral one-dimensional photonic crystal with negligible index contrast...... to the formally equivalent problem of linearly polarized light incident on-axis on a non-chiral structure with index contrast. We derive analytical expressions for the first-order shifts of the band gaps for negligible index contrast. These are modified to give good approximations to the band gap shifts also...

  15. On spectral pollution

    International Nuclear Information System (INIS)

    Llobet, X.; Appert, K.; Bondeson, A.; Vaclavik, J.


    Finite difference and finite element approximations of eigenvalue problems, under certain circumstances exhibit spectral pollution, i.e. the appearance of eigenvalues that do not converge to the correct value when the mesh density is increased. In the present paper this phenomenon is investigated in a homogeneous case by means of discrete dispersion relations: the polluting modes belong to a branch of the dispersion relation that is strongly distorted by the discretization method employed, or to a new, spurious branch. The analysis is applied to finite difference methods and to finite element methods, and some indications about how to avoiding polluting schemes are given. (author) 5 figs., 10 refs


    Directory of Open Access Journals (Sweden)

    Artem O. Donskikh*


    Full Text Available The paper considers methods of classification of grain mixture components based on spectral analysis in visible and near-infrared wavelength ranges using various measurement approaches - reflection, transmission and combined spectrum methods. It also describes the experimental measuring units used and suggests the prototype of a multispectral grain mixture analyzer. The results of the spectral measurement were processed using neural network based classification algorithms. The probabilities of incorrect recognition for various numbers of spectral parts and combinations of spectral methods were estimated. The paper demonstrates that combined usage of two spectral analysis methods leads to higher classification accuracy and allows for reducing the number of the analyzed spectral parts. A detailed description of the proposed measurement device for high-performance real-time multispectral analysis of the components of grain mixtures is given.

  17. Neural network technologies for image classification (United States)

    Korikov, A. M.; Tungusova, A. V.


    We analyze the classes of problems with an objective necessity to use neural network technologies, i.e. representation and resolution problems in the neural network logical basis. Among these problems, image recognition takes an important place, in particular the classification of multi-dimensional data based on information about textural characteristics. These problems occur in aerospace and seismic monitoring, materials science, medicine and other. We reviewed different approaches for the texture description: statistical, structural, and spectral. We developed a neural network technology for resolving a practical problem of cloud image classification for satellite snapshots from the spectroradiometer MODIS. The cloud texture is described by the statistical characteristics of the GLCM (Gray Level Co- Occurrence Matrix) method. From the range of neural network models that might be applied for image classification, we chose the probabilistic neural network model (PNN) and developed an implementation which performs the classification of the main types and subtypes of clouds. Also, we chose experimentally the optimal architecture and parameters for the PNN model which is used for image classification.

  18. New spectral types in NGC 3603 (United States)

    Morrell, N.; Melena, N.; Massey, P.; Zangari, A.

    NGC 3603 is a giant H II region known to harbor a large population of massive stars. Its central cluster is the closest galactic counterpart to the R136 cluster in 30 Dor, in the Large Magellanic Cloud (Walborn 1973). It is very compact (76 arcsecs in diameter) which makes it an extremely difficult target for individual stars spectroscopy. Some stars lying mostly in the periphery of NGC 3603 have been classified from the ground by Moffat (1983), but for the highly crowded core only one study was available at present (Drissen et al. 1995), which was performed with the Faint Object Spectrograph on board of the Hubble Space Telescope (HST). Among the massive members of NGC 3603 there are some of the objects showing H-rich WN + abs spectra, also found in the R136 cluster in 30 Doradus (Massey & Hunter 1998). During 2 nights in April 2006, we have made use of the excellent seeing and large aperture of the Magellan telescopes to obtain individual spectroscopy for stars in the crowded core of NGC 3603. We used the IMACS spectrograph in F4 mode at the Baade (Magellan I) telescope, with a 600 l/mm grating and a 0.7 arcsec long slit. From these observations we were able to derive new spectral types for 26 stars: 16 of which are classified here for the first time, while for the remaining 10 we have revised previous spectral classifications, finding very good general agreement, but exact coincidence for only 2 of them. This rises to 38 the number of stars in this massive star forming region, for which spectral classification is available. Not surprisingly, most of the newly classified spectra belong to the earliest O-subtypes. This work is part of a more comprehensive study (Melena et al. 2007) in which archival HST/ACS-HRC images (P.I. Maiz-Apellaniz) have been used to derive new photometry for stars in the cluster, including those for which there is spectroscopy. Having new spectral types and improved photometry, allowed us to determine new values for the reddening (E (B

  19. Is spectral reflectance of the face a reliable biometric? (United States)

    Uzair, Muhammad; Mahmood, Arif; Shafait, Faisal; Nansen, Christian; Mian, Ajmal


    Over a decade ago, Pan et al. [IEEE TPAMI 25, 1552 (2003)] performed face recognition using only the spectral reflectance of the face at six points and reported around 95% recognition rate. Since their database is private, no one has been able to replicate these results. Moreover, due to the unavailability of public datasets, there has been no detailed study in the literature on the viability of facial spectral reflectance for person identification. In this study, we introduce a new public database of facial spectral reflectance profiles measured with a high precision spectrometer. For each of the 40 subjects, spectral reflectance was measured at the same six points as Pan et al. [IEEE TPAMI 25, 1552 (2003)] in multiple sessions and with time lapse. Furthermore, we sample the facial spectral reflectance from two public hyperspectral face image datasets and analyzed the data using state of the art face classification techniques. The best performing classifier achieved the maximum rank-1 identification rate of 53.8%. We conclude that facial spectral reflectance alone is not a reliable biometric for unconstrained face recognition.

  20. Biologically-inspired data decorrelation for hyper-spectral imaging

    Directory of Open Access Journals (Sweden)

    Ghita Ovidiu


    Full Text Available Abstract Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA, linear discriminant analysis (LDA, wavelet decomposition (WD, or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

  1. Revisiting Existing Classification Approaches for Building Materials Based on Hyperspectral Data (United States)

    Ilehag, R.; Weinmann, M.; Schenk, A.; Keller, S.; Jutzi, B.; Hinz, S.


    Classification of materials found in urban areas using remote sensing, in particular with hyperspectral data, has in recent times increased in importance. This study is conducting classification of materials found on building using hyperspectral data, by using an existing spectral library and collected data acquired with a spectrometer. Two commonly used classification algorithms, Support Vector Machine and Random Forest, were used to classify the materials. In addition, dimensionality reduction and band selection were performed to determine if selected parts of the full spectral domain, such as the Short Wave Infra-Red domain, are sufficient to classify the different materials. We achieved the best classification results for the two datasets using dimensionality reduction based on a Principal Component Analysis in combination with a Random Forest classification. Classification using the full domain achieved the best results, followed by the Short Wave Infra-Red domain.

  2. Photometric Redshift and Classification for the XMM-COSMOS Sources

    NARCIS (Netherlands)

    Salvato, M.; Hasinger, G.; Ilbert, O.; Zamorani, G.; Brusa, M.; Scoville, N. Z.; Rau, A.; Capak, P.; Arnouts, S.; Aussel, H.; Bolzonella, M.; Buongiorno, A.; Cappelluti, N.; Caputi, K.; Civano, F.; Cook, R.; Elvis, M.; Gilli, R.; Jahnke, K.; Kartaltepe, J. S.; Impey, C. D.; Lamareille, F.; Le Floch, E.; Lilly, S.; Mainieri, V.; McCarthy, P.; McCracken, H.; Mignoli, M.; Mobasher, B.; Murayama, T.; Sasaki, S.; Sanders, D. B.; Schiminovich, D.; Shioya, Y.; Shopbell, P.; Silverman, J.; Smolcic, V.; Surace, J.; Taniguchi, Y.; Thompson, D.; Trump, J. R.; Urry, M.; Zamojski, M.


    We present photometric redshifts and spectral energy distribution (SED) classifications for a sample of 1542 optically identified sources detected with XMM in the COSMOS field. Our template fitting classifies 46 sources as stars and 464 as nonactive galaxies, while the remaining 1032 require

  3. Decision tree approach for classification of remotely sensed satellite ...

    Indian Academy of Sciences (India)

    The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source data mining software. The classified image is compared with the image classified using classical ISODATA clustering and Maximum Likelihood Classifier (MLC) algorithms. Classification result ...

  4. Transferability of decision trees for land cover classification in a ...

    African Journals Online (AJOL)


    heterogeneous, as geographical complexity can have a negative effect on the spectral separability of .... environment. The 30m resolution imagery was pansharpened to 15m, while the two thermal bands .... Slope gradient and aspect values were calculated and incorporated into the classification as additional features. 4.

  5. Intensity Conserving Spectral Fitting (United States)

    Klimchuk, J. A.; Patsourakos, S.; Tripathi, D.


    The detailed shapes of spectral line profiles provide valuable information about the emitting plasma, especially when the plasma contains an unresolved mixture of velocities, temperatures, and densities. As a result of finite spectral resolution, the intensity measured by a spectrometer is the average intensity across a wavelength bin of non-zero size. It is assigned to the wavelength position at the center of the bin. However, the actual intensity at that discrete position will be different if the profile is curved, as it invariably is. Standard fitting routines (spline, Gaussian, etc.) do not account for this difference, and this can result in significant errors when making sensitive measurements. Detection of asymmetries in solar coronal emission lines is one example. Removal of line blends is another. We have developed an iterative procedure that corrects for this effect. It can be used with any fitting function, but we employ a cubic spline in a new analysis routine called Intensity Conserving Spline Interpolation (ICSI). As the name implies, it conserves the observed intensity within each wavelength bin, which ordinary fits do not. Given the rapid convergence, speed of computation, and ease of use, we suggest that ICSI be made a standard component of the processing pipeline for spectroscopic data.

  6. A systematic scoping review of the evidence for consumer involvement in organisations undertaking systematic reviews: focus on Cochrane. (United States)

    Morley, Richard F; Norman, Gill; Golder, Su; Griffith, Polly


    Cochrane is the largest international producer of systematic reviews of clinical trial evidence. We looked for published evidence that reports where consumers (patients and the public) have been involved in Cochrane systematic reviews, and also in reviews published by other organisations.We found 36 studies that reported about consumer involvement either in individual systematic reviews, or in other organisations. The studies showed that consumers were involved in reviews in a range of different ways: coordinating and producing reviews, making reviews more accessible, and spreading the results of reviews ("knowledge transfer"). The most common role was commenting on reviews ("peer reviewing"). Consumers also had other general roles, for example in educating people about evidence or helping other consumers. There were some interesting examples of new ways of involving consumers. The studies showed that most consumers came from rich and English speaking countries. There was little evidence about how consumer involvement had changed the reviews ("impact"). The studies found that consumer involvement needed to be properly supported.In future we believe that more research should be done to understand what kind of consumer involvement has the best impact; that more review authors should report how consumers have been involved; and that consumers who help with reviews should come from more varied backgrounds. Background Cochrane is the largest international producer of systematic reviews, and is committed to consumer involvement in the production and dissemination of its reviews. The review aims to systematically scope the evidence base for consumer involvement in organisations which commission, undertake or support systematic reviews; with an emphasis on Cochrane. Methods In June 2015 we searched six databases and other sources for studies of consumer involvement in organisations which commission, undertake or support systematic reviews, or in individual systematic

  7. Building capacity to use and undertake research in health organisations: a survey of training needs and priorities among staff. (United States)

    Barratt, Helen; Fulop, Naomi J


    Efforts to improve healthcare and population health depend partly on the ability of health organisations to use research knowledge and participate in its production. We report the findings of a survey conducted to prioritise training needs among healthcare and public health staff, in relation to the production and implementation of research, across an applied health research collaboration. A questionnaire survey using a validated tool, the Hennessy-Hicks Training Needs Assessment Questionnaire. Participants rated 25 tasks on a five-point scale with regard to both their confidence in performing the task, and its importance to their role. A questionnaire weblink was distributed to a convenience sample of 35 healthcare and public health organisations in London and South East England, with a request that they cascade the information to relevant staff. 203 individuals responded, from 20 healthcare and public health organisations. None. Training needs were identified by comparing median importance and performance scores for each task. Individuals were also invited to describe up to three priority areas in which they require training. Across the study sample, evaluation; teaching; making do with limited resources; coping with change and managing competing demands were identified as key tasks. Assessing the relevance of research and learning about new developments were the most relevant research-related tasks. Participants' training priorities included evaluation; finding, appraising and applying research evidence; and data analysis. Key barriers to involvement included time and resources, as well as a lack of institutional support for undertaking research. We identify areas in which healthcare and public health professionals may benefit from support to facilitate their involvement in and use of applied health research. We also describe barriers to participation and differing perceptions of research between professional groups. Published by the BMJ Publishing Group Limited

  8. An investigation in to the impact of acquisition location on error type and rate when undertaking panoramic radiography. (United States)

    Loughlin, A; Drage, N; Greenall, C; Farnell, D J J


    Panoramic radiography is a common radiographic examination carried out in the UK. This study was carried out to determine if acquisition site has an impact on image quality. An image quality audit was carried out in South Wales across a number of dental and general radiology settings. The image quality was assessed retrospectively against national standards. A total of 174 radiographs were assessed from general radiology departments and 141 from dental radiology units. Chi-squared analysis was used to investigate whether there were differences in the grading between dental radiology units and general radiology departments. Differences between the two settings in terms of the number of errors in the radiographs was analysed using the Mann-Whitney test. Chi-squared analysis was used to see if there were differences between the types of errors in the two clinical settings. There was a significant association (p = 0.021) between the quality of the radiograph grading and type of radiology department. However when excellent and diagnostically acceptable radiographs were grouped together there was no significant difference between the two clinical settings. Although the vast majority of radiographs were diagnostic (89% for general radiology and 92% for dental radiology units), neither reached the required standards. The most common errors were patient positioning errors (54.6% radiographs affected) and preparation/instructional errors (47.9% radiographs affected). Errors in panoramic radiography are relatively high and further instruction to staff undertaking these procedures is required to ensure the targets are reached. Copyright © 2017 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

  9. Resumption of menstruation and pituitary response to gonadotropin-releasing hormone in functional hypothalamic amenorrhea subjects undertaking estrogen replacement therapy. (United States)

    Shen, Z Q; Xu, J J; Lin, J F


    Functional hypothalamic amenorrhea (FHA) refers to a functional menstrual disorder with various causes and presentations. Recovery of menstrual cyclicity is common in long-term follow-up but the affecting factors remain unknown. To explore factors affecting the menstrual resumption and to evaluate the pituitary response to gonadotropin-releasing hormone (GnRH) in FHA. Thirty cases with FHA were recruited. All subjects were put on continuous 1 mg/day estradiol valerate orally and followed up monthly. Recovery was defined as the occurrence of at least three consecutive regular cycles. Responder referred to those who recovered within two years of therapy. Gonadotropin response to the 50 μg GnRH challenge was tested every three months. Nineteen (63.3%) subjects recovered with a mean time to recovery of 26.8 months. Time to recovery was negatively correlated with body mass index (BMI) before and by amenorrhea. Twentyone cases had undertaken therapy for more than two years and 10 of them recovered. BMI before and by amenorrhea were negatively correlated with the recovery. Significant increase of serum luteinizing hormone (LH) and LH response to GnRH were noted after recovery. Menstrual resumption was common in FHA undertaking estrogen replacement therapy (ERT). The likelihood of recovery was affected by their BMI before and by amenorrhea but not by the weight gain during therapy. Low serum LH and attenuated LH response to GnRH were the main features of pituitary deficiency in FHA. The menstrual resumption in FHA was accompanied by the recovery of serum LH and the LH response to GnRH.

  10. Recursive heuristic classification (United States)

    Wilkins, David C.


    The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

  11. Security classification of information

    Energy Technology Data Exchange (ETDEWEB)

    Quist, A.S.


    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  12. Knowledge Discovery in Spectral Data by Means of Complex Networks

    Directory of Open Access Journals (Sweden)

    Stefano Boccaletti


    Full Text Available In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.

  13. Automated structural classification of lipids by machine learning. (United States)

    Taylor, Ryan; Miller, Ryan H; Miller, Ryan D; Porter, Michael; Dalgleish, James; Prince, John T


    Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome far exceeds the number currently classified, despite a decade of work. Automated classification would benefit ongoing classification efforts by decreasing the time needed and increasing the accuracy of classification while providing classifications for mass spectral identification algorithms. We introduce a tool that automates classification into the LIPID MAPS ontology of known lipids with >95% accuracy and novel lipids with 63% accuracy. The classification is based upon simple chemical characteristics and modern machine learning algorithms. The decision trees produced are intelligible and can be used to clarify implicit assumptions about the current LIPID MAPS classification scheme. These characteristics and decision trees are made available to facilitate alternative implementations. We also discovered many hundreds of lipids that are currently misclassified in the LIPID MAPS database, strongly underscoring the need for automated classification. Source code and chemical characteristic lists as SMARTS search strings are available under an open-source license at © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:

  14. Spectral feature variations in x-ray diffraction imaging systems (United States)

    Wolter, Scott D.; Greenberg, Joel A.


    Materials with different atomic or molecular structures give rise to unique scatter spectra when measured by X-ray diffraction. The details of these spectra, though, can vary based on both intrinsic (e.g., degree of crystallinity or doping) and extrinsic (e.g., pressure or temperature) conditions. While this sensitivity is useful for detailed characterizations of the material properties, these dependences make it difficult to perform more general classification tasks, such as explosives threat detection in aviation security. A number of challenges, therefore, currently exist for reliable substance detection including the similarity in spectral features among some categories of materials combined with spectral feature variations from materials processing and environmental factors. These factors complicate the creation of a material dictionary and the implementation of conventional classification and detection algorithms. Herein, we report on two prominent factors that lead to variations in spectral features: crystalline texture and temperature variations. Spectral feature comparisons between materials categories will be described for solid metallic sheet, aqueous liquids, polymer sheet, and metallic, organic, and inorganic powder specimens. While liquids are largely immune to texture effects, they are susceptible to temperature changes that can modify their density or produce phase changes. We will describe in situ temperature-dependent measurement of aqueous-based commercial goods in the temperature range of -20°C to 35°C.

  15. Spectral clustering for water body spectral types analysis (United States)

    Huang, Leping; Li, Shijin; Wang, Lingli; Chen, Deqing


    In order to study the spectral types of water body in the whole country, the key issue of reservoir research is to obtain and to analyze the information of water body in the reservoir quantitatively and accurately. A new type of weight matrix is constructed by utilizing the spectral features and spatial features of the spectra from GF-1 remote sensing images comprehensively. Then an improved spectral clustering algorithm is proposed based on this weight matrix to cluster representative reservoirs in China. According to the internal clustering validity index which called Davies-Bouldin(DB) index, the best clustering number 7 is obtained. Compared with two clustering algorithms, the spectral clustering algorithm based only on spectral features and the K-means algorithm based on spectral features and spatial features, simulation results demonstrate that the proposed spectral clustering algorithm based on spectral features and spatial features has a higher clustering accuracy, which can better reflect the spatial clustering characteristics of representative reservoirs in various provinces in China - similar spectral properties and adjacent geographical locations.

  16. Ministerial Decree of 15 February 1974 establishing the inventory of qualified experts and physicians authorized to undertake the health physics and medical supervision of protection against ionizing radiations

    International Nuclear Information System (INIS)


    This Decree was made in implementation of DPR No. 185 of 13 February 1964 and provides for the legal and administrative acknowledgment of experts and physicians who are required to undertake supervision of protection against the hazards of ionizing radiations. (NEA) [fr

  17. Spectral Automorphisms in Quantum Logics (United States)

    Ivanov, Alexandru; Caragheorgheopol, Dan


    In quantum mechanics, the Hilbert space formalism might be physically justified in terms of some axioms based on the orthomodular lattice (OML) mathematical structure (Piron in Foundations of Quantum Physics, Benjamin, Reading, 1976). We intend to investigate the extent to which some fundamental physical facts can be described in the more general framework of OMLs, without the support of Hilbert space-specific tools. We consider the study of lattice automorphisms properties as a “substitute” for Hilbert space techniques in investigating the spectral properties of observables. This is why we introduce the notion of spectral automorphism of an OML. Properties of spectral automorphisms and of their spectra are studied. We prove that the presence of nontrivial spectral automorphisms allow us to distinguish between classical and nonclassical theories. We also prove, for finite dimensional OMLs, that for every spectral automorphism there is a basis of invariant atoms. This is an analogue of the spectral theorem for unitary operators having purely point spectrum.

  18. Classification of Flotation Frothers

    Directory of Open Access Journals (Sweden)

    Jan Drzymala


    Full Text Available In this paper, a scheme of flotation frothers classification is presented. The scheme first indicates the physical system in which a frother is present and four of them i.e., pure state, aqueous solution, aqueous solution/gas system and aqueous solution/gas/solid system are distinguished. As a result, there are numerous classifications of flotation frothers. The classifications can be organized into a scheme described in detail in this paper. The frother can be present in one of four physical systems, that is pure state, aqueous solution, aqueous solution/gas and aqueous solution/gas/solid system. It results from the paper that a meaningful classification of frothers relies on choosing the physical system and next feature, trend, parameter or parameters according to which the classification is performed. The proposed classification can play a useful role in characterizing and evaluation of flotation frothers.

  19. Comparison between Possibilistic c-Means (PCM and Artificial Neural Network (ANN Classification Algorithms in Land use/ Land cover Classification

    Directory of Open Access Journals (Sweden)

    Ganchimeg Ganbold


    Full Text Available There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC, the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

  20. Rectangular spectral collocation

    KAUST Repository

    Driscoll, Tobin A.


    Boundary conditions in spectral collocation methods are typically imposed by removing some rows of the discretized differential operator and replacing them with others that enforce the required conditions at the boundary. A new approach based upon resampling differentiated polynomials into a lower-degree subspace makes differentiation matrices, and operators built from them, rectangular without any row deletions. Then, boundary and interface conditions can be adjoined to yield a square system. The resulting method is both flexible and robust, and avoids ambiguities that arise when applying the classical row deletion method outside of two-point scalar boundary-value problems. The new method is the basis for ordinary differential equation solutions in Chebfun software, and is demonstrated for a variety of boundary-value, eigenvalue and time-dependent problems.

  1. VizieR Online Data Catalog: Spectral properties of 441 radio pulsars (Jankowski+, 2018) (United States)

    Jankowski, F.; van Straten, W.; Keane, E. F.; Bailes, M.; Barr, E. D.; Johnston, S.; Kerr, M.


    We present spectral parameters for 441 radio pulsars. These were obtained from observations centred at 728, 1382 and 3100MHz using the 10-50cm and the 20cm multibeam receiver at the Parkes radio telescope. In particular, we list the pulsar names (J2000), the calibrated, band-integrated flux densities at 728, 1382 and 3100MHz, the spectral classifications, the frequency ranges the spectral classifications were performed over, the spectral indices for pulsars with simple power-law spectra and the robust modulation indices at all three centre frequencies for pulsars of which we have at least six measurement epochs. The flux density uncertainties include scintillation and a systematic contribution, in addition to the statistical uncertainty. Upper limits are reported at the 3σ level and all other uncertainties at the 1σ level. (1 data file).

  2. A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery (United States)

    Zhao, Bei; Zhong, Yanfei; Zhang, Liangpei


    Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral-structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental

  3. [Review of digital ground object spectral library]. (United States)

    Zhou, Xiao-Hu; Zhou, Ding-Wu


    A higher spectral resolution is the main direction of developing remote sensing technology, and it is quite important to set up the digital ground object reflectance spectral database library, one of fundamental research fields in remote sensing application. Remote sensing application has been increasingly relying on ground object spectral characteristics, and quantitative analysis has been developed to a new stage. The present article summarized and systematically introduced the research status quo and development trend of digital ground object reflectance spectral libraries at home and in the world in recent years. Introducing the spectral libraries has been established, including desertification spectral database library, plants spectral database library, geological spectral database library, soil spectral database library, minerals spectral database library, cloud spectral database library, snow spectral database library, the atmosphere spectral database library, rocks spectral database library, water spectral database library, meteorites spectral database library, moon rock spectral database library, and man-made materials spectral database library, mixture spectral database library, volatile compounds spectral database library, and liquids spectral database library. In the process of establishing spectral database libraries, there have been some problems, such as the lack of uniform national spectral database standard and uniform standards for the ground object features as well as the comparability between different databases. In addition, data sharing mechanism can not be carried out, etc. This article also put forward some suggestions on those problems.

  4. Spectral Theory of Chemical Bonding

    National Research Council Canada - National Science Library

    Langhoff, P. W; Boatz, J. A; Hinde, R. J; Sheehy, J. A


    New theoretical methods are reported for obtaining the binding energies of molecules and other chemical aggregates employing the spectral eigenstates and related properties of their atomic constituents...

  5. Ontologies vs. Classification Systems

    DEFF Research Database (Denmark)

    Madsen, Bodil Nistrup; Erdman Thomsen, Hanne


    What is an ontology compared to a classification system? Is a taxonomy a kind of classification system or a kind of ontology? These are questions that we meet when working with people from industry and public authorities, who need methods and tools for concept clarification, for developing meta...... data sets or for obtaining advanced search facilities. In this paper we will present an attempt at answering these questions. We will give a presentation of various types of ontologies and briefly introduce terminological ontologies. Furthermore we will argue that classification systems, e.g. product...... classification systems and meta data taxonomies, should be based on ontologies....

  6. Spectral dimensionality reduction based on intergrated bispectrum phase for hyperspectral image analysis (United States)

    Saipullah, Khairul Muzzammil; Kim, Deok-Hwan


    In this paper, we propose a method to reduce spectral dimension based on the phase of integrated bispectrum. Because of the excellent and robust information extracted from the bispectrum, the proposed method can achieve high spectral classification accuracy even with low dimensional feature. The classification accuracy of bispectrum with one dimensional feature is 98.8%, whereas those of principle component analysis (PCA) and independent component analysis (ICA) are 41.2% and 63.9%, respectively. The unsupervised segmentation accuracy of bispectrum is also 20% and 40% greater than those of PCA and ICA, respectively.

  7. Astrophysical Information from Objective Prism Digitized Images: Classification with an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Bratsolis Emmanuel


    Full Text Available Stellar spectral classification is not only a tool for labeling individual stars but is also useful in studies of stellar population synthesis. Extracting the physical quantities from the digitized spectral plates involves three main stages: detection, extraction, and classification of spectra. Low-dispersion objective prism images have been used and automated methods have been developed. The detection and extraction problems have been presented in previous works. In this paper, we present a classification method based on an artificial neural network (ANN. We make a brief presentation of the entire automated system and we compare the new classification method with the previously used method of maximum correlation coefficient (MCC. Digitized photographic material has been used here. The method can also be used on CCD spectral images.

  8. Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery (United States)

    Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn


    The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

  9. Understanding Soliton Spectral Tunneling as a Spectral Coupling Effect

    DEFF Research Database (Denmark)

    Guo, Hairun; Wang, Shaofei; Zeng, Xianglong


    Soliton eigenstate is found corresponding to a dispersive phase profile under which the soliton phase changes induced by the dispersion and nonlinearity are instantaneously counterbalanced. Much like a waveguide coupler relying on a spatial refractive index profile that supports mode coupling...... between channels, here we suggest that the soliton spectral tunneling effect can be understood supported by a spectral phase coupler. The dispersive wave number in the spectral domain must have a coupler-like symmetric profile for soliton spectral tunneling to occur. We show that such a spectral coupler...... exactly implies phase as well as group-velocity matching between the input soliton and tunneled soliton, namely a soliton phase matching condition. Examples in realistic photonic crystal fibers are also presented....

  10. Linear Classification Functions. (United States)

    Huberty, Carl J.; Smith, Jerry D.

    Linear classification functions (LCFs) arise in a predictive discriminant analysis for the purpose of classifying experimental units into criterion groups. The relative contribution of the response variables to classification accuracy may be based on LCF-variable correlations for each group. It is proved that, if the raw response measures are…

  11. Classification, confusion and misclassification

    African Journals Online (AJOL)

    Classifications change and in that process, we can see that someone or some group has recognise that a previous classification hindered understanding or moulded ... to a pathologist's ability to distinguish, had led to confusion and mismanagement by gynaecologists. What is worrying if not the word. 'complex'? But this is ...

  12. Spectroscopic classification of transients

    DEFF Research Database (Denmark)

    Stritzinger, M. D.; Fraser, M.; Hummelmose, N. N.


    We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017.......We report the spectroscopic classification of several transients based on observations taken with the Nordic Optical Telescope (NOT) equipped with ALFOSC, over the nights 23-25 August 2017....

  13. Library Classification 2020 (United States)

    Harris, Christopher


    In this article the author explores how a new library classification system might be designed using some aspects of the Dewey Decimal Classification (DDC) and ideas from other systems to create something that works for school libraries in the year 2020. By examining what works well with the Dewey Decimal System, what features should be carried…

  14. Colombia: Territorial classification

    International Nuclear Information System (INIS)

    Mendoza Morales, Alberto


    The article is about the approaches of territorial classification, thematic axes, handling principles and territorial occupation, politician and administrative units and administration regions among other topics. Understanding as Territorial Classification the space distribution on the territory of the country, of the geographical configurations, the human communities, the political-administrative units and the uses of the soil, urban and rural, existent and proposed


    Directory of Open Access Journals (Sweden)



    Full Text Available Using spectral analysis is very common in technical areas but rather unusual in economics and finance, where ARIMA and GARCH modeling are much more in use. To show that spectral analysis can be useful in determining hidden periodic components for high-frequency finance data as well, we use the example of foreign exchange rates

  16. Use of UAV-Borne Spectrometer for Land Cover Classification

    Directory of Open Access Journals (Sweden)

    Sowmya Natesan


    Full Text Available Unmanned aerial vehicles (UAV are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS and Inertial Measurement Unit (IMU navigation sensors, and a Raspberry Pi processor and camera module. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral measurements. RGB images and spectrometer data were captured simultaneously. As spectrometer data do not provide continuous terrain coverage, the locations of their ground elliptical footprints were determined from the bundle adjustment solution of the captured images. For each of the spectrometer ground ellipses, the land cover signature at the footprint location was determined to enable the characterization, identification, and classification of land cover elements. To attain a continuous land cover classification map, spatial interpolation was carried out from the irregularly distributed labeled spectrometer points. The accuracy of the classification was assessed using spatial intersection with the object-based image classification performed using the RGB images. Results show that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed classes, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the contribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve land cover classification.

  17. DOE LLW classification rationale

    International Nuclear Information System (INIS)

    Flores, A.Y.


    This report was about the rationale which the US Department of Energy had with low-level radioactive waste (LLW) classification. It is based on the Nuclear Regulatory Commission's classification system. DOE site operators met to review the qualifications and characteristics of the classification systems. They evaluated performance objectives, developed waste classification tables, and compiled dose limits on the waste. A goal of the LLW classification system was to allow each disposal site the freedom to develop limits to radionuclide inventories and concentrations according to its own site-specific characteristics. This goal was achieved with the adoption of a performance objectives system based on a performance assessment, with site-specific environmental conditions and engineered disposal systems

  18. Intersection numbers of spectral curves

    CERN Document Server

    Eynard, B


    We compute the symplectic invariants of an arbitrary spectral curve with only 1 branchpoint in terms of integrals of characteristic classes in the moduli space of curves. Our formula associates to any spectral curve, a characteristic class, which is determined by the laplace transform of the spectral curve. This is a hint to the key role of Laplace transform in mirror symmetry. When the spectral curve is y=\\sqrt{x}, the formula gives Kontsevich--Witten intersection numbers, when the spectral curve is chosen to be the Lambert function \\exp{x}=y\\exp{-y}, the formula gives the ELSV formula for Hurwitz numbers, and when one chooses the mirror of C^3 with framing f, i.e. \\exp{-x}=\\exp{-yf}(1-\\exp{-y}), the formula gives the topological vertex formula, i.e. the generating function of Gromov-Witten invariants of C^3. In some sense this formula generalizes ELSV formula, and Mumford formula.

  19. Spectral imagery collection experiment (United States)

    Romano, Joao M.; Rosario, Dalton; Farley, Vincent; Sohr, Brian


    The Spectral and Polarimetric Imagery Collection Experiment (SPICE) is a collaborative effort between the US Army ARDEC and ARL for the collection of mid-wave and long-wave infrared imagery using hyperspectral, polarimetric, and broadband sensors. The objective of the program is to collect a comprehensive database of the different modalities over the course of 1 to 2 years to capture sensor performance over a wide variety of adverse weather conditions, diurnal, and seasonal changes inherent to Picatinny's northern New Jersey location. Using the Precision Armament Laboratory (PAL) tower at Picatinny Arsenal, the sensors will autonomously collect the desired data around the clock at different ranges where surrogate 2S3 Self-Propelled Howitzer targets are positioned at different viewing perspectives at 549 and 1280m from the sensor location. The collected database will allow for: 1) Understand of signature variability under the different weather conditions; 2) Development of robust algorithms; 3) Development of new sensors; 4) Evaluation of hyperspectral and polarimetric technologies; and 5) Evaluation of fusing the different sensor modalities. In this paper, we will present the SPICE data collection objectives, the ongoing effort, the sensors that are currently deployed, and how this work will assist researches on the development and evaluation of sensors, algorithms, and fusion applications.


    Energy Technology Data Exchange (ETDEWEB)



    As a member of the science-support part of the ITT-lead LISA development program, BNL is tasked with the acquisition of UV Raman spectral fingerprints and associated scattering cross-sections for those chemicals-of-interest to the program's sponsor. In support of this role, the present report contains the first installment of UV Raman spectral fingerprint data on the initial subset of chemicals. Because of the unique nature associated with the acquisition of spectral fingerprints for use in spectral pattern matching algorithms (i.e., CLS, PLS, ANN) great care has been undertaken to maximize the signal-to-noise and to minimize unnecessary spectral subtractions, in an effort to provide the highest quality spectral fingerprints. This report is divided into 4 sections. The first is an Experimental section that outlines how the Raman spectra are performed. This is then followed by a section on Sample Handling. Following this, the spectral fingerprints are presented in the Results section where the data reduction process is outlined. Finally, a Photographs section is included.

  1. Cancer patients undertaking bone scans in a department of Nuclear Medicine have significant stress related to the examination

    International Nuclear Information System (INIS)

    Sioka, C.; Manetou, M.; Dimakopoulos, N.; Christidi, S.; Kouraklis, G.


    Bone scanning is a standard screening procedure for evaluation of metastases in cancer patient. In addition to the staging procedures, bone scan is a valuable test for deciding palliative therapeutic options in selected patients. The aim of this study was to investigate if patients with cancer who were undertaking routine bone scans had any stress related to the test. We asked 83 consecutive patients with various types of cancer if they had anxiety just prior to undergoing the test. Overall, we found that 53 (64%) patients had increased anxiety related to the examination and 30 (36%) patients did not. Among the 53 patients who were anxious about the bone scan, 32 were concerned about the results of the examination, 13 worried about the effects of the radiation, 4 were anxious for both results/radiation, and 4 patients had stress but could not specify the reason. Among the 32 patients who were concerned about the results of the examination, 15 were having their first bone scans, while 17 had already undergone the procedure before. Among the 13 patients who were mainly concerned about the risks of the radiation exposure during the test, 9 were having bone scans for the first time. Out of the 4 patients who feared both the results and radiation, 3 were having bone scans for the first time and 1 had it for several times. Finally, out of the 4 patients who had anxiety about the test but could not identify the reason, 3 were having bone scans for the first time and one had the test before but was claustrophobic. Our findings indicate that most patients (64%) with cancer who underwent a routine bone scan to check for metastatic disease had intense stress related either to the results or the side effects of the examination. However, there were more patients who were concerned about the results of the test rather than the effects of radiation. Among the patients who feared the effects of radioactivity most were having the test for the first time. A previous study in a

  2. Archiving Spectral Libraries in the Planetary Data System (United States)

    Slavney, S.; Guinness, E. A.; Scholes, D.; Zastrow, A.


    Spectral libraries are becoming popular candidates for archiving in PDS. With the increase in the number of individual investigators funded by programs such as NASA's PDART, the PDS Geosciences Node is receiving many requests for support from proposers wishing to archive various forms of laboratory spectra. To accommodate the need for a standardized approach to archiving spectra, the Geosciences Node has designed the PDS Spectral Library Data Dictionary, which contains PDS4 classes and attributes specifically for labeling spectral data, including a classification scheme for samples. The Reflectance Experiment Laboratory (RELAB) at Brown University, which has long been a provider of spectroscopy equipment and services to the science community, has provided expert input into the design of the dictionary. Together the Geosciences Node and RELAB are preparing the whole of the RELAB Spectral Library, consisting of many thousands of spectra collected over the years, to be archived in PDS. An online interface for searching, displaying, and downloading selected spectra is planned, using the Spectral Library metadata recorded in the PDS labels. The data dictionary and online interface will be extended to include spectral libraries submitted by other data providers. The Spectral Library Data Dictionary is now available from PDS at It can be used in PDS4 labels for reflectance spectra as well as for Raman, XRF, XRD, LIBS, and other types of spectra. Ancillary data such as images, chemistry, and abundance data are also supported. To help generate PDS4-compliant labels for spectra, the Geosciences Node provides a label generation program called MakeLabels ( which creates labels from a template, and which can be used for any kind of PDS4 label. For information, contact the Geosciences Node at


    Directory of Open Access Journals (Sweden)

    J. Avbelj


    Full Text Available Building extraction from imagery has been an active research area for decades. However, the precise building detection from hyperspectral (HSI images solely is a less often addressed research question due to the low spatial resolution of data. The building boundaries are usually represented by spectrally mixed pixels, and classical edge detector algorithms fail to detect borders with sufficient completeness. The idea of the proposed method is to use fraction of materials in mixed pixels to derive weights for adjusting building boundaries. The building regions are detected using seeded region growing and merging in a HSI image; for the initial seed point selection the digital surface model (DSM is used. Prior to region growing, the seeds are statistically tested for outliers on the basis of their spectral characteristics. Then, the border pixels of building regions are compared in spectrum to the seed points by calculating spectral dissimilarity. From this spectral dissimilarity the weights for weighted and constrained least squares (LS adjustment are derived. We used the Spectral Angle Mapper (SAM for spectral similarity measure, but the proposed boundary estimation method could benefit from soft classification or spectral unmixing results. The method was tested on a HSI image with spatial resolution of 4 m, and buildings of rectangular shape. The importance of constraints to the relations between building parts, e.g. perpendicularity is shown on example with a building with inner yards. The adjusted building boundaries are compared to the laser DSM, and have a relative accuracy of boundaries 1/4 of a pixel.

  4. Oro-facial pain and temporomandibular disorders classification systems: A critical appraisal and future directions. (United States)

    Klasser, G D; Manfredini, D; Goulet, J-P; De Laat, A


    It is a difficult undertaking to design a classification system for any disease entity, let alone for oro-facial pain (OFP) and more specifically for temporomandibular disorders (TMD). A further complication of this task is that both physical and psychosocial variables must be included. To augment this process, a two-step systematic review, adhering to PRISMA guidelines, of the classification systems published during the last 20 years for OFP and TMD was performed. The first search step identified 190 potential citations which ultimately resulted in only 17 articles being included for in-depth analysis and review. The second step resulted in only 5 articles being selected for inclusion in this review. Five additional articles and four classification guidelines/criteria were also included due to expansion of the search criteria. Thus, in total, 14 documents comprising articles and guidelines/criteria (8 proposals of classification systems for OFP; 6 for TMD) were selected for inclusion in the systematic review. For each, a discussion as to their advantages, strengths and limitations was provided. Suggestions regarding the future direction for improving the classification process with the use of ontological principles rather than taxonomy are discussed. Furthermore, the potential for expanding the scope of axes included in existing classification systems, to include genetic, epigenetic and neurobiological variables, is explored. It is therefore recommended that future classification system proposals be based on combined approaches aiming to provide archetypal treatment-oriented classifications. © 2017 John Wiley & Sons Ltd.

  5. Kappa Coefficients for Circular Classifications

    NARCIS (Netherlands)

    Warrens, Matthijs J.; Pratiwi, Bunga C.


    Circular classifications are classification scales with categories that exhibit a certain periodicity. Since linear scales have endpoints, the standard weighted kappas used for linear scales are not appropriate for analyzing agreement between two circular classifications. A family of kappa

  6. Oral epithelial dysplasia classification systems

    DEFF Research Database (Denmark)

    Warnakulasuriya, S; Reibel, J; Bouquot, J


    . In this report, we review the oral epithelial dysplasia classification systems. The three classification schemes [oral epithelial dysplasia scoring system, squamous intraepithelial neoplasia and Ljubljana classification] were presented and the Working Group recommended epithelial dysplasia grading for routine...

  7. Ministerial Decree of 12 May 1980 authorising Agip Nucleare S.p.a. in Rome to undertake health physics and medical supervision of protection against ionizing radiation

    International Nuclear Information System (INIS)


    Section 83 of Decree No. 185 of 13 February 1964 on protection against ionizing radiation provides that institutions previously authorised by the Minister of Labour and Social Security may, on condition that they are adequately equipped for such services, be authorised to undertake health physics and medical supervision of personnel. This Decree accordingly authorises the Agip Nucleare Company to carry out this work. (NEA) [fr


    Directory of Open Access Journals (Sweden)

    L. Xi


    Full Text Available Super resolution-based spectral unmixing (SRSU is a recently developed method for spectral unmixing of remotely sensed imagery, but it is too complex to implement for common users who are interested in land cover mapping. This study makes use of spatial interpolation as an alternative approach to achieve super resolution reconstruction in SRSU. An ASTER image with three spectral bands was used as the test data. The algorithm is evaluated using root mean square error (RMSE compared with linear spectral unmixing and hard classification. The result shows that the proposed algorithm has higher unmixing accuracy than those of the other comparative algorithms, and it is proved as an efficient and convenient spectral unmixing tool of remotely sensed imagery.

  9. The Ultraviolet Spectral Morphology of a Sample of B Supergiants in the Small Magellanic Cloud (United States)

    McNeil, R. C.; Borchers, A. L.; Sonneborn, G.; Fahey, R. P.


    A study of the ultraviolet spectra of a sample of B supergiants in the Small Magellanic Cloud is being undertaken as a means of addressing some questions about the nature and evolution of massive stars. All spectra are new or archival low-dispersion SWP spectra (1200International Ultraviolet Explorer. As a first step in this study, the ultraviolet spectral morphology of approximately 50 program stars is being examined for consistency with their published spectral classifications. Analysis includes a tabulation of ultraviolet spectral features, evaluation of their variation with spectral type and luminosity class, and comparison with IUE spectral sequences of standard stars. The data analysis was performed at the IUE Data Analysis Center at Goddard Space Flight Center. Partial support of this work by NASA and Northern Kentucky University through the Joint Ventures (JOVE) program, and support of the Laboratory for Astronomy and Solar Physics at GSFC, is gratefully acknowledged.

  10. Cluster Based Text Classification Model

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock


    We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...... datasets. Our model also outperforms A Decision Cluster Classification (ADCC) and the Decision Cluster Forest Classification (DCFC) models on the Reuters-21578 dataset....

  11. Classification of hand eczema

    DEFF Research Database (Denmark)

    Agner, T; Aalto-Korte, K; Andersen, K E


    BACKGROUND: Classification of hand eczema (HE) is mandatory in epidemiological and clinical studies, and also important in clinical work. OBJECTIVES: The aim was to test a recently proposed classification system of HE in clinical practice in a prospective multicentre study. METHODS: Patients were......%) could not be classified. 38% had one additional diagnosis and 26% had two or more additional diagnoses. Eczema on feet was found in 30% of the patients, statistically significantly more frequently associated with hyperkeratotic and vesicular endogenous eczema. CONCLUSION: We find that the classification...

  12. Pitch Based Sound Classification

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Hansen, Lars Kai; Kjems, U


    A sound classification model is presented that can classify signals into music, noise and speech. The model extracts the pitch of the signal using the harmonic product spectrum. Based on the pitch estimate and a pitch error measure, features are created and used in a probabilistic model with soft......-max output function. Both linear and quadratic inputs are used. The model is trained on 2 hours of sound and tested on publicly available data. A test classification error below 0.05 with 1 s classification windows is achieved. Further more it is shown that linear input performs as well as a quadratic...


    Directory of Open Access Journals (Sweden)

    Sushil Chandra


    Full Text Available The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification.

  14. Hyperspectral Biofilm Classification Analysis for Carrying Capacity of Migratory Birds in the South Bay Salt Ponds (United States)

    Hsu, Wei-Chen; Kuss, Amber Jean; Ketron, Tyler; Nguyen, Andrew; Remar, Alex Covello; Newcomer, Michelle; Fleming, Erich; Debout, Leslie; Debout, Brad; Detweiler, Angela; hide


    Tidal marshes are highly productive ecosystems that support migratory birds as roosting and over-wintering habitats on the Pacific Flyway. Microphytobenthos, or more commonly 'biofilms' contribute significantly to the primary productivity of wetland ecosystems, and provide a substantial food source for macroinvertebrates and avian communities. In this study, biofilms were characterized based on taxonomic classification, density differences, and spectral signatures. These techniques were then applied to remotely sensed images to map biofilm densities and distributions in the South Bay Salt Ponds and predict the carrying capacity of these newly restored ponds for migratory birds. The GER-1500 spectroradiometer was used to obtain in situ spectral signatures for each density-class of biofilm. The spectral variation and taxonomic classification between high, medium, and low density biofilm cover types was mapped using in-situ spectral measurements and classification of EO-1 Hyperion and Landsat TM 5 images. Biofilm samples were also collected in the field to perform laboratory analyses including chlorophyll-a, taxonomic classification, and energy content. Comparison of the spectral signatures between the three density groups shows distinct variations useful for classification. Also, analysis of chlorophyll-a concentrations show statistically significant differences between each density group, using the Tukey-Kramer test at an alpha level of 0.05. The potential carrying capacity in South Bay Salt Ponds is estimated to be 250,000 birds.


    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. CLASSIFICATION OF VIRUSES. On basis of morphology. On basis of chemical composition. On basis of structure of genome. On basis of mode of replication. Notes:

  16. Update on diabetes classification. (United States)

    Thomas, Celeste C; Philipson, Louis H


    This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Learning Apache Mahout classification

    CERN Document Server

    Gupta, Ashish


    If you are a data scientist who has some experience with the Hadoop ecosystem and machine learning methods and want to try out classification on large datasets using Mahout, this book is ideal for you. Knowledge of Java is essential.

  18. Spectral Unmixing With Multiple Dictionaries (United States)

    Cohen, Jeremy E.; Gillis, Nicolas


    Spectral unmixing aims at recovering the spectral signatures of materials, called endmembers, mixed in a hyperspectral or multispectral image, along with their abundances. A typical assumption is that the image contains one pure pixel per endmember, in which case spectral unmixing reduces to identifying these pixels. Many fully automated methods have been proposed in recent years, but little work has been done to allow users to select areas where pure pixels are present manually or using a segmentation algorithm. Additionally, in a non-blind approach, several spectral libraries may be available rather than a single one, with a fixed number (or an upper or lower bound) of endmembers to chose from each. In this paper, we propose a multiple-dictionary constrained low-rank matrix approximation model that address these two problems. We propose an algorithm to compute this model, dubbed M2PALS, and its performance is discussed on both synthetic and real hyperspectral images.

  19. Special topics in spectral distributions

    International Nuclear Information System (INIS)

    French, J.B.


    We discuss two problems which relate to the foundations of the subject, and a third about asymptotic properties of spectral distributions. We give also a brief list of topics which should be further explored

  20. Substitution dynamical systems spectral analysis

    CERN Document Server

    Queffélec, Martine


    This volume mainly deals with the dynamics of finitely valued sequences, and more specifically, of sequences generated by substitutions and automata. Those sequences demonstrate fairly simple combinatorical and arithmetical properties and naturally appear in various domains. As the title suggests, the aim of the initial version of this book was the spectral study of the associated dynamical systems: the first chapters consisted in a detailed introduction to the mathematical notions involved, and the description of the spectral invariants followed in the closing chapters. This approach, combined with new material added to the new edition, results in a nearly self-contained book on the subject. New tools - which have also proven helpful in other contexts - had to be developed for this study. Moreover, its findings can be concretely applied, the method providing an algorithm to exhibit the spectral measures and the spectral multiplicity, as is demonstrated in several examples. Beyond this advanced analysis, many...

  1. Latent classification models

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre


    parametric family ofdistributions.  In this paper we propose a new set of models forclassification in continuous domains, termed latent classificationmodels. The latent classification model can roughly be seen ascombining the \\NB model with a mixture of factor analyzers,thereby relaxing the assumptions...... classification model, and wedemonstrate empirically that the accuracy of the proposed model issignificantly higher than the accuracy of other probabilisticclassifiers....

  2. Multilingual News Article Classification


    Skjennum, Patrick L


    News is an ever-growing and global resource, reliant on robust distribution networks to spread information. This thesis investigates how exploiting semantic, contextual and ontological information may form a basis for a language independent news article classification system. In light of the above, a scalable multi-label news article classification system, based exclusively on extracted DBpedia entities, and a predetermined standardized set of fixed-size IPTC Media Topic categories, is p...

  3. Applying object-based image analysis and knowledge-based classification to ADS-40 digital aerial photographs to facilitate complex forest land cover classification (United States)

    Hsieh, Yi-Ta; Chen, Chaur-Tzuhn; Chen, Jan-Chang


    In general, considerable human and material resources are required for performing a forest inventory survey. Using remote sensing technologies to save forest inventory costs has thus become an important topic in forest inventory-related studies. Leica ADS-40 digital aerial photographs feature advantages such as high spatial resolution, high radiometric resolution, and a wealth of spectral information. As a result, they have been widely used to perform forest inventories. We classified ADS-40 digital aerial photographs according to the complex forest land cover types listed in the Fourth Forest Resource Survey in an effort to establish a classification method for categorizing ADS-40 digital aerial photographs. Subsequently, we classified the images using the knowledge-based classification method in combination with object-based analysis techniques, decision tree classification techniques, classification parameters such as object texture, shape, and spectral characteristics, a class-based classification method, and geographic information system mapping information. Finally, the results were compared with manually interpreted aerial photographs. Images were classified using a hierarchical classification method comprised of four classification levels (levels 1 to 4). The classification overall accuracy (OA) of levels 1 to 4 is within a range of 64.29% to 98.50%. The final result comparisons showed that the proposed classification method achieved an OA of 78.20% and a kappa coefficient of 0.7597. On the basis of the image classification results, classification errors occurred mostly in images of sunlit crowns because the image values for individual trees varied. Such a variance was caused by the crown structure and the incident angle of the sun. These errors lowered image classification accuracy and warrant further studies. This study corroborates the high feasibility for mapping complex forest land cover types using ADS-40 digital aerial photographs.

  4. Spectral dimensionality reduction for HMMs


    Foster, Dean P.; Rodu, Jordan; Ungar, Lyle H.


    Hidden Markov Models (HMMs) can be accurately approximated using co-occurrence frequencies of pairs and triples of observations by using a fast spectral method in contrast to the usual slow methods like EM or Gibbs sampling. We provide a new spectral method which significantly reduces the number of model parameters that need to be estimated, and generates a sample complexity that does not depend on the size of the observation vocabulary. We present an elementary proof giving bounds on the rel...

  5. Compressive spectroscopy by spectral modulation (United States)

    Oiknine, Yaniv; August, Isaac; Stern, Adrian


    We review two compressive spectroscopy techniques based on modulation in the spectral domain that we have recently proposed. Both techniques achieve a compression ratio of approximately 10:1, however each with a different sensing mechanism. The first technique uses a liquid crystal cell as a tunable filter to modulate the spectral signal, and the second technique uses a Fabry-Perot etalon as a resonator. We overview the specific properties of each of the techniques.

  6. Spectral unmixing: estimating partial abundances

    CSIR Research Space (South Africa)

    Debba, Pravesh


    Full Text Available the ingredients for this chocolate cake? Debba (CSIR) Spectral Unmixing LQM 2009 3 / 22 Background and Research Question Ingredients Quantity unsweetened chocolate unsweetened cocoa powder boiling water flour baking powder baking soda salt unsalted... butter white sugar eggs pure vanilla extract milk Table: Chocolate cake ingredients Debba (CSIR) Spectral Unmixing LQM 2009 4 / 22 Background and Research Question Ingredients Quantity unsweetened chocolate 120 grams unsweetened cocoa powder 28...

  7. Hierarchical discriminant manifold learning for dimensionality reduction and image classification (United States)

    Chen, Weihai; Zhao, Changchen; Ding, Kai; Wu, Xingming; Chen, Peter C. Y.


    In the field of image classification, it has been a trend that in order to deliver a reliable classification performance, the feature extraction model becomes increasingly more complicated, leading to a high dimensionality of image representations. This, in turn, demands greater computation resources for image classification. Thus, it is desirable to apply dimensionality reduction (DR) methods for image classification. It is necessary to apply DR methods to relieve the computational burden as well as to improve the classification accuracy. However, traditional DR methods are not compatible with modern feature extraction methods. A framework that combines manifold learning based DR and feature extraction in a deeper way for image classification is proposed. A multiscale cell representation is extracted from the spatial pyramid to satisfy the locality constraints for a manifold learning method. A spectral weighted mean filtering is proposed to eliminate noise in the feature space. A hierarchical discriminant manifold learning is proposed which incorporates both category label and image scale information to guide the DR process. Finally, the image representation is generated by concatenating dimensionality reduced cell representations from the same image. Extensive experiments are conducted to test the proposed algorithm on both scene and object recognition datasets in comparison with several well-established and state-of-the-art methods with respect to classification precision and computational time. The results verify the effectiveness of incorporating manifold learning in the feature extraction procedure and imply that the multiscale cell representations may be distributed on a manifold.

  8. Deep learning for tumor classification in imaging mass spectrometry. (United States)

    Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter


    Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. or Supplementary data are available at Bioinformatics online.

  9. Change Detection Analysis With Spectral Thermal Imagery

    National Research Council Canada - National Science Library

    Behrens, Richard


    ... (LWIR) region. This study used analysis techniques of differencing, histograms, and principal components analysis to detect spectral changes and investigate the utility of spectral change detection...

  10. A New Classification Approach Based on Multiple Classification Rules


    Zhongmei Zhou


    A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when t...

  11. Development and evaluation of spectral transformation algorithms for analysis and characterization of forest vegetation (United States)

    Zhao, Guang


    This research reviewed and evaluated some of the most important statistically based spectral transformation algorithms. Two spectral transformation algorithms, canonical discriminant analysis (CDA) and multiple logistic regression (MLR) transformations were developed and evaluated in two independent studies. The objectives were to investigate if the methods are capable of solving the two fundamental questions raised in the beginning: separating spectral overlap and quantifying spatial variability under forest conditions. It was generalized from previous research that spectral transformations are usually performed to complete one or more tasks, with ultimate goal of optimizing data structure for improving visual interpretation, analysis, and classification performance. PCA is the most widely used spectral transformation techniques. Kauth-Thomas Tasseled Cap transformed components are important vegetation indices, and they are developed using sensor and scene physical characteristics and Gram-Schmidt orthogonalization process. A theoretical comparison was conducted to identify major differences among Tasseled Cap, PCA, and CDA transformations in their objectives, prior knowledge requirements, limitations, processes, and variance-covariance usage. CDA was a better "separation" algorithm than PCA in improving overall classification accuracy. CDA was used as a transformation technique to not only increase class separation, but also reduce data dimension and noise. The last two canonical components usually contain largely noise variances, which hold less than 1 percent of the variance found in source variables. A sub-dimension (the first four components) is preferable for final classifications than the whole derived canonical component data sets, as the noise variances associated with the last two components were removed. Comparison of CDA and PCA eigenstructure matrices revealed that there is no distinct pattern in terms of source variable contribution and load signs

  12. SCORPIO - II. Spectral indices of weak Galactic radio sources (United States)

    Cavallaro, F.; Trigilio, C.; Umana, G.; Franzen, T. M. O.; Norris, R. P.; Leto, P.; Ingallinera, A.; Buemi, C. S.; Marvil, J.; Agliozzo, C.; Bufano, F.; Cerrigone, L.; Riggi, S.


    In the next few years the classification of radio sources observed by the large surveys will be a challenging problem and spectral index is a powerful tool for addressing it. Here we present an algorithm to estimate the spectral index of sources from multiwavelength radio images. We have applied our algorithm to SCORPIO, a Galactic plane survey centred around 2.1 GHz carried out with Australian Telescope Compact Array and found we can measure reliable spectral indices only for sources stronger than 40 times the rms noise. Above a threshold of 1 mJy, the source density in SCORPIO is 20 per cent greater than in a typical extragalactic field, like Australia Telescope Large Area Survey because of the presence of Galactic sources. Among this excess population, 16 sources per square degree have a spectral index of about zero suggesting optically thin thermal emission such as H II regions and planetary nebulae, while 12 per square degree present a rising spectrum, suggesting optically thick thermal emission such as stars and UCH II regions.

  13. The Infrared Telescope Facility (IRTF) Spectral Library: Cool Stars (United States)

    Rayner, John T.; Cushing, Michael C.; Vacca, William D.


    We present a 0.8-5 μm spectral library of 210 cool stars observed at a resolving power of R ≡ λ/Δλ ~ 2000 with the medium-resolution infrared spectrograph, SpeX, at the 3.0 m NASA Infrared Telescope Facility (IRTF) on Mauna Kea, Hawaii. The stars have well-established MK spectral classifications and are mostly restricted to near-solar metallicities. The sample not only contains the F, G, K, and M spectral types with luminosity classes between I and V, but also includes some AGB, carbon, and S stars. In contrast to some other spectral libraries, the continuum shape of the spectra is measured and preserved in the data reduction process. The spectra are absolutely flux calibrated using the Two Micron All Sky Survey photometry. Potential uses of the library include studying the physics of cool stars, classifying and studying embedded young clusters and optically obscured regions of the Galaxy, evolutionary population synthesis to study unresolved stellar populations in optically obscured regions of galaxies and synthetic photometry. The library is available in digital form from the IRTF Web site.

  14. Evolving spectral transformations for multitemporal information extraction using evolutionary computation (United States)

    Momm, Henrique; Easson, Greg


    Remote sensing plays an important role in assessing temporal changes in land features. The challenge often resides in the conversion of large quantities of raw data into actionable information in a timely and cost-effective fashion. To address this issue, research was undertaken to develop an innovative methodology integrating biologically-inspired algorithms with standard image classification algorithms to improve information extraction from multitemporal imagery. Genetic programming was used as the optimization engine to evolve feature-specific candidate solutions in the form of nonlinear mathematical expressions of the image spectral channels (spectral indices). The temporal generalization capability of the proposed system was evaluated by addressing the task of building rooftop identification from a set of images acquired at different dates in a cross-validation approach. The proposed system generates robust solutions (kappa values > 0.75 for stage 1 and > 0.4 for stage 2) despite the statistical differences between the scenes caused by land use and land cover changes coupled with variable environmental conditions, and the lack of radiometric calibration between images. Based on our results, the use of nonlinear spectral indices enhanced the spectral differences between features improving the clustering capability of standard classifiers and providing an alternative solution for multitemporal information extraction.

  15. A classification method based on principal components of SELDI spectra to diagnose of lung adenocarcinoma.

    Directory of Open Access Journals (Sweden)

    Qiang Lin

    Full Text Available Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.


    Directory of Open Access Journals (Sweden)

    N. Jamshidpour


    Full Text Available Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

  17. Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information (United States)

    Jamshidpour, N.; Homayouni, S.; Safari, A.


    Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

  18. Classification of objects on hyperspectral images — further method development

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Williams, Paul James

    Classification of objects (such as tablets, cereals, fruits, etc.) is one of the very important applications of hyperspectral imaging and image analysis. Quite often, a hyperspectral image is represented and analyzed just as a bunch of spectra without taking into account spatial information about...... the pixels. This makes classification of the objects inefficient. Recently, several methods, which combine spectral and spatial information, has been also proposed and this approach becomes more and more wide-spread. The methods use local rank, topology, spectral features calculated for separate objects...... and other spatial characteristics. In this work we would like to show several improvements to the classification method, which utilizes spectral features calculated for individual objects [1]. The features are based (in general) on descriptors of spatial patterns of individual object’s pixels in a common...

  19. Classification of objects on hyperspectral images — further developments

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Williams, Paul

    Classification of objects (such as tablets, cereals, fruits, etc.) is one of the very important applications of hyperspectral imaging and image analysis. Quite often, a hyperspectral image is represented and analyzed just as a bunch of spectra without taking into account spatial information about...... the pixels, which makes classification objects inefficient. Recently, several methods, which combine spectral and spatial information, has been also developed and this approach becomes more and more wide-spread. The methods use local rank, topology, spectral features calculated for separate objects and other...... spatial characteristics. In this work we would like to show several improvements to the classification method, which utilizes spectral features calculated for individual objects [1]. The features are based (in general) on descriptors of spatial patterns of individual object’s pixels in a common principal...

  20. Clinical classification of syncope. (United States)

    Sutton, Richard


    Syncope is a presenting symptom, and in itself is not a diagnosis. An etiology or a mechanism must be sought in all cases. Currently, most clinicians classify syncope on clinical grounds by attempting to ascertain its etiology. They then use this classification to guide further management. Using this approach, reflex syncope is the most common form of syncope, occurring in approximately 60% of syncope presentations. Orthostatic hypotension presents in around 15% with arrhythmic syncope in 10% and structural heart disease as the cause of syncope in 5%; in 10% of patients no diagnosis is made. An alternative classification system uses the mechanism of syncope derived from an implanted ECG loop recorder (ILR). While this approach may be of value for optimizing therapy, it cannot be considered as the primary classification since ILRs are not typically implanted early in the evaluation process of most patients. ILRs are usually placed after "risk stratification" in those deemed not to be at high risk but remain in the uncertain etiology category. Furthermore, there exists, in current ILR technology, lack of ambulatory blood pressure monitoring capability. Thus, vasodilation leading to hypotension, the main trigger of cerebral hypoperfusion other than bradycardia, cannot be detected and is currently unavailable for use in a mechanistic-based classification. Thus, the etiological classification remains the basis for both risk stratification and subsequent clinical management. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images. (United States)

    Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng


    In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

  2. Acoustic and spectral characteristics of young children's fricative productions: A developmental perspective (United States)

    Nissen, Shawn L.; Fox, Robert Allen


    Scientists have made great strides toward understanding the mechanisms of speech production and perception. However, the complex relationships between the acoustic structures of speech and the resulting psychological percepts have yet to be fully and adequately explained, especially in speech produced by younger children. Thus, this study examined the acoustic structure of voiceless fricatives (/f, θ, s, /sh/) produced by adults and typically developing children from 3 to 6 years of age in terms of multiple acoustic parameters (durations, normalized amplitude, spectral slope, and spectral moments). It was found that the acoustic parameters of spectral slope and variance (commonly excluded from previous studies of child speech) were important acoustic parameters in the differentiation and classification of the voiceless fricatives, with spectral variance being the only measure to separate all four places of articulation. It was further shown that the sibilant contrast between /s/ and /sh/ was less distinguished in children than adults, characterized by a dramatic change in several spectral parameters at approximately five years of age. Discriminant analysis revealed evidence that classification models based on adult data were sensitive to these spectral differences in the five-year-old age group.

  3. Spectral filtering for plant production

    Energy Technology Data Exchange (ETDEWEB)

    Young, R.E.; McMahon, M.J.; Rajapakse, N.C.; Becoteau, D.R.


    Research to date suggests that spectral filtering can be an effective alternative to chemical growth regulators for altering plant development. If properly implemented, it can be nonchemical and environmentally friendly. The aqueous CuSO{sub 4}, and CuCl{sub 2} solutions in channelled plastic panels have been shown to be effective filters, but they can be highly toxic if the solutions contact plants. Some studies suggest that spectral filtration limited to short EOD intervals can also alter plant development. Future research should be directed toward confirmation of the influence of spectral filters and exposure times on a broader range of plant species and cultivars. Efforts should also be made to identify non-noxious alternatives to aqueous copper solutions and/or to incorporate these chemicals permanently into plastic films and panels that can be used in greenhouse construction. It would also be informative to study the impacts of spectral filters on insect and microbal populations in plant growth facilities. The economic impacts of spectral filtering techniques should be assessed for each delivery methodology.

  4. Solar Spectral Irradiance and Climate (United States)

    Pilewskie, P.; Woods, T.; Cahalan, R.


    Spectrally resolved solar irradiance is recognized as being increasingly important to improving our understanding of the manner in which the Sun influences climate. There is strong empirical evidence linking total solar irradiance to surface temperature trends - even though the Sun has likely made only a small contribution to the last half-century's global temperature anomaly - but the amplitudes cannot be explained by direct solar heating alone. The wavelength and height dependence of solar radiation deposition, for example, ozone absorption in the stratosphere, absorption in the ocean mixed layer, and water vapor absorption in the lower troposphere, contribute to the "top-down" and "bottom-up" mechanisms that have been proposed as possible amplifiers of the solar signal. New observations and models of solar spectral irradiance are needed to study these processes and to quantify their impacts on climate. Some of the most recent observations of solar spectral variability from the mid-ultraviolet to the near-infrared have revealed some unexpected behavior that was not anticipated prior to their measurement, based on an understanding from model reconstructions. The atmospheric response to the observed spectral variability, as quantified in climate model simulations, have revealed similarly surprising and in some cases, conflicting results. This talk will provide an overview on the state of our understanding of the spectrally resolved solar irradiance, its variability over many time scales, potential climate impacts, and finally, a discussion on what is required for improving our understanding of Sun-climate connections, including a look forward to future observations.

  5. The process of undertaking a quantitative dissertation for a taught M.Sc: Personal insights gained from supporting and examining students in the UK and Ireland

    International Nuclear Information System (INIS)

    Marshall, Gill; Brennan, Patrick


    Purpose: This article discusses the roles of the student and the supervisor in the process of undertaking and writing a dissertation, a potentially daunting process. Results: The authors have supervised and examined students within 20 institutions and the personal insights gained result in the guidance provided within this article. Conclusion: The authors conclude that much can be done by students working with their supervisors, to improve progress in both performing and writing up the dissertation. Taking account of these factors will ease the dissertation process and move students progressively towards the production of a well-written dissertation

  6. Spectral region identification versus individual channel selection in supervised dimensionality reduction of hyperspectral image data (United States)

    Aria, S. Enayat Hosseini; Menenti, Massimo; Gorte, Ben G. H.


    Hyperspectral images may be applied to classify objects in a scene. The redundancy in hyperspectral data implies that fewer spectral features might be sufficient for discriminating the objects captured in a scene. The availability of labeled classes of several areas in a scene paves the way for a supervised dimensionality reduction, i.e., using a discrimination measure between the classes in a scene to select spectral features. We show that averaging adjacent spectral channels and using wider spectral regions yield a better class separability than the selection of individual channels from the original hyperspectral dataset. We used a method named spectral region splitting (SRS), which creates a new feature space by averaging neighboring channels. In addition to the common benefits of channel selection methods, the algorithm constructs wider spectral regions when it is useful. Using different class separability measures over various datasets resulted in a better discrimination between the classes than the best-selected channels using the same measure. The reason is that the wider spectral regions led to a reduction in intraclass distances and an improvement in class discrimination. The overall classification accuracy of two hyperspectral scenes gave an increase of about two-percent when using the spectral regions determined by applying SRS.

  7. Cellular image classification

    CERN Document Server

    Xu, Xiang; Lin, Feng


    This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical...

  8. Bosniak classification system

    DEFF Research Database (Denmark)

    Graumann, Ole; Osther, Susanne Sloth; Karstoft, Jens


    BACKGROUND: The Bosniak classification was originally based on computed tomographic (CT) findings. Magnetic resonance (MR) and contrast-enhanced ultrasonography (CEUS) imaging may demonstrate findings that are not depicted at CT, and there may not always be a clear correlation between the findings...... at MR and CEUS imaging and those at CT. PURPOSE: To compare diagnostic accuracy of MR, CEUS, and CT when categorizing complex renal cystic masses according to the Bosniak classification. MATERIAL AND METHODS: From February 2011 to June 2012, 46 complex renal cysts were prospectively evaluated by three...... readers. Each mass was categorized according to the Bosniak classification and CT was chosen as gold standard. Kappa was calculated for diagnostic accuracy and data was compared with pathological results. RESULTS: CT images found 27 BII, six BIIF, seven BIII, and six BIV. Forty-three cysts could...

  9. Medical imbalanced data classification

    Directory of Open Access Journals (Sweden)

    Sara Belarouci


    Full Text Available In general, the imbalanced dataset is a problem often found in health applications. In medical data classification, we often face the imbalanced number of data samples where at least one of the classes constitutes only a very small minority of the data. In the same time, it represent a difficult problem in most of machine learning algorithms. There have been many works dealing with classification of imbalanced dataset. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS algorithm that penalizes errors of different samples with different weights and some rules of thumb to determine those weights. After the balancing phase, we apply the different techniques (Support Vector Machine [SVM], K- Nearest Neighbor [K-NN] and Multilayer perceptron [MLP] for the balanced datasets. We have also compared the obtained results before and after balancing method. We have obtained best results compared to literature with a classification accuracy of 100%.

  10. Acoustic classification of dwellings

    DEFF Research Database (Denmark)

    Berardi, Umberto; Rasmussen, Birgit


    Schemes for the classification of dwellings according to different building performances have been proposed in the last years worldwide. The general idea behind these schemes relates to the positive impact a higher label, and thus a better performance, should have. In particular, focusing on sound...... insulation performance, national schemes for sound classification of dwellings have been developed in several European countries. These schemes define acoustic classes according to different levels of sound insulation. Due to the lack of coordination among countries, a significant diversity in terms...... of descriptors, number of classes, and class intervals occurred between national schemes. However, a proposal “acoustic classification scheme for dwellings” has been developed recently in the European COST Action TU0901 with 32 member countries. This proposal has been accepted as an ISO work item. This paper...

  11. Tree Classification with Fused Mobile Laser Scanning and Hyperspectral Data (United States)

    Puttonen, Eetu; Jaakkola, Anttoni; Litkey, Paula; Hyyppä, Juha


    Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin. PMID:22163894

  12. Spectral element simulation of ultrafiltration

    DEFF Research Database (Denmark)

    Hansen, M.; Barker, Vincent A.; Hassager, Ole


    A spectral element method for simulating stationary 2-D ultrafiltration is presented. The mathematical model is comprised of the Navier-Stokes equations for the velocity field of the fluid and a transport equation for the concentration of the solute. In addition to the presence of the velocity...... vector in the transport equation, the system is coupled by the dependency of the fluid viscosity on the solute concentration and by a concentration-dependent boundary condition for the Navier-Stokes equations at the membrane surface. The spectral element discretization yields a nonlinear algebraic system....... The performance of the spectral element code when applied to several ultrafiltration problems is reported. (C) 1998 Elsevier Science Ltd. All rights reserved....

  13. Spectrally Compatible Iterative Water Filling (United States)

    Verlinden, Jan; Bogaert, Etienne Vanden; Bostoen, Tom; Zanier, Francesca; Luise, Marco; Cendrillon, Raphael; Moonen, Marc


    Until now static spectrum management has ensured that DSL lines in the same cable are spectrally compatible under worst-case crosstalk conditions. Recently dynamic spectrum management (DSM) has been proposed aiming at an increased capacity utilization by adaptation of the transmit spectra of DSL lines to the actual crosstalk interference. In this paper, a new DSM method for downstream ADSL is derived from the well-known iterative water-filling (IWF) algorithm. The amount of boosting of this new DSM method is limited, such that it is spectrally compatible with ADSL. Hence it is referred to as spectrally compatible iterative water filling (SC-IWF). This paper focuses on the performance gains of SC-IWF. This method is an autonomous DSM method (DSM level 1) and it will be investigated together with two other DSM level-1 algorithms, under various noise conditions, namely, iterative water-filling algorithm, and flat power back-off (flat PBO).

  14. Spectrally Compatible Iterative Water Filling

    Directory of Open Access Journals (Sweden)

    Cendrillon Raphael


    Full Text Available Until now static spectrum management has ensured that DSL lines in the same cable are spectrally compatible under worst-case crosstalk conditions. Recently dynamic spectrum management (DSM has been proposed aiming at an increased capacity utilization by adaptation of the transmit spectra of DSL lines to the actual crosstalk interference. In this paper, a new DSM method for downstream ADSL is derived from the well-known iterative water-filling (IWF algorithm. The amount of boosting of this new DSM method is limited, such that it is spectrally compatible with ADSL. Hence it is referred to as spectrally compatible iterative water filling (SC-IWF. This paper focuses on the performance gains of SC-IWF. This method is an autonomous DSM method (DSM level 1 and it will be investigated together with two other DSM level-1 algorithms, under various noise conditions, namely, iterative water-filling algorithm, and flat power back-off (flat PBO.

  15. The SED Machine: A Robotic Spectrograph for Fast Transient Classification (United States)

    Blagorodnova, Nadejda; Neill, James D.; Walters, Richard; Kulkarni, Shrinivas R.; Fremling, Christoffer; Ben-Ami, Sagi; Dekany, Richard G.; Fucik, Jason R.; Konidaris, Nick; Nash, Reston; Ngeow, Chow-Choong; Ofek, Eran O.; O’ Sullivan, Donal; Quimby, Robert; Ritter, Andreas; Vyhmeister, Karl E.


    Current time domain facilities are finding several hundreds of transient astronomical events a year. The discovery rate is expected to increase in the future as soon as new surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Sky Survey (LSST) come online. Presently, the rate at which transients are classified is approximately one order or magnitude lower than the discovery rate, leading to an increasing “follow-up drought”. Existing telescopes with moderate aperture can help address this deficit when equipped with spectrographs optimized for spectral classification. Here, we provide an overview of the design, operations and first results of the Spectral Energy Distribution Machine (SEDM), operating on the Palomar 60-inch telescope (P60). The instrument is optimized for classification and high observing efficiency. It combines a low-resolution (R ∼ 100) integral field unit (IFU) spectrograph with “Rainbow Camera” (RC), a multi-band field acquisition camera which also serves as multi-band (ugri) photometer. The SEDM was commissioned during the operation of the intermediate Palomar Transient Factory (iPTF) and has already lived up to its promise. The success of the SEDM demonstrates the value of spectrographs optimized for spectral classification.

  16. Silence–breathing–snore classification from snore-related sounds

    International Nuclear Information System (INIS)

    Karunajeewa, Asela S; Abeyratne, Udantha R; Hukins, Craig


    Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. The first task in the automatic analysis of snore-related sounds (SRS) is to segment the SRS data as accurately as possible into three main classes: snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. SRS data are generally contaminated with background noise. In this paper, we present classification performance of a new segmentation algorithm based on pattern recognition. We considered four features derived from SRS to classify samples of SRS into three classes. The features—number of zero crossings, energy of the signal, normalized autocorrelation coefficient at 1 ms delay and the first predictor coefficient of linear predictive coding (LPC) analysis—in combination were able to achieve a classification accuracy of 90.74% in classifying a set of test data. We also investigated the performance of the algorithm when three commonly used noise reduction (NR) techniques in speech processing—amplitude spectral subtraction (ASS), power spectral subtraction (PSS) and short time spectral amplitude (STSA) estimation—are used for noise reduction. We found that noise reduction together with a proper choice of features could improve the classification accuracy to 96.78%, making the automated analysis a possibility

  17. Minimum Error Entropy Classification

    CERN Document Server

    Marques de Sá, Joaquim P; Santos, Jorge M F; Alexandre, Luís A


    This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.

  18. Classification of iconic images


    Zrianina, Mariia; Kopf, Stephan


    Iconic images represent an abstract topic and use a presentation that is intuitively understood within a certain cultural context. For example, the abstract topic “global warming” may be represented by a polar bear standing alone on an ice floe. Such images are widely used in media and their automatic classification can help to identify high-level semantic concepts. This paper presents a system for the classification of iconic images. It uses a variation of the Bag of Visual Words approach wi...

  19. Information gathering for CLP classification

    Directory of Open Access Journals (Sweden)

    Ida Marcello


    Full Text Available Regulation 1272/2008 includes provisions for two types of classification: harmonised classification and self-classification. The harmonised classification of substances is decided at Community level and a list of harmonised classifications is included in the Annex VI of the classification, labelling and packaging Regulation (CLP. If a chemical substance is not included in the harmonised classification list it must be self-classified, based on available information, according to the requirements of Annex I of the CLP Regulation. CLP appoints that the harmonised classification will be performed for carcinogenic, mutagenic or toxic to reproduction substances (CMR substances and for respiratory sensitisers category 1 and for other hazard classes on a case-by-case basis. The first step of classification is the gathering of available and relevant information. This paper presents the procedure for gathering information and to obtain data. The data quality is also discussed.

  20. A spectral atlas of λ Bootis stars

    Directory of Open Access Journals (Sweden)

    Paunzen E.


    Full Text Available Since the discovery of λ Bootis stars, a permanent confusion about their classification can be found in literature. This group of non-magnetic, Population I, metal-poor A to F-type stars, has often been used as some sort of trash can for "exotic" and spectroscopically dubious objects. Some attempts have been made to establish a homogeneous group of stars which share the same common properties. Unfortunately, the flood of "new" information (e.g. UV and IR data led again to a whole zoo of objects classified as λ Bootis stars, which, however, are apparent non-members. To overcome this unsatisfying situation, a spectral atlas of well established λ Bootis stars for the classical optical domain was compiled. It includes intermediate dispersion (40 and 120Å mm-1 spectra of three λ Bootis, as well as appropriate MK standard stars. Furthermore, "suspicious" objects, such as shell and Field Horizontal Branch stars, have been considered in order to provide to classifiers a homogeneous reference. As a further step, a high resolution (8Å mm-1 spectrum of one "classical" λ Bootis star in the same wavelength region (3800-4600Å is presented. In total, 55 lines can be used for this particular star to derive detailed abundances for nine heavy elements (Mg, Ca, Sc, Ti, Cr, Mn, Fe, Sr and Ba.

  1. Detector response artefacts in spectral reconstruction (United States)

    Olsen, Ulrik L.; Christensen, Erik D.; Khalil, Mohamad; Gu, Yun; Kehres, Jan


    Energy resolved detectors are gaining traction as a tool to achieve better material contrast. K-edge imaging and tomography is an example of a method with high potential that has evolved on the capabilities of photon counting energy dispersive detectors. Border security is also beginning to see instruments taking advantage of energy resolved detectors. The progress of the field is halted by the limitations of the detectors. The limitations include nonlinear response for both x-ray intensity and x-ray spectrum. In this work we investigate how the physical interactions in the energy dispersive detectors affect the quality of the reconstruction and how corrections restore the quality. We have modeled detector responses for the primary detrimental effects occurring in the detector; escape peaks, charge sharing/loss and pileup. The effect of the change in the measured spectra is evaluated based on the artefacts occurring in the reconstructed images. We also evaluate the effect of a correction algorithm for reducing these artefacts on experimental data acquired with a setup using Multix ME-100 V-2 line detector modules. The artefacts were seen to introduce 20% deviation in the reconstructed attenuation coefficient for the uncorrected detector. We performed tomography experiments on samples with various materials interesting for security applications and found the SSIM to increase > 5% below 60keV. Our work shows that effective corrections schemes are necessary for the accurate material classification in security application promised by the advent of high flux detectors for spectral tomography

  2. Spectral scheme for spacetime physics

    International Nuclear Information System (INIS)

    Seriu, Masafumi


    Based on the spectral representation of spatial geometry, we construct an analysis scheme for spacetime physics and cosmology, which enables us to compare two or more universes with each other. In this scheme the spectral distance plays a central role, which is the measure of closeness between two geometries defined in terms of the spectra. We apply this scheme for analyzing the averaging problem in cosmology; we explicitly investigate the time evolution of the spectra, distance between two nearby spatial geometries, simulating the relation between the real Universe and its model. We then formulate the criteria for a model to be a suitable one

  3. Spectral ellipsometry of nanodiamond composite

    International Nuclear Information System (INIS)

    Yastrebov, S.G.; Ivanov-Omskij, V.I.; Gordeev, S.K.; Garriga, M.; Alonso, I.A.


    Methods of spectral ellipsometry were applied for analysis of optical properties of nanodiamond based composite in spectral region 1.4-5 eV. The nanocomposite was synthesized by molding of ultradispersed nanodiamond powder in the course of heterogeneous chemical reaction of decomposition of methane, forming pyrocarbon interconnecting nanodiamond grains. The energy of σ + π plasmon of pyrocarbon component of nanodiamond composite was restored which proves to be ∼ 24 eV; using this value, an estimation was done of pyrocarbon matrix density, which occurs to be 2 g/cm 3 [ru

  4. Morphological Interpretation of Reflectance Spectrum (MIRS using libraries looking towards soil classification

    Directory of Open Access Journals (Sweden)

    José Alexandre Melo Demattê


    Full Text Available The search for tools to perform soil surveying faster and cheaper has led to the development of technological innovations such as remote sensing (RS and the so-called spectral libraries in recent years. However, there are no studies which collate all the RS background to demonstrate how to use this technology for soil classification. The present study aims to describe a simple method of how to classify soils by the morphology of spectra associated with a quantitative view (400-2,500 nm. For this, we constructed three spectral libraries: (i one for quantitative model performance; (ii a second to function as the spectral patterns; and (iii a third to serve as a validation stage. All samples had their chemical and granulometric attributes determined by laboratory analysis and prediction models were created based on soil spectra. The system is based on seven steps summarized as follows: i interpretation of the spectral curve intensity; ii observation of the general shape of curves; iii evaluation of absorption features; iv comparison of spectral curves between the same profile horizons; v quantification of soil attributes by spectral library models; vi comparison of a pre-existent spectral library with unknown profile spectra; vii most probable soil classification. A soil cannot be classified from one spectral curve alone. The behavior between the horizons of a profile, however, was correlated with its classification. In fact, the validation showed 85 % accuracy between the Morphological Interpretation of Reflectance Spectrum (MIRS method and the traditional classification, showing the importance and potential of a combination of descriptive and quantitative evaluations.

  5. Classification of merged AVHRR and SMMR Arctic data with neural networks (United States)

    Key, J.; Maslanik, J. A.; Schweiger, A. J.


    A forward-feed back-propagation neural network is used to classify merged AVHRR and SMMR summer Arctic data. Four surface and eight cloud classes are identified. Partial memberships of each pixel to each class are examined for spectral ambiguities. Classification results are compared to manual interpretations and to those determined by a supervised maximum likelihood procedure. Results indicate that a neural network approach offers advantages in ease of use, interpretability, and utility for indistinct and time-variant spectral classes.


    Directory of Open Access Journals (Sweden)

    P. Karakus


    Full Text Available Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

  7. Effect of Pansharpened Image on Some of Pixel Based and Object Based Classification Accuracy (United States)

    Karakus, P.; Karabork, H.


    Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

  8. An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation (United States)

    Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.


    Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

  9. Intelligent image processing for vegetation classification using multispectral LANDSAT data (United States)

    Santos, Stewart R.; Flores, Jorge L.; Garcia-Torales, G.


    We propose an intelligent computational technique for analysis of vegetation imaging, which are acquired with multispectral scanner (MSS) sensor. This work focuses on intelligent and adaptive artificial neural network (ANN) methodologies that allow segmentation and classification of spectral remote sensing (RS) signatures, in order to obtain a high resolution map, in which we can delimit the wooded areas and quantify the amount of combustible materials present into these areas. This could provide important information to prevent fires and deforestation of wooded areas. The spectral RS input data, acquired by the MSS sensor, are considered in a random propagation remotely sensed scene with unknown statistics for each Thematic Mapper (TM) band. Performing high-resolution reconstruction and adding these spectral values with neighbor pixels information from each TM band, we can include contextual information into an ANN. The biggest challenge in conventional classifiers is how to reduce the number of components in the feature vector, while preserving the major information contained in the data, especially when the dimensionality of the feature space is high. Preliminary results show that the Adaptive Modified Neural Network method is a promising and effective spectral method for segmentation and classification in RS images acquired with MSS sensor.

  10. Sunspot Pattern Classification using PCA and Neural Networks (Poster) (United States)

    Rajkumar, T.; Thompson, D. E.; Slater, G. L.


    The sunspot classification scheme presented in this paper is considered as a 2-D classification problem on archived datasets, and is not a real-time system. As a first step, it mirrors the Zuerich/McIntosh historical classification system and reproduces classification of sunspot patterns based on preprocessing and neural net training datasets. Ultimately, the project intends to move from more rudimentary schemes, to develop spatial-temporal-spectral classes derived by correlating spatial and temporal variations in various wavelengths to the brightness fluctuation spectrum of the sun in those wavelengths. Once the approach is generalized, then the focus will naturally move from a 2-D to an n-D classification, where "n" includes time and frequency. Here, the 2-D perspective refers both to the actual SOH0 Michelson Doppler Imager (MDI) images that are processed, but also refers to the fact that a 2-D matrix is created from each image during preprocessing. The 2-D matrix is the result of running Principal Component Analysis (PCA) over the selected dataset images, and the resulting matrices and their eigenvalues are the objects that are stored in a database, classified, and compared. These matrices are indexed according to the standard McIntosh classification scheme.

  11. Iterative Self Organized Data Algorithm for Fault Classification of Mechanical System


    Jayamala K. Patil; P. B. Ghewari; S. S. Nagtilak


    The challenging issue for mechanical industry is to develop fast & reliable fault diagnosis systems before total breakdown of machine. Fault diagnosis & classification of faults of mechanical systems is a difficult task. It improves productivity & reduces cost of production. This paper presents an approach for classification of commonly observed faults in gears of mechanical system. These faults include weared gear, gear with one tooth broken & gear with crack on one tooth. The Power Spectral...

  12. Optimal classification of standoff bioaerosol measurements using evolutionary algorithms (United States)

    Nyhavn, Ragnhild; Moen, Hans J. F.; Farsund, Øystein; Rustad, Gunnar


    Early warning systems based on standoff detection of biological aerosols require real-time signal processing of a large quantity of high-dimensional data, challenging the systems efficiency in terms of both computational complexity and classification accuracy. Hence, optimal feature selection is essential in forming a stable and efficient classification system. This involves finding optimal signal processing parameters, characteristic spectral frequencies and other data transformations in large magnitude variable space, stating the need for an efficient and smart search algorithm. Evolutionary algorithms are population-based optimization methods inspired by Darwinian evolutionary theory. These methods focus on application of selection, mutation and recombination on a population of competing solutions and optimize this set by evolving the population of solutions for each generation. We have employed genetic algorithms in the search for optimal feature selection and signal processing parameters for classification of biological agents. The experimental data were achieved with a spectrally resolved lidar based on ultraviolet laser induced fluorescence, and included several releases of 5 common simulants. The genetic algorithm outperform benchmark methods involving analytic, sequential and random methods like support vector machines, Fisher's linear discriminant and principal component analysis, with significantly improved classification accuracy compared to the best classical method.

  13. Event Classification using Concepts

    NARCIS (Netherlands)

    Boer, M.H.T. de; Schutte, K.; Kraaij, W.


    The semantic gap is one of the challenges in the GOOSE project. In this paper a Semantic Event Classification (SEC) system is proposed as an initial step in tackling the semantic gap challenge in the GOOSE project. This system uses semantic text analysis, multiple feature detectors using the BoW

  14. Shark Teeth Classification (United States)

    Brown, Tom; Creel, Sally; Lee, Velda


    On a recent autumn afternoon at Harmony Leland Elementary in Mableton, Georgia, students in a fifth-grade science class investigated the essential process of classification--the act of putting things into groups according to some common characteristics or attributes. While they may have honed these skills earlier in the week by grouping their own…

  15. Classification system: Netherlands

    NARCIS (Netherlands)

    Hartemink, A.E.


    Although people have always classified soils, it is only since the mid 19th century that soil classification emerged as an important topic within soil science. It forced soil scientists to think systematically about soils and its genesis and developed to facilitate communication between soil

  16. Text document classification

    Czech Academy of Sciences Publication Activity Database

    Novovičová, Jana

    č. 62 (2005), s. 53-54 ISSN 0926-4981 R&D Projects: GA AV ČR IAA2075302; GA AV ČR KSK1019101; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : document representation * categorization * classification Subject RIV: BD - Theory of Information

  17. Classifications in popular music

    NARCIS (Netherlands)

    van Venrooij, A.; Schmutz, V.; Wright, J.D.


    The categorical system of popular music, such as genre categories, is a highly differentiated and dynamic classification system. In this article we present work that studies different aspects of these categorical systems in popular music. Following the work of Paul DiMaggio, we focus on four

  18. Mimicking human texture classification

    NARCIS (Netherlands)

    Rogowitz, B.E.; van Rikxoort, Eva M.; van den Broek, Egon; Pappas, T.N.; Schouten, Theo E.; Daly, S.J.


    In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was

  19. Classification of myocardial infarction

    DEFF Research Database (Denmark)

    Saaby, Lotte; Poulsen, Tina Svenstrup; Hosbond, Susanne


    The classification of myocardial infarction into 5 types was introduced in 2007 as an important component of the universal definition. In contrast to the plaque rupture-related type 1 myocardial infarction, type 2 myocardial infarction is considered to be caused by an imbalance between demand...

  20. Faculty Assignment Classification System. (United States)

    Whatcom Community Coll., Ferndale, WA.

    This document outlines the point-based faculty assignment classification system in effect at Whatcom Community College (Washington). The purpose of the point system is to provide an equitable and flexible means of compensating faculty members based on a system of assigning quantitative values to tasks. Teaching, which includes classroom…

  1. Steel column base classification


    Jaspart, J.P.; Wald, F.; Weynand, K.; Gresnigt, A.M.


    The influence of the rotational characteristics of the column bases on the structural frame response is discussed and specific design criteria for stiffness classification into semi-rigid and rigid joints are derived. The particular case of an industrial portal frame is then considered. Peer reviewed

  2. Principles for ecological classification (United States)

    Dennis H. Grossman; Patrick Bourgeron; Wolf-Dieter N. Busch; David T. Cleland; William Platts; G. Ray; C. Robins; Gary Roloff


    The principal purpose of any classification is to relate common properties among different entities to facilitate understanding of evolutionary and adaptive processes. In the context of this volume, it is to facilitate ecosystem stewardship, i.e., to help support ecosystem conservation and management objectives.

  3. Ecosystem classification, Chapter 2 (United States)

    M.J. Robin-Abbott; L.H. Pardo


    The ecosystem classification in this report is based on the ecoregions developed through the Commission for Environmental Cooperation (CEC) for North America (CEC 1997). Only ecosystems that occur in the United States are included. CEC ecoregions are described, with slight modifications, below (CEC 1997) and shown in Figures 2.1 and 2.2. We chose this ecosystem...

  4. Ontologies vs. Classification Systems

    DEFF Research Database (Denmark)

    Madsen, Bodil Nistrup; Erdman Thomsen, Hanne


    data sets or for obtaining advanced search facilities. In this paper we will present an attempt at answering these questions. We will give a presentation of various types of ontologies and briefly introduce terminological ontologies. Furthermore we will argue that classification systems, e.g. product...

  5. Bosniak classification system

    DEFF Research Database (Denmark)

    Graumann, Ole; Osther, Susanne S; Karstoft, Jens


    BACKGROUND: The Bosniak classification is a diagnostic tool for the differentiation of cystic changes in the kidney. The process of categorizing renal cysts may be challenging, involving a series of decisions that may affect the final diagnosis and clinical outcome such as surgical management. PU...

  6. An automated cirrus classification (United States)

    Gryspeerdt, Edward; Quaas, Johannes; Sourdeval, Odran; Goren, Tom


    Cirrus clouds play an important role in determining the radiation budget of the earth, but our understanding of the lifecycle and controls on cirrus clouds remains incomplete. Cirrus clouds can have very different properties and development depending on their environment, particularly during their formation. However, the relevant factors often cannot be distinguished using commonly retrieved satellite data products (such as cloud optical depth). In particular, the initial cloud phase has been identified as an important factor in cloud development, but although back-trajectory based methods can provide information on the initial cloud phase, they are computationally expensive and depend on the cloud parametrisations used in re-analysis products. In this work, a classification system (Identification and Classification of Cirrus, IC-CIR) is introduced. Using re-analysis and satellite data, cirrus clouds are separated in four main types: frontal, convective, orographic and in-situ. The properties of these classes show that this classification is able to provide useful information on the properties and initial phase of cirrus clouds, information that could not be provided by instantaneous satellite retrieved cloud properties alone. This classification is designed to be easily implemented in global climate models, helping to improve future comparisons between observations and models and reducing the uncertainty in cirrus clouds properties, leading to improved cloud parametrisations.

  7. Support Vector Machines for Hyperspectral Remote Sensing Classification (United States)

    Gualtieri, J. Anthony; Cromp, R. F.


    The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

  8. An improved hyperspectral image classification approach based on ISODATA and SKR method (United States)

    Hong, Pu; Ye, Xiao-feng; Yu, Hui; Zhang, Zhi-jie; Cai, Yu-fei; Tang, Xin; Tang, Wei; Wang, Chensheng


    Hyper-spectral images can not only provide spatial information but also a wealth of spectral information. A short list of applications includes environmental mapping, global change research, geological research, wetlands mapping, assessment of trafficability, plant and mineral identification and abundance estimation, crop analysis, and bathymetry. A crucial aspect of hyperspectral image analysis is the identification of materials present in an object or scene being imaged. Classification of a hyperspectral image sequence amounts to identifying which pixels contain various spectrally distinct materials that have been specified by the user. Several techniques for classification of multi-hyperspectral pixels have been used from minimum distance and maximum likelihood classifiers to correlation matched filter-based approaches such as spectral signature matching and the spectral angle mapper. In this paper, an improved hyperspectral images classification algorithm is proposed. In the proposed method, an improved similarity measurement method is applied, in which both the spectrum similarity and space similarity are considered. We use two different weighted matrix to estimate the spectrum similarity and space similarity between two pixels, respectively. And then whether these two pixels represent the same material can be determined. In order to reduce the computational cost the wavelet transform is also applied prior to extract the spectral and space features. The proposed method is tested using hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory. Experimental results the efficiency of this new method on hyperspectral images associated with space object material identification.

  9. Discrimination of fungal infections on grape berries via spectral signatures (United States)

    Molitor, Daniel; Griesser, Michaela; Schütz, Erich; Khuen, Marie-Therese; Schefbeck, Christa; Ronellenfitsch, Franz Kai; Schlerf, Martin; Beyer, Marco; Schoedl-Hummel, Katharina; Anhalt, Ulrike; Forneck, Astrid


    The fungal pathogens Botrytis cinerea and Penicillium expansum are causing economic damages on grapevine worldwide. Especially the simultaneous occurrence of both often results in off-flavours highly threatening wine quality. For the classification of grape quality as well as for the determination of targeted enological treatments, the knowledge of the level of fungal attack is of highest interest. However, visual assessment and pathogen discrimination are cost-intensive. Consequently, a pilot laboratory study aimed at (i) detecting differences in spectral signatures between grape berry lots with different levels of infected berries (B. cinerea and/or P. expansum) and (ii) detecting links between spectral signatures and biochemical as well as quantitative molecular markers for fungal attack. To this end, defined percentages (infection levels) of table grape berries were inoculated with fungal spore suspensions. Spectral measurements were taken using a FieldSpec 3 Max spectroradiometer (ASD Inc., Boulder/Colorado, USA) in regular intervals after inoculation. In addition, fungal attack was determined enzymatically) and quantitatively (real-time PCR). In addition, gluconic acid concentrations (as a potential markers for fungal attack) were determined photometrically. Results indicate that based on spectral signatures, a discrimination of P. expansum and B. cinerea infections as well as of different B. cinerea infection levels is possible. Real-time PCR analyses, detecting DNA levels of both fungi, showed yet a low detection level. Whereas the gluconic acid concentrations turned out to be specific for the two fungi tested (B. cinerea vs. P. expansum) and thus may serve as a differentiating biochemical marker. Correlation analyses between spectral measurements and biological data (gluconic acid concentrations, fungi DNA) as well as further common field and laboratory trials are targeted.

  10. Visual inspection by spectral features in the ceramics industry (United States)

    Kukkonen, Saku; Kalviainen, Heikki A.; Parkkinen, Jussi P. S.


    Visual quality control is an important application area of machine vision. In ceramics industry, it is essential that in each set of ceramic tiles every single tile looks similar, while considering e.g. color and texture. Our goal is to design a machine vision system that can estimate the sufficient similarity or same appearance to the human eye. Currently, the estimation is usually done by human vision. Our main approach is to use accurate spectral representation of color, and compare spectral features to the RGB color features. The authors have recently proposed preliminary methods and results for the classification of color features. In this paper the approach is developed further to cope with illumination effects and to take more advantage of spectral features more. Experiments with five classes of brown tiles are discussed. Besides the k-NN classifier, a neural network, called the Self-Organizing Map (SOM) is used for understanding spectral features. Every single spectrum in each tile is used as input to a 2-D SOM with 30 X 30 nodes or neurons. The SOM is analyzed in order to understand how spectra are clustered. As a result, the nodes are labeled according to the classes. Another interest is to know whether we can find the order of spectral colors. In our approach, all spectra are clustered by 32 nodes in a 1-D SOM, and each pixel (spectrum) is presented by pseudocolors according to the trained nodes. Thus, each node corresponds to one pseudocolor and every spectrum is mapped into one of these nodes. Finally, the results are compared to experiments with human vision.


    African Journals Online (AJOL)

    ABSTRACT: The illuminated current-voltage characteristics of thin film a-Si:H. p-i-n solar cells were measured for the visible and near infrared spectral regions. The fill factor, the conversion efficiency, the open circuit Voltage and the short circuit current were compared to the parameters of crystalline silicon pit-junction.

  12. Speech recognition from spectral dynamics

    Indian Academy of Sciences (India)

    Abstract. Information is carried in changes of a signal. The paper starts with revis- iting Dudley's concept of the carrier nature of speech. It points to its close connection to modulation spectra of speech and argues against short-term spectral envelopes as dominant carriers of the linguistic information in speech. The history of ...

  13. Optical Spectral Variability of Blazars

    Indian Academy of Sciences (India)


    Jan 27, 2016 ... It is well established that blazars show flux variations in the complete electromagnetic (EM) spectrum on all possible time scales ranging from a few tens of minutes to several years. Here, we report the review of optical flux and spectral variability properties of different classes of blazars on IDV and STV ...

  14. Speech recognition from spectral dynamics

    Indian Academy of Sciences (India)


    Aug 26, 2016 ... Some of the history of gradual infusion of the modulation spectrum concept into Automatic recognition of speech (ASR) comes next, pointing to the relationship of modulation spectrum processing to wellaccepted ASR techniques such as dynamic speech features or RelAtive SpecTrAl (RASTA) filtering. Next ...

  15. Spectral problems for operator matrices

    NARCIS (Netherlands)

    Bátkai, A.; Binding, P.; Dijksma, A.; Hryniv, R.; Langer, H.


    We study spectral properties of 2 × 2 block operator matrices whose entries are unbounded operators between Banach spaces and with domains consisting of vectors satisfying certain relations between their components. We investigate closability in the product space, essential spectra and generation of

  16. Spectral Methods for Numerical Relativity

    Directory of Open Access Journals (Sweden)

    Grandclément Philippe


    Full Text Available Equations arising in general relativity are usually too complicated to be solved analytically and one must rely on numerical methods to solve sets of coupled partial differential equations. Among the possible choices, this paper focuses on a class called spectral methods in which, typically, the various functions are expanded in sets of orthogonal polynomials or functions. First, a theoretical introduction of spectral expansion is given with a particular emphasis on the fast convergence of the spectral approximation. We then present different approaches to solving partial differential equations, first limiting ourselves to the one-dimensional case, with one or more domains. Generalization to more dimensions is then discussed. In particular, the case of time evolutions is carefully studied and the stability of such evolutions investigated. We then present results obtained by various groups in the field of general relativity by means of spectral methods. Work, which does not involve explicit time-evolutions, is discussed, going from rapidly-rotating strange stars to the computation of black-hole–binary initial data. Finally, the evolution of various systems of astrophysical interest are presented, from supernovae core collapse to black-hole–binary mergers.

  17. Functional Analysis-Spectral Theoryl

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 6; Issue 4. Functional Analysis - Spectral Theory1. Cherian Varughese. Book Review Volume 6 Issue 4 April 2001 pp 91-92 ... Author Affiliations. Cherian Varughese1. Indian Statistical Institute, 8th Mile, Mysore Road, Bangalore 560 059, India.

  18. Spectral Diagonal Ensemble Kalman Filters

    Czech Academy of Sciences Publication Activity Database

    Kasanický, Ivan; Mandel, Jan; Vejmelka, Martin


    Roč. 22, č. 4 (2015), s. 485-497 ISSN 1023-5809 R&D Projects: GA ČR GA13-34856S Grant - others:NSF(US) DMS -1216481 Institutional support: RVO:67985807 Keywords : data assimilation * ensemble Kalman filter * spectral representation Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.321, year: 2015

  19. Speech recognition from spectral dynamics

    Indian Academy of Sciences (India)

    Some of the history of gradual infusion of the modulation spectrum concept into Automatic recognition of speech (ASR) comes next, pointing to the relationship of modulation spectrum processing to wellaccepted ASR techniques such as dynamic speech features or RelAtive SpecTrAl (RASTA) filtering. Next, the frequency ...

  20. Speech recognition from spectral dynamics

    Indian Academy of Sciences (India)

    automatic recognition of speech (ASR). Instead, likely for historical reasons, envelopes of power spectrum were adopted as main carrier of linguistic information in ASR. However, the relationships between phonetic values of sounds and their short-term spectral envelopes are not straightforward. Consequently, this asks for ...

  1. Spectral representation of Gaussian semimartingales

    DEFF Research Database (Denmark)

    Basse-O'Connor, Andreas


    The aim of the present paper is to characterize the spectral representation of Gaussian semimartingales. That is, we provide necessary and sufficient conditions on the kernel K for X t =∫ K t (s) dN s to be a semimartingale. Here, N denotes an independently scattered Gaussian random measure...

  2. Multiple Sparse Representations Classification (United States)

    Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik


    Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and


    Directory of Open Access Journals (Sweden)



    Full Text Available Problem statement. Ecopolices are the newest stage of the urban planning. They have to be consideredsuchas material and energy informational structures, included to the dynamic-evolutionary matrix netsofex change processes in the ecosystems. However, there are not made the ecopolice classifications, developing on suchapproaches basis. And this determined the topicality of the article. Analysis of publications on theoretical and applied aspects of the ecopolices formation showed, that the work on them is managed mainly in the context of the latest scientific and technological achievements in the various knowledge fields. These settlements are technocratic. They are connected with the morphology of space, network structures of regional and local natural ecosystems, without independent stability, can not exist without continuous man support. Another words, they do not work in with an ecopolices idea. It is come to a head for objective, symbiotic searching of ecopolices concept with the development of their classifications. Purpose statement is to develop the objective evidence for ecopolices and to propose their new classification. Conclusion. On the base of the ecopolices classification have to lie an elements correlation idea of their general plans and men activity type according with natural mechanism of accepting, reworking and transmission of material, energy and information between geo-ecosystems, planet, man, ecopolices material part and Cosmos. New ecopolices classification should be based on the principles of multi-dimensional, time-spaced symbiotic clarity with exchange ecosystem networks. The ecopolice function with this approach comes not from the subjective anthropocentric economy but from the holistic objective of Genesis paradigm. Or, otherwise - not from the Consequence, but from the Cause.

  4. Assessing FRET using Spectral Techniques (United States)

    Leavesley, Silas J.; Britain, Andrea L.; Cichon, Lauren K.; Nikolaev, Viacheslav O.; Rich, Thomas C.


    Förster resonance energy transfer (FRET) techniques have proven invaluable for probing the complex nature of protein–protein interactions, protein folding, and intracellular signaling events. These techniques have traditionally been implemented with the use of one or more fluorescence band-pass filters, either as fluorescence microscopy filter cubes, or as dichroic mirrors and band-pass filters in flow cytometry. In addition, new approaches for measuring FRET, such as fluorescence lifetime and acceptor photobleaching, have been developed. Hyperspectral techniques for imaging and flow cytometry have also shown to be promising for performing FRET measurements. In this study, we have compared traditional (filter-based) FRET approaches to three spectral-based approaches: the ratio of acceptor-to-donor peak emission, linear spectral unmixing, and linear spectral unmixing with a correction for direct acceptor excitation. All methods are estimates of FRET efficiency, except for one-filter set and three-filter set FRET indices, which are included for consistency with prior literature. In the first part of this study, spectrofluorimetric data were collected from a CFP–Epac–YFP FRET probe that has been used for intracellular cAMP measurements. All comparisons were performed using the same spectrofluorimetric datasets as input data, to provide a relevant comparison. Linear spectral unmixing resulted in measurements with the lowest coefficient of variation (0.10) as well as accurate fits using the Hill equation. FRET efficiency methods produced coefficients of variation of less than 0.20, while FRET indices produced coefficients of variation greater than 8.00. These results demonstrate that spectral FRET measurements provide improved response over standard, filter-based measurements. Using spectral approaches, single-cell measurements were conducted through hyperspectral confocal microscopy, linear unmixing, and cell segmentation with quantitative image analysis

  5. 78 FR 54970 - Cotton Futures Classification: Optional Classification Procedure (United States)


    ... industry and ICE, AMS proposes to offer a futures classification option whereby cotton bales may be... contract month. It is anticipated that AMS would make the futures classification option available December... Sec. 121.201). Establishing the registration option for cotton futures classification will not...

  6. 78 FR 68983 - Cotton Futures Classification: Optional Classification Procedure (United States)


    ... response to requests from the U.S. cotton industry and ICE, AMS will offer a futures classification option... parameters established by that exchange. AMS anticipates that the futures classification option will be... competition in the marketplace; (5) The futures classification option is expected to streamline marketing and...

  7. Hyperspectral imaging of polymer banknotes for building and analysis of spectral library (United States)

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham


    The use of counterfeit banknotes increases crime rates and cripples the economy. New countermeasures are required to stop counterfeiters who use advancing technologies with criminal intent. Many countries started adopting polymer banknotes to replace paper notes, as polymer notes are more durable and have better quality. The research on authenticating such banknotes is of much interest to the forensic investigators. Hyperspectral imaging can be employed to build a spectral library of polymer notes, which can then be used for classification to authenticate these notes. This is however not widely reported and has become a research interest in forensic identification. This paper focuses on the use of hyperspectral imaging on polymer notes to build spectral libraries, using a pushbroom hyperspectral imager which has been previously reported. As an initial study, a spectral library will be built from three arbitrarily chosen regions of interest of five circulated genuine polymer notes. Principal component analysis is used for dimension reduction and to convert the information in the spectral library to principal components. A 99% confidence ellipse is formed around the cluster of principal component scores of each class and then used as classification criteria. The potential of the adopted methodology is demonstrated by the classification of the imaged regions as training samples.

  8. Experimental study on spectral absorbance in fog as a function of temperature, liquid water content, and particle size (United States)

    Mäyrä, A.; Hietala, E.; Kutila, M.; Pyykönen, P.; Tiihonen, M.; Jokela, T.


    The ECSEL joint undertaking RobustSENSE1 focuses on technologies and solutions for automated driving in adverse weather conditions. One of the main technology challenges is to improve laser scanner performance in fog where the existing 905 nm lidar reliability degrades below tolerances. This report briefly summarizes the results of experimental fog absorbance measurements, which were conducted in VTT icing wind tunnel located in VTT's premises. The content of the presentation will focus on spectral absorbance measurements in artificial fog in near infrared band.

  9. An Ecological Diagnostic Classification Plan. (United States)

    Hurst, James C.; McKinley, Donna L.


    Discusses the value of diagnostic classification systems to counseling professionals. Describes the Ecological Diagnostic Classification Plan, an approach to diagnosis that includes the environment as a possible cause of pathology and target of intervention. (Author/KS)

  10. Obstacles to researching the researchers: a case study of the ethical challenges of undertaking methodological research investigating the reporting of randomised controlled trials. (United States)

    McKenzie, Joanne E; Herbison, G Peter; Roth, Paul; Paul, Charlotte


    Recent cohort studies of randomised controlled trials have provided evidence of within-study selective reporting bias; where statistically significant outcomes are more likely to be more completely reported compared to non-significant outcomes. Bias resulting from selective reporting can impact on meta-analyses, influencing the conclusions of systematic reviews, and in turn, evidence based clinical practice guidelines.In 2006 we received funding to investigate if there was evidence of within-study selective reporting in a cohort of RCTs submitted to New Zealand Regional Ethics Committees in 1998/99. This research involved accessing ethics applications, their amendments and annual reports, and comparing these with corresponding publications. We did not plan to obtain informed consent from trialists to view their ethics applications for practical and scientific reasons. In November 2006 we sought ethical approval to undertake the research from our institutional ethics committee. The Committee declined our application on the grounds that we were not obtaining informed consent from the trialists to view their ethics application. This initiated a seventeen month process to obtain ethical approval. This publication outlines what we planned to do, the issues we encountered, discusses the legal and ethical issues, and presents some potential solutions. Methodological research such as this has the potential for public benefit and there is little or no harm for the participants (trialists) in undertaking it. Further, in New Zealand, there is freedom of information legislation, which in this circumstance, unambiguously provided rights of access and use of the information in the ethics applications. The decision of our institutional ethics committee defeated this right and did not recognise the nature of this observational research. Methodological research, such as this, can be used to develop processes to improve quality in research reporting. Recognition of the potential

  11. Classification of Osteogenesis Imperfecta revisited

    NARCIS (Netherlands)

    van Dijk, F. S.; Pals, G.; van Rijn, R. R.; Nikkels, P. G. J.; Cobben, J. M.


    In 1979 Sillence proposed a classification of Osteogenesis Imperfecta (OI) in OI types I, II, III and IV. In 2004 and 2007 this classification was expanded with OI types V-VIII because of distinct clinical features and/or different causative gene mutations. We propose a revised classification of OI

  12. IAEA Classification of Uranium Deposits

    International Nuclear Information System (INIS)

    Bruneton, Patrice


    Classifications of uranium deposits follow two general approaches, focusing on: • descriptive features such as the geotectonic position, the host rock type, the orebody morphology, …… : « geologic classification »; • or on genetic aspects: « genetic classification »

  13. Quantitative method to assess caries via fluorescence imaging from the perspective of autofluorescence spectral analysis (United States)

    Chen, Q. G.; Zhu, H. H.; Xu, Y.; Lin, B.; Chen, H.


    A quantitative method to discriminate caries lesions for a fluorescence imaging system is proposed in this paper. The autofluorescence spectral investigation of 39 teeth samples classified by the International Caries Detection and Assessment System levels was performed at 405 nm excitation. The major differences in the different caries lesions focused on the relative spectral intensity range of 565-750 nm. The spectral parameter, defined as the ratio of wavebands at 565-750 nm to the whole spectral range, was calculated. The image component ratio R/(G + B) of color components was statistically computed by considering the spectral parameters (e.g. autofluorescence, optical filter, and spectral sensitivity) in our fluorescence color imaging system. Results showed that the spectral parameter and image component ratio presented a linear relation. Therefore, the image component ratio was graded as 1.62 to quantitatively classify sound, early decay, established decay, and severe decay tissues, respectively. Finally, the fluorescence images of caries were experimentally obtained, and the corresponding image component ratio distribution was compared with the classification result. A method to determine the numerical grades of caries using a fluorescence imaging system was proposed. This method can be applied to similar imaging systems.

  14. Quantitative method to assess caries via fluorescence imaging from the perspective of autofluorescence spectral analysis

    International Nuclear Information System (INIS)

    Chen, Q G; Xu, Y; Zhu, H H; Chen, H; Lin, B


    A quantitative method to discriminate caries lesions for a fluorescence imaging system is proposed in this paper. The autofluorescence spectral investigation of 39 teeth samples classified by the International Caries Detection and Assessment System levels was performed at 405 nm excitation. The major differences in the different caries lesions focused on the relative spectral intensity range of 565–750 nm. The spectral parameter, defined as the ratio of wavebands at 565–750 nm to the whole spectral range, was calculated. The image component ratio R/(G + B) of color components was statistically computed by considering the spectral parameters (e.g. autofluorescence, optical filter, and spectral sensitivity) in our fluorescence color imaging system. Results showed that the spectral parameter and image component ratio presented a linear relation. Therefore, the image component ratio was graded as <0.66, 0.66–1.06, 1.06–1.62, and >1.62 to quantitatively classify sound, early decay, established decay, and severe decay tissues, respectively. Finally, the fluorescence images of caries were experimentally obtained, and the corresponding image component ratio distribution was compared with the classification result. A method to determine the numerical grades of caries using a fluorescence imaging system was proposed. This method can be applied to similar imaging systems. (paper)

  15. Piroxicam derivatives THz classification (United States)

    Sterczewski, Lukasz A.; Grzelczak, Michal P.; Nowak, Kacper; Szlachetko, Bogusław; Plinska, Stanislawa; Szczesniak-Siega, Berenika; Malinka, Wieslaw; Plinski, Edward F.


    In this paper we report a new approach to linking the terahertz spectral shapes of drug candidates having a similar molecular structure to their chemical and physical parameters. We examined 27 newly-synthesized derivatives of a well-known nonsteroidal anti-inflammatory drug Piroxicam used for treatment of inflammatory arthritis and chemoprevention of colon cancer. The testing was carried out by means of terahertz pulsed spectroscopy (TPS). Using chemometric techniques we evaluated their spectral similarity in the terahertz range and attempted to link the position on the principal component analysis (PCA) score map to the similarity of molecular descriptors. A simplified spectral model preserved 75% and 85.1% of the variance in 2 and 3 dimensions respectively, compared to the input 1137. We have found that in 85% of the investigated samples a similarity of the physical and chemical parameters corresponds to a similarity in the terahertz spectra. The effects of data preprocessing on the generated maps are also discussed. The technique presented can support the choice of the most promising drug candidates for clinical trials in pharmacological research.

  16. [Research on identification of cabbages and weeds combining spectral imaging technology and SAM taxonomy]. (United States)

    Zu, Qin; Zhang, Shui-fa; Cao, Yang; Zhao, Hui-yi; Dang, Chang-qing


    Weeds automatic identification is the key technique and also the bottleneck for implementation of variable spraying and precision pesticide. Therefore, accurate, rapid and non-destructive automatic identification of weeds has become a very important research direction for precision agriculture. Hyperspectral imaging system was used to capture the hyperspectral images of cabbage seedlings and five kinds of weeds such as pigweed, barnyard grass, goosegrass, crabgrass and setaria with the wavelength ranging from 1000 to 2500 nm. In ENVI, by utilizing the MNF rotation to implement the noise reduction and de-correlation of hyperspectral data and reduce the band dimensions from 256 to 11, and extracting the region of interest to get the spectral library as standard spectra, finally, using the SAM taxonomy to identify cabbages and weeds, the classification effect was good when the spectral angle threshold was set as 0. 1 radians. In HSI Analyzer, after selecting the training pixels to obtain the standard spectrum, the SAM taxonomy was used to distinguish weeds from cabbages. Furthermore, in order to measure the recognition accuracy of weeds quantificationally, the statistical data of the weeds and non-weeds were obtained by comparing the SAM classification image with the best classification effects to the manual classification image. The experimental results demonstrated that, when the parameters were set as 5-point smoothing, 0-order derivative and 7-degree spectral angle, the best classification result was acquired and the recognition rate of weeds, non-weeds and overall samples was 80%, 97.3% and 96.8% respectively. The method that combined the spectral imaging technology and the SAM taxonomy together took full advantage of fusion information of spectrum and image. By applying the spatial classification algorithms to establishing training sets for spectral identification, checking the similarity among spectral vectors in the pixel level, integrating the advantages of

  17. Spectral behavior of some modal soil profiles from São Paulo State, Brazil

    Directory of Open Access Journals (Sweden)

    José Alexandre Melo Demattê


    Full Text Available Remote sensing has a high potential for environmental evaluation. However, a necessity exists for a better understanding of the relations between the soil attributes and spectral data. The objective of this work was to analyze the spectral behavior of some soil profiles from the region of Piracicaba, São Paulo State, using a laboratory spectroradiometer (400 to 2500 nm. The relations between the reflected electromagnetic energy and the soil physical, chemical and mineralogical attributes were analyzed, verifying the spectral variations of soil samples in depth along the profiles with their classification and discrimination. Sandy soil reflected more, presenting a spectral curve with an ascendant form, opposite to clayey soils. The 1900 nm band discriminated soil with 2:1 mineralogy from the 1:1 and oxidic soils. It was possible to detect the presence of kaolinite, gibbsite, hematite and goethite in the soils through the descriptive aspects of curves, absorption features and reflectance intensity. A relation exists between the weathering stage and spectral data. The evaluation of the superficial and subsuperficial horizon samples allowed characterizing and discriminating the analytical variability of the profile, helping to soil distinguishing and classification.

  18. Minimum-Light Spectral Types for Four Mira Variables (United States)

    Wing, R. F.; Yuan, Y.; Benfer, S. R.


    Spectral types for four little-studied Mira variables have been derived from narrow-band classification photometry obtained within three weeks of minimum light. The observations were made on May 24, 1997, with the CTIO 1.5-m telescope and ASCAP photometer when targets of a primary program were not accessible. The following stars were selected for observation based on AAVSO predicted times of minimum light. Star P V Sp.T.(pub) Phase I(104) Sp.T.(8c) RR Aqr 182.5 9.1--14.4 M2e--M4e 0.52 7.63 M7.5 RS Aqr 214.6 9.5--14.4 Me 0.62 6.63 M7.5 Z Aql 129.2 8.2--14.8 M3 0.67 7.07 M8.3 RU Cap 347.4 9.2--15.2 M9e 0.68 5.49 M8.8 The first four columns give data from the GCVS: the star's name, mean period in days, observed range in visual magnitude, and published spectral type. The last three columns refer to our own observations: the phase on 1997 May 24 (according to the AAVSO predictions), the apparent magnitude in a narrow band centered at 10400 Angstroms, and our spectral classification on the eight-color system, which is based on measurements of TiO at 7120 Angstroms and VO at 10540 Angstroms. The very late published spectral type of RU Cap (which appears to have been observed only at minimum) is confirmed. The three other stars, which had not previously been observed later than M4, all showed very strong TiO and significant absorption by VO, indicative of types M7.5 or later. These and earlier observations show that published spectral types of Mira variables --- even those that are brighter than V = 10 at maximum --- are often very incomplete and can be misleading. Indeed, it is difficult to find Miras that do not attain types of at least M7 at minimum.

  19. BIRADS classification in mammography. (United States)

    Balleyguier, Corinne; Ayadi, Salma; Van Nguyen, Kim; Vanel, Daniel; Dromain, Clarisse; Sigal, Robert


    The Breast Imaging Report and Data System (BIRADS) of the American College of Radiology (ACR) is today largely used in most of the countries where breast cancer screening is implemented. It is a tool defined to reduce variability between radiologists when creating the reports in mammography, ultrasonography or MRI. Some changes in the last version of the BIRADStrade mark have been included to reduce the inaccuracy of some categories, especially for category 4. The BIRADStrade mark includes a lexicon and descriptive diagrams of the anomalies, recommendations for the mammographic report as well as councils and examples of mammographic cases. This review describes the mammographic items of the BIRADS classification with its more recent developments, while detailing the advantages and limits of this classification.

  20. Sound classification of dwellings

    DEFF Research Database (Denmark)

    Rasmussen, Birgit


    National schemes for sound classification of dwellings exist in more than ten countries in Europe, typically published as national standards. The schemes define quality classes reflecting different levels of acoustical comfort. Main criteria concern airborne and impact sound insulation between...... dwellings, facade sound insulation and installation noise. The schemes have been developed, implemented and revised gradually since the early 1990s. However, due to lack of coordination between countries, there are significant discrepancies, and new standards and revisions continue to increase the diversity...... is needed, and a European COST Action TU0901 "Integrating and Harmonizing Sound Insulation Aspects in Sustainable Urban Housing Constructions", has been established and runs 2009-2013, one of the main objectives being to prepare a proposal for a European sound classification scheme with a number of quality...

  1. [Headache: classification and diagnosis]. (United States)

    Carbaat, P A T; Couturier, E G M


    There are many types of headache and, moreover, many people have different types of headache at the same time. Adequate treatment is possible only on the basis of the correct diagnosis. Technically and in terms of content the current diagnostics process for headache is based on the 'International Classification of Headache Disorders' (ICHD-3-beta) that was produced under the auspices of the International Headache Society. This classification is based on a distinction between primary and secondary headaches. The most common primary headache types are the tension type headache, migraine and the cluster headache. Application of uniform diagnostic concepts is essential to come to the most appropriate treatment of the various types of headache.

  2. Using Social Cognitive Theory to Explain the Intention of Final-year Pharmacy Students to Undertake a Higher Degree in Pharmacy Practice Research. (United States)

    Carter, Stephen R; Moles, Rebekah J; Krass, Ines; Kritikos, Vicki S


    Objective. To develop and test a conceptual model that hypothesized student intention to undertake a higher degree in pharmacy practice research (PPR) would be increased by self-efficacy, outcome expectancy, and the social influence of faculty members. Methods. Cross-sectional surveys were completed by 387 final-year pharmacy undergraduates enrolled in 2012 and 2013. Structural equation modeling was used to explore relationships between variables and intention. Results. Fit indices were good. The model explained 55% of the variation in intention. As hypothesized, faculty social influence increased self-efficacy and indirectly increased outcome expectancy and intention. Conclusion. To increase pharmacy students' orientation towards a career in PPR, faculty members could use their social influence by highlighting PPR in their teaching.



    Natalia Romanova


    New types of criminal groups are emerging in modern society.  These types have their special criminal subculture. The research objective is to develop new parameters of classification of modern criminal groups, create a new typology of criminal groups and identify some features of their subculture. Research methodology is based on the system approach that includes using the method of analysis of documentary sources (materials of a criminal case), method of conversations with themembers of the...

  4. Classification and regression trees

    CERN Document Server

    Breiman, Leo; Olshen, Richard A; Stone, Charles J


    The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

  5. Classification Constrained Dimensionality Reduction


    Raich, Raviv; Costa, Jose A.; Damelin, Steven B.; Hero III, Alfred O.


    Dimensionality reduction is a topic of recent interest. In this paper, we present the classification constrained dimensionality reduction (CCDR) algorithm to account for label information. The algorithm can account for multiple classes as well as the semi-supervised setting. We present an out-of-sample expressions for both labeled and unlabeled data. For unlabeled data, we introduce a method of embedding a new point as preprocessing to a classifier. For labeled data, we introduce a method tha...

  6. Classification of nanopolymers

    Energy Technology Data Exchange (ETDEWEB)

    Larena, A; Tur, A [Department of Chemical Industrial Engineering and Environment, Universidad Politecnica de Madrid, E.T.S. Ingenieros Industriales, C/ Jose Gutierrez Abascal, Madrid (Spain); Baranauskas, V [Faculdade de Engenharia Eletrica e Computacao, Departamento de Semicondutores, Instrumentos e Fotonica, Universidade Estadual de Campinas, UNICAMP, Av. Albert Einstein N.400, 13 083-852 Campinas SP Brasil (Brazil)], E-mail:


    Nanopolymers with different structures, shapes, and functional forms have recently been prepared using several techniques. Nanopolymers are the most promising basic building blocks for mounting complex and simple hierarchical nanosystems. The applications of nanopolymers are extremely broad and polymer-based nanotechnologies are fast emerging. We propose a nanopolymer classification scheme based on self-assembled structures, non self-assembled structures, and on the number of dimensions in the nanometer range (nD)

  7. Decimal Classification Editions

    Directory of Open Access Journals (Sweden)

    Zenovia Niculescu


    Full Text Available The study approaches the evolution of Dewey Decimal Classification editions from the perspective of updating the terminology, reallocating and expanding the main and auxilary structure of Dewey indexing language. The comparative analysis of DDC editions emphasizes the efficiency of Dewey scheme from the point of view of improving the informational offer, through basic index terms, revised and developed, as well as valuing the auxilary notations.

  8. Classification of mitral insufficiency and stenosis using MLP neural network and neuro-fuzzy system. (United States)

    Barýpçý, Necaattin; Ergün, Uçman; Ilkay, Erdoğan; Serhatlýoğlu, Selami; Hardalaç, Firat; Güler, Inan


    Cardiac Doppler signals recorded from mitral valve of 60 patients were transferred to a personal computer by using a 16-bit sound card. The power spectral density (PSD) was applied to the recorded signal from each patient. In order to do a good interpretation and rapid diagnosis, PSD values classified using multilayer perceptron (MLP) and neuro-fuzzy system. Our findings demonstrated that 93.33% classification success rate was obtained from MLP, 90% classification success rate was obtained from neuro-fuzzy system. The classification results show that MLP offers best results in the case of diagnosis.

  9. Optimized extreme learning machine for urban land cover classification using hyperspectral imagery (United States)

    Su, Hongjun; Tian, Shufang; Cai, Yue; Sheng, Yehua; Chen, Chen; Najafian, Maryam


    This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficient C and Gaussian kernel σ for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.

  10. Neuromuscular disease classification system (United States)

    Sáez, Aurora; Acha, Begoña; Montero-Sánchez, Adoración; Rivas, Eloy; Escudero, Luis M.; Serrano, Carmen


    Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

  11. Classifications of track structures

    International Nuclear Information System (INIS)

    Paretzke, H.G.


    When ionizing particles interact with matter they produce random topological structures of primary activations which represent the initial boundary conditions for all subsequent physical, chemical and/or biological reactions. There are two important aspects of research on such track structures, namely their experimental or theoretical determination on one hand and the quantitative classification of these complex structures which is a basic pre-requisite for the understanding of mechanisms of radiation actions. This paper deals only with the latter topic, i.e. the problems encountered in and possible approaches to quantitative ordering and grouping of these multidimensional objects by their degrees of similarity with respect to their efficiency in producing certain final radiation effects, i.e. to their ''radiation quality.'' Various attempts of taxonometric classification with respect to radiation efficiency have been made in basic and applied radiation research including macro- and microdosimetric concepts as well as track entities and stopping power based theories. In this paper no review of those well-known approaches is given but rather an outline and discussion of alternative methods new to this field of radiation research which have some very promising features and which could possibly solve at least some major classification problems

  12. [Myelodysplastic syndrome classification]. (United States)

    Ghariani, Ines; Braham, Najia; Hassine, Mohsen; Kortas, Mondher


    Myelodysplastic syndromes (MDS) are myeloid disorders with various clinical and biological presentations. The French-American-British (FAB-1982) classification included five categories basing on morphology and bone marrow blast count. Three criteria are taken into account: 1) the percentage of blasts in peripheral blood and bone marrow, 2) the percentage of ringed sideroblasts, and 3) the number of monocytes in peripheral blood. The World Health Organization classification (WHO 2001, 2008) modifies the FAB system by also taking cytogenetic characteristics and molecular biology into consideration. The last classification (WHO-2008) takes into account: 1) the number of peripheral cytopenia, 2) the percentage of blasts in peripheral blood and bone marrow, 3) the percentage of ringed sideroblasts, 4) the possible presence of Auer Rods, and 5) the detection of a cytogenetic abnormality (the isolated 5q deletion). The following subgroups are defined: refractory cytopenia with unilineage dysplasia, refractory anemia with ringed sideroblasts, refractory cytopenia with multilineage dysplasia, refractory anemia with excess blasts, myelodysplastic syndrome unclassifiable and myelodysplastic syndrome with isolated del(5q).

  13. Spectral Tensor-Train Decomposition

    DEFF Research Database (Denmark)

    Bigoni, Daniele; Engsig-Karup, Allan Peter; Marzouk, Youssef M.


    discretizations of the target function. We assess the performance of the method on a range of numerical examples: a modified set of Genz functions with dimension up to 100, and functions with mixed Fourier modes or with local features. We observe significant improvements in performance over an anisotropic......The accurate approximation of high-dimensional functions is an essential task in uncertainty quantification and many other fields. We propose a new function approximation scheme based on a spectral extension of the tensor-train (TT) decomposition. We first define a functional version of the TT.......e., the “cores”) comprising the functional TT decomposition. This result motivates an approximation scheme employing polynomial approximations of the cores. For functions with appropriate regularity, the resulting spectral tensor-train decomposition combines the favorable dimension-scaling of the TT...

  14. Spectral computations for bounded operators

    CERN Document Server

    Ahues, Mario; Limaye, Balmohan


    Exact eigenvalues, eigenvectors, and principal vectors of operators with infinite dimensional ranges can rarely be found. Therefore, one must approximate such operators by finite rank operators, then solve the original eigenvalue problem approximately. Serving as both an outstanding text for graduate students and as a source of current results for research scientists, Spectral Computations for Bounded Operators addresses the issue of solving eigenvalue problems for operators on infinite dimensional spaces. From a review of classical spectral theory through concrete approximation techniques to finite dimensional situations that can be implemented on a computer, this volume illustrates the marriage of pure and applied mathematics. It contains a variety of recent developments, including a new type of approximation that encompasses a variety of approximation methods but is simple to verify in practice. It also suggests a new stopping criterion for the QR Method and outlines advances in both the iterative refineme...

  15. Segmentation Based Fuzzy Classification of High Resolution Images (United States)

    Rao, Mukund; Rao, Suryaprakash; Masser, Ian; Kasturirangan, K.

    Information extraction from satellite images is the process of delineation of entities in the image which pertain to some feature on the earth and to which on associating an attribute, a classification of the image is obtained. Classification is a common technique to extract information from remote sensing data and, by and large, the common classification techniques mainly exploit the spectral characteristics of remote sensing images and attempt to detect patterns in spectral information to classify images. These are based on a per-pixel analysis of the spectral information, "clustering" or "grouping" of pixels is done to generate meaningful thematic information. Most of the classification techniques apply statistical pattern recognition of image spectral vectors to "label" each pixel with appropriate class information from a set of training information. On the other hand, Segmentation is not new, but it is yet seldom used in image processing of remotely sensed data. Although there has been a lot of development in segmentation of grey tone images in this field and other fields, like robotic vision, there has been little progress in segmentation of colour or multi-band imagery. Especially within the last two years many new segmentation algorithms as well as applications were developed, but not all of them lead to qualitatively convincing results while being robust and operational. One reason is that the segmentation of an image into a given number of regions is a problem with a huge number of possible solutions. Newer algorithms based on fractal approach could eventually revolutionize image processing of remotely sensed data. The paper looks at applying spatial concepts to image processing, paving the way to algorithmically formulate some more advanced aspects of cognition and inference. In GIS-based spatial analysis, vector-based tools already have been able to support advanced tasks generating new knowledge. By identifying objects (as segmentation results) from

  16. The paradox of atheoretical classification

    DEFF Research Database (Denmark)

    Hjørland, Birger


    be very successful. The best example of a successful “atheoretical” classification is probably the prestigious Diagnostic and Statistical Manual of Mental Disorders (DSM) since its third edition from 1980. Based on such successes one may ask: Should the claim that classifications ideally are natural......A distinction can be made between “artificial classifications” and “natural classifications,” where artificial classifications may adequately serve some limited purposes, but natural classifications are overall most fruitful by allowing inference and thus many different purposes. There is strong...

  17. Pulse Analysis Spectroradiometer System for Measuring the Spectral Distribution of Flash Solar Simulators: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Andreas, A. M.; Myers, D. R.


    Flashing artificial light sources are used extensively in photovoltaic module performance testing and plant production lines. There are several means of attempting to measure the spectral distribution of a flash of light; however, many of these approaches generally capture the entire pulse energy. We report here on the design and performance of a system to capture the waveform of flash at individual wavelengths of light. Any period within the flash duration can be selected, over which to integrate the flux intensity at each wavelength. The resulting spectral distribution is compared with the reference spectrum, resulting in a solar simulator classification.

  18. Spatial-spectral dimensionality reduction of hyperspectral imagery with partial knowledge of class labels (United States)

    Cahill, Nathan D.; Chew, Selene E.; Wenger, Paul S.


    Laplacian Eigenmaps (LE) and Schroedinger Eigenmaps (SE) are effective dimensionality reduction algorithms that are capable of integrating both the spatial and spectral information inherent in a hyperspectral image. In this paper, we consider how to extend LE- and SE-based spatial-spectral dimensionality reduction algorithms to situations where partial knowledge of class labels exists, for example, when a subset of pixels has been manually labeled by an expert user. This partial knowledge is incorporated through the use of cluster potentials, turning each underlying algorithm into an instance of SE. Using publicly available data, we show that incorporating this partial knowledge improves the performance of subsequent classification algorithms.

  19. Biomarkers and Biological Spectral Imaging (United States)


    karyotyping (SKY) in hematological neoplasia [4259-13] B. S. Preiss, R. K. Pedersen, G. B. Kerndrup, Odense Univ. Hospital (Denmark) 60 Structure of...astronomy and airborne monitoring to forensic and biomedical sciences or industrial qualit\\ and process monitoring. There is growing need for a sensitive...SPIE Vol. 4259 55 Spectral Karyotyping (SKY) in Hematologic Neoplasia. Birgitte S. Preiss*a, Rikke K. Pedersena, Gitte B. Kerndrupa aInstitute of

  20. Chebyshev and Fourier spectral methods

    CERN Document Server

    Boyd, John P


    Completely revised text focuses on use of spectral methods to solve boundary value, eigenvalue, and time-dependent problems, but also covers Hermite, Laguerre, rational Chebyshev, sinc, and spherical harmonic functions, as well as cardinal functions, linear eigenvalue problems, matrix-solving methods, coordinate transformations, methods for unbounded intervals, spherical and cylindrical geometry, and much more. 7 Appendices. Glossary. Bibliography. Index. Over 160 text figures.

  1. Abundance estimation of spectrally similar minerals

    CSIR Research Space (South Africa)

    Debba, Pravesh


    Full Text Available This paper evaluates a spectral unmixing method for estimating the partial abundance of spectrally similar minerals in complex mixtures. The method requires formulation of a linear function of individual spectra of individual minerals. The first...

  2. Maximum mutual information regularized classification

    KAUST Repository

    Wang, Jim Jing-Yan


    In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

  3. Calibration with near-continuous spectral measurements

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Rasmussen, Michael; Madsen, Henrik


    In chemometrics traditional calibration in case of spectral measurements express a quantity of interest (e.g. a concentration) as a linear combination of the spectral measurements at a number of wavelengths. Often the spectral measurements are performed at a large number of wavelengths and in thi...... by an example in which the octane number of gasoline is related to near infrared spectral measurements. The performance is found to be much better that for the traditional calibration methods....

  4. USGS Spectral Library Version 7 (United States)

    Kokaly, Raymond F.; Clark, Roger N.; Swayze, Gregg A.; Livo, K. Eric; Hoefen, Todd M.; Pearson, Neil C.; Wise, Richard A.; Benzel, William M.; Lowers, Heather A.; Driscoll, Rhonda L.; Klein, Anna J.


    We have assembled a library of spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns [μm]). Laboratory samples of specific minerals, plants, chemical compounds, and manmade materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically constructed as well as mathematically computed mixtures. Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of Analytical Spectral Devices (ASD) field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Measurements of rocks, soils, and natural mixtures of minerals were made in laboratory and field settings. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. This report describes the instruments used, the organization of materials into chapters, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements. To facilitate greater application of the spectra, the library has also been convolved to selected spectrometer and imaging spectrometers sampling and

  5. Calibrating spectral images using penalized likelihood

    NARCIS (Netherlands)

    Heijden, van der G.W.A.M.; Glasbey, C.


    A new method is presented for automatic correction of distortions and for spectral calibration (which band corresponds to which wavelength) of spectral images recorded by means of a spectrograph. The method consists of recording a bar-like pattern with an illumination source with spectral bands

  6. Spectral properties of generalized eigenparameter dependent ...

    African Journals Online (AJOL)

    Jost function, spectrum, the spectral singularities, and the properties of the principal vectors corresponding to the spectral singularities of L, if. ∞Σn=1 n(∣1 - an∣ + ∣bnl) < ∞. Mathematics Subject Classication (2010): 34L05, 34L40, 39A70, 47A10, 47A75. Key words: Discrete equations, eigenparameter, spectral analysis, ...

  7. Spectral Lag Evolution among -Ray Burst Pulses

    Indian Academy of Sciences (India)


    Jan 27, 2016 ... We analyse the spectral lag evolution of -ray burst (GRB) pulses with observations by CGRO/BATSE. No universal spectral lag evolution feature and pulse luminosity-lag relation within a GRB is observed.Our results suggest that the spectral lag would be due to radiation physics and dynamics of a given ...

  8. Cardiac arrhythmia classification using multi-modal signal analysis. (United States)

    Kalidas, V; Tamil, L S


    In this paper, as a contribution to the Physionet/Computing in Cardiology 2015 Challenge, we present individual algorithms to accurately classify five different life threatening arrhythmias with the goal of suppressing false alarm generation in intensive care units. Information obtained by analysing electrocardiogram, photoplethysmogram and arterial blood pressure signals was utilized to develop the classification models. Prior to classification, the signals were subject to a signal pre-processing stage for quality analysis. Classification was performed using a combination of support vector machine based machine learning approach and logical analysis techniques. The predicted result for a certain arrhythmia classification model was verified by logical analysis to aid in reduction of false alarms. Separate feature vectors were formed for predicting the presence or absence of each arrhythmia, using both spectral and time-domain information. The training and test data were obtained from the Physionet/CinC Challenge 2015 database. Classification algorithms were written for two different categories of data, namely real-time and retrospective, whose data lengths were 10 s and an additional 30 s, respectively. For the real-time test dataset, sensitivity of 94% and specificity of 82% were obtained. Similarly, for the retrospective test dataset, sensitivity of 94% and specificity of 86% were obtained.

  9. 14 CFR 1203.412 - Classification guides. (United States)


    ... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Classification guides. 1203.412 Section... PROGRAM Guides for Original Classification § 1203.412 Classification guides. (a) General. A classification guide, based upon classification determinations made by appropriate program and classification...

  10. Conditions and Motivations to Undertake

    Directory of Open Access Journals (Sweden)

    Flor Ángela Marulanda Valencia


    Full Text Available This study aims at deepening in the analysis of motivations shown by a group of entrepreneurs in Medellin, Antioquia. It also describes the different perceptions about the enablers and obstacles for the development of entrepreneurship in appropriate environments to promote it. It was found that independence was the principal motivation for entrepreneurship and that the city offered the most favourable environment to foster it. Additionally, it was found that the most important obstacle to develop it was the difficulties to access a bank credit.

  11. 3D and Multispectral Imaging For Subcutaneous Structures Classification And Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Paquit, Vincent C [ORNL; Tobin Jr, Kenneth William [ORNL; Price, Jeffery R [ORNL; Meriaudeau, Fabrice [ORNL


    A classification method to differentiate subcutaneous structures is presented. To obtain characteristic spectral signatures, we are investigating light propagation phenomena in biological tissues by combining visible to near-infrared multi-wavelength skin imaging and three dimensional topographic imaging of the skin surface.

  12. Combined principal component preprocessing and n-tuple neural networks for improved classification

    DEFF Research Database (Denmark)

    Høskuldsson, Agnar; Linneberg, Christian


    We present a combined principal component analysis/neural network scheme for classification. The data used to illustrate the method consist of spectral fluorescence recordings from seven different production facilities, and the task is to relate an unknown sample to one of these seven factories. ...

  13. Automatic training sample selection for a multi-evidence based crop classification approach

    DEFF Research Database (Denmark)

    Chellasamy, Menaka; Ferre, Ty; Greve, Mogens Humlekrog

    three Multi-Layer Perceptron (MLP) neural networks trained separately with spectral, texture and vegetation indices; classification labels were then assigned based on Endorsement Theory. The present study proposes an approach to feed this ensemble classifier with automatically selected training samples...

  14. Development of a Wave Resource Classification System (United States)

    Ahn, S.; Haas, K. A.; Neary, V. S.; Bredin, S.


    As the wave energy industry develops, detailed classification systems for wave resource assessments are beneficial for characterizing the wave resource statistics of particular sites and codifying opportunities and risks at these sites. Despite the wide spread availability of wave buoy data for the United States, this is currently insufficient to develop a classification scheme. Therefore, data from the 3rd generation wave model (WWIII) is utilized. Key wave resource statistics for the entire US territorial waters are computed and retained as time-series using a 30-year hindcast (1980-2009) wave spectra including: Annual available energy (AAE), omni-directional wave power, significant wave height, energy period, spectral width, direction of maximum directionally resolved wave power and directionality coefficient. The hindcast data are extensively validated with the available buoy wave measurements using the validation methodology recommended by the IEC standard for wave energy resource assessments (IEC TS 62600-101). As a high level wave classification, the AAE density is the primary indicator of wave energy resources. The AAE is analogous to annual energy production (AEP) without considering the energy conversion process. It can be thought as the theoretical available wave energy resource for any particular location. The AAE is separated into four different classes of ascending energy levels, 0, I, II, and III. Because of the dependence of wave energy devices on wave frequencies, the AAE for the US territorial waters are computed for the full spectrum along with different frequency bands corresponding to wind sea, swell and transitional sea states. The geographic distribution for the different wave classes within each frequency band have been determined. Finally, subclasses based on the extreme wave conditions will also be presented.

  15. Spectral multitude and spectral dynamics reflect changing conjugation length in single molecules of oligophenylenevinylenes

    KAUST Repository

    Kobayashi, Hiroyuki


    Single-molecule study of phenylenevinylene oligomers revealed distinct spectral forms due to different conjugation lengths which are determined by torsional defects. Large spectral jumps between different spectral forms were ascribed to torsional flips of a single phenylene ring. These spectral changes reflect the dynamic nature of electron delocalization in oligophenylenevinylenes and enable estimation of the phenylene torsional barriers. © 2012 The Owner Societies.

  16. Cool stars: spectral library of high-resolution echelle spectra and database of stellar parameters (United States)

    Montes, D.


    During the last years our group have undertake several high resolution spectroscopic surveys of nearby FGKM stars with different spectrographs (FOCES, SARG, SOFIN, FIES, HERMES). A large number of stars have been already observed and we have already determined spectral types, rotational velocities as well as radial velocities, Lithium abundance and several chromospheric activity indicators. We are working now in a homogeneous determination of the fundamental stellar parameters (T_{eff}, log{g}, ξ and [Fe/H]) and chemical abundances of many elements of all these stars. Some fully reduced spectra in FITS format have been available via ftp and in the {}{Worl Wide Web} (Montes et al. 1997, A&AS, 123, 473; Montes et al. 1998, A&AS, 128, 485; and Montes et al. 1999, ApJS, 123, 283) and some particular spectral regions of the echelle spectra are available at VizieR by López-Santiago et al. 2010, A&A, 514, A97. We are now working in made accessible all the spectra of our different surveys in a Virtual Observatory ({}{VO}) compliant library and database accessible using a common web interface following the standards of the International Virtual Observatory Alliance ({}{IVOA}). The spectral library includes F, G, K and M field stars, from dwarfs to giants. The spectral coverage is from 3800 to 10000 Å, with spectral resolution ranging from 40000 to 80000. The database will provide in addition the stellar parameters determined for these spectra using {}{StePar} (Tabernero et al. 2012, A&A, 547, A13).

  17. [Spectral analysis in nanometer material science]. (United States)

    Chen, Wei; Sun, Shi-gang


    Spectral analysis is an important means in studies of nanometer scale systems, and is essential for deep understanding the structure and properties of nanometer materials. This paper reviews the recent progresses made in studies of nanometer materials using spectral analysis methods such as UV-Visible spectroscopy, FTIR spectroscopy, Raman spectroscopy, Mössbauer spectroscopy, positron annihilation and photoacoustic spectroscopy. The principle, characteristics and applications of most frequently employed spectral methods are introduced briefly and illustrated with typical examples. Future perspectives of spectral analysis in nanometer field are discussed. New directions of establishing spectral analysis methods at nanometer scale resolution and developing new spectroscopy technology in nanometer material studies are also emphasized.

  18. Study of RS data classification based on rough sets and C4.5 algorithm (United States)

    Yu, Ming; Ai, Ting-hua


    The classification by extracting of remote sensing (RS) data is the primary information source for GIS in land resource application. Automatic and accurate mapping of region LUCC from high spatial resolution satellite image is still a challenge. The paper discussed remote sensing image data classification techniques based on C4.5 algorithm and rough sets and the combination of C4.5 algorithm and rough sets. On the basis of the theories and methods of spatial data mining, we improve the classification accuracy. Finally validates its effectiveness taking a test area as example. We took the outskirts of Fuzhou with complicated land use in Fujian Province as study area. The classification rules are discovered from the samples through decision tree C4.5 algorithm, Rough Sets and both with together, which integrates spectral, textural and the topography characters. And the classification test is performed based on these rules. The traditional maximum likelihood classification is also compared to check the classification accuracy. The results have shown that the accuracy of classification based on knowledge is markedly higher than the traditional maximum likelihood classification. Especially the method based on combine Rough Sets and decision tree C4.5 algorithm is the best.

  19. Classification in Medical Imaging

    DEFF Research Database (Denmark)

    Chen, Chen

    Classification is extensively used in the context of medical image analysis for the purpose of diagnosis or prognosis. In order to classify image content correctly, one needs to extract efficient features with discriminative properties and build classifiers based on these features. In addition...... to segment breast tissue and pectoral muscle area from the background in mammogram. The second focus is the choices of metric and its influence to the feasibility of a classifier, especially on k-nearest neighbors (k-NN) algorithm, with medical applications on breast cancer prediction and calcification...


    Directory of Open Access Journals (Sweden)

    I. P. Prokopenko


    Full Text Available Correctly organized nutritive and pharmacological support is an important component of an athlete's preparation for competitions, an optimal shape maintenance, fast recovery and rehabilitation after traumas and defatigation. Special products of enhanced biological value (BAS for athletes nutrition are used with this purpose. Easy-to-use energy sources are administered into athlete's organism, yielded materials and biologically active substances which regulate and activate exchange reactions which proceed with difficulties during certain physical trainings. The article presents sport supplements classification which can be used before warm-up and trainings, after trainings and in competitions breaks.