WorldWideScience

Sample records for classification schemes based

  1. A Novel Fault Classification Scheme Based on Least Square SVM

    OpenAIRE

    Dubey, Harishchandra; Tiwari, A. K.; Nandita; Ray, P. K.; Mohanty, S. R.; Kishor, Nand

    2016-01-01

    This paper presents a novel approach for fault classification and section identification in a series compensated transmission line based on least square support vector machine. The current signal corresponding to one-fourth of the post fault cycle is used as input to proposed modular LS-SVM classifier. The proposed scheme uses four binary classifier; three for selection of three phases and fourth for ground detection. The proposed classification scheme is found to be accurate and reliable in ...

  2. A new Fourier transform based CBIR scheme for mammographic mass classification: a preliminary invariance assessment

    Science.gov (United States)

    Gundreddy, Rohith Reddy; Tan, Maxine; Qui, Yuchen; Zheng, Bin

    2015-03-01

    The purpose of this study is to develop and test a new content-based image retrieval (CBIR) scheme that enables to achieve higher reproducibility when it is implemented in an interactive computer-aided diagnosis (CAD) system without significantly reducing lesion classification performance. This is a new Fourier transform based CBIR algorithm that determines image similarity of two regions of interest (ROI) based on the difference of average regional image pixel value distribution in two Fourier transform mapped images under comparison. A reference image database involving 227 ROIs depicting the verified soft-tissue breast lesions was used. For each testing ROI, the queried lesion center was systematically shifted from 10 to 50 pixels to simulate inter-user variation of querying suspicious lesion center when using an interactive CAD system. The lesion classification performance and reproducibility as the queried lesion center shift were assessed and compared among the three CBIR schemes based on Fourier transform, mutual information and Pearson correlation. Each CBIR scheme retrieved 10 most similar reference ROIs and computed a likelihood score of the queried ROI depicting a malignant lesion. The experimental results shown that three CBIR schemes yielded very comparable lesion classification performance as measured by the areas under ROC curves with the p-value greater than 0.498. However, the CBIR scheme using Fourier transform yielded the highest invariance to both queried lesion center shift and lesion size change. This study demonstrated the feasibility of improving robustness of the interactive CAD systems by adding a new Fourier transform based image feature to CBIR schemes.

  3. New classification scheme for ozone monitoring stations based on frequency distribution of hourly data.

    Science.gov (United States)

    Tapia, O; Escudero, M; Lozano, Á; Anzano, J; Mantilla, E

    2016-02-15

    According to European Union (EU) legislation, ozone (O3) monitoring sites can be classified regarding their location (rural background, rural, suburban, urban) or based on the presence of emission sources (background, traffic, industrial). There have been attempts to improve these classifications aiming to reduce their ambiguity and subjectivity, but although scientifically sound, they lack the simplicity needed for operational purposes. We present a simple methodology for classifying O3 stations based on the characteristics of frequency distribution curves which are indicative of the actual impact of combustion sources emitting NO that consumes O3 via titration. Four classes are identified using 1998-2012 hourly data from 72 stations widely distributed in mainland Spain and the Balearic Islands. Types 1 and 2 present unimodal bell-shaped distribution with very low amount of data near zero reflecting a limited influence of combustion sources while Type 4 has a primary mode close to zero, showing the impact of combustion sources, and a minor mode for higher concentrations. Type 3 stations present bimodal distributions with the main mode in the higher levels. We propose a quantitative metric based on the Gini index with the objective of reproducing this classification and finding empirical ranges potentially useful for future classifications. The analysis of the correspondence with the EUROAIRNET classes for the 72 stations reveals that the proposed scheme is only dependent on the impact of combustion sources and not on climatic or orographic aspects. It is demonstrated that this classification is robust since in 87% of the occasions the classification obtained for individual years coincide with the global classification obtained for the 1998-2012 period. Finally, case studies showing the applicability of the new classification scheme for assessing the impact on O3 of a station relocation and performing a critical evaluation of an air quality monitoring network are

  4. A new classification scheme of plastic wastes based upon recycling labels

    Energy Technology Data Exchange (ETDEWEB)

    Özkan, Kemal, E-mail: kozkan@ogu.edu.tr [Computer Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Ergin, Semih, E-mail: sergin@ogu.edu.tr [Electrical Electronics Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Işık, Şahin, E-mail: sahini@ogu.edu.tr [Computer Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Işıklı, İdil, E-mail: idil.isikli@bilecik.edu.tr [Electrical Electronics Engineering Dept., Bilecik University, 11210 Bilecik (Turkey)

    2015-01-15

    Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple

  5. A new classification scheme of plastic wastes based upon recycling labels

    International Nuclear Information System (INIS)

    Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple

  6. Assessing lidar-based classification schemes for polar stratospheric clouds based on 16 years of measurements at Esrange, Sweden

    Science.gov (United States)

    Achtert, P.; Tesche, M.

    2014-02-01

    Lidar measurements of polar stratospheric clouds (PSCs) are commonly analyzed in classification schemes that apply the backscatter ratio and the particle depolarization ratio. This similarity of input data suggests comparable results of different classification schemes—despite measurements being performed with a variety of mostly custom-made instruments. Based on a time series of 16 years of lidar measurements at Esrange (68°N, 21°E), Sweden, we show that PSC classification differs substantially depending on the applied scheme. The discrepancies result from varying threshold values of lidar-derived parameters used to define certain PSC types. The resulting inconsistencies could impact the understanding of long-term PSC observations documented in the literature. We identify two out of seven considered classification schemes that are most likely to give reliable results and should be used in future lidar-based studies. Using polarized backscatter ratios gives the advantage of increased contrast for observations of weakly backscattering and weakly depolarizing particles. Improved confidence in PSC classification can be achieved by a more comprehensive consideration of the effect of measurement uncertainties. The particle depolarization ratio is the key to a reliable identification of different PSC types. Hence, detailed information on the calibration of the polarization-sensitive measurement channels should be provided to assess the findings of a study. Presently, most PSC measurements with lidar are performed at 532 nm only. The information from additional polarization-sensitive measurements in the near infrared could lead to an improved PSC classification. Coincident lidar-based temperature measurements at PSC level might provide useful information for an assessment of PSC classification.

  7. Small-scale classification schemes

    DEFF Research Database (Denmark)

    Hertzum, Morten

    2004-01-01

    important means of discretely balancing the contractual aspect of requirements engineering against facilitating the users in an open-ended search for their system requirements. The requirements classification is analysed in terms of the complementary concepts of boundary objects and coordination mechanisms......Small-scale classification schemes are used extensively in the coordination of cooperative work. This study investigates the creation and use of a classification scheme for handling the system requirements during the redevelopment of a nation-wide information system. This requirements...... classification inherited a lot of its structure from the existing system and rendered requirements that transcended the framework laid out by the existing system almost invisible. As a result, the requirements classification became a defining element of the requirements-engineering process, though its main...

  8. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

    Science.gov (United States)

    Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

    2016-01-01

    Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI. PMID:26880873

  9. A classification scheme for chimera states

    OpenAIRE

    Kemeth, Felix P.; Haugland, Sindre W.; Schmidt, Lennart; Kevrekidis, Ioannis G.; Krischer, Katharina

    2016-01-01

    We present a universal characterization scheme for chimera states applicable to both numerical and experimental data sets. The scheme is based on two correlation measures that enable a meaningful definition of chimera states as well as their classification into three categories: stationary, turbulent and breathing. In addition, these categories can be further subdivided according to the time-stationarity of these two measures. We demonstrate that this approach both is consistent with previous...

  10. Lyrics-Based Genre Classification Using Variant tf-idf Weighting Schemes

    Directory of Open Access Journals (Sweden)

    Teh Chao Ying

    2015-01-01

    Full Text Available Music documents are often classified based on genre and mood. In recent years, features from lyrics text have been used for classification of musical documents and the feasibility of lyrics features to classify musical documents has been shown. In this study an approach to lyrics based musical genre classification was presented which utilizing mood information. From the analysis of the lyrics text in the data collection, correlation of terms between genre and mood was observed. Based on this correlation of terms, new weighting equation with combine weights from genre and mood was introduced and implemented in two different ways. Ten musical genre and mood categories were selected respectively based on a summary from the literature. Musical genre classification experiments were performed using a test collection consists of 1000 English songs. To confirm present approach can improve the genre classification, experiments were conducted using similar weighting metric from previous study. Experimental results with new weighting equation reveal improvement in musical genre classification.

  11. Parallel Implementation of Morphological Profile Based Spectral-Spatial Classification Scheme for Hyperspectral Imagery

    Science.gov (United States)

    Kumar, B.; Dikshit, O.

    2016-06-01

    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.

  12. A new classification scheme for deep geothermal systems based on geologic controls

    Science.gov (United States)

    Moeck, I.

    2012-04-01

    A key element in the characterization, assessment and development of geothermal energy systems is the resource classification. Throughout the past 30 years many classifications and definitions were published mainly based on temperature and thermodynamic properties. In the past classification systems, temperature has been the essential measure of the quality of the resource and geothermal systems have been divided into three different temperature (or enthalpy) classes: low-temperature, moderate-temperature and high-temperature. There are, however, no uniform temperature ranges for these classes. It is still a key requirement of a geothermal classification that resource assessment provides logical and consistent frameworks simplified enough to communicate important aspects of geothermal energy potential to both non-experts and general public. One possible solution may be to avoid classifying geothermal resources by temperature and simply state the range of temperatures at the individual site. Due to technological development, in particular in EGS (Enhanced Geothermal Systems or Engineered Geothermal Systems; both terms are considered synonymously in this thesis) technology, currently there are more geothermal systems potentially economic than 30 years ago. An alternative possibility is to classify geothermal energy systems by their geologic setting. Understanding and characterizing the geologic controls on geothermal systems has been an ongoing focus on different scales from plate tectonics to local tectonics/structural geology. In fact, the geologic setting has a fundamental influence on the potential temperature, on the fluid composition, the reservoir characteristics and whether the system is a predominantly convective or conductive system. The key element in this new classification for geothermal systems is the recognition that a geothermal system is part of a geological system. The structural geological and plate tectonic setting has a fundamental influence on

  13. A classification scheme for LWR fuel assemblies

    International Nuclear Information System (INIS)

    With over 100 light water nuclear reactors operating nationwide, representing designs by four primary vendors, and with reload fuel manufactured by these vendors and additional suppliers, a wide variety of fuel assembly types are in existence. At Oak Ridge National Laboratory, both the Systems Integration Program and the Characteristics Data Base project required a classification scheme for these fuels. This scheme can be applied to other areas and is expected to be of value to many Office of Civilian Radioactive Waste Management programs. To develop the classification scheme, extensive information on the fuel assemblies that have been and are being manufactured by the various nuclear fuel vendors was compiled, reviewed, and evaluated. It was determined that it is possible to characterize assemblies in a systematic manner, using a combination of physical factors. A two-stage scheme was developed consisting of 79 assembly types, which are grouped into 22 assembly classes. The assembly classes are determined by the general design of the reactor cores in which the assemblies are, or were, used. The general BWR and PWR classes are divided differently but both are based on reactor core configuration. 2 refs., 15 tabs

  14. A classification scheme for risk assessment methods.

    Energy Technology Data Exchange (ETDEWEB)

    Stamp, Jason Edwin; Campbell, Philip LaRoche

    2004-08-01

    This report presents a classification scheme for risk assessment methods. This scheme, like all classification schemes, provides meaning by imposing a structure that identifies relationships. Our scheme is based on two orthogonal aspects--level of detail, and approach. The resulting structure is shown in Table 1 and is explained in the body of the report. Each cell in the Table represent a different arrangement of strengths and weaknesses. Those arrangements shift gradually as one moves through the table, each cell optimal for a particular situation. The intention of this report is to enable informed use of the methods so that a method chosen is optimal for a situation given. This report imposes structure on the set of risk assessment methods in order to reveal their relationships and thus optimize their usage.We present a two-dimensional structure in the form of a matrix, using three abstraction levels for the rows and three approaches for the columns. For each of the nine cells in the matrix we identify the method type by name and example. The matrix helps the user understand: (1) what to expect from a given method, (2) how it relates to other methods, and (3) how best to use it. Each cell in the matrix represent a different arrangement of strengths and weaknesses. Those arrangements shift gradually as one moves through the table, each cell optimal for a particular situation. The intention of this report is to enable informed use of the methods so that a method chosen is optimal for a situation given. The matrix, with type names in the cells, is introduced in Table 2 on page 13 below. Unless otherwise stated we use the word 'method' in this report to refer to a 'risk assessment method', though often times we use the full phrase. The use of the terms 'risk assessment' and 'risk management' are close enough that we do not attempt to distinguish them in this report. The remainder of this report is organized as follows. In

  15. A DSmT Based Combination Scheme for Multi-Class Classification

    OpenAIRE

    Nassim Abbas; Youcef Chiban; Zineb Belhadi; Mehdia Hedir

    2015-01-01

    This paper presents a new combination scheme for reducing the number of focal elements to manipulate in order to reduce the complexity of the combination process in the multiclass framework. The basic idea consists in using of p sources of information involved in the global scheme providing p kinds of complementary information to feed each set of p one class support vector machine classifiers independently of each other, which are designed for detecting the ou...

  16. A hierarchical classification scheme of psoriasis images

    DEFF Research Database (Denmark)

    Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

    2003-01-01

    A two-stage hierarchical classification scheme of psoriasis lesion images is proposed. These images are basically composed of three classes: normal skin, lesion and background. The scheme combines conventional tools to separate the skin from the background in the first stage, and the lesion from...

  17. Faults Classification Scheme for Three Phase Induction Motor

    OpenAIRE

    Mohammed Obaid Mustafa; George Nikolakopoulos; Thomas Gustafsson

    2014-01-01

    In every kind of industrial application, the operation of fault detection and diagnosis for induction motors is of paramount importance. Fault diagnosis and detection led to minimize the downtime and improves its reliability and availability of the systems. In this article, a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel–cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction...

  18. A suggested expansion of the NLM classification scheme for dentistry.

    Science.gov (United States)

    Strauss, C D

    1973-07-01

    The National Library of Medicine Classification is excellent for the shelf arrangement of books in a medical library. However, it is too general for a very specialized dental collection such as ours at Northwestern University Dental School Library. We suggest an expansion of the WU category based on the pattern followed in other areas of the NLM classification. An index for the expanded scheme is included. PMID:4725344

  19. Classification schemes for dental school libraries.

    Science.gov (United States)

    McMaugh, D R

    1979-12-01

    The provision of an efficient and acceptable library system for the dental literature is examined. It is suggested that an index to the dental literature is best provided by a combination of Index Medicus and Medical Subject Headings. The Library of Congress scheme would be best for an autonomous dental school and, where a dental school library is provided by a large medical library, the National Library of Medicine Classification would be suitable for dental student use. PMID:395935

  20. Fiction Classification Schemes: The Principles behind Them and Their Success.

    Science.gov (United States)

    Baker, Sharon L.; Shepherd, Gay W.

    1987-01-01

    Describes the five major principles of fiction classification laid out by early theorists and discusses studies that measure patron reactions to fiction classification schemes. Areas for further research are suggested, and 15 references are listed.(MES)

  1. The Impact of Industry Classification Schemes on Financial Research

    OpenAIRE

    Weiner, Christian

    2005-01-01

    This paper investigates industry classification systems. During the last 50 years there has been a considerable discussion of problems regarding the classification of economic data by industries. From my perspective, the central point of each classification is to determine a balance between aggregation of similar firms and differentiation between industries. This paper examines the structure and content of industrial classification schemes and how they affect financial research. I use classif...

  2. 15 CFR Appendix I to Part 921 - Biogeographic Classification Scheme

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Biogeographic Classification Scheme I... RESOURCE MANAGEMENT NATIONAL ESTUARINE RESEARCH RESERVE SYSTEM REGULATIONS Pt. 921, App. I Appendix I to Part 921—Biogeographic Classification Scheme Acadian 1. Northern of Maine (Eastport to the...

  3. Enriching User-Oriented Class Associations for Library Classification Schemes.

    Science.gov (United States)

    Pu, Hsiao-Tieh; Yang, Chyan

    2003-01-01

    Explores the possibility of adding user-oriented class associations to hierarchical library classification schemes. Analyses a log of book circulation records from a university library in Taiwan and shows that classification schemes can be made more adaptable by analyzing circulation patterns of similar users. (Author/LRW)

  4. International proposal for an acoustic classification scheme for dwellings

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2014-01-01

    Acoustic classification schemes specify different quality levels for acoustic conditions. Regulations and classification schemes for dwellings typically include criteria for airborne and impact sound insulation, façade sound insulation and service equipment noise. However, although important for...... European countries have introduced classification schemes. The schemes typically include four classes. Comparative studies have shown significant discrepancies between countries due to national development of schemes. The diversity is an obstacle for exchange of construction experience for different...... classes, implying also trade barriers. Thus, a harmonized classification scheme would be useful, and the European COST Action TU0901 "Integrating and Harmonizing Sound Insulation Aspects in Sustainable Urban Housing Constructions", running 2009-2013 with members from 32 countries, including three overseas...

  5. Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: Localized search method based on anatomical classification

    International Nuclear Information System (INIS)

    We developed an advanced computer-aided diagnostic (CAD) scheme for the detection of various types of lung nodules on chest radiographs intended for implementation in clinical situations. We used 924 digitized chest images (992 noncalcified nodules) which had a 500x500 matrix size with a 1024 gray scale. The images were divided randomly into two sets which were used for training and testing of the computerized scheme. In this scheme, the lung field was first segmented by use of a ribcage detection technique, and then a large search area (448x448 matrix size) within the chest image was automatically determined by taking into account the locations of a midline and a top edge of the segmented ribcage. In order to detect lung nodule candidates based on a localized search method, we divided the entire search area into 7x7 regions of interest (ROIs: 64x64 matrix size). In the next step, each ROI was classified anatomically into apical, peripheral, hilar, and diaphragm/heart regions by use of its image features. Identification of lung nodule candidates and extraction of image features were applied for each localized region (128x128 matrix size), each having its central part (64x64 matrix size) located at a position corresponding to a ROI that was classified anatomically in the previous step. Initial candidates were identified by use of the nodule-enhanced image obtained with the average radial-gradient filtering technique, in which the filter size was varied adaptively depending on the location and the anatomical classification of the ROI. We extracted 57 image features from the original and nodule-enhanced images based on geometric, gray-level, background structure, and edge-gradient features. In addition, 14 image features were obtained from the corresponding locations in the contralateral subtraction image. A total of 71 image features were employed for three sequential artificial neural networks (ANNs) in order to reduce the number of false-positive candidates. All

  6. Sound classification of dwellings - Comparison of schemes in Europe

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2009-01-01

    , there are significant discrepancies. Descriptors, number of quality classes, class intervals, class levels and status of the classification schemes in relation to legal requirements vary. In some countries the building code and the classification standard are incoherent. In other countries, they are...... strongly ”integrated”, implying that the building code refers to a specific class in a classification standard rather than describing requirements. Although the schemes prove useful on a national basis, the diversity in Europe is an obstacle for exchange of experience and for further development of design......National sound classification schemes for dwellings exist in nine countries in Europe, and proposals are under preparation in more countries. The schemes specify class criteria concerning several acoustic aspects, the main criteria being about airborne and impact sound insulation between dwellings...

  7. On the Classification of Psychology in General Library Classification Schemes.

    Science.gov (United States)

    Soudek, Miluse

    1980-01-01

    Holds that traditional library classification systems are inadequate to handle psychological literature, and advocates the establishment of new theoretical approaches to bibliographic organization. (FM)

  8. Towards a Collaborative Intelligent Tutoring System Classification Scheme

    Science.gov (United States)

    Harsley, Rachel

    2014-01-01

    This paper presents a novel classification scheme for Collaborative Intelligent Tutoring Systems (CITS), an emergent research field. The three emergent classifications of CITS are unstructured, semi-structured, and fully structured. While all three types of CITS offer opportunities to improve student learning gains, the full extent to which these…

  9. Development and test of a classification scheme for human factors in incident reports

    International Nuclear Information System (INIS)

    The Research Center System Safety of the Berlin University of Technology conducted a research project on the analysis of Human Factors (HF) aspects in incident reported by German Nuclear Power Plants. Based on psychological theories and empirical studies a classification scheme was developed which permits the identification of human involvement in incidents. The classification scheme was applied in an epidemiological study to a selection of more than 600 HF - relevant incidents. The results allow insights into HF related problem areas. An additional study proved that the application of the classification scheme produces results which are reliable and independent from raters. (author). 13 refs, 1 fig

  10. Texture Classification based on Gabor Wavelet

    OpenAIRE

    Amandeep Kaur; Savita Gupta

    2012-01-01

    This paper presents the comparison of Texture classification algorithms based on Gabor Wavelets. The focus of this paper is on feature extraction scheme for texture classification. The texture feature for an image can be classified using texture descriptors. In this paper we have used Homogeneous texture descriptor that uses Gabor Wavelets concept. For texture classification, we have used online texture database that is Brodatz’s database and three advanced well known classifiers: Support Vec...

  11. Pitch Based Sound Classification

    OpenAIRE

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

    2006-01-01

    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 classif...

  12. Discovery of User-Oriented Class Associations for Enriching Library Classification Schemes.

    Science.gov (United States)

    Pu, Hsiao-Tieh

    2002-01-01

    Presents a user-based approach to exploring the possibility of adding user-oriented class associations to hierarchical library classification schemes. Classes not grouped in the same subject hierarchies yet relevant to users' knowledge are obtained by analyzing a log book of a university library's circulation records, using collaborative filtering…

  13. Pitch Based Sound Classification

    DEFF Research Database (Denmark)

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

    2006-01-01

    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...

  14. A novel pattern classification scheme using the Baker's map

    OpenAIRE

    Rogers, Alan; Keating, John; Shorten, Robert

    2003-01-01

    We demonstrate a novel application of nonlinear systems in the design of pattern classification systems. We show that pattern classification systems can be designed based upon training algorithms designed to control the qualitative behaviour of a nonlinear system. Our paradigm is illustrated by means of a simple chaotic system-the Baker's map. Algorithms for training the system are presented and examples are given to illustrate the operation and learning of the system for pattern classificati...

  15. Cluster Based Text Classification Model

    DEFF Research Database (Denmark)

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

    2011-01-01

    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....

  16. CANDELS Visual Classifications: Scheme, Data Release, and First Results

    CERN Document Server

    Kartaltepe, Jeyhan S; Kocevski, Dale; McIntosh, Daniel H; Lotz, Jennifer; Bell, Eric F; Faber, Sandra; Ferguson, Henry; Koo, David; Bassett, Robert; Bernyk, Maksym; Blancato, Kirsten; Bournaud, Frederic; Cassata, Paolo; Castellano, Marco; Cheung, Edmond; Conselice, Christopher J; Croton, Darren; Dahlen, Tomas; de Mello, Duilia F; DeGroot, Laura; Donley, Jennifer; Guedes, Javiera; Grogin, Norman; Hathi, Nimish; Hilton, Matt; Hollon, Brett; Inami, Hanae; Kassin, Susan; Koekemoer, Anton; Lani, Caterina; Liu, Nick; Lucas, Ray A; Martig, Marie; McGrath, Elizabeth; McPartland, Conor; Mobasher, Bahram; Morlock, Alice; Mutch, Simon; O'Leary, Erin; Peth, Mike; Pforr, Janine; Pillepich, Annalisa; Poole, Gregory B; Rizer, Zachary; Rosario, David; Soto, Emmaris; Straughn, Amber; Telford, Olivia; Sunnquist, Ben; Weiner, Benjamin; Wuyts, Stijn

    2014-01-01

    We have undertaken an ambitious program to visually classify all galaxies in the five CANDELS fields down to H<24.5 involving the dedicated efforts of 65 individual classifiers. Once completed, we expect to have detailed morphological classifications for over 50,000 galaxies up to z<4 over all the fields. Here, we present our detailed visual classification scheme, which was designed to cover a wide range of CANDELS science goals. This scheme includes the basic Hubble sequence types, but also includes a detailed look at mergers and interactions, the clumpiness of galaxies, $k$-corrections, and a variety of other structural properties. In this paper, we focus on the first field to be completed -- GOODS-S. The wide area coverage spanning the full field includes 7634 galaxies that have been classified by at least three different people. In the deep area of the field, 2534 galaxies have been classified by at least five different people at three different depths. With this paper, we release to the public all ...

  17. New classification scheme for atrial fibrillation symptom severity and burden.

    Science.gov (United States)

    Koci, Florian; Forbes, Peter; Mansour, Moussa C; Heist, E Kevin; Singh, Jagmeet P; Ellinor, Patrick T; Ruskin, Jeremy N

    2014-07-15

    Although atrial fibrillation (AF) symptom severity is used to guide clinical care, a simple, standardized assessment tool is not available for routine clinical use. We sought to develop and validate a patient-generated score and classification scheme for AF-related symptom severity and burden. Atrial Fibrillation Symptom and Burden, a simple 2-part questionnaire, was designed to assess (1) AF symptom severity using 8 questions to determine how symptoms affect daily life and (2) AF burden using 6 questions to measure AF frequency, duration, and health-care utilization. The resulting score was used to classify patients into 4 classes of symptom and burden severity. Patients were asked to complete the questionnaire, a survey evaluating the questionnaire, and an Short Form-12v2 generic health-related quality-of-life form. Validation of the questionnaire included assessments of its reliability and construct and known groups validity. The strength of interrater agreement between patient-generated and blinded provider-generated classifications of AF symptom severity was also assessed. The survey had good internal consistency (Cronbach α>0.82) and reproducibility (intraclass correlation coefficient=0.93). There was a good linear correlation with health-related quality-of-life aggregates measured by Pearson correlation coefficient (r=0.62 and 0.42 vs physical component summary and mental component summary, respectively). Compared with physical and mental component summary scores, the patient-generated symptom severity classification scheme showed robust discrimination between mild and moderate severity (pcorrelated with standardized quality-of-life measures. PMID:24878121

  18. CANDELS Visual Classifications: Scheme, Data Release, and First Results

    Science.gov (United States)

    Kartaltepe, Jeyhan S.; Mozena, Mark; Kocevski, Dale; McIntosh, Daniel H.; Lotz, Jennifer; Bell, Eric F.; Faber, Sandy; Ferguson, Harry; Koo, David; Bassett, Robert; Bernyk, Maksym; Blancato, Kirsten; Bournaud, Frederic; Cassata, Paolo; Castellano, Marco; Cheung, Edmond; Conselice, Christopher J.; Croton, Darren; Dahlen, Tomas; de Mello, Duilia F.; DeGroot, Laura; Donley, Jennifer; Guedes, Javiera; Grogin, Norman; Hathi, Nimish; Hilton, Matt; Hollon, Brett; Koekemoer, Anton; Liu, Nick; Lucas, Ray A.; Martig, Marie; McGrath, Elizabeth; McPartland, Conor; Mobasher, Bahram; Morlock, Alice; O'Leary, Erin; Peth, Mike; Pforr, Janine; Pillepich, Annalisa; Rosario, David; Soto, Emmaris; Straughn, Amber; Telford, Olivia; Sunnquist, Ben; Trump, Jonathan; Weiner, Benjamin; Wuyts, Stijn

    2015-11-01

    We have undertaken an ambitious program to visually classify all galaxies in the five CANDELS fields down to H visual classification scheme, which was designed to cover a wide range of CANDELS science goals. This scheme includes the basic Hubble sequence types, but also includes a detailed look at mergers and interactions, the clumpiness of galaxies, k-corrections, and a variety of other structural properties. In this paper, we focus on the first field to be completed—GOODS-S, which has been classified at various depths. The wide area coverage spanning the full field (wide+deep+ERS) includes 7634 galaxies that have been classified by at least three different people. In the deep area of the field, 2534 galaxies have been classified by at least five different people at three different depths. With this paper, we release to the public all of the visual classifications in GOODS-S along with the Perl/Tk GUI that we developed to classify galaxies. We present our initial results here, including an analysis of our internal consistency and comparisons among multiple classifiers as well as a comparison to the Sérsic index. We find that the level of agreement among classifiers is quite good (>70% across the full magnitude range) and depends on both the galaxy magnitude and the galaxy type, with disks showing the highest level of agreement (>50%) and irregulars the lowest (frame colors shows a clear separation between disk and spheroid populations. Finally, we explore morphological k-corrections between the V-band and H-band observations and find that a small fraction (84 galaxies in total) are classified as being very different between these two bands. These galaxies typically have very clumpy and extended morphology or are very faint in the V-band.

  19. LWE-based Identification Schemes

    CERN Document Server

    Silva, Rosemberg; Dahab, Ricardo

    2011-01-01

    Some hard problems from lattices, like LWE (Learning with Errors), are particularly suitable for application in Cryptography due to the possibility of using worst-case to average-case reductions as evidence of strong security properties. In this work, we show two LWE-based constructions of zero-knowledge identification schemes and discuss their performance and security. We also highlight the design choices that make our solution of both theoretical and practical interest.

  20. An Empirical Study on User-oriented Association Analysis of Library Classification Schemes

    Directory of Open Access Journals (Sweden)

    Hsiao-Tieh Pu

    2002-12-01

    Full Text Available Library classification schemes are mostly organized based on disciplines with a hierarchical structure. From the user point of view, some highly related yet non-hierarchical classes may not be easy to perceive in these schemes. This paper is to discover hidden associations between classes by analyzing users’ usage of library collections. The proposed approach employs collaborative filtering techniques to discover associated classes based on the circulation patterns of similar users. Many associated classes scattered across different subject hierarchies could be discovered from the circulation patterns of similar users. The obtained association norms between classes were found to be useful in understanding users' subject preferences for a given class. Classification schemes can, therefore, be made more adaptable to changes of users and the uses of different library collections. There are implications for applications in information organization and retrieval as well. For example, catalogers could refer to the ranked associated classes when they perform multi-classification, and users could also browse the associated classes for related subjects in an enhanced OPAC system. In future research, more empirical studies will be needed to validate the findings, and methods for obtaining user-oriented associations can still be improved.[Article content in Chinese

  1. Texture Classification based on Gabor Wavelet

    Directory of Open Access Journals (Sweden)

    Amandeep Kaur

    2012-07-01

    Full Text Available This paper presents the comparison of Texture classification algorithms based on Gabor Wavelets. The focus of this paper is on feature extraction scheme for texture classification. The texture feature for an image can be classified using texture descriptors. In this paper we have used Homogeneous texture descriptor that uses Gabor Wavelets concept. For texture classification, we have used online texture database that is Brodatz’s database and three advanced well known classifiers: Support Vector Machine, K-nearest neighbor method and decision tree induction method. The results shows that classification using Support vector machines gives better results as compare to the other classifiers. It can accurately discriminate between a testing image data and training data.

  2. 15 CFR 921.3 - National Estuarine Research Reserve System biogeographic classification scheme and estuarine...

    Science.gov (United States)

    2010-01-01

    ... biogeographic classification scheme and estuarine typologies. (a) National Estuarine Research Reserves are... biogeographic classification scheme is used to ensure that the National Estuarine Research Reserve System... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false National Estuarine Research...

  3. Harmonization of sound insulation descriptors and classification schemes in Europe: COST Action TU0901

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    Regulatory sound insulation requirements for dwellings exist in more than 30 countries in Europe. Classification schemes exist at present (2010) in 10 countries. In some countries, sound insulation requirements have existed since the 1950s. The first classification schemes for dwellings were...... implemented in the early 1990s. Findings from comparative studies of regulatory sound insulation requirements in 24 countries in Europe and sound classification schemes in 10 countries show that sound insulation descriptors, regulatory requirements and classification schemes in Europe represent a high degree......, harmonization of sound insulation requirements seems unrealistic. However, by preparing a harmonized European classification scheme with a number of quality classes, member states could select a "harmonized" class fitting the national needs and conditions. A joint European Action, COST Action TU0901...

  4. Fair Electronic Payment Scheme Based on DSA

    Institute of Scientific and Technical Information of China (English)

    WANG Shao-bin; HONG Fan; ZHU Xian

    2005-01-01

    We present a multi-signature scheme based on DSA and describes a fair electronic payment scheme based on improved DSA signatures. The scheme makes both sides in equal positions during the course of electronic transaction. A Trusted Third Party (TTP) is involved in the scheme to guarantee the fairness of the scheme for both sides. However, only during the course of registration and dispute resolution will TTP be needed. TTP is not needed during the normal payment stage.

  5. Acoustic classification schemes in Europe – Applicability for new, existing and renovated housing

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2016-01-01

    The first acoustic classification schemes for dwellings were published in the 1990’es as national standards with the main purpose to introduce the possibility of specifying easily stricter acoustic criteria for new-build than the minimum requirements found in building regulations. Since then, more...... countries have introduced acoustic classification schemes, the first countries updated more times and some countries introduced acoustic classification also for other building categories. However, the classification schemes continued to focus on new buildings and have in general limited applicability for...... international scheme for classification of dwellings under development in ISO/TC43/SC2 will be explained. One of several key characteristics of the proposal is a wide range of classes, implying applicability to a major part of the existing housing stock in Europe, thus enabling acoustic labelling like energy...

  6. A novel adaptive classification scheme for digital modulations in satellite communication

    Institute of Scientific and Technical Information of China (English)

    Wu Dan; Gu Xuemai; Guo Qing

    2007-01-01

    To make the modulation classification system more suitable for signals in a wide range of signal to noise ratios (SNRs) , a novel adaptive modulation classification scheme is presented in this paper. Different from traditional schemes, the proposed scheme employs a new SNR estimation algorithm for small samples before modulation classification, which makes the modulation classifier work adaptively according to estimated SNRs. Furthermore, it uses three efficient features and support vector machines (SVM) in modulation classification. Computer simulation shows that the scheme can adaptively classify ten digital modulation types (i.e. 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, 16QAM, TFM, π/4QPSK and OQPSK) at SNRS ranging from OdB to 25 dB and success rates are over 95% when SNR is not lower than 3dB. Accuracy, efficiency and simplicity of the proposed scheme are obviously improved, which make it more adaptive to engineering applications.

  7. Optimal Timer Based Selection Schemes

    CERN Document Server

    Shah, Virag; Yim, Raymond

    2009-01-01

    Timer-based mechanisms are often used to help a given (sink) node select the best helper node among many available nodes. Specifically, a node transmits a packet when its timer expires, and the timer value is a monotone non-increasing function of its local suitability metric. The best node is selected successfully if no other node's timer expires within a 'vulnerability' window after its timer expiry, and so long as the sink can hear the available nodes. In this paper, we show that the optimal metric-to-timer mapping that (i) maximizes the probability of success or (ii) minimizes the average selection time subject to a minimum constraint on the probability of success, maps the metric into a set of discrete timer values. We specify, in closed-form, the optimal scheme as a function of the maximum selection duration, the vulnerability window, and the number of nodes. An asymptotic characterization of the optimal scheme turns out to be elegant and insightful. For any probability distribution function of the metri...

  8. coupling factor classification schemes for common cause failure analysis

    International Nuclear Information System (INIS)

    Given the existence of the root cause as a mechanism of a transition of state from available to that of failed or functionally unavailable, the second concept of importance is linking or coupling mechanism, which is what leads to multiple equipment failure. A common cause failure could occur as a result of an event at the plant resulting from a cause in the component compartment. The cause is the root cause of failure of the two components. The coupling factor is the fact that both components are located in the same compartment. Coupling factor is a characteristic of a group of components or piece arts that identifies them as susceptible to the same causal mechanisms of failure. Such factors include similarity in design, location, environment, mission, and operational, maintenance, and test procedures. The most important purpose of the proposed coupling factor classification system is providing a tool for evaluating plant defenses against multiple failures. The hierarchical structure of coding system proposed for coupling factors is particularly useful in event classification since the level of detail in available information can vary from event to event. The events will be sorted based on coupling factors coding system in two categories, operating and standby systems. This approach allows the analyst to evaluate possible defense and determine which class of coupling factors is pertinent to the plant under consideration

  9. Malware Detection, Supportive Software Agents and Its Classification Schemes

    Directory of Open Access Journals (Sweden)

    Adebayo, Olawale Surajudeen

    2012-12-01

    Full Text Available Over time, the task of curbing the emergence of malware and its dastard activities has been identified interms of analysis, detection and containment of malware. Malware is a general term that is used todescribe the category of malicious software that is part of security threats to the computer and internetsystem. It is a malignant program designed to hamper the effectiveness of a computer and internetsystem. This paper aims at identifying the malware as one of the most dreaded threats to an emergingcomputer and communication technology. The paper identified the category of malware, malwareclassification algorithms, malwares activities and ways of preventing and removing malware if iteventually infects system.The research also describes tools that classify malware dataset using a rule-based classification schemeand machine learning algorithms to detect the malicious program from normal program through patternrecognition.

  10. A New Classification Approach Based on Multiple Classification Rules

    OpenAIRE

    Zhongmei Zhou

    2014-01-01

    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. A classification scheme for positive solutions of second order nonlinear iterative differential equations

    Directory of Open Access Journals (Sweden)

    Xianling Fan

    2000-03-01

    Full Text Available This article presents a classification scheme for eventually-positive solutions of second-order nonlinear iterative differential equations, in terms of their asymptotic magnitudes. Necessary and sufficient conditions for the existence of solutions are also provided.

  12. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers.

    Science.gov (United States)

    Jack, Clifford R; Bennett, David A; Blennow, Kaj; Carrillo, Maria C; Feldman, Howard H; Frisoni, Giovanni B; Hampel, Harald; Jagust, William J; Johnson, Keith A; Knopman, David S; Petersen, Ronald C; Scheltens, Philip; Sperling, Reisa A; Dubois, Bruno

    2016-08-01

    Biomarkers have become an essential component of Alzheimer disease (AD) research and because of the pervasiveness of AD pathology in the elderly, the same biomarkers are used in cognitive aging research. A number of current issues suggest that an unbiased descriptive classification scheme for these biomarkers would be useful. We propose the "A/T/N" system in which 7 major AD biomarkers are divided into 3 binary categories based on the nature of the pathophysiology that each measures. "A" refers to the value of a β-amyloid biomarker (amyloid PET or CSF Aβ42); "T," the value of a tau biomarker (CSF phospho tau, or tau PET); and "N," biomarkers of neurodegeneration or neuronal injury ([(18)F]-fluorodeoxyglucose-PET, structural MRI, or CSF total tau). Each biomarker category is rated as positive or negative. An individual score might appear as A+/T+/N-, or A+/T-/N-, etc. The A/T/N system includes the new modality tau PET. It is agnostic to the temporal ordering of mechanisms underlying AD pathogenesis. It includes all individuals in any population regardless of the mix of biomarker findings and therefore is suited to population studies of cognitive aging. It does not specify disease labels and thus is not a diagnostic classification system. It is a descriptive system for categorizing multidomain biomarker findings at the individual person level in a format that is easy to understand and use. Given the present lack of consensus among AD specialists on terminology across the clinically normal to dementia spectrum, a biomarker classification scheme will have broadest acceptance if it is independent from any one clinically defined diagnostic scheme. PMID:27371494

  13. The software invention cube: A classification scheme for software inventions

    OpenAIRE

    Bergstra, J.A.; Klint, Paul

    2008-01-01

    The patent system protects inventions. The requirement that a software invention should make ‘a technical contribution’ turns out to be untenable in practice and this raises the question, what constitutes an invention in the realm of software. The authors developed the Software Invention Cube (SWIC), a classification of software inventions and used this classification to explore the meaning of the notions ‘novelty’, ‘inventive step’ and ‘someone skilled in the art’ for software inventions. Th...

  14. Development the EarthCARE aerosol classification scheme

    Science.gov (United States)

    Wandinger, Ulla; Baars, Holger; Hünerbein, Anja; Donovan, Dave; van Zadelhoff, Gerd-Jan; Fischer, Jürgen; von Bismarck, Jonas; Eisinger, Michael; Lajas, Dulce; Wehr, Tobias

    2015-04-01

    the consistency of EarthCARE retrievals, to support aerosol description in the EarthCARE simulator ECSIM, and to facilitate a uniform specification of broad-band aerosol optical properties, a hybrid end-to-end aerosol classification model (HETEAC) is developed which serves as a baseline for EarthCARE algorithm development and evaluation procedures. The model's theoretical description of aerosol microphysics (bi-modal size distribution, spectral refractive index, and particle shape distribution) is adjusted to experimental data of aerosol optical properties, i.e. lidar ratio, depolarization ratio, Ångström exponents (hybrid approach). The experimental basis is provided by ground-based observations with sophisticated multi-wavelength, polarization lidars applied in the European Aerosol Research Lidar Network (EARLINET) and in dedicated field campaigns in the Sahara (SAMUM-1), Cape Verde (SAMUM-2), Barbados (SALTRACE), Atlantic Ocean (Polarstern and Meteor cruises), and Amazonia. The model is designed such that it covers the entire loop from aerosol microphysics via aerosol classification to optical and radiative properties of the respective types and allows consistency checks of modeled and measured parameters (end-to-end approach). Optical modeling considers scattering properties of spherical and non-spherical particles. A suitable set of aerosol types is defined which includes dust, clean marine, clean continental, pollution, smoke, and stratospheric aerosol. Mixtures of these types are included as well. The definition is consistent with CALIPSO approaches and will thus enable the establishment of a long-term global four-dimensional aerosol dataset.

  15. Biogeography based Satellite Image Classification

    CERN Document Server

    Panchal, V K; Kaur, Navdeep; Kundra, Harish

    2009-01-01

    Biogeography is the study of the geographical distribution of biological organisms. The mindset of the engineer is that we can learn from nature. Biogeography Based Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. Satellite image classification is an important task because it is the only way we can know about the land cover map of inaccessible areas. Though satellite images have been classified in past by using various techniques, the researchers are always finding alternative strategies for satellite image classification so that they may be prepared to select the most appropriate technique for the feature extraction task in hand. This paper is focused on classification of the satellite image of a particular land cover using the theory of Biogeography based Optimization. The original BBO algorithm does not have the inbuilt property of clustering which is required during image classification. Hence modifications have been proposed to the original algorithm and...

  16. Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification

    OpenAIRE

    Kim, Yoon; Zhang, Owen

    2014-01-01

    We provide a simple but novel supervised weighting scheme for adjusting term frequency in tf-idf for sentiment analysis and text classification. We compare our method to baseline weighting schemes and find that it outperforms them on multiple benchmarks. The method is robust and works well on both snippets and longer documents.

  17. Sound classification of dwellings – A diversity of national schemes in Europe

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2011-01-01

    Sound classification schemes for dwellings exist in ten countries in Europe, typically prepared and published as national standards. The schemes define quality classes intended to reflect different levels of acoustical comfort. The main criteria concern airborne and impact sound insulation betwee...

  18. Sound classification schemes in Europe - Quality classes intended for renovated housing

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2010-01-01

    exposure in the home included in the proposed main objectives for a housing policy. In most countries in Europe, building regulations specify minimum requirements concerning acoustical conditions for new dwellings. In addition, several countries have introduced sound classification schemes with classes...... intended to reflect different levels of acoustical comfort. Consequently, acoustic requirements for a dwelling can be specified as the legal minimum requirements or as a specific class in a classification scheme. Most schemes have both higher classes than corresponding to the regulatory requirements and at...

  19. Classification-based reasoning

    Science.gov (United States)

    Gomez, Fernando; Segami, Carlos

    1991-01-01

    A representation formalism for N-ary relations, quantification, and definition of concepts is described. Three types of conditions are associated with the concepts: (1) necessary and sufficient properties, (2) contingent properties, and (3) necessary properties. Also explained is how complex chains of inferences can be accomplished by representing existentially quantified sentences, and concepts denoted by restrictive relative clauses as classification hierarchies. The representation structures that make possible the inferences are explained first, followed by the reasoning algorithms that draw the inferences from the knowledge structures. All the ideas explained have been implemented and are part of the information retrieval component of a program called Snowy. An appendix contains a brief session with the program.

  20. Sound classification of dwellings – A diversity of national schemes in Europe

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2011-01-01

    Sound classification schemes for dwellings exist in ten countries in Europe, typically prepared and published as national standards. The schemes define quality classes intended to reflect different levels of acoustical comfort. The main criteria concern airborne and impact sound insulation betwee...... Housing Constructions", has been established and runs 2009-2013. The main objectives of TU0901 are to prepare proposals for harmonized sound insulation descriptors and for a European sound classification scheme with a number of quality classes for dwellings.......Sound classification schemes for dwellings exist in ten countries in Europe, typically prepared and published as national standards. The schemes define quality classes intended to reflect different levels of acoustical comfort. The main criteria concern airborne and impact sound insulation between...... dwellings, facade sound insulation and installation noise. This paper presents the sound classification schemes in Europe and compares the class criteria for sound insulation between dwellings. The schemes have been implemented and revised gradually since the early 1990s. However, due to lack of...

  1. Toward standard classification schemes for nursing language: recommendations of the American Nurses Association Steering Committee on Databases to Support Clinical Nursing Practice.

    OpenAIRE

    McCormick, K A; Lang, N.; Zielstorff, R; Milholland, D K; Saba, V.; Jacox, A

    1994-01-01

    The American Nurses Association (ANA) Cabinet on Nursing Practice mandated the formation of the Steering Committee on Databases to Support Clinical Nursing Practice. The Committee has established the process and the criteria by which to review and recommend nursing classification schemes based on the ANA Nursing Process Standards and elements contained in the Nursing Minimum Data Set (NMDS) for inclusion of nursing data elements in national databases. Four classification schemes have been rec...

  2. Quantum Authentication Scheme Based on Entanglement Swapping

    Science.gov (United States)

    Penghao, Niu; Yuan, Chen; Chong, Li

    2016-01-01

    Based on the entanglement swapping, a quantum authentication scheme with a trusted- party is proposed in this paper. With this scheme, two users can perform mutual identity authentication to confirm each other's validity. In addition, the scheme is proved to be secure under circumstances where a malicious attacker is capable of monitoring the classical and quantum channels and has the power to forge all information on the public channel.

  3. Market-Based Debt-Reduction Schemes

    OpenAIRE

    Krugman, Paul R.

    1988-01-01

    Recently much attention has been given to the idea of reducing the debt of developing countries through a "menu approach" of schemes that attempt to harness the discounts on debt in the secondary market. This paper, after reviewing the rationale for the orthodox strategy of concerted lending and the case for debt forgiveness, examines the logic behind several market-based debt reduction schemes. It shows that such schemes will ordinarily benefit both debtor and creditor only when the debtor i...

  4. Contextual completeness and a classification scheme for theories

    CERN Document Server

    Jaroszkiewicz, George

    2015-01-01

    We discuss the role of propositions, truth, context and observers in scientific theories. We introduce the concept of generalized proposition and use it to define an algorithm for the classification of any scientific theory. The algorithm assigns a number 0, 1, 2 or 3 to a given theory, thereby classifying it as of metaphysical, mathematical, classical or quantum class. The objective is to provide an impartial method of assessing the scientific status of any theory.

  5. Joint efforts to harmonize sound insulation descriptors and classification schemes in Europe (COST TU0901)

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2010-01-01

    Sound insulation descriptors, regulatory requirements and classification schemes in Europe represent a high degree of diversity. One implication is very little exchange of experience of housing design and construction details for different levels of sound insulation; another is trade barriers for...... building systems and products. Unfortunately, there is evidence for a development in the "wrong" direction. For example, sound classification schemes for dwellings exist in nine countries. There is no sign on increasing harmonization, rather the contrary, as more countries are preparing proposals with new...... Aspects in Sustainable Urban Housing Constructions", has been approved and runs 2009-2013. The main objectives are to prepare proposals for harmonized sound insulation descriptors and for a European sound classification scheme. Other goals are e.g. to establish a catalogue of sound insulation data and an...

  6. Complete symmetry classifications of the generalized nonlinear second-order equation and partial difference schemes

    International Nuclear Information System (INIS)

    This paper is concerned with the generalized nonlinear second-order equation and its discrete case furnished with different lattices. The complete symmetry classifications of the equations are performed by symmetry analysis. In the sense of point symmetry, all of the vector fields of the continuous nonlinear equation are obtained. As a special case, the vector fields of some important nonlinear systems are provided. Then, we develop the unified symmetry classification method for dealing with the partial difference schemes (PΔS), all of the point symmetries of the difference schemes are presented with respect to the given arbitrary function p(u) and the different lattices. (paper)

  7. Investment Cash Flow Sensitivity and Managerial Optimism: A Literature Review via the Classification Scheme Technique

    Directory of Open Access Journals (Sweden)

    Ezzeddine Ben Mohamed

    2013-06-01

    Full Text Available In this paper, we present a literature review and classification scheme for investment cash flow sensitivity under behavioral corporate finance (hereafter, BCF. The former consists of all published articles between 2000 and 2011 in different journals that are appropriate outlets for BCF research. The articles are classified and results of these are presented and analyzed. The classification of articles was based on nine criteria; journals, date of publication, paper nature, the context of the study adopted behavioral biases, adopted approach, behavioral biases measurement, the adopted assumption, econometric approach and empirical findings. Literature on investment cash flow sensitivity under behavioral corporate finance isn’t well developed. In fact, the behavioral corporate finance is very young. Our review shows that behavioral biases (optimism and overconfidence have an explanatory power and they can succeed to explain the dependence of corporate investment on the internal cash flow availability. This result is protected in the most cases by the some restrictive assumptions: the absence of agency costs and asymmetric information. Based on the review, suggestions for future research are likewise provided.

  8. A High Dimensional Clustering Scheme for Data Classification

    Directory of Open Access Journals (Sweden)

    Tejalal choudhary

    2015-09-01

    Full Text Available The data mining is the knowledge extraction or finding the hidden patterns from large data these data may be in different form as well from different resources. The data mining systems can be used in various research domains like health, share market analysis, super market, weather forecasting and many other domains. Data mining systems use the computer oriented algorithms. These algorithms can be categorized as supervised and unsupervised respectively. The classification or prediction algorithms belong to supervised category and clustering algorithms are the type of unsupervised. The clustering is an approach to group the similar data objects to the same cluster and the main aspect of clustering is that the distance between data objects in same cluster should be as minimum as and distance of objects in inter cluster should be high. k-means is one of the most common clustering algorithm. K-means is very easy to use and efficient but has also some weakness because of random or inappropriate selection of initial centroids so need to improve k-means. The proposed work is an attempt to improve k means by using genetic algorithm for selection of initial cluster centroid.

  9. Classification of basic facilities for high-rise residential: A survey from 100 housing scheme in Kajang area

    Science.gov (United States)

    Ani, Adi Irfan Che; Sairi, Ahmad; Tawil, Norngainy Mohd; Wahab, Siti Rashidah Hanum Abd; Razak, Muhd Zulhanif Abd

    2016-08-01

    High demand for housing and limited land in town area has increasing the provision of high-rise residential scheme. This type of housing has different owners but share the same land lot and common facilities. Thus, maintenance works of the buildings and common facilities must be well organized. The purpose of this paper is to identify and classify basic facilities for high-rise residential building hoping to improve the management of the scheme. The method adopted is a survey on 100 high-rise residential schemes that ranged from affordable housing to high cost housing by using a snowball sampling. The scope of this research is within Kajang area, which is rapidly developed with high-rise housing. The objective of the survey is to list out all facilities in every sample of the schemes. The result confirmed that pre-determined 11 classifications hold true and can provide the realistic classification for high-rise residential scheme. This paper proposed for redefinition of facilities provided to create a better management system and give a clear definition on the type of high-rise residential based on its facilities.

  10. A Novel Broadcast Scheme DSR-based Mobile Adhoc Networks

    Directory of Open Access Journals (Sweden)

    Muneer Bani Yassein

    2016-04-01

    Full Text Available Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS. Despite many studies that have placed a lot of emphasis on broadcast communication, broadcasting in MANETs is still a problematic issue. Due to the absence of the fixed infrastructure in MANETs, broadcast is an essential operation for all network nodes. Although the blind flooding is the simplest broadcasting technique, it is inefficient and lacks resource utilization efficiency. One of the proposed schemes to mitigate the blind flooding deficiency is the counter based broadcast scheme that depends on the number of received duplicate packets between the node and its neighbors, where the node compares the duplicate packet itself and each neighbor node that previously re-broadcasted a packet. Due to the fact that existing counter-based schemes are mainly based on the fixed counter based approach, these schemes are not efficient in different operating conditions. Thus, unlike existing studies, this paper proposes a dynamic counter based threshold value and examines its effectiveness under the Dynamic Source Routing Protocol (DSR which is one of the well-known on-demand routing protocols. Specifically, we develop in this paper a new counter based broadcast algorithm under the umbrella of the DSR, namely, Inspired Counter Based Broadcasting (DSR-ICB. Using various simulation experiments, DSR-ICB has shown good performance especially in terms of delay and the number of redundant packets.

  11. Classification for Estuarine Ecosystems: A Review and Comparison of Selected Classification Schemes

    Science.gov (United States)

    Estuarine scientists have devoted considerable effort to classifying coastal, estuarine and marine environments and their watersheds, for a variety of purposes. These classifications group systems with similarities – most often in physical and hydrodynamic properties – in order ...

  12. Palmprint based multidimensional fuzzy vault scheme.

    Science.gov (United States)

    Liu, Hailun; Sun, Dongmei; Xiong, Ke; Qiu, Zhengding

    2014-01-01

    Fuzzy vault scheme (FVS) is one of the most popular biometric cryptosystems for biometric template protection. However, error correcting code (ECC) proposed in FVS is not appropriate to deal with real-valued biometric intraclass variances. In this paper, we propose a multidimensional fuzzy vault scheme (MDFVS) in which a general subspace error-tolerant mechanism is designed and embedded into FVS to handle intraclass variances. Palmprint is one of the most important biometrics; to protect palmprint templates; a palmprint based MDFVS implementation is also presented. Experimental results show that the proposed scheme not only can deal with intraclass variances effectively but also could maintain the accuracy and meanwhile enhance security. PMID:24892094

  13. Dose classification scheme for digital imaging techniques in diagnostic radiology

    International Nuclear Information System (INIS)

    CT all clinical questions can be answered with certainty and regardless of clinical experience of the involved physician. They are often recommended by the equipment manufacturers and should be reviewed critically because of their high radiation exposure. Conclusion: the classification of applicable doses in three classes can generally be considered as a practicable way of dose reduction. (author)

  14. Hybrid Support Vector Machines-Based Multi-fault Classification

    Institute of Scientific and Technical Information of China (English)

    GAO Guo-hua; ZHANG Yong-zhong; ZHU Yu; DUAN Guang-huang

    2007-01-01

    Support Vector Machines (SVM) is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 (one versus one) and 1-v-r (one versus rest), are respectively adopted at different classification levels. At the parallel classification level, using 1-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.

  15. Modulation classification based on spectrogram

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The aim of modulation classification (MC) is to identify the modulation type of a communication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-based modulation classification methods are proposed. Their reccgnition scope and performance are investigated or evaluated by theoretical analysis and extensive simulation studies. The method taking moment-like features is robust to frequency offset while the other two, which make use of principal component analysis (PCA) with different transformation inputs,can achieve satisfactory accuracy even at low SNR (as low as 2 dB). Due to the properties of spectrogram, the statistical pattern recognition techniques, and the image preprocessing steps, all of our methods are insensitive to unknown phase and frequency offsets, timing errors, and the arriving sequence of symbols.

  16. Improved code-based identification scheme

    CERN Document Server

    Cayrel, Pierre-Louis

    2010-01-01

    We revisit the 3-pass code-based identification scheme proposed by Stern at Crypto'93, and give a new 5-pass protocol for which the probability of the cheater is 1/2 (instead of 2/3 in the original Stern's proposal). Furthermore, we propose to use quasi-cyclic construction in order to dramatically reduce the size of the public key. The proposed scheme is zero-knowledge and relies on an NP-complete problem coming from coding theory (namely the q-ary Syndrome Decoding problem). Taking into account a recent study of a generalization of Stern's information-set-decoding algorithm for decoding linear codes over arbitrary finite fields Fq we suggest parameters so that the public key be 34Kbits while those of Stern's scheme is about 66Kbits. This provides a very practical identification (and possibly signature) scheme which is mostly attractive for light-weight cryptography

  17. Threshold Ring Signature Scheme Based on TPM

    Institute of Scientific and Technical Information of China (English)

    Gong Bei; Jiang Wei; Lin Li; Li Yu; Zhang Xing

    2012-01-01

    The conventional ring signature schemes cannot address the scenario where the rank of members of the ring needs to be distinguished, for example, in electronically commerce application. To solve this problem, we presented a Trusted Platform Module (TPM)-based threshold ring signature schen. Employing a reliable secret Share Distribution Center (SDC), the proposed approach can authenticate the TPM-based identity rank of members of the ring but not track a specific member's identity. A subset including t members with the same identity rank is built. With the signing cooperation of t members of the subset, the ring signature based on Chinese remainder theorem is generated. We proved the anonymity and unforgeability of the proposed scheme and compared it with the threshold ring signature based on Lagrange interpolation polynomial. Our scheme is relatively simpler to calculate.

  18. An unsupervised classification scheme for improving predictions of prokaryotic TIS

    Directory of Open Access Journals (Sweden)

    Meinicke Peter

    2006-03-01

    Full Text Available Abstract Background Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes. Results We introduce a clustering algorithm for completely unsupervised scoring of potential TIS, based on positionally smoothed probability matrices. The algorithm requires an initial gene prediction and the genomic sequence of the organism to perform the reannotation. As compared with other methods for improving predictions of gene starts in bacterial genomes, our approach is not based on any specific assumptions about prokaryotic TIS. Despite the generality of the underlying algorithm, the prediction rate of our method is competitive on experimentally verified test data from E. coli and B. subtilis. Regarding genomes with high G+C content, in contrast to some previously proposed methods, our algorithm also provides good performance on P. aeruginosa, B. pseudomallei and R. solanacearum. Conclusion On reliable test data we showed that our method provides good results in post-processing the predictions of the widely-used program GLIMMER. The underlying clustering algorithm is robust with respect to variations in the initial TIS annotation and does not require specific assumptions about prokaryotic gene starts. These features are particularly useful on genomes with high G+C content. The algorithm has been implemented in the tool »TICO«(TIs COrrector which is

  19. LEARNING WITH ERROR BASED SEARCHABLE ENCRYPTION SCHEME

    Institute of Scientific and Technical Information of China (English)

    Zhang Jiuling; Deng Beixing; Li Xing

    2012-01-01

    A learning with error problem based encryption scheme that allows secure searching over the cipher text is proposed.Both the generation of cipher text and the trapdoor of the query are based on the problem of learning with errors.By performing an operation over the trapdoor and the cipher text,it is able to tell if the cipher text is the encryption of a plaintext.The secure searchable encryption scheme is both cipher text and trapdoor indistinguishable.The probabilities of missing and failing match occurrence in searching are both exponentially small.

  20. An ECC-Based Blind Signature Scheme

    Directory of Open Access Journals (Sweden)

    Fuh-Gwo Jeng

    2010-08-01

    Full Text Available Cryptography is increasingly applied to the E-commerce world, especially to the untraceable payment system and the electronic voting system. Protocols for these systems strongly require the anonymous digital signature property, and thus a blind signature strategy is the answer to it. Chaum stated that every blind signature protocol should hold two fundamental properties, blindness and intractableness. All blind signature schemes proposed previously almost are based on the integer factorization problems, discrete logarithm problems, or the quadratic residues, which are shown by Lee et al. that none of the schemes is able to meet the two fundamental properties above. Therefore, an ECC-based blind signature scheme that possesses both the above properties is proposed in this paper.

  1. Projection Classification Based Iterative Algorithm

    Science.gov (United States)

    Zhang, Ruiqiu; Li, Chen; Gao, Wenhua

    2015-05-01

    Iterative algorithm has good performance as it does not need complete projection data in 3D image reconstruction area. It is possible to be applied in BGA based solder joints inspection but with low convergence speed which usually acts with x-ray Laminography that has a worse reconstruction image compared to the former one. This paper explores to apply one projection classification based method which tries to separate the object to three parts, i.e. solute, solution and air, and suppose that the reconstruction speed decrease from solution to two other parts on both side lineally. And then SART and CAV algorithms are improved under the proposed idea. Simulation experiment result with incomplete projection images indicates the fast convergence speed of the improved iterative algorithms and the effectiveness of the proposed method. Less the projection images, more the superiority is also founded.

  2. Evaluation of rock mass classification schemes: a case study from the Bowen Basin, Australia

    Science.gov (United States)

    Brook, Martin; Hebblewhite, Bruce; Mitra, Rudrajit

    2016-04-01

    The development of an accurate engineering geological model and adequate knowledge of spatial variation in rock mass conditions are important prerequisites for slope stability analyses, tunnel design, mine planning and risk management. Rock mass classification schemes such as Rock Mass Rating (RMR), Coal Mine Roof Rating (CMRR), Q-system and Roof Strength Index (RSI) have been used for a range of engineering geological applications, including transport tunnels, "hard rock" mining and underground and open-cut coal mines. Often, rock mass classification schemes have been evaluated on subaerial exposures, where weathering has affected joint characteristics and intact strength. In contrast, the focus of this evaluation of the above classification schemes is an underground coal mine in the Bowen Basin, central Queensland, Australia, 15 km east of the town of Moranbah. Rock mass classification was undertaken at 68 sites across the mine. Both the target coal seam and overlying rock show marked spatial variability in terms of RMR, CMRR and Q, but RSI showed limited sensitivity to changes in rock mass condition. Relationships were developed between different parameters with varying degrees of success. A mine-wide analysis of faulting was undertaken, and compared with in situ stress field and local-scale measurements of joint and cleat. While there are no unequivocal relationships between rock mass classification parameters and faulting, a central graben zone shows heterogeneous rock mass properties. The corollary is that if geological features can be accurately defined by remote sensing technologies, then this can assist in predicting rock mass conditions and risk management ahead of development and construction.

  3. Feasibility analysis of two identity- based proxy ring signature schemes

    Institute of Scientific and Technical Information of China (English)

    Wang Huaqun; Zhang Lijun; Zhao Junxi

    2007-01-01

    Recently , proxy ring signature schemes have been shown to be useful in various applications , such as electronic polling, electronic payment, etc. Although many proxy ring signature schemes have been proposed, there are only two identity- based proxy ring signature schemes have been proposed until now, I.e., Cheng's scheme and Lang's scheme. It's unlucky that the two identity- based proxy ring signature schemes are unfeasible . This paper points out the reasons why the two identity- based proxy ring signature schemes are unfeasible. In order to design feasible and efficient identity-based proxy ring signature schemes from bilinear pairings , we have to search for other methods .

  4. DREAM: Classification scheme for dialog acts in clinical research query mediation.

    Science.gov (United States)

    Hoxha, Julia; Chandar, Praveen; He, Zhe; Cimino, James; Hanauer, David; Weng, Chunhua

    2016-02-01

    Clinical data access involves complex but opaque communication between medical researchers and query analysts. Understanding such communication is indispensable for designing intelligent human-machine dialog systems that automate query formulation. This study investigates email communication and proposes a novel scheme for classifying dialog acts in clinical research query mediation. We analyzed 315 email messages exchanged in the communication for 20 data requests obtained from three institutions. The messages were segmented into 1333 utterance units. Through a rigorous process, we developed a classification scheme and applied it for dialog act annotation of the extracted utterances. Evaluation results with high inter-annotator agreement demonstrate the reliability of this scheme. This dataset is used to contribute preliminary understanding of dialog acts distribution and conversation flow in this dialog space. PMID:26657707

  5. Attribute Based Multisignature Scheme for Wireless Communications

    OpenAIRE

    2015-01-01

    With rapidly development of wireless communication, more mobile devices are used in our daily life. Although the need for accessing a wireless network is evident, new problems, such as keeping and preserving user identity’s privacy, should be greatly concerned. Attribute based signature scheme is an important cryptographic primitive which provides a powerful way for user to control their privacy. In wireless environment, the capacity of wireless channel is also valuable resources which is lim...

  6. Arabic Text Mining Using Rule Based Classification

    OpenAIRE

    Fadi Thabtah; Omar Gharaibeh; Rashid Al-Zubaidy

    2012-01-01

    A well-known classification problem in the domain of text mining is text classification, which concerns about mapping textual documents into one or more predefined category based on its content. Text classification arena recently attracted many researchers because of the massive amounts of online documents and text archives which hold essential information for a decision-making process. In this field, most of such researches focus on classifying English documents while there are limited studi...

  7. Domain-Based Classification of CSCW Systems

    Directory of Open Access Journals (Sweden)

    M. Khan

    2011-11-01

    Full Text Available CSCW systems are widely used for group activities in different organizations and setups. This study briefly describes the existing classifications of CSCW systems and their shortcomings. These existing classifications are helpful to categorize systems based on a general set of CSCW characteristics but do not provide any guidance towards system design and evaluation. After literature review of ACM CSCW conference (1986-2010, a new classification is proposed to categorize CSCW systems on the basis of domains. This proposed classification may help researchers to come up with more effective design and evaluation methods for CSCW systems.

  8. A Dynamic Multimedia User-Weight Classification Scheme for IEEE_802.11 WLANs

    CERN Document Server

    Rebai, Ahmed Riadh; 10.5121/ijcnc.2011.3214

    2011-01-01

    In this paper we expose a dynamic traffic-classification scheme to support multimedia applications such as voice and broadband video transmissions over IEEE 802.11 Wireless Local Area Networks (WLANs). Obviously, over a Wi-Fi link and to better serve these applications - which normally have strict bounded transmission delay or minimum link rate requirement - a service differentiation technique can be applied to the media traffic transmitted by the same mobile node using the well-known 802.11e Enhanced Distributed Channel Access (EDCA) protocol. However, the given EDCA mode does not offer user differentiation, which can be viewed as a deficiency in multi-access wireless networks. Accordingly, we propose a new inter-node priority access scheme for IEEE 802.11e networks which is compatible with the EDCA scheme. The proposed scheme joins a dynamic user-weight to each mobile station depending on its outgoing data, and therefore deploys inter-node priority for the channel access to complement the existing EDCA inte...

  9. A proposed radiographic classification scheme for congenital thoracic vertebral malformations in brachycephalic "screw-tailed" dog breeds.

    Science.gov (United States)

    Gutierrez-Quintana, Rodrigo; Guevar, Julien; Stalin, Catherine; Faller, Kiterie; Yeamans, Carmen; Penderis, Jacques

    2014-01-01

    Congenital vertebral malformations are common in brachycephalic "screw-tailed" dog breeds such as French bulldogs, English bulldogs, Boston terriers, and pugs. The aim of this retrospective study was to determine whether a radiographic classification scheme developed for use in humans would be feasible for use in these dog breeds. Inclusion criteria were hospital admission between September 2009 and April 2013, neurologic examination findings available, diagnostic quality lateral and ventro-dorsal digital radiographs of the thoracic vertebral column, and at least one congenital vertebral malformation. Radiographs were retrieved and interpreted by two observers who were unaware of neurologic status. Vertebral malformations were classified based on a classification scheme modified from a previous human study and a consensus of both observers. Twenty-eight dogs met inclusion criteria (12 with neurologic deficits, 16 with no neurologic deficits). Congenital vertebral malformations affected 85/362 (23.5%) of thoracic vertebrae. Vertebral body formation defects were the most common (butterfly vertebrae 6.6%, ventral wedge-shaped vertebrae 5.5%, dorsal hemivertebrae 0.8%, and dorso-lateral hemivertebrae 0.5%). No lateral hemivertebrae or lateral wedge-shaped vertebrae were identified. The T7 vertebra was the most commonly affected (11/28 dogs), followed by T8 (8/28 dogs) and T12 (8/28 dogs). The number and type of vertebral malformations differed between groups (P = 0.01). Based on MRI, dorsal, and dorso-lateral hemivertebrae were the cause of spinal cord compression in 5/12 (41.6%) of dogs with neurologic deficits. Findings indicated that a modified human radiographic classification system of vertebral malformations is feasible for use in future studies of brachycephalic "screw-tailed" dogs. PMID:24833506

  10. Fuzzy Commitment Scheme based on Reed Solomon Codes

    OpenAIRE

    Chauhan, Sonam; Sharma, Ajay

    2016-01-01

    The conventional commitment scheme requires both commitment string and a valid key for the sender to verify his commitment. Differ from the conventional commitment scheme; fuzzy commitment scheme accepts the key that is similar to the original key. The new opening key, not identical to the original key, differs from the initial key in some suitable metrics. The fuzziness in the fuzzy commitment scheme tolerate small amount of corruptions. The fuzzy commitment scheme based on the cryptographic...

  11. Texture Classification Based on Texton Features

    Directory of Open Access Journals (Sweden)

    U Ravi Babu

    2012-08-01

    Full Text Available Texture Analysis plays an important role in the interpretation, understanding and recognition of terrain, biomedical or microscopic images. To achieve high accuracy in classification the present paper proposes a new method on textons. Each texture analysis method depends upon how the selected texture features characterizes image. Whenever a new texture feature is derived it is tested whether it precisely classifies the textures. Here not only the texture features are important but also the way in which they are applied is also important and significant for a crucial, precise and accurate texture classification and analysis. The present paper proposes a new method on textons, for an efficient rotationally invariant texture classification. The proposed Texton Features (TF evaluates the relationship between the values of neighboring pixels. The proposed classification algorithm evaluates the histogram based techniques on TF for a precise classification. The experimental results on various stone textures indicate the efficacy of the proposed method when compared to other methods.

  12. Climatological characteristics of the tropics in China: climate classification schemes between German scientists and Huang Bingwei

    Institute of Scientific and Technical Information of China (English)

    ManfredDomroes

    2003-01-01

    Reviewing some important German scientists who have developed climatic regionalization schemes either on a global or Chinese scale, their various definitions of the tropical climate characteristics in China are discussed and compared with Huang Bingwei's climate classification scheme and the identification of the tropical climate therein. It can be seen that, due to different methodological approaches of the climatic regionalization schemes, the definitions of the tropics vary and hence also their spatial distribution in China. However, it is found that the tropical climate type occupies only a peripheral part of southern China, though it firmly represents a distinctive type of climate that is subsequently associated with a great economic importance for China. As such, the tropical climate type was mostly identified with its agro-climatological significance, that is by giving favourable growing conditions all-year round for perennial crops with a great heat demand. Tropical climate is, hence, conventionally regarded to be governed by all-year round summer conditions "where winter never comes".

  13. Fingerprint Classification based on Orientaion Estimation

    Directory of Open Access Journals (Sweden)

    Manish Mathuria

    2013-06-01

    Full Text Available The geometric characteristics of an object make it distinguishable. The objects present in the Environment known by their features and properties. The fingerprint image as object may classify into sub classes based on minutiae structure. The minutiae structure may categorize as ridge curves generated by the orientation estimation. The extracted curves are invariant to location, rotation and scaling. This classification approach helps to manage fingerprints along their classes. This research provides a better collaboration of data mining based on classification.

  14. Secure Order-Specified Multisignature Scheme Based on DSA

    Institute of Scientific and Technical Information of China (English)

    YANG Muxiang; SU Li; LI Jun; HONG Fan

    2006-01-01

    In multisignature schemes signers can sign either in a linear order or not in any specified order, but neither of them is adequate in some scenarios where require mixture using of orderless and ordered multisignature. Most order-specified multisignatures specified the orders as linear ones. In this paper, we proposed an order-specified multisignature scheme based on DSA secure against active insider attack. To our knowledge, it is the first order-specified multisignature scheme based on DSA signature scheme, in which signers can sign in flexible order represented by series-parallel graphs. In the multisignature scheme verification to both signers and signing order are available. The security of the scheme is proved by reduce to an identification scheme that is proved have some concrete security. The running time of verifying a signature is comparable to previous schemes while the running time of multisignature generation and the space needed is less than those schemes.

  15. A proper Land Cover and Forest Type Classification Scheme for Mexico

    Science.gov (United States)

    Gebhardt, S.; Maeda, P.; Wehrmann, T.; Argumedo Espinoza, J.; Schmidt, M.

    2015-04-01

    The imminent implementation of a REDD+ MRV system in Mexico in 2015, demanding operational annual land cover change reporting, requires highly accurate, annual and high resolution forest type maps; not only for monitoring but also to establish the historical baseline from the 1990s onwards. The employment of any supervised classifier demands exhaustive definition of land cover classes and the representation of all classes in the training stage. This paper reports the process of a data driven class separability analysis and the definition and application of a national land cover classification scheme. All Landsat data recorded over Mexico in the year 2000 with cloud coverage below 10 percent and a national digital elevation model have been used. Automatic wall-2-wall image classification has been performed trained by national reference data on land use and vegetation types with 66 classes. Validation has been performed against field plots of the national forest inventory. Groups of non-separable classes have subsequently been discerned by automatic iterative class aggregation. Class aggregations have finally been manually revised and modified towards a proposed national land cover classification scheme at 4 levels with 35 classes at the highest level including 13 classes for primary temperate and tropical forests, 2 classes for secondary temperate and tropical forest, 1 for induced or cultivated forest, as also 8 different scrubland classes. The remaining 11 classes cover agriculture, grassland, wetland, water bodies, urban and other vegetation land cover classes. The remaining 3 levels provide further hierarchic aggregations with 14, 10, and 8 classes, respectively. Trained by the relabeled training dataset wall-2-wall classification towards the 35 classes has been performed. The final national land cover dataset has been validated against more than 200,000 polygons randomly distributed all over the country with class labels derived by manual interpretation. The

  16. Blind Signature Scheme Based on Chebyshev Polynomials

    Directory of Open Access Journals (Sweden)

    Maheswara Rao Valluri

    2011-12-01

    Full Text Available A blind signature scheme is a cryptographic protocol to obtain a valid signature for a message from a signer such that signer’s view of the protocol can’t be linked to the resulting message signature pair. This paper presents blind signature scheme using Chebyshev polynomials. The security of the given scheme depends upon the intractability of the integer factorization problem and discrete logarithms ofChebyshev polynomials.

  17. Sound classification of dwellings in the Nordic countries – Differences and similarities between the five national schemes

    DEFF Research Database (Denmark)

    Rasmussen, Birgit

    2012-01-01

    In all five Nordic countries, sound classification schemes for dwellings have been published in national standards being implemented and revised gradually since the late 1990s. The national classification criteria for dwellings originate from a common Nordic INSTA-B proposal from the 1990s, thus...... having several similarities. In 2012, status is that number and denotations of classes for dwellings are identical in the Nordic countries, but the structures of the standards and several details are quite different. Also the issues dealt with are different. Examples of differences are sound insulation...... for classification of such buildings. This paper presents and compares the main class criteria for sound insulation of dwellings and summarizes differences and similarities in criteria and in structures of standards. Classification schemes for dwellings also exist in several other countries in Europe...

  18. A Classification-based Review Recommender

    Science.gov (United States)

    O'Mahony, Michael P.; Smyth, Barry

    Many online stores encourage their users to submit product/service reviews in order to guide future purchasing decisions. These reviews are often listed alongside product recommendations but, to date, limited attention has been paid as to how best to present these reviews to the end-user. In this paper, we describe a supervised classification approach that is designed to identify and recommend the most helpful product reviews. Using the TripAdvisor service as a case study, we compare the performance of several classification techniques using a range of features derived from hotel reviews. We then describe how these classifiers can be used as the basis for a practical recommender that automatically suggests the mosthelpful contrasting reviews to end-users. We present an empirical evaluation which shows that our approach achieves a statistically significant improvement over alternative review ranking schemes.

  19. An Authentication Technique Based on Classification

    Institute of Scientific and Technical Information of China (English)

    李钢; 杨杰

    2004-01-01

    We present a novel watermarking approach based on classification for authentication, in which a watermark is embedded into the host image. When the marked image is modified, the extracted watermark is also different to the original watermark, and different kinds of modification lead to different extracted watermarks. In this paper, different kinds of modification are considered as classes, and we used classification algorithm to recognize the modifications with high probability. Simulation results show that the proposed method is potential and effective.

  20. Evaluating arguments based on Toulmin's scheme

    NARCIS (Netherlands)

    Verheij, Bart; Hitchcock, D; Verheij, B

    2006-01-01

    Toulmin's scheme for the layout of arguments (1958) represents an influential tool for the analysis of arguments. The scheme enriches the traditional premises-conclusion model of arguments by distinguishing additional elements, like warrant, backing and rebuttal. The present paper contains a formal

  1. A new threshold signature scheme based on fuzzy biometric identity

    Institute of Scientific and Technical Information of China (English)

    Yongquan Cai; Ke Zhang

    2009-01-01

    The focus of this paper is to present the first threshold signature scheme based on biometric identity, which is acquired from a recently proposed fuzzy identities-based encryption scheme. An important feature of this scheme, which is different from other previous ID-based threshold signature schemes, is that it can be applied to situations using not only average personal attributes in social contact but also people's noisy biometric inputs as identities. The security of our scheme in the selective-lD model reduces the limit in the hardness of the Decisional BDH Assumption.

  2. A network condition classification scheme for supporting video delivery over wireless Internet

    Institute of Scientific and Technical Information of China (English)

    CHAN Siu-ping; SUN Ming-ting

    2006-01-01

    Real-time video transport over wireless Internet faces many challenges due to the heterogeneous environment including wireline and wireless networks. A robust network condition classification algorithm using multiple end-to-end metrics and Support Vector Machine (SVM) is proposed to classify different network events and model the transition pattern of network conditions.End-to-end Quality-of-Service (QoS) mechanisms like congestion control, error control, and power control can benefit from the network condition information and react to different network situations appropriately. The proposed network condition classification algorithm uses SVM as a classifier to cluster different end-to-end metrics such as end-to-end delay, delay jitter, throughput and packet loss-rate for the UDP traffic with TCP-friendly Rate Control (TFRC), which is used for video transport. The algorithm is also flexible for classifying different numbers of states representing different levels of network events such as wireline congestion and wireless channel loss. Simulation results using network simulator 2 (ns2) showed the effectiveness of the proposed scheme.

  3. Polarimetric radar observations during an orographic rain event and the performance of a hydrometeor classification scheme

    Directory of Open Access Journals (Sweden)

    M. Frech

    2014-07-01

    Full Text Available An intense orographic precipitation event is analysed using two polarimetric C-Band radars situated north of the Alps on 5 January 2013. One radar is operated at DWD's meteorological observatory Hohenpeißenberg (MHP, 1006 m a.s.l. – above sea level and the Memmingen (MEM, 65 km west of MHP, 600 m a.s.l. radar is part of DWD's operational radar network. The event lasted about 1.5 days and in total 44 mm precipitation was measured at Hohenpeißenberg. Detailed high resolution observation on the vertical structure of this event is obtained through a birdbath scan at 90° elevation which is part of the operational scanning. This scan is acquired every 5 min and provides meteorological profiles at high spatial resolution. In the course of this event, the melting layer (ML descends until the transition from rain into snow is observed at ground level. This transition from rain into snow is well documented by local weather observers and a present-weather sensor. The orographic precipitation event reveals mesoscale variability above the melting layer which is unexpected from a meteorological point of view. It corresponds to a substantial increase in rain rate at the surface. The performance of the newly developed hydrometeor classification scheme "Hymec" using Memmingen radar data over Hohenpeißenberg is analyzed. The detection in location and timing of the ML agrees well with the Hohenpeißenberg radar data. Considering the size of the Memmingen radar sensing volume, the detected hydrometeor (HM types are consistent for measurements at or in a ML, even though surface observation indicate for example rain whereas the predominant HM is classified as wet snow. To better link the HM classification with the surface observation, either better thermodynamic input is needed for Hymec or a statistical correction of the HM classification similar to a model output statistics (MOS approach may be needed.

  4. A NEW EFFICIENT ID-BASED PROXY BLIND SIGNATURE SCHEME

    Institute of Scientific and Technical Information of China (English)

    Ming Yang; Wang Yumin

    2008-01-01

    In a proxy blind signature scheme, the proxy signer is allowed to generate a blind signature on behalf of the original signer. The proxy blind signature scheme is useful in several applications such as e-voting, e-payment, etc. Recently, Zheng, et al. presented an IDentity (ID)-based proxy blind signature. In this paper, a new efficient ID-based proxy blind signature scheme from bilinear pairings is proposed, which can satisfy the security properties of both the proxy signatures and the blind signature schemes. Analysis of the scheme efficiency shows that the new scheme is more efficient than Zheng, et al.'s scheme. The proposed scheme is more practical in the real world.

  5. Cryptanalysis and Improvement of Digital Multisignature Scheme Based on RSA

    Institute of Scientific and Technical Information of China (English)

    SU Li; CUI Guo-hua; CHEN Jing; YUAN Jun

    2007-01-01

    Zhang et al. proposed a sequential multisignature scheme based on RSA. The scheme has advantages of low computation and communication costs, and so on. However, we find a problem in their scheme that the verifier can not distinguish whether the multisignature is signed by all the signers of the group or only by the last signer. Thus, any single signature created by the last signer can be used as a multisignature created by the whole group members. This paper proposes an improved scheme that can overcome the defect. In the new scheme, the identity messages of all the signers are added in the multisignature and used in verification phase, so that the verifier can know the signature is generated by which signers. Performance analysis shows that the proposed scheme costs less computation than the original scheme in both signature and verification phases. Furthermore, each partial signature is based on the signer's identity certificate, which makes the scheme more secure.

  6. Normalization Benefits Microarray-Based Classification

    Directory of Open Access Journals (Sweden)

    Chen Yidong

    2006-01-01

    Full Text Available When using cDNA microarrays, normalization to correct labeling bias is a common preliminary step before further data analysis is applied, its objective being to reduce the variation between arrays. To date, assessment of the effectiveness of normalization has mainly been confined to the ability to detect differentially expressed genes. Since a major use of microarrays is the expression-based phenotype classification, it is important to evaluate microarray normalization procedures relative to classification. Using a model-based approach, we model the systemic-error process to generate synthetic gene-expression values with known ground truth. These synthetic expression values are subjected to typical normalization methods and passed through a set of classification rules, the objective being to carry out a systematic study of the effect of normalization on classification. Three normalization methods are considered: offset, linear regression, and Lowess regression. Seven classification rules are considered: 3-nearest neighbor, linear support vector machine, linear discriminant analysis, regular histogram, Gaussian kernel, perceptron, and multiple perceptron with majority voting. The results of the first three are presented in the paper, with the full results being given on a complementary website. The conclusion from the different experiment models considered in the study is that normalization can have a significant benefit for classification under difficult experimental conditions, with linear and Lowess regression slightly outperforming the offset method.

  7. Normalization Benefits Microarray-Based Classification

    Directory of Open Access Journals (Sweden)

    Edward R. Dougherty

    2006-08-01

    Full Text Available When using cDNA microarrays, normalization to correct labeling bias is a common preliminary step before further data analysis is applied, its objective being to reduce the variation between arrays. To date, assessment of the effectiveness of normalization has mainly been confined to the ability to detect differentially expressed genes. Since a major use of microarrays is the expression-based phenotype classification, it is important to evaluate microarray normalization procedures relative to classification. Using a model-based approach, we model the systemic-error process to generate synthetic gene-expression values with known ground truth. These synthetic expression values are subjected to typical normalization methods and passed through a set of classification rules, the objective being to carry out a systematic study of the effect of normalization on classification. Three normalization methods are considered: offset, linear regression, and Lowess regression. Seven classification rules are considered: 3-nearest neighbor, linear support vector machine, linear discriminant analysis, regular histogram, Gaussian kernel, perceptron, and multiple perceptron with majority voting. The results of the first three are presented in the paper, with the full results being given on a complementary website. The conclusion from the different experiment models considered in the study is that normalization can have a significant benefit for classification under difficult experimental conditions, with linear and Lowess regression slightly outperforming the offset method.

  8. Review of habitat classification schemes appropriate to streams, rivers, and connecting channels in the Great Lakes drainage system

    Science.gov (United States)

    Hudson, Patrick L.; Griffiths, R.W.; Wheaton, T.J.

    1992-01-01

    Studies of lotic classification, zonation, and distribution carried out since the turn of the century were reviewed for their use in developing a habitat classification scheme for flowing water in the Great Lakes drainage basin. Seventy papers, dealing mainly with fish but including benthos, were organized into four somewhat distinct groups. A heirarchical scale of habitat measurements is suggested, and sources of data and inventory methods, including statistical treatment, are reviewed. An outline is also provided for developing a classification system for riverine habitat in the Great Lakes drainage basin.

  9. Verifiable (t, n) Threshold Signature Scheme Based on Elliptic Curve

    Institute of Scientific and Technical Information of China (English)

    WANG Hua-qun; ZHAO Jun-xi; ZHANG Li-jun

    2005-01-01

    Based on the difficulty of solving the ECDLP (elliptic curve discrete logarithm problem) on the finite field,we present a (t, n) threshold signature scheme and a verifiable key agreement scheme without trusted party. Applying a modified elliptic curve signature equation, we get a more efficient signature scheme than the existing ECDSA (elliptic curve digital signature algorithm) from the computability and security view. Our scheme has a shorter key, faster computation, and better security.

  10. A New Efficient Certificate-Based Signature Scheme

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yichen; LI Jiguo; WANG Zhiwei; YAO Wei

    2015-01-01

    Certificate-based cryptography is a new kind of public key algorithm, which combines the merits of traditional Public key infrastructure (PKI) and identity-based cryptography. It removes the inherent key escrow problem in the identity-based cryptography and eliminates the certificate revocation problem and third-party queries in the traditional PKI. In this paper, we propose an effi-cient certificate-based signature scheme based on bilinear pairings. Under the strong security model of certificate-based signature scheme, we prove that our scheme is exis-tentially unforgeable against adaptive chosen message and identity attacks in the random oracle. In our scheme, only two pairing operations are needed in the signing and ver-ification processes. Compared with some certificate-based signature schemes from bilinear pairings, our scheme en-joys more advantage in computational cost and communi-cational cost.

  11. Commercial Shot Classification Based on Multiple Features Combination

    Science.gov (United States)

    Liu, Nan; Zhao, Yao; Zhu, Zhenfeng; Ni, Rongrong

    This paper presents a commercial shot classification scheme combining well-designed visual and textual features to automatically detect TV commercials. To identify the inherent difference between commercials and general programs, a special mid-level textual descriptor is proposed, aiming to capture the spatio-temporal properties of the video texts typical of commercials. In addition, we introduce an ensemble-learning based combination method, named Co-AdaBoost, to interactively exploit the intrinsic relations between the visual and textual features employed.

  12. Threshold Signature Scheme Based on Discrete Logarithm and Quadratic Residue

    Institute of Scientific and Technical Information of China (English)

    FEI Ru-chun; WANG Li-na

    2004-01-01

    Digital signature scheme is a very important research field in computer security and modern cryptography.A(k,n) threshold digital signature scheme is proposed by integrating digital signature scheme with Shamir secret sharing scheme.It can realize group-oriented digital signature, and its security is based on the difficulty in computing discrete logarithm and quadratic residue on some special conditions.In this scheme, effective digital signature can not be generated by any k-1 or fewer legal users, or only by signature executive.In addition, this scheme can identify any legal user who presents incorrect partial digital signature to disrupt correct signature, or any illegal user who forges digital signature.A method of extending this scheme to an Abelian group such as elliptical curve group is also discussed.The extended scheme can provide rapider computing speed and stronger security in the case of using shorter key.

  13. Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

    OpenAIRE

    Wenyu Zhang; Zhenjiang Zhang

    2015-01-01

    Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any ty...

  14. The Performance-based Funding Scheme of Universities

    Directory of Open Access Journals (Sweden)

    Juha KETTUNEN

    2016-05-01

    Full Text Available The purpose of this study is to analyse the effectiveness of the performance-based funding scheme of the Finnish universities that was adopted at the beginning of 2013. The political decision-makers expect that the funding scheme will create incentives for the universities to improve performance, but these funding schemes have largely failed in many other countries, primarily because public funding is only a small share of the total funding of universities. This study is interesting because Finnish universities have no tuition fees, unlike in many other countries, and the state allocates funding based on the objectives achieved. The empirical evidence of the graduation rates indicates that graduation rates increased when a new scheme was adopted, especially among male students, who have more room for improvement than female students. The new performance-based funding scheme allocates the funding according to the output-based indicators and limits the scope of strategic planning and the autonomy of the university. The performance-based funding scheme is transformed to the strategy map of the balanced scorecard. The new funding scheme steers universities in many respects but leaves the research and teaching skills to the discretion of the universities. The new scheme has also diminished the importance of the performance agreements between the university and the Ministry. The scheme increases the incentives for universities to improve the processes and structures in order to attain as much public funding as possible. It is optimal for the central administration of the university to allocate resources to faculties and other organisational units following the criteria of the performance-based funding scheme. The new funding scheme has made the universities compete with each other, because the total funding to the universities is allocated to each university according to the funding scheme. There is a tendency that the funding schemes are occasionally

  15. ID-Based Signature Scheme with Weil Pairing

    Directory of Open Access Journals (Sweden)

    Neetu Sharma

    2013-09-01

    Full Text Available Digital signature is an essential component in cryptography. Digital signaturesguarantee end-to-end message integrity and authentication information about the origin of a message. In this paper we propose a new identification based digital signature scheme with weil pairing. Also we analyze security and efficiency of our scheme. Security of our scheme is based on expressing the torsion point of curve into linear combination of its basis points; it is more complicated than solving ECDLP(Elliptic Curve Discrete Logarithm Problem. We claim that our new identification based digital signature scheme is more secure and efficient than the existing scheme of Islam et al(S. K. Hafizul Islam, G.P. Biswas, An Efficient and Provably-secure Digital signature Scheme based on Elliptic Curve Bilinear Pairings, Theoretical and Applied Informatics ISSN 18965334 Vol.24, no. 2, 2012, pp. 109 118 based on bilinear pairing.

  16. Adaptive Image Transmission Scheme over Wavelet-Based OFDM System

    Institute of Scientific and Technical Information of China (English)

    GAOXinying; YUANDongfeng; ZHANGHaixia

    2005-01-01

    In this paper an adaptive image transmission scheme is proposed over Wavelet-based OFDM (WOFDM) system with Unequal error protection (UEP) by the design of non-uniform signal constellation in MLC. Two different data division schemes: byte-based and bitbased, are analyzed and compared. Different bits are protected unequally according to their different contribution to the image quality in bit-based data division scheme, which causes UEP combined with this scheme more powerful than that with byte-based scheme. Simulation results demonstrate that image transmission by UEP with bit-based data division scheme presents much higher PSNR values and surprisingly better image quality. Furthermore, by considering the tradeoff of complexity and BER performance, Haar wavelet with the shortest compactly supported filter length is the most suitable one among orthogonal Daubechies wavelet series in our proposed system.

  17. Interaction profile-based protein classification of death domain

    Directory of Open Access Journals (Sweden)

    Pio Frederic

    2004-06-01

    learning approach yielded an 89% average accuracy. Conclusion We have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.

  18. A Proxy Blind Signature Scheme Based on ECDLP

    Institute of Scientific and Technical Information of China (English)

    WANGHaiyan; WANGRuchuan

    2005-01-01

    While proxy signature scheme enables an original signer to fully authorize a proxy to sign a message on his or her behalf legally and undeniably, blind signature scheme keeps the message blind from the signer so that the signer cannot make a linkage between the signature and the identity of requester (receiver). Both schemes have been widely applied in the electronic business. A new ECDLP (Elliptic curve discrete problem)-based proxy blind signature scheme is to be proposed in this paper by integrating the security properties of both schemes.

  19. Quantum election scheme based on anonymous quantum key distribution

    International Nuclear Information System (INIS)

    An unconditionally secure authority-certified anonymous quantum key distribution scheme using conjugate coding is presented, based on which we construct a quantum election scheme without the help of an entanglement state. We show that this election scheme ensures the completeness, soundness, privacy, eligibility, unreusability, fairness, and verifiability of a large-scale election in which the administrator and counter are semi-honest. This election scheme can work even if there exist loss and errors in quantum channels. In addition, any irregularity in this scheme is sensible. (general)

  20. Design Scheme of Remote Monitoring System Based on Qt

    Directory of Open Access Journals (Sweden)

    Xu Dawei

    2015-01-01

    Full Text Available This paper introduces a design scheme of remote monitoring system based on Qt, the scheme of remote monitoring system based on S3C2410 and Qt, with the aid of cross platform development tools Qt and powerful ARM platform design and implementation. The development of remote video surveillance system based on embedded terminal has practical significance and value.

  1. Classifying Obstructive and Nonobstructive Code Clones of Type I Using Simplified Classification Scheme: A Case Study

    Directory of Open Access Journals (Sweden)

    Miroslaw Staron

    2015-01-01

    Full Text Available Code cloning is a part of many commercial and open source development products. Multiple methods for detecting code clones have been developed and finding the clones is often used in modern quality assurance tools in industry. There is no consensus whether the detected clones are negative for the product and therefore the detected clones are often left unmanaged in the product code base. In this paper we investigate how obstructive code clones of Type I (duplicated exact code fragments are in large software systems from the perspective of the quality of the product after the release. We conduct a case study at Ericsson and three of its large products, which handle mobile data traffic. We show how to use automated analogy-based classification to decrease the classification effort required to determine whether a clone pair should be refactored or remain untouched. The automated method allows classifying 96% of Type I clones (both algorithms and data declarations leaving the remaining 4% for the manual classification. The results show that cloning is common in the studied commercial software, but that only 1% of these clones are potentially obstructive and can jeopardize the quality of the product if left unmanaged.

  2. Application of a 5-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants in the InSiGHT locus-specific database

    DEFF Research Database (Denmark)

    Thompson, Bryony A; Spurdle, Amanda B; Plazzer, John-Paul;

    2014-01-01

    and apply a standardized classification scheme to constitutional variants in the Lynch syndrome-associated genes MLH1, MSH2, MSH6 and PMS2. Unpublished data submission was encouraged to assist in variant classification and was recognized through microattribution. The scheme was refined by multidisciplinary...

  3. An Encoder/Decoder Scheme of OCDMA Based on Waveguide

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    A new encoder/decoder scheme of OCDMA based on waveguide isproposed in this paper. The principle as well as the structure of waveguide encoder/decoder is given. It can be seen that all-optical OCDMA encoder/decoder can be realized by the proposed scheme of the waveguide encoder/decoder. It can also make the OCDMA encoder/decoder integrated easily and the access controlled easily. The system based on this scheme can work under the entirely asynchronous condition.

  4. Improved ID-Based Signature Scheme Solving Key Escrow

    Institute of Scientific and Technical Information of China (English)

    LIAO Jian; QI Ying-hao; HUANG Pei-wei; RONG Men-tian; LI Sheng-hong

    2006-01-01

    Key escrow is an inherent disadvantage for traditional ID-based cryptosystem, i.e. , the dishonest private key generator (PKG) can forge the signature of any user, meanwhile, the user can deny the signature actually signed by him/herself. To avoid the keyescrow problem, an ID-based signature scheme was presented without trusted PKG. The exact proof of security was presented to demonstrate that our scheme is secure against existential forgery on adaptively chosen message and ID attacks assuming the complexity of computational Diffie-Hellman(CDH) problem. Compared with other signature schemes, the proposed scheme is more efficient.

  5. Malware Classification based on Call Graph Clustering

    OpenAIRE

    Kinable, Joris; Kostakis, Orestis

    2010-01-01

    Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations...

  6. An Agent Based Classification Model

    CERN Document Server

    Gu, Feng; Greensmith, Julie

    2009-01-01

    The major function of this model is to access the UCI Wisconsin Breast Can- cer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classifi cation can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artifi cial Immune Sys- tems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to prob- lem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifi cally for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based mod- elling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environ- ...

  7. Image-based Vehicle Classification System

    CERN Document Server

    Ng, Jun Yee

    2012-01-01

    Electronic toll collection (ETC) system has been a common trend used for toll collection on toll road nowadays. The implementation of electronic toll collection allows vehicles to travel at low or full speed during the toll payment, which help to avoid the traffic delay at toll road. One of the major components of an electronic toll collection is the automatic vehicle detection and classification (AVDC) system which is important to classify the vehicle so that the toll is charged according to the vehicle classes. Vision-based vehicle classification system is one type of vehicle classification system which adopt camera as the input sensing device for the system. This type of system has advantage over the rest for it is cost efficient as low cost camera is used. The implementation of vision-based vehicle classification system requires lower initial investment cost and very suitable for the toll collection trend migration in Malaysia from single ETC system to full-scale multi-lane free flow (MLFF). This project ...

  8. Improved Identification Schemes Based on Error-Correcting Codes

    OpenAIRE

    Véron, Pascal

    1997-01-01

    As it is often the case in public-key cryptography, the first practical identification schemes were based on hard problems from number theory (factoring, discrete logarithms). The security of the proposed scheme depends on an NP- complete problem from the theory of error correcting codes: the syndrome decoding problem which relies on the hardness of decoding a binary word of given weight and given syndrome. Starting from Stern's scheme [18], we define a dual version which, unlike the other sc...

  9. An Image Fusion Algorithm Based on Lifting Scheme

    Institute of Scientific and Technical Information of China (English)

    BAI Di; FAN Qibin; SHU Qian

    2005-01-01

    Taking the advantage of the lifting scheme's characters that can build wavelet transforms for transforming from integer to integer and the quality ofthe reconstructing imageis independent of the topology way adopted by the boundary, an image fusion algorithm based on lifting scheme is proposed. This paper discusses the fundamental theory of lifting scheme firstly and then after taking transform analysis according to a kind of images that need to be confused.

  10. Cost Based Droop Schemes for Economic Dispatch in Islanded Microgrids

    DEFF Research Database (Denmark)

    Chen, Feixiong; Chen, Minyou; Li, Qiang;

    2016-01-01

    In this paper, cost based droop schemes are proposed, to minimize the total active power generation cost in an islanded microgrid (MG), while the simplicity and decentralized nature of the droop control are retained. In cost based droop schemes, the incremental costs of distributed generators (DGs......) are embedded into the droop schemes, where the incremental cost is a derivative of the DG cost function with respect to output power. In the steady state, DGs share a single common frequency, and cost based droop schemes equate incremental costs of DGs, thus minimizing the total active power generation cost......, in terms of the equal incremental cost principle. Finally, simulation results in an islanded MG with high penetration of intermittent renewable energy sources are presented, to demonstrate the eectiveness, as well as plug and play capability of the cost based droop schemes....

  11. The "chessboard" classification scheme of mineral deposits: Mineralogy and geology from aluminum to zirconium

    Science.gov (United States)

    Dill, Harald G.

    2010-06-01

    Economic geology is a mixtum compositum of all geoscientific disciplines focused on one goal, finding new mineral depsosits and enhancing their exploitation. The keystones of this mixtum compositum are geology and mineralogy whose studies are centered around the emplacement of the ore body and the development of its minerals and rocks. In the present study, mineralogy and geology act as x- and y-coordinates of a classification chart of mineral resources called the "chessboard" (or "spreadsheet") classification scheme. Magmatic and sedimentary lithologies together with tectonic structures (1 -D/pipes, 2 -D/veins) are plotted along the x-axis in the header of the spreadsheet diagram representing the columns in this chart diagram. 63 commodity groups, encompassing minerals and elements are plotted along the y-axis, forming the lines of the spreadsheet. These commodities are subjected to a tripartite subdivision into ore minerals, industrial minerals/rocks and gemstones/ornamental stones. Further information on the various types of mineral deposits, as to the major ore and gangue minerals, the current models and the mode of formation or when and in which geodynamic setting these deposits mainly formed throughout the geological past may be obtained from the text by simply using the code of each deposit in the chart. This code can be created by combining the commodity (lines) shown by numbers plus lower caps with the host rocks or structure (columns) given by capital letters. Each commodity has a small preface on the mineralogy and chemistry and ends up with an outlook into its final use and the supply situation of the raw material on a global basis, which may be updated by the user through a direct link to databases available on the internet. In this case the study has been linked to the commodity database of the US Geological Survey. The internal subdivision of each commodity section corresponds to the common host rock lithologies (magmatic, sedimentary, and

  12. A novel chaotic encryption scheme based on pseudorandom bit padding

    CERN Document Server

    Sadra, Yaser; Fard, Zahra Arasteh

    2012-01-01

    Cryptography is always very important in data origin authentications, entity authentication, data integrity and confidentiality. In recent years, a variety of chaotic cryptographic schemes have been proposed. These schemes has typical structure which performed the permutation and the diffusion stages, alternatively. The random number generators are intransitive in cryptographic schemes and be used in the diffusion functions of the image encryption for diffused pixels of plain image. In this paper, we propose a chaotic encryption scheme based on pseudorandom bit padding that the bits be generated by a novel logistic pseudorandom image algorithm. To evaluate the security of the cipher image of this scheme, the key space analysis, the correlation of two adjacent pixels and differential attack were performed. This scheme tries to improve the problem of failure of encryption such as small key space and level of security.

  13. A novel chaotic encryption scheme based on pseudorandom bit padding

    Directory of Open Access Journals (Sweden)

    Sodeif Ahadpour

    2012-01-01

    Full Text Available Cryptography is always very important in data origin authentications, entity authentication, data integrity and confidentiality. In recent years, a variety of chaotic cryptographic schemes have been proposed. These schemes has typical structure which performed the permutation and the diffusion stages, alternatively. The random number generators are intransitive in cryptographic schemes and be used in the diffusion functions of the image encryption for diffused pixels of plain image. In this paper, we propose a chaotic encryption scheme based on pseudorandom bit padding that the bits be generated by a novel logistic pseudorandom image algorithm. To evaluate the security of the cipher image of this scheme, the key space analysis, the correlation of two adjacent pixels and differential attack were performed. This scheme tries to improve the problem of failure of encryption such as small key space and level of security.

  14. Movie Review Classification and Feature based Summarization of Movie Reviews

    Directory of Open Access Journals (Sweden)

    Sabeeha Mohammed Basheer#1, Syed Farook

    2013-07-01

    Full Text Available Sentiment classification and feature based summarization are essential steps involved with the classification and summarization of movie reviews. The movie review classification is based on sentiment classification and condensed descriptions of movie reviews are generated from the feature based summarization. Experiments are conducted to identify the best machine learning based sentiment classification approach. Latent Semantic Analysis and Latent Dirichlet Allocation were compared to identify features which in turn affects the summary size. The focus of the system design is on classification accuracy and system response time.

  15. Optimized entanglement purification schemes for modular based quantum computers

    Science.gov (United States)

    Krastanov, Stefan; Jiang, Liang

    The choice of entanglement purification scheme strongly depends on the fidelities of quantum gates and measurements, as well as the imperfection of initial entanglement. For instance, the purification scheme optimal at low gate fidelities may not necessarily be the optimal scheme at higher gate fidelities. We employ an evolutionary algorithm that efficiently optimizes the entanglement purification circuit for given system parameters. Such optimized purification schemes will boost the performance of entanglement purification, and consequently enhance the fidelity of teleportation-based non-local coupling gates, which is an indispensible building block for modular-based quantum computers. In addition, we study how these optimized purification schemes affect the resource overhead caused by error correction in modular based quantum computers.

  16. Mechanism-based drug exposure classification in pharmacoepidemiological studies

    NARCIS (Netherlands)

    Verdel, B.M.

    2010-01-01

    Mechanism-based classification of drug exposure in pharmacoepidemiological studies In pharmacoepidemiology and pharmacovigilance, the relation between drug exposure and clinical outcomes is crucial. Exposure classification in pharmacoepidemiological studies is traditionally based on pharmacotherapeu

  17. Decision of Multimodal Transportation Scheme Based on Swarm Intelligence

    OpenAIRE

    Kai Lei; Xiaoning Zhu; Jianfei Hou; Wencheng Huang

    2014-01-01

    In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detai...

  18. PILOT-BASED FREQUENCY OFFSET DETECTION SCHEME IN OFDM SYSTEM

    Institute of Scientific and Technical Information of China (English)

    Du Zheng; Zhu Jinkang

    2003-01-01

    The frequency offset information is extracted from local pilot amplitude characteristics, which suffer much less distortion in frequency-selective fading channels than those utilizing frequency domain correlation techniques. Simulation shows that the performance of this scheme has better performance than the existing frequency domain pilot-based frequency offset detection scheme.

  19. A FRACTAL-BASED STOCHASTIC INTERPOLATION SCHEME IN SUBSURFACE HYDROLOGY

    Science.gov (United States)

    The need for a realistic and rational method for interpolating sparse data sets is widespread. Real porosity and hydraulic conductivity data do not vary smoothly over space, so an interpolation scheme that preserves irregularity is desirable. Such a scheme based on the properties...

  20. Gemstones and geosciences in space and time. Digital maps to the "Chessboard classification scheme of mineral deposits"

    Science.gov (United States)

    Dill, Harald G.; Weber, Berthold

    2013-12-01

    The gemstones, covering the spectrum from jeweler's to showcase quality, have been presented in a tripartite subdivision, by country, geology and geomorphology realized in 99 digital maps with more than 2600 mineralized sites. The various maps were designed based on the "Chessboard classification scheme of mineral deposits" proposed by Dill (2010a, 2010b) to reveal the interrelations between gemstone deposits and mineral deposits of other commodities and direct our thoughts to potential new target areas for exploration. A number of 33 categories were used for these digital maps: chromium, nickel, titanium, iron, manganese, copper, tin-tungsten, beryllium, lithium, zinc, calcium, boron, fluorine, strontium, phosphorus, zirconium, silica, feldspar, feldspathoids, zeolite, amphibole (tiger's eye), olivine, pyroxenoid, garnet, epidote, sillimanite-andalusite, corundum-spinel - diaspore, diamond, vermiculite-pagodite, prehnite, sepiolite, jet, and amber. Besides the political base map (gems by country) the mineral deposit is drawn on a geological map, illustrating the main lithologies, stratigraphic units and tectonic structure to unravel the evolution of primary gemstone deposits in time and space. The geomorphological map is to show the control of climate and subaerial and submarine hydrography on the deposition of secondary gemstone deposits. The digital maps are designed so as to be plotted as a paper version of different scale and to upgrade them for an interactive use and link them to gemological databases.

  1. Module-Based Breast Cancer Classification

    OpenAIRE

    Zhang, Yuji; Xuan, Jianhua; Clarke, Robert; Ressom, Habtom W

    2013-01-01

    The reliability and reproducibility of gene biomarkers for classification of cancer patients has been challenged due to measurement noise and biological heterogeneity among patients. In this paper, we propose a novel module-based feature selection framework, which integrates biological network information and gene expression data to identify biomarkers not as individual genes but as functional modules. Results from four breast cancer studies demonstrate that the identified module biomarkers i...

  2. Contextual Deep CNN Based Hyperspectral Classification

    OpenAIRE

    Lee, Hyungtae; Kwon, Heesung

    2016-01-01

    In this paper, we describe a novel deep convolutional neural networks (CNN) based approach called contextual deep CNN that can jointly exploit spatial and spectral features for hyperspectral image classification. The contextual deep CNN first concurrently applies multiple 3-dimensional local convolutional filters with different sizes jointly exploiting spatial and spectral features of a hyperspectral image. The initial spatial and spectral feature maps obtained from applying the variable size...

  3. An ID-based Blind Signature Scheme from Bilinear Pairings

    Directory of Open Access Journals (Sweden)

    B.Umaprasada Rao

    2010-03-01

    Full Text Available Blind signatures, introduced by Chaum, allow a user to obtain a signature on a message without revealing any thing about the message to the signer. Blind signatures play on important role in plenty of applications such as e-voting, e-cash system where anonymity is of great concern. Identity based(ID-based public key cryptography can be a good alternative for certified based public key setting, especially when efficient key management and moderate security are required. In this paper, we propose an ID-based blind signature scheme from bilinear pairings. The proposed scheme is based on the Hess ID- based digital signature scheme. Also we analyze security and efficiency of the proposed scheme.

  4. A Neuro-Fuzzy based System for Classification of Natural Textures

    Science.gov (United States)

    Jiji, G. Wiselin

    2016-06-01

    A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. In this paper, two issues are being addressed; one is the extraction of texture features from the fuzzy texture spectrum in the chromatic and achromatic domains from each colour component histogram of natural texture images and the second issue is the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification, when compared with other works.

  5. An Identity-Based Encryption Scheme with Compact Ciphertexts

    Institute of Scientific and Technical Information of China (English)

    LIU Sheng-li; GUO Bao-an; ZHANG Qing-sheng

    2009-01-01

    This paper proposes an identity-based encryption scheme with the help of bilinear pairings, where the identity information of a user functions as the user's public key. The advantage of an identity-based public key system is that it can avoid public key certificates and certificate management. Our identity-based encryption scheme enjoys short ciphertexts and provable security against chosen-ciphertext attack (CCA).

  6. A New Proxy Blind Signature Scheme based on ECDLP

    Directory of Open Access Journals (Sweden)

    Daniyal M Alghazzawi

    2011-05-01

    Full Text Available A proxy blind signature scheme is a special form of blind signature which allows a designated person called proxy signer to sign on behalf of two or more original signers without knowing the content of the message or document. It combines the advantages of proxy signature, blind signature and multi-signature scheme and satisfies the security properties of both proxy and blind signature scheme. Most of the exiting proxy blind signature schemes were developed based on the mathematical hard problems integer factorization (IFP and simple discrete logarithm (DLP which take sub-exponential time to solve. This paper describes an secure simple proxy blind signature scheme based on Elliptic Curve Discrete Logarithm Problem (ECDLP takes fully-exponential time. This can be implemented in low power and small processor mobile devices such as smart card, PDA etc. Here also we describes implementation issues of various scalar multiplication for ECDLP.

  7. Ship Classification with High Resolution TerraSAR-X Imagery Based on Analytic Hierarchy Process

    Directory of Open Access Journals (Sweden)

    Zhi Zhao

    2013-01-01

    Full Text Available Ship surveillance using space-borne synthetic aperture radar (SAR, taking advantages of high resolution over wide swaths and all-weather working capability, has attracted worldwide attention. Recent activity in this field has concentrated mainly on the study of ship detection, but the classification is largely still open. In this paper, we propose a novel ship classification scheme based on analytic hierarchy process (AHP in order to achieve better performance. The main idea is to apply AHP on both feature selection and classification decision. On one hand, the AHP based feature selection constructs a selection decision problem based on several feature evaluation measures (e.g., discriminability, stability, and information measure and provides objective criteria to make comprehensive decisions for their combinations quantitatively. On the other hand, we take the selected feature sets as the input of KNN classifiers and fuse the multiple classification results based on AHP, in which the feature sets’ confidence is taken into account when the AHP based classification decision is made. We analyze the proposed classification scheme and demonstrate its results on a ship dataset that comes from TerraSAR-X SAR images.

  8. Collaborative Representation based Classification for Face Recognition

    CERN Document Server

    Zhang, Lei; Feng, Xiangchu; Ma, Yi; Zhang, David

    2012-01-01

    By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored. In this paper we discuss how SRC works, and show that the collaborative representation mechanism used in SRC is much more crucial to its success of face classification. The SRC is a special case of collaborative representation based classification (CRC), which has various instantiations by applying different norms to the coding residual and coding coefficient. More specifically, the l1 or l2 norm characterization of coding residual is related to the robustness of CRC to outlier facial pixels, while the l1 or l2 norm c...

  9. Texture feature based liver lesion classification

    Science.gov (United States)

    Doron, Yeela; Mayer-Wolf, Nitzan; Diamant, Idit; Greenspan, Hayit

    2014-03-01

    Liver lesion classification is a difficult clinical task. Computerized analysis can support clinical workflow by enabling more objective and reproducible evaluation. In this paper, we evaluate the contribution of several types of texture features for a computer-aided diagnostic (CAD) system which automatically classifies liver lesions from CT images. Based on the assumption that liver lesions of various classes differ in their texture characteristics, a variety of texture features were examined as lesion descriptors. Although texture features are often used for this task, there is currently a lack of detailed research focusing on the comparison across different texture features, or their combinations, on a given dataset. In this work we investigated the performance of Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), Gabor, gray level intensity values and Gabor-based LBP (GLBP), where the features are obtained from a given lesion`s region of interest (ROI). For the classification module, SVM and KNN classifiers were examined. Using a single type of texture feature, best result of 91% accuracy, was obtained with Gabor filtering and SVM classification. Combination of Gabor, LBP and Intensity features improved the results to a final accuracy of 97%.

  10. A Color image encryption scheme based on Generalized Synchronization Theorem

    Directory of Open Access Journals (Sweden)

    Han shuangshuang

    2013-07-01

    Full Text Available Base on a generalized synchronization theorem (GCS for discrete chaotic system, this paper introduces a new 6-dimensional generalized chaos synchronization system based on 3D-Lorenz map. Numerical simulation showed that two pair variables of the synchronization system achieve generalized synchronization via a transform H.Combining with the 2-Dimension non equilateral Arnold transformation, a color image encryption scheme was designed. Analyzing the key sensitivity, key space, histogram, information entropy and correlation of adjacent pixels, it showed that the scheme have sound encryption and decryption effects. Numerical simulations reveal that the scheme is effective in commercial network communication for its strong anti-interference ability.

  11. Triangle based TVD schemes for hyperbolic conservation laws

    Science.gov (United States)

    Durlofsky, Louis J.; Osher, Stanley; Engquist, Bjorn

    1990-01-01

    A triangle based total variation diminishing (TVD) scheme for the numerical approximation of hyperbolic conservation laws in two space dimensions is constructed. The novelty of the scheme lies in the nature of the preprocessing of the cell averaged data, which is accomplished via a nearest neighbor linear interpolation followed by a slope limiting procedures. Two such limiting procedures are suggested. The resulting method is considerably more simple than other triangle based non-oscillatory approximations which, like this scheme, approximate the flux up to second order accuracy. Numerical results for linear advection and Burgers' equation are presented.

  12. Ladar-based terrain cover classification

    Science.gov (United States)

    Macedo, Jose; Manduchi, Roberto; Matthies, Larry H.

    2001-09-01

    An autonomous vehicle driving in a densely vegetated environment needs to be able to discriminate between obstacles (such as rocks) and penetrable vegetation (such as tall grass). We propose a technique for terrain cover classification based on the statistical analysis of the range data produced by a single-axis laser rangefinder (ladar). We first present theoretical models for the range distribution in the presence of homogeneously distributed grass and of obstacles partially occluded by grass. We then validate our results with real-world cases, and propose a simple algorithm to robustly discriminate between vegetation and obstacles based on the local statistical analysis of the range data.

  13. A threshold key escrow scheme based on public key cryptosystem

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    In key escrow field it is important to solve the problem thatuser's secret key completely depends on the trusted escrow agency. In 1995, some methods of solving the problem were presented. But these methods are no better than that of directly using threshold cryptography. In this paper, we present a common pattern of threshold key escrow scheme based on public key cryptosystem, and a detailed design based on the improved RSA algorithm is given. The above problem is solved by this scheme.

  14. Image Integrity Authentication Scheme Based On Fixed Point Theory

    OpenAIRE

    Li, Xu; Sun, Xingming; Liu, Quansheng

    2013-01-01

    Based on fixed point theory, this paper proposes a new scheme for image integrity authentication, which is different from Digital Signature and Fragile Watermarking. A realization of the new scheme is given based on Gaussian Convolution and Deconvolution (GCD) functions. For a given image, if it is invariant under a GCD function, we call it GCD fixed point image. An existence theorem of fixed points for GCD functions is proved and an iterative algorithm is presented for finding fixed points. ...

  15. A Secure Code-Based Authentication Scheme for RFID Systems

    Directory of Open Access Journals (Sweden)

    Noureddine Chikouche

    2015-08-01

    Full Text Available Two essential problems are still posed in terms of Radio Frequency Identification (RFID systems, including: security and limitation of resources. Recently, Li et al.'s proposed a mutual authentication scheme for RFID systems in 2014, it is based on Quasi Cyclic-Moderate Density Parity Check (QC-MDPC McEliece cryptosystem. This cryptosystem is designed to reducing the key sizes. In this paper, we found that this scheme does not provide untraceability and forward secrecy properties. Furthermore, we propose an improved version of this scheme to eliminate existing vulnerabilities of studied scheme. It is based on the QC-MDPC McEliece cryptosystem with padding the plaintext by a random bit-string. Our work also includes a security comparison between our improved scheme and different code-based RFID authentication schemes. We prove secrecy and mutual authentication properties by AVISPA (Automated Validation of Internet Security Protocols and Applications tools. Concerning the performance, our scheme is suitable for low-cost tags with resource limitation.

  16. Digital image-based classification of biodiesel.

    Science.gov (United States)

    Costa, Gean Bezerra; Fernandes, David Douglas Sousa; Almeida, Valber Elias; Araújo, Thomas Souto Policarpo; Melo, Jessica Priscila; Diniz, Paulo Henrique Gonçalves Dias; Véras, Germano

    2015-07-01

    This work proposes a simple, rapid, inexpensive, and non-destructive methodology based on digital images and pattern recognition techniques for classification of biodiesel according to oil type (cottonseed, sunflower, corn, or soybean). For this, differing color histograms in RGB (extracted from digital images), HSI, Grayscale channels, and their combinations were used as analytical information, which was then statistically evaluated using Soft Independent Modeling by Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and variable selection using the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). Despite good performances by the SIMCA and PLS-DA classification models, SPA-LDA provided better results (up to 95% for all approaches) in terms of accuracy, sensitivity, and specificity for both the training and test sets. The variables selected Successive Projections Algorithm clearly contained the information necessary for biodiesel type classification. This is important since a product may exhibit different properties, depending on the feedstock used. Such variations directly influence the quality, and consequently the price. Moreover, intrinsic advantages such as quick analysis, requiring no reagents, and a noteworthy reduction (the avoidance of chemical characterization) of waste generation, all contribute towards the primary objective of green chemistry. PMID:25882407

  17. BROAD PHONEME CLASSIFICATION USING SIGNAL BASED FEATURES

    Directory of Open Access Journals (Sweden)

    Deekshitha G

    2014-12-01

    Full Text Available Speech is the most efficient and popular means of human communication Speech is produced as a sequence of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC features derived through short time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme classification is achieved using features derived directly from the speech at the signal level itself. Broad phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR, short time energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first three formants. Features derived from short time frames of training speech are used to train a multilayer feedforward neural network based classifier with manually marked class label as output and classification accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction which is useful for applications such as automatic speech recognition and automatic language identification.

  18. Nominated Texture Based Cervical Cancer Classification

    Directory of Open Access Journals (Sweden)

    Edwin Jayasingh Mariarputham

    2015-01-01

    Full Text Available Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern histogram, tamura features, and edge orientation histogram. Few types of support vector machine (SVM and neural network (NN classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes.

  19. Hyperspectral remote sensing image classification based on decision level fusion

    Institute of Scientific and Technical Information of China (English)

    Peijun Du; Wei Zhang; Junshi Xia

    2011-01-01

    @@ To apply decision level fusion to hyperspectral remote sensing (HRS) image classification, three decision level fusion strategies are experimented on and compared, namely, linear consensus algorithm, improved evidence theory, and the proposed support vector machine (SVM) combiner.To evaluate the effects of the input features on classification performance, four schemes are used to organize input features for member classifiers.In the experiment, by using the operational modular imaging spectrometer (OMIS) II HRS image, the decision level fusion is shown as an effective way for improving the classification accuracy of the HRS image, and the proposed SVM combiner is especially suitable for decision level fusion.The results also indicate that the optimization of input features can improve the classification performance.%To apply decision level fusion to hyperspectral remote sensing (HRS) image classification, three decision level fusion strategies are experimented on and compared, namely, linear consensus algorithm, improved evidence theory, and the proposed support vector machine (SVM) combiner. To evaluate the effects of the input features on classification performance, four schemes are used to organize input features for member classifiers. In the experiment, by using the operational modular imaging spectrometer (OMIS) Ⅱ HRS image, the decision level fusion is shown as an effective way for improving the classification accuracy of the HRS image, and the proposed SVM combiner is especially suitable for decision level fusion. The results also indicate that the optimization of input features can improve the classification performance.

  20. Physiotherapy movement based classification approaches to low back pain: comparison of subgroups through review and developer/expert survey

    Directory of Open Access Journals (Sweden)

    Karayannis Nicholas V

    2012-02-01

    Full Text Available Abstract Background Several classification schemes, each with its own philosophy and categorizing method, subgroup low back pain (LBP patients with the intent to guide treatment. Physiotherapy derived schemes usually have a movement impairment focus, but the extent to which other biological, psychological, and social factors of pain are encompassed requires exploration. Furthermore, within the prevailing 'biological' domain, the overlap of subgrouping strategies within the orthopaedic examination remains unexplored. The aim of this study was "to review and clarify through developer/expert survey, the theoretical basis and content of physical movement classification schemes, determine their relative reliability and similarities/differences, and to consider the extent of incorporation of the bio-psycho-social framework within the schemes". Methods A database search for relevant articles related to LBP and subgrouping or classification was conducted. Five dominant movement-based schemes were identified: Mechanical Diagnosis and Treatment (MDT, Treatment Based Classification (TBC, Pathoanatomic Based Classification (PBC, Movement System Impairment Classification (MSI, and O'Sullivan Classification System (OCS schemes. Data were extracted and a survey sent to the classification scheme developers/experts to clarify operational criteria, reliability, decision-making, and converging/diverging elements between schemes. Survey results were integrated into the review and approval obtained for accuracy. Results Considerable diversity exists between schemes in how movement informs subgrouping and in the consideration of broader neurosensory, cognitive, emotional, and behavioural dimensions of LBP. Despite differences in assessment philosophy, a common element lies in their objective to identify a movement pattern related to a pain reduction strategy. Two dominant movement paradigms emerge: (i loading strategies (MDT, TBC, PBC aimed at eliciting a phenomenon

  1. A MapReduce based Parallel SVM for Email Classification

    OpenAIRE

    Ke Xu; Cui Wen; Qiong Yuan; Xiangzhu He; Jun Tie

    2014-01-01

    Support Vector Machine (SVM) is a powerful classification and regression tool. Varying approaches including SVM based techniques are proposed for email classification. Automated email classification according to messages or user-specific folders and information extraction from chronologically ordered email streams have become interesting areas in text machine learning research. This paper presents a parallel SVM based on MapReduce (PSMR) algorithm for email classification. We discuss the chal...

  2. Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

    Science.gov (United States)

    Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

    2015-11-01

    The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis. PMID:26356597

  3. An Efficient Signature Scheme based on Factoring and Discrete Logarithm

    OpenAIRE

    Ciss, Abdoul Aziz; Cheikh, Ahmed Youssef Ould

    2012-01-01

    This paper proposes a new signature scheme based on two hard problems : the cube root extraction modulo a composite moduli (which is equivalent to the factorisation of the moduli, IFP) and the discrete logarithm problem(DLP). By combining these two cryptographic assumptions, we introduce an efficient and strongly secure signature scheme. We show that if an adversary can break the new scheme with an algorithm $\\mathcal{A},$ then $\\mathcal{A}$ can be used to sove both the DLP and the IFP. The k...

  4. Cost-based droop scheme for DC microgrid

    DEFF Research Database (Denmark)

    Nutkani, Inam Ullah; Wang, Peng; Loh, Poh Chiang;

    2014-01-01

    DC microgrids are gaining interest due to higher efficiencies of DC distribution compared with AC. The benefits of DC systems have been widely researched for data centers, IT facilities and residential applications. The research focus, however, has been more on system architecture and optimal...... voltage level, less on optimized operation and control of generation sources. The latter theme is perused in this paper, where cost-based droop scheme is proposed for distributed generators (DGs) in DC microgrids. Unlike traditional proportional power sharing based droop scheme, the proposed scheme...... considers the generation costs of DGs and dynamically tunes their droop gradients to produce more power from less costly DGs and vice versa. The proposed scheme is fully autonomous, simple to implement in dispatchable and non-dispatchable sources coupled with storage, support islanded and grid...

  5. Evaluation of the NS1 Rapid Test and the WHO Dengue Classification Schemes for Use as Bedside Diagnosis of Acute Dengue Fever in Adults

    OpenAIRE

    Chaterji, Shera; Allen, John Carson; Chow, Angelia; Leo, Yee-Sin; Ooi, Eng-Eong

    2011-01-01

    Because healthcare facilities in many dengue endemic countries lack laboratory support, early dengue diagnosis must rely on either clinical recognition or a bedside diagnostic test. We evaluated the sensitivity and specificity of the 1997 and 2009 World Health Organization (WHO) dengue classification schemes and the NS1 strip test in acute sera from 154 virologically confirmed dengue patients and 200 patients with other febrile illnesses. Both WHO classification schemes had high sensitivity b...

  6. Regional assessment of lake ecological states using Landsat: A classification scheme for alkaline-saline, flamingo lakes in the East African Rift Valley

    Science.gov (United States)

    Tebbs, E. J.; Remedios, J. J.; Avery, S. T.; Rowland, C. S.; Harper, D. M.

    2015-08-01

    In situ reflectance measurements and Landsat satellite imagery were combined to develop an optical classification scheme for alkaline-saline lakes in the Eastern Rift Valley. The classification allows the ecological state and consequent value, in this case to Lesser Flamingos, to be determined using Landsat satellite imagery. Lesser Flamingos depend on a network of 15 alkaline-saline lakes in East African Rift Valley, where they feed by filtering cyanobacteria and benthic diatoms from the lakes' waters. The classification developed here was based on a decision tree which used the reflectance in Landsat ETM+ bands 2-4 to assign one of six classes: low phytoplankton biomass; suspended sediment-dominated; microphytobenthos; high cyanobacterial biomass; cyanobacterial scum and bleached cyanobacterial scum. The classification accuracy was 77% when verified against in situ measurements. Classified imagery and timeseries were produced for selected lakes, which show the different ecological behaviours of these complex systems. The results have highlighted the importance to flamingos of the food resources offered by the extremely remote Lake Logipi. This study has demonstrated the potential of high spatial resolution, low spectral resolution sensors for providing ecologically valuable information at a regional scale, for alkaline-saline lakes and similar hypereutrophic inland waters.

  7. Pixel classification based color image segmentation using quaternion exponent moments.

    Science.gov (United States)

    Wang, Xiang-Yang; Wu, Zhi-Fang; Chen, Liang; Zheng, Hong-Liang; Yang, Hong-Ying

    2016-02-01

    Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we propose a pixel classification based color image segmentation using quaternion exponent moments. Firstly, the pixel-level image feature is extracted based on quaternion exponent moments (QEMs), which can capture effectively the image pixel content by considering the correlation between different color channels. Then, the pixel-level image feature is used as input of twin support vector machines (TSVM) classifier, and the TSVM model is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained TSVM model. The proposed scheme has the following advantages: (1) the effective QEMs is introduced to describe color image pixel content, which considers the correlation between different color channels, (2) the excellent TSVM classifier is utilized, which has lower computation time and higher classification accuracy. Experimental results show that our proposed method has very promising segmentation performance compared with the state-of-the-art segmentation approaches recently proposed in the literature. PMID:26618250

  8. A Unicast Retransmission Scheme Based on Network Coding

    OpenAIRE

    Manssour, Jawad; Osseiran, Afif; Ben Slimane, Slimane

    2012-01-01

    A novel scheme for data retransmission for wireless unicast communication is presented. The scheme is based on a transmitter and receiver structure and bit-level data processing using a combination of channel coding and network coding that allows retransmissions to contain the previously incorrectly received information and new information, both destined to the same receiver. Results show that, for the chosen forward error codes, up to 68.75% retransmission throughput gains are achieved compa...

  9. Markov chaotic sequences for correlation based watermarking schemes

    International Nuclear Information System (INIS)

    In this paper, statistical analysis of watermarking schemes based on correlation detection is presented. Statistical properties of watermark sequences generated by piecewise-linear Markov maps are exploited, resulting in superior watermark detection reliability. Correlation/spectral properties of such sequences are easily controllable, a fact that affects the watermarking system performance. A family of chaotic maps, namely the skew tent map family, is proposed for use in watermarking schemes

  10. LU based Beamforming schemes for MIMO systems

    OpenAIRE

    Mbaye, Moustapha; Diallo, Moussa; Mboup, Mamadou

    2016-01-01

    —We present a time-domain broadband beamforming based on a Unimodular-Upper polynomial matrix decomposition. The unimodular factor is the product of elementary J-orthogonal matrices and a lower triangular matrix with ones on the diagonal, as in the constant matrix LU decomposition. This leads to a J-Orthogonal LU polynomial matrix decomposition, as a combination of two classical matrix factorization methods: Smith canonical form and LU Gaussian elimination. The inversion of the unimodular fac...

  11. Cirrhosis classification based on texture classification of random features.

    Science.gov (United States)

    Liu, Hui; Shao, Ying; Guo, Dongmei; Zheng, Yuanjie; Zhao, Zuowei; Qiu, Tianshuang

    2014-01-01

    Accurate staging of hepatic cirrhosis is important in investigating the cause and slowing down the effects of cirrhosis. Computer-aided diagnosis (CAD) can provide doctors with an alternative second opinion and assist them to make a specific treatment with accurate cirrhosis stage. MRI has many advantages, including high resolution for soft tissue, no radiation, and multiparameters imaging modalities. So in this paper, multisequences MRIs, including T1-weighted, T2-weighted, arterial, portal venous, and equilibrium phase, are applied. However, CAD does not meet the clinical needs of cirrhosis and few researchers are concerned with it at present. Cirrhosis is characterized by the presence of widespread fibrosis and regenerative nodules in the hepatic, leading to different texture patterns of different stages. So, extracting texture feature is the primary task. Compared with typical gray level cooccurrence matrix (GLCM) features, texture classification from random features provides an effective way, and we adopt it and propose CCTCRF for triple classification (normal, early, and middle and advanced stage). CCTCRF does not need strong assumptions except the sparse character of image, contains sufficient texture information, includes concise and effective process, and makes case decision with high accuracy. Experimental results also illustrate the satisfying performance and they are also compared with typical NN with GLCM. PMID:24707317

  12. Waste-acceptance criteria and risk-based thinking for radioactive-waste classification

    International Nuclear Information System (INIS)

    The US system of radioactive-waste classification and its development provide a reference point for the discussion of risk-based thinking in waste classification. The official US system is described and waste-acceptance criteria for disposal sites are introduced because they constitute a form of de facto waste classification. Risk-based classification is explored and it is found that a truly risk-based system is context-dependent: risk depends not only on the waste-management activity but, for some activities such as disposal, it depends on the specific physical context. Some of the elements of the official US system incorporate risk-based thinking, but like many proposed alternative schemes, the physical context of disposal is ignored. The waste-acceptance criteria for disposal sites do account for this context dependence and could be used as a risk-based classification scheme for disposal. While different classes would be necessary for different management activities, the waste-acceptance criteria would obviate the need for the current system and could better match wastes to disposal environments saving money or improving safety or both

  13. Fuzzy Rule Base System for Software Classification

    Directory of Open Access Journals (Sweden)

    Adnan Shaout

    2013-07-01

    Full Text Available Given the central role that software development plays in the delivery and application of informationtechnology, managers have been focusing on process improvement in the software development area. Thisimprovement has increased the demand for software measures, or metrics to manage the process. Thismetrics provide a quantitative basis for the development and validation of models during the softwaredevelopment process. In this paper a fuzzy rule-based system will be developed to classify java applicationsusing object oriented metrics. The system will contain the following features:Automated method to extract the OO metrics from the source code,Default/base set of rules that can be easily configured via XML file so companies, developers, teamleaders,etc, can modify the set of rules according to their needs,Implementation of a framework so new metrics, fuzzy sets and fuzzy rules can be added or removeddepending on the needs of the end user,General classification of the software application and fine-grained classification of the java classesbased on OO metrics, andTwo interfaces are provided for the system: GUI and command.

  14. A Chemistry-Based Classification for Peridotite Xenoliths

    Science.gov (United States)

    Block, K. A.; Ducea, M.; Raye, U.; Stern, R. J.; Anthony, E. Y.; Lehnert, K. A.

    2007-12-01

    The development of a petrological and geochemical database for mantle xenoliths is important for interpreting EarthScope geophysical results. Interpretation of compositional characteristics of xenoliths requires a sound basis for comparing geochemical results, even when no petrographic modes are available. Peridotite xenoliths are generally classified on the basis of mineralogy (Streckeisen, 1973) derived from point-counting methods. Modal estimates, particularly on heterogeneous samples, are conducted using various methodologies and are therefore subject to large statistical error. Also, many studies simply do not report the modes. Other classifications for peridotite xenoliths based on host matrix or tectonic setting (cratonic vs. non-cratonic) are poorly defined and provide little information on where samples from transitional settings fit within a classification scheme (e.g., xenoliths from circum-cratonic locations). We present here a classification for peridotite xenoliths based on bulk rock major element chemistry, which is one of the most common types of data reported in the literature. A chemical dataset of over 1150 peridotite xenoliths is compiled from two online geochemistry databases, the EarthChem Deep Lithosphere Dataset and from GEOROC (http://www.earthchem.org), and is downloaded with the rock names reported in the original publications. Ternary plots of combinations of the SiO2- CaO-Al2O3-MgO (SCAM) components display sharp boundaries that define the dunite, harzburgite, lherzolite, or wehrlite-pyroxenite fields and provide a graphical basis for classification. In addition, for the CaO-Al2O3-MgO (CAM) diagram, a boundary between harzburgite and lherzolite at approximately 19% CaO is defined by a plot of over 160 abyssal peridotite compositions calculated from observed modes using the methods of Asimow (1999) and Baker and Beckett (1999). We anticipate that our SCAM classification is a first step in the development of a uniform basis for

  15. PSG-Based Classification of Sleep Phases

    OpenAIRE

    Králík, M.

    2015-01-01

    This work is focused on classification of sleep phases using artificial neural network. The unconventional approach was used for calculation of classification features using polysomnographic data (PSG) of real patients. This approach allows to increase the time resolution of the analysis and, thus, to achieve more accurate results of classification.

  16. Communication scheme based on evolutionary spatial 2×2 games

    Science.gov (United States)

    Ziaukas, Pranas; Ragulskis, Tautvydas; Ragulskis, Minvydas

    2014-06-01

    A visual communication scheme based on evolutionary spatial 2×2 games is proposed in this paper. Self-organizing patterns induced by complex interactions between competing individuals are exploited for hiding and transmitting secret visual information. Properties of the proposed communication scheme are discussed in details. It is shown that the hiding capacity of the system (the minimum size of the detectable primitives and the minimum distance between two primitives) is sufficient for the effective transmission of digital dichotomous images. Also, it is demonstrated that the proposed communication scheme is resilient to time backwards, plain image attacks and is highly sensitive to perturbations of private and public keys. Several computational experiments are used to demonstrate the effectiveness of the proposed communication scheme.

  17. Kinetic regimes and limiting cases of gas uptake and heterogeneous reactions in atmospheric aerosols and clouds: a general classification scheme

    Directory of Open Access Journals (Sweden)

    T. Berkemeier

    2013-01-01

    Full Text Available Heterogeneous reactions are important to atmospheric chemistry and are therefore an area of intense research. In multiphase systems such as aerosols and clouds, chemical reactions are usually strongly coupled to a complex sequence of mass transport processes and results are often not easy to interpret.

    Here we present a systematic classification scheme for gas uptake by aerosol or cloud particles which distinguishes two major regimes: a reaction-diffusion regime and a mass-transfer regime. Each of these regimes includes four distinct limiting cases, characterized by a dominant reaction location (surface or bulk and a single rate-limiting process: chemical reaction, bulk diffusion, gas-phase diffusion or mass accommodation.

    The conceptual framework enables efficient comparison of different studies and reaction systems, going beyond the scope of previous classification schemes by explicitly resolving interfacial transport processes and surface reactions limited by mass transfer from the gas phase. The use of kinetic multi-layer models instead of resistor model approaches increases the flexibility and enables a broader treatment of the subject, including cases which do not fit into the strict limiting cases typical of most resistor model formulations. The relative importance of different kinetic parameters such as diffusion, reaction rate and accommodation coefficients in this system is evaluated by a quantitative global sensitivity analysis. We outline the characteristic features of each limiting case and discuss the potential relevance of different regimes and limiting cases for various reaction systems. In particular, the classification scheme is applied to three different data sets for the benchmark system of oleic acid reacting with ozone. In light of these results, future directions of research needed to elucidate the multiphase chemical kinetics in this and other reaction systems are discussed.

  18. Kinetic regimes and limiting cases of gas uptake and heterogeneous reactions in atmospheric aerosols and clouds: a general classification scheme

    Directory of Open Access Journals (Sweden)

    T. Berkemeier

    2013-07-01

    Full Text Available Heterogeneous reactions are important to atmospheric chemistry and are therefore an area of intense research. In multiphase systems such as aerosols and clouds, chemical reactions are usually strongly coupled to a complex sequence of mass transport processes and results are often not easy to interpret. Here we present a systematic classification scheme for gas uptake by aerosol or cloud particles which distinguishes two major regimes: a reaction-diffusion regime and a mass transfer regime. Each of these regimes includes four distinct limiting cases, characterised by a dominant reaction location (surface or bulk and a single rate-limiting process: chemical reaction, bulk diffusion, gas-phase diffusion or mass accommodation. The conceptual framework enables efficient comparison of different studies and reaction systems, going beyond the scope of previous classification schemes by explicitly resolving interfacial transport processes and surface reactions limited by mass transfer from the gas phase. The use of kinetic multi-layer models instead of resistor model approaches increases the flexibility and enables a broader treatment of the subject, including cases which do not fit into the strict limiting cases typical of most resistor model formulations. The relative importance of different kinetic parameters such as diffusion, reaction rate and accommodation coefficients in this system is evaluated by a quantitative global sensitivity analysis. We outline the characteristic features of each limiting case and discuss the potential relevance of different regimes and limiting cases for various reaction systems. In particular, the classification scheme is applied to three different datasets for the benchmark system of oleic acid reacting with ozone in order to demonstrate utility and highlight potential issues. In light of these results, future directions of research needed to elucidate the multiphase chemical kinetics in this and other reaction systems

  19. Malware Classification based on Call Graph Clustering

    CERN Document Server

    Kinable, Joris

    2010-01-01

    Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations away, and enable the detection of structural similarities between samples. The ability to cluster similar samples together will make more generic detection techniques possible, thereby targeting the commonalities of the samples within a cluster. To compare call graphs mutually, we compute pairwise graph similarity scores via graph matchings which approximately minimize the graph edit distance. Next, to facilitate the discovery of similar malware samples, we employ several clustering algorithms, including k-medoids and DB...

  20. A Nominative Multi- Proxy Signature Scheme Based on ECC

    Institute of Scientific and Technical Information of China (English)

    MA Chuan-gui; GAO Feng-xiu; WANG Yan

    2005-01-01

    A nominative multi-proxy signature in which the original signer authorizes a group of proxy signers is presented. Meanwhile, our proposed scheme is based on elliptic curve cryptosystem which is more efficient than the corresponding one based on traditional discrete logarithm.

  1. Automatic web services classification based on rough set theory

    Institute of Scientific and Technical Information of China (English)

    陈立; 张英; 宋自林; 苗壮

    2013-01-01

    With development of web services technology, the number of existing services in the internet is growing day by day. In order to achieve automatic and accurate services classification which can be beneficial for service related tasks, a rough set theory based method for services classification was proposed. First, the services descriptions were preprocessed and represented as vectors. Elicited by the discernibility matrices based attribute reduction in rough set theory and taking into account the characteristic of decision table of services classification, a method based on continuous discernibility matrices was proposed for dimensionality reduction. And finally, services classification was processed automatically. Through the experiment, the proposed method for services classification achieves approving classification result in all five testing categories. The experiment result shows that the proposed method is accurate and could be used in practical web services classification.

  2. Judgement of Design Scheme Based on Flexible Constraint in ICAD

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The conception of flexible constraint is proposed in the paper. The solution of flexible constraint is in special range, and maybe different in different instances of same design scheme. The paper emphasis on how to evaluate and optimize a design scheme with flexible constraints based on the satisfaction degree function defined on flexible constraints. The conception of flexible constraint is used to solve constraint conflict and design optimization in complicated constraint-based assembly design by the PFM parametrization assembly design system. An instance of gear-box design is used for verifying optimization method.

  3. Optical tomographic detection of rheumatoid arthritis with computer-aided classification schemes

    Science.gov (United States)

    Klose, Christian D.; Klose, Alexander D.; Netz, Uwe; Beuthan, Jürgen; Hielscher, Andreas H.

    2009-02-01

    A recent research study has shown that combining multiple parameters, drawn from optical tomographic images, leads to better classification results to identifying human finger joints that are affected or not affected by rheumatic arthritis RA. Building up on the research findings of the previous study, this article presents an advanced computer-aided classification approach for interpreting optical image data to detect RA in finger joints. Additional data are used including, for example, maximum and minimum values of the absorption coefficient as well as their ratios and image variances. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index and area under the curve AUC. Results were compared to different benchmarks ("gold standard"): magnet resonance, ultrasound and clinical evaluation. Maximum accuracies (AUC=0.88) were reached when combining minimum/maximum-ratios and image variances and using ultrasound as gold standard.

  4. A scheme for the classification of explosions in the chemical process industry

    International Nuclear Information System (INIS)

    All process industry accidents fall under three broad categories-fire, explosion, and toxic release. Of these fire is the most common, followed by explosions. Within these broad categories occur a large number of sub-categories, each depicting a specific sub-type of a fire/explosion/toxic release. But whereas clear and self-consistent sub-classifications exist for fires and toxic releases, the situation is not as clear vis a vis explosions. In this paper the inconsistencies and/or shortcomings associated with the classification of different types of explosions, which are seen even in otherwise highly authentic and useful reference books on process safety, are reviewed. In its context a new classification is attempted which may, hopefully, provide a frame-of-reference for the future.

  5. Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes.

    Science.gov (United States)

    Yates, Katherine L; Mellin, Camille; Caley, M Julian; Radford, Ben T; Meeuwig, Jessica J

    2016-01-01

    Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are

  6. Graph-based Methods for Orbit Classification

    Energy Technology Data Exchange (ETDEWEB)

    Bagherjeiran, A; Kamath, C

    2005-09-29

    An important step in the quest for low-cost fusion power is the ability to perform and analyze experiments in prototype fusion reactors. One of the tasks in the analysis of experimental data is the classification of orbits in Poincare plots. These plots are generated by the particles in a fusion reactor as they move within the toroidal device. In this paper, we describe the use of graph-based methods to extract features from orbits. These features are then used to classify the orbits into several categories. Our results show that existing machine learning algorithms are successful in classifying orbits with few points, a situation which can arise in data from experiments.

  7. A Detection Scheme for Cavity-based Dark Matter Searches

    CERN Document Server

    Bukhari, M H S

    2016-01-01

    We present here proposal of a scheme and some useful ideas for resonant cavity-based detection of cold dark matter axions with hope to improve the existing endeavors. The scheme is based upon our idea of a detector, which incorporates an integrated tunnel diode and a GaAs HEMT or HFET, High Electron Mobility Transistor or Heterogenous FET, for resonance detection and amplification from a resonant cavity (in a strong transverse magnetic field from a cylindrical array of halbach magnets). The idea of a TD-oscillator-amplifier combination could possibly serve as a more sensitive and viable resonance detection regime while maintaining an excellent performance with low noise temperature, whereas the halbach magnets array may offer a compact and permanent solution replacing the conventional electromagnets scheme. We believe that all these factors could possibly increase the sensitivity and accuracy of axion detection searches and reduce complications (and associated costs) in the experiments, in addition to help re...

  8. Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

    Science.gov (United States)

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

    2009-01-01

    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

  9. Classification of High-Rise Residential Building Facilities: A Descriptive Survey on 170 Housing Scheme in Klang Valley

    Directory of Open Access Journals (Sweden)

    Abd Wahab Siti Rashidah Hanum

    2016-01-01

    Full Text Available High-rise residential building is a type of housing that has multi-dwelling units built on the same land. This type of housing has become popular each year in urban area due to the increasing cost of land. There are several common facilities provided in high-rise residential building. For example playground, swimming pool, gymnasium, 24 hours security system such as CCTV, access card and so on. Thus, maintenance works of the common facilities must be well organised. The purpose of this paper is to identify the classification of facilities provided at high rise residential building. The survey was done on 170 high-rise residential schemes by using stratified random sampling technique. The scope of this research is within Klang Valley area. This area is rapidly developed with high-rise residential building. The objective of this survey is to list down all the facilities provided in each sample of the schemes. The result, there are nine classification of facilities provided for high-rise residential building.

  10. A MapReduce based Parallel SVM for Email Classification

    Directory of Open Access Journals (Sweden)

    Ke Xu

    2014-06-01

    Full Text Available Support Vector Machine (SVM is a powerful classification and regression tool. Varying approaches including SVM based techniques are proposed for email classification. Automated email classification according to messages or user-specific folders and information extraction from chronologically ordered email streams have become interesting areas in text machine learning research. This paper presents a parallel SVM based on MapReduce (PSMR algorithm for email classification. We discuss the challenges that arise from differences between email foldering and traditional document classification. We show experimental results from an array of automated classification methods and evaluation methodologies, including Naive Bayes, SVM and PSMR method of foldering results on the Enron datasets based on the timeline. By distributing, processing and optimizing the subsets of the training data across multiple participating nodes, the parallel SVM based on MapReduce algorithm reduces the training time significantly

  11. Classification techniques based on AI application to defect classification in cast aluminum

    Science.gov (United States)

    Platero, Carlos; Fernandez, Carlos; Campoy, Pascual; Aracil, Rafael

    1994-11-01

    This paper describes the Artificial Intelligent techniques applied to the interpretation process of images from cast aluminum surface presenting different defects. The whole process includes on-line defect detection, feature extraction and defect classification. These topics are discussed in depth through the paper. Data preprocessing process, as well as segmentation and feature extraction are described. At this point, algorithms employed along with used descriptors are shown. Syntactic filter has been developed to modelate the information and to generate the input vector to the classification system. Classification of defects is achieved by means of rule-based systems, fuzzy models and neural nets. Different classification subsystems perform together for the resolution of a pattern recognition problem (hybrid systems). Firstly, syntactic methods are used to obtain the filter that reduces the dimension of the input vector to the classification process. Rule-based classification is achieved associating a grammar to each defect type; the knowledge-base will be formed by the information derived from the syntactic filter along with the inferred rules. The fuzzy classification sub-system uses production rules with fuzzy antecedent and their consequents are ownership rates to every defect type. Different architectures of neural nets have been implemented with different results, as shown along the paper. In the higher classification level, the information given by the heterogeneous systems as well as the history of the process is supplied to an Expert System in order to drive the casting process.

  12. Auction-based schemes for multipath routing in selfish networks

    OpenAIRE

    Zhou, H; Leung, KC; Li, VOK

    2013-01-01

    We study multipath routing with traffic assignment in selfish networks. Based on the Vickrey-Clarke-Groves (VCG) auction, an optimal and strategy-proof scheme, known as optimal auction-based multipath routing (OAMR), is developed. However, OAMR is computationally expensive and cannot run in real time when the network size is large. Therefore, we propose sequential auction-based multipath routing (SAMR). SAMR handles routing requests sequentially using some greedy strategies. In particular, wi...

  13. Improved Readout Scheme for SQUID-Based Thermometry

    Science.gov (United States)

    Penanen, Konstantin

    2007-01-01

    An improved readout scheme has been proposed for high-resolution thermometers, (HRTs) based on the use of superconducting quantum interference devices (SQUIDs) to measure temperature- dependent magnetic susceptibilities. The proposed scheme would eliminate counting ambiguities that arise in the conventional scheme, while maintaining the superior magnetic-flux sensitivity of the conventional scheme. The proposed scheme is expected to be especially beneficial for HRT-based temperature control of multiplexed SQUIDbased bolometer sensor arrays. SQUID-based HRTs have become standard for measuring and controlling temperatures in the sub-nano-Kelvin temperature range in a broad range of low-temperature scientific and engineering applications. A typical SQUIDbased HRT that utilizes the conventional scheme includes a coil wound on a core made of a material that has temperature- dependent magnetic susceptibility in the temperature range of interest. The core and the coil are placed in a DC magnetic field provided either by a permanent magnet or as magnetic flux inside a superconducting outer wall. The aforementioned coil is connected to an input coil of a SQUID. Changes in temperature lead to changes in the susceptibility of the core and to changes in the magnetic flux detected by the SQUID. The SQUID readout instrumentation is capable of measuring magnetic-flux changes that correspond to temperature changes down to a noise limit .0.1 nK/Hz1/2. When the flux exceeds a few fundamental flux units, which typically corresponds to a temperature of .100 nK, the SQUID is reset. The temperature range can be greatly expanded if the reset events are carefully tracked and counted, either by a computer running appropriate software or by a dedicated piece of hardware.

  14. Small Sample Issues for Microarray-Based Classification

    OpenAIRE

    Dougherty, Edward R

    2006-01-01

    In order to study the molecular biological differences between normal and diseased tissues, it is desirable to perform classification among diseases and stages of disease using microarray-based gene-expression values. Owing to the limited number of microarrays typically used in these studies, serious issues arise with respect to the design, performance and analysis of classifiers based on microarray data. This paper reviews some fundamental issues facing small-sample classification: classific...

  15. Gender Classification Based on Geometry Features of Palm Image

    OpenAIRE

    Ming Wu; Yubo Yuan

    2014-01-01

    This paper presents a novel gender classification method based on geometry features of palm image which is simple, fast, and easy to handle. This gender classification method based on geometry features comprises two main attributes. The first one is feature extraction by image processing. The other one is classification system with polynomial smooth support vector machine (PSSVM). A total of 180 palm images were collected from 30 persons to verify the validity of the proposed gender classi...

  16. Security problems with a chaos-based deniable authentication scheme

    International Nuclear Information System (INIS)

    Recently, a new scheme was proposed for deniable authentication. Its main originality lied on applying a chaos-based encryption-hash parallel algorithm and the semi-group property of the Chebyshev chaotic map. Although original and practicable, its insecurity and inefficiency are shown in this paper, thus rendering it inadequate for adoption in e-commerce

  17. WEAKNESS ON CRYPTOGRAPHIC SCHEMES BASED ON REGULAR LDPC CODES

    Directory of Open Access Journals (Sweden)

    Omessaad Hamdi

    2010-01-01

    Full Text Available We propose a method to recover the structure of a randomly permuted chained code and how to cryptanalyse cryptographic schemes based on these kinds of error coding. As application of these methods is a cryptographic schema using regular Low Density Parity Check (LDPC Codes. This result prohibits the use of chained code and particularly regular LDPC codes on cryptography.

  18. WEAKNESS ON CRYPTOGRAPHIC SCHEMES BASED ON REGULAR LDPC CODES

    OpenAIRE

    Omessaad Hamdi; Manel abdelhedi; Ammar Bouallegue; Sami Harari

    2010-01-01

    We propose a method to recover the structure of a randomly permuted chained code and how to cryptanalyse cryptographic schemes based on these kinds of error coding. As application of these methods is a cryptographic schema using regular Low Density Parity Check (LDPC) Codes. This result prohibits the use of chained code and particularly regular LDPC codes on cryptography.

  19. Design Validations for Discrete Logarithm Based Signature Schemes

    OpenAIRE

    Brickell, Ernest; Pointcheval, David; Vaudenay, Serge

    2000-01-01

    A number of signature schemes and standards have been recently designed, based on the discrete logarithm problem. Examples of standards are the DSA and the KCDSA. Very few formal design/security validations have already been conducted for both the KCDSA and the DSA, but in the "full" so-called random oracle model. In this paper we try to minimize the use of ideal hash functions for several Discrete Logarithm (DSS-like) signatures (abstracted as generic schemes). Namely, we show that the follo...

  20. DWT-Based Watermarking Scheme for Digital Images

    Institute of Scientific and Technical Information of China (English)

    何泉; 苏广川

    2003-01-01

    A watermarking scheme for digital images is introduced. This method is based on discrete wavelet transform and spread spectrum technique. A discrete wavelet transformed binary signature image is expanded by an m-sequence and added to the large wavelet coefficients of a host image with a scale factor. Good balance between transparency and robustness is achieved by the selection of the scale factor. In addition, the spread spectrum technique is adopted to increase the robustness of this watermarking scheme. The experimental results show that the proposed method is of good performance and robustness for common image operations such as JPEG lossy compression, etc.

  1. A Fair E-Cash Payment Scheme Based on Credit

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A new fair e-cash payment scheme based on credit is present in this paper. In the scheme, an overdraft credit certificate is issued to user by bank. Using the overdraft credit certificate, user can produce e-cash himself to pay in exchanges. Merchant can verify the e-cash received from user. Bank can make a fair dispute resolution when there is a dissension between user and merchant. It can avoid the problem of partition e-cash for changes, prevent from reusing e-cash and faking e-cash. It fits justice, anonymity, non-deny and impartiality.

  2. DNA sequence analysis using hierarchical ART-based classification networks

    Energy Technology Data Exchange (ETDEWEB)

    LeBlanc, C.; Hruska, S.I. [Florida State Univ., Tallahassee, FL (United States); Katholi, C.R.; Unnasch, T.R. [Univ. of Alabama, Birmingham, AL (United States)

    1994-12-31

    Adaptive resonance theory (ART) describes a class of artificial neural network architectures that act as classification tools which self-organize, work in real-time, and require no retraining to classify novel sequences. We have adapted ART networks to provide support to scientists attempting to categorize tandem repeat DNA fragments from Onchocerca volvulus. In this approach, sequences of DNA fragments are presented to multiple ART-based networks which are linked together into two (or more) tiers; the first provides coarse sequence classification while the sub- sequent tiers refine the classifications as needed. The overall rating of the resulting classification of fragments is measured using statistical techniques based on those introduced to validate results from traditional phylogenetic analysis. Tests of the Hierarchical ART-based Classification Network, or HABclass network, indicate its value as a fast, easy-to-use classification tool which adapts to new data without retraining on previously classified data.

  3. A classification scheme of Amino Acids in the Genetic Code by Group Theory

    CERN Document Server

    Sachse, Sebastian

    2012-01-01

    We derive the amino acid assignment to one codon representation (typical 64-dimensional irreducible representation) of the basic classical Lie superalgebra osp(5|2) from biochemical arguments. We motivate the approach of mathematical symmetries to the classification of the building constituents of the biosphere by analogy of its success in particle physics and chemistry. The model enables to calculate polarity and molecular volume of amino acids to a good approximation.

  4. An Improved Biometrics-Based Remote User Authentication Scheme with User Anonymity

    OpenAIRE

    Muhammad Khurram Khan; Saru Kumari

    2013-01-01

    The authors review the biometrics-based user authentication scheme proposed by An in 2012. The authors show that there exist loopholes in the scheme which are detrimental for its security. Therefore the authors propose an improved scheme eradicating the flaws of An's scheme. Then a detailed security analysis of the proposed scheme is presented followed by its efficiency comparison. The proposed scheme not only withstands security problems found in An's scheme but also provides some extra feat...

  5. Risk-based classification system of nanomaterials

    International Nuclear Information System (INIS)

    Various stakeholders are increasingly interested in the potential toxicity and other risks associated with nanomaterials throughout the different stages of a product's life cycle (e.g., development, production, use, disposal). Risk assessment methods and tools developed and applied to chemical and biological materials may not be readily adaptable for nanomaterials because of the current uncertainty in identifying the relevant physico-chemical and biological properties that adequately describe the materials. Such uncertainty is further driven by the substantial variations in the properties of the original material due to variable manufacturing processes employed in nanomaterial production. To guide scientists and engineers in nanomaterial research and application as well as to promote the safe handling and use of these materials, we propose a decision support system for classifying nanomaterials into different risk categories. The classification system is based on a set of performance metrics that measure both the toxicity and physico-chemical characteristics of the original materials, as well as the expected environmental impacts through the product life cycle. Stochastic multicriteria acceptability analysis (SMAA-TRI), a formal decision analysis method, was used as the foundation for this task. This method allowed us to cluster various nanomaterials in different ecological risk categories based on our current knowledge of nanomaterial physico-chemical characteristics, variation in produced material, and best professional judgments. SMAA-TRI uses Monte Carlo simulations to explore all feasible values for weights, criteria measurements, and other model parameters to assess the robustness of nanomaterial grouping for risk management purposes.

  6. Classification of CMEs Based on Their Dynamics

    Science.gov (United States)

    Nicewicz, J.; Michalek, G.

    2016-05-01

    A large set of coronal mass ejections CMEs (6621) has been selected to study their dynamics seen with the Large Angle and Spectroscopic Coronagraph (LASCO) onboard the Solar and Heliospheric Observatory (SOHO) field of view (LFOV). These events were selected based on having at least six height-time measurements so that their dynamic properties, in the LFOV, can be evaluated with reasonable accuracy. Height-time measurements (in the SOHO/LASCO catalog) were used to determine the velocities and accelerations of individual CMEs at successive distances from the Sun. Linear and quadratic functions were fitted to these data points. On the basis of the best fits to the velocity data points, we were able to classify CMEs into four groups. The types of CMEs do not only have different dynamic behaviors but also different masses, widths, velocities, and accelerations. We also show that these groups of events are initiated by different onset mechanisms. The results of our study allow us to present a consistent classification of CMEs based on their dynamics.

  7. Structure-Based Algorithms for Microvessel Classification

    KAUST Repository

    Smith, Amy F.

    2015-02-01

    © 2014 The Authors. Microcirculation published by John Wiley & Sons Ltd. Objective: Recent developments in high-resolution imaging techniques have enabled digital reconstruction of three-dimensional sections of microvascular networks down to the capillary scale. To better interpret these large data sets, our goal is to distinguish branching trees of arterioles and venules from capillaries. Methods: Two novel algorithms are presented for classifying vessels in microvascular anatomical data sets without requiring flow information. The algorithms are compared with a classification based on observed flow directions (considered the gold standard), and with an existing resistance-based method that relies only on structural data. Results: The first algorithm, developed for networks with one arteriolar and one venular tree, performs well in identifying arterioles and venules and is robust to parameter changes, but incorrectly labels a significant number of capillaries as arterioles or venules. The second algorithm, developed for networks with multiple inlets and outlets, correctly identifies more arterioles and venules, but is more sensitive to parameter changes. Conclusions: The algorithms presented here can be used to classify microvessels in large microvascular data sets lacking flow information. This provides a basis for analyzing the distinct geometrical properties and modelling the functional behavior of arterioles, capillaries, and venules.

  8. Enhanced ensemble-based 4DVar scheme for data assimilation

    OpenAIRE

    Yang, Yin; Robinson, Cordelia; Heitz, Dominique; Mémin, Etienne

    2015-01-01

    International audience Ensemble based optimal control schemes combine the components of ensemble Kalman filters and variational data assimilation (4DVar). They are trendy because they are easier to implement than 4DVar. In this paper, we evaluate a modified version of an ensemble based optimal control strategy for image data assimilation. This modified method is assessed with a Shallow Water model combined with synthetic data and original incomplete experimental depth sensor observations. ...

  9. A Color image encryption scheme based on Generalized Synchronization Theorem

    OpenAIRE

    Han shuangshuang

    2013-01-01

    Base on a generalized synchronization theorem (GCS) for discrete chaotic system, this paper introduces a new 6-dimensional generalized chaos synchronization system based on 3D-Lorenz map. Numerical simulation showed that two pair variables of the synchronization system achieve generalized synchronization via a transform H.Combining with the 2-Dimension non equilateral Arnold transformation, a color image encryption scheme was designed. Analyzing the key sensitivity, key space, histogram, info...

  10. An Extensible, Kinematically-Based Gesture Annotation Scheme

    OpenAIRE

    Martell, Craig H.

    2002-01-01

    Chapter 1 in the book: Advances in Natural Multimodal Dialogue Systems Annotated corpora have played a critical role in speech and natural language research; and, there is an increasing interest in corpora-based research in sign language and gesture as well. We present a non-semantic, geometrically-based annotation scheme, FORM, which allows an annotator to capture the kinematic information in a gesture just from videos of speakers. In addition, FORM stores this gestural in...

  11. Classification problems in object-based representation systems

    OpenAIRE

    Napoli, Amedeo

    1999-01-01

    Classification is a process that consists in two dual operations: generating a set of classes and then classifying given objects into the created classes. The class generation may be understood as a learning process and object classification as a problem-solving process. The goal of this position paper is to introduce and to make precise the notion of a classification problem in object-based representation systems, e.g. a query against a class hierarchy, to define a subsumption relation betwe...

  12. Fuzzy Inference System & Fuzzy Cognitive Maps based Classification

    OpenAIRE

    Kanika Bhutani; Gaurav; Megha Kumar

    2015-01-01

    Fuzzy classification is very necessary because it has the ability to use interpretable rules. It has got control over the limitations of crisp rule based classifiers. This paper mainly deals with classification on the basis of soft computing techniques fuzzy cognitive maps and fuzzy inference system on the lenses dataset. The results obtained with FIS shows 100% accuracy. Sometimes the data available for classification contain missing or ambiguous data so Neutrosophic logic is used for cla...

  13. A new classification algorithm based on RGH-tree search

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In this paper, we put forward a new classification algorithm based on RGH-Tree search and perform the classification analysis and comparison study. This algorithm can save computing resource and increase the classification efficiency. The experiment shows that this algorithm can get better effect in dealing with three dimensional multi-kind data. We find that the algorithm has better generalization ability for small training set and big testing result.

  14. Development of a classification scheme for disease-related enzyme information

    Directory of Open Access Journals (Sweden)

    Söhngen Carola

    2011-08-01

    Full Text Available Abstract Background BRENDA (BRaunschweig ENzyme DAtabase, http://www.brenda-enzymes.org is a major resource for enzyme related information. First and foremost, it provides data which are manually curated from the primary literature. DRENDA (Disease RElated ENzyme information DAtabase complements BRENDA with a focus on the automatic search and categorization of enzyme and disease related information from title and abstracts of primary publications. In a two-step procedure DRENDA makes use of text mining and machine learning methods. Results Currently enzyme and disease related references are biannually updated as part of the standard BRENDA update. 910,897 relations of EC-numbers and diseases were extracted from titles or abstracts and are included in the second release in 2010. The enzyme and disease entity recognition has been successfully enhanced by a further relation classification via machine learning. The classification step has been evaluated by a 5-fold cross validation and achieves an F1 score between 0.802 ± 0.032 and 0.738 ± 0.033 depending on the categories and pre-processing procedures. In the eventual DRENDA content every category reaches a classification specificity of at least 96.7% and a precision that ranges from 86-98% in the highest confidence level, and 64-83% for the smallest confidence level associated with higher recall. Conclusions The DRENDA processing chain analyses PubMed, locates references with disease-related information on enzymes and categorises their focus according to the categories causal interaction, therapeutic application, diagnostic usage and ongoing research. The categorisation gives an impression on the focus of the located references. Thus, the relation categorisation can facilitate orientation within the rapidly growing number of references with impact on diseases and enzymes. The DRENDA information is available as additional information in BRENDA.

  15. On Cryptographic Schemes Based on Discrete Logarithms and Factoring

    Science.gov (United States)

    Joye, Marc

    At CRYPTO 2003, Rubin and Silverberg introduced the concept of torus-based cryptography over a finite field. We extend their setting to the ring of integers modulo N. We so obtain compact representations for cryptographic systems that base their security on the discrete logarithm problem and the factoring problem. This results in smaller key sizes and substantial savings in memory and bandwidth. But unlike the case of finite fields, analogous trace-based compression methods cannot be adapted to accommodate our extended setting when the underlying systems require more than a mere exponentiation. As an application, we present an improved, torus-based implementation of the ACJT group signature scheme.

  16. Fuzzy classification rules based on similarity

    Czech Academy of Sciences Publication Activity Database

    Holeňa, Martin; Štefka, D.

    Seňa : PONT s.r.o., 2012 - (Horváth, T.), s. 25-31 ISBN 978-80-971144-0-4. [ITAT 2012. Conference on Theory and Practice of Information Technologies. Ždiar (SK), 17.09.2012-21.09.2012] R&D Projects: GA ČR GA201/08/0802 Institutional support: RVO:67985807 Keywords : classification rules * fuzzy classification * fuzzy integral * fuzzy measure * similarity Subject RIV: IN - Informatics, Computer Science

  17. Classification of types of stuttering symptoms based on brain activity.

    Directory of Open Access Journals (Sweden)

    Jing Jiang

    Full Text Available Among the non-fluencies seen in speech, some are more typical (MT of stuttering speakers, whereas others are less typical (LT and are common to both stuttering and fluent speakers. No neuroimaging work has evaluated the neural basis for grouping these symptom types. Another long-debated issue is which type (LT, MT whole-word repetitions (WWR should be placed in. In this study, a sentence completion task was performed by twenty stuttering patients who were scanned using an event-related design. This task elicited stuttering in these patients. Each stuttered trial from each patient was sorted into the MT or LT types with WWR put aside. Pattern classification was employed to train a patient-specific single trial model to automatically classify each trial as MT or LT using the corresponding fMRI data. This model was then validated by using test data that were independent of the training data. In a subsequent analysis, the classification model, just established, was used to determine which type the WWR should be placed in. The results showed that the LT and the MT could be separated with high accuracy based on their brain activity. The brain regions that made most contribution to the separation of the types were: the left inferior frontal cortex and bilateral precuneus, both of which showed higher activity in the MT than in the LT; and the left putamen and right cerebellum which showed the opposite activity pattern. The results also showed that the brain activity for WWR was more similar to that of the LT and fluent speech than to that of the MT. These findings provide a neurological basis for separating the MT and the LT types, and support the widely-used MT/LT symptom grouping scheme. In addition, WWR play a similar role as the LT, and thus should be placed in the LT type.

  18. Enhancing Community Detection By Affinity-based Edge Weighting Scheme

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Andy [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanders, Geoffrey [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Henson, Van [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Vassilevski, Panayot [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-10-05

    Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is ideal for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.

  19. Security of a biometric identity-based encryption scheme

    CERN Document Server

    Tian, Miaomiao; Huang, Liusheng

    2011-01-01

    Biometric identity-based encryption (Bio-IBE) is a kind of fuzzy identity-based encryption (fuzzy IBE) where a ciphertext encrypted under an identity w' can be decrypted using a secret key corresponding to the identity w which is close to w' as measured by some metric. Recently, Yang et al. proposed a constant-size Bio-IBE scheme and proved that it is secure against adaptive chosen-ciphertext attack (CCA2) in the random oracle model. Unfortunately, in this paper, we will show that their Bio-IBE scheme is even not chosen-plaintext secure. Specifically, user w using his secret key is able to decrypt any ciphertext encrypted under an identity w' even though w is not close to w'.

  20. A Color Image Digital Watermarking Scheme Based on SOFM

    Directory of Open Access Journals (Sweden)

    J. Anitha

    2010-09-01

    Full Text Available Digital watermarking technique has been presented and widely researched to solve some important issues in the digital world, such as copyright protection, copy protection and content authentication. Several robust watermarking schemes based on vector quantization (VQ have been presented. In this paper, we present a new digital image watermarking method based on SOFM vector quantizer for color images. This method utilizes the codebook partition technique in which the watermark bit is embedded into the selected VQ encoded block. The main feature of this scheme is that the watermark exists both in VQ compressed image and in the reconstructed image. The watermark extraction can be performed without the original image. The watermark is hidden inside the compressed image, so much transmission time and storage space can be saved when the compressed data are transmitted over the Internet. Simulation results demonstrate that the proposed method has robustness against various image processing operations without sacrificing compression performance and the computational speed.

  1. Preliminary Research on Grassland Fine-classification Based on MODIS

    International Nuclear Information System (INIS)

    Grassland ecosystem is important for climatic regulation, maintaining the soil and water. Research on the grassland monitoring method could provide effective reference for grassland resource investigation. In this study, we used the vegetation index method for grassland classification. There are several types of climate in China. Therefore, we need to use China's Main Climate Zone Maps and divide the study region into four climate zones. Based on grassland classification system of the first nation-wide grass resource survey in China, we established a new grassland classification system which is only suitable for this research. We used MODIS images as the basic data resources, and use the expert classifier method to perform grassland classification. Based on the 1:1,000,000 Grassland Resource Map of China, we obtained the basic distribution of all the grassland types and selected 20 samples evenly distributed in each type, then used NDVI/EVI product to summarize different spectral features of different grassland types. Finally, we introduced other classification auxiliary data, such as elevation, accumulate temperature (AT), humidity index (HI) and rainfall. China's nation-wide grassland classification map is resulted by merging the grassland in different climate zone. The overall classification accuracy is 60.4%. The result indicated that expert classifier is proper for national wide grassland classification, but the classification accuracy need to be improved

  2. Functions and Design Scheme of Tibet High Altitude Test Base

    Institute of Scientific and Technical Information of China (English)

    Yu Yongqing; Guo Jian; Yin Yu; Mao Yan; Li Guangfan; Fan Jianbin; Lu Jiayu; Su Zhiyi; Li Peng; Li Qingfeng; Liao Weiming; Zhou Jun

    2010-01-01

    @@ The functional orientation of the Tibet High Altitude Test Base, subordinated to the State Grid Corporation of China (SGCC), is to serve power transmission projects in high altitude areas, especially to provide technical support for southwestern hydropower delivery projects by UHVDC transmission and Qinghai-Tibet grid interconnection project. This paper presents the matters concerned during siting and planning, functions,design scheme, the main performances and parameters of the test facilities, as well as the tests and research tasks already carried out.

  3. An Indexing Scheme for Case-Based Manufacturing Vision Development

    DEFF Research Database (Denmark)

    Wang, Chengbo; Johansen, John; Luxhøj, James T.

    2004-01-01

    This paper focuses on one critical element, indexing – retaining and representing knowledge in an applied case-based reasoning (CBR) model for supporting strategic manufacturing vision development (CBRM). Manufacturing vision (MV) is a kind of knowledge management concept and process concerned wi...... summarize the methods, primary conclusions of test runs with the indexing scheme. Further research work to refine the index vocabulary is discussed as well....

  4. Four-Party Quantum Broadcast Scheme Based on Aharonov State

    International Nuclear Information System (INIS)

    In this paper, we propose a solution based on four-qubit Aharonov state to an old problem by using the property of congruence. The proposed scheme may realize the broadcast among four participants, therefore, it makes progress to the three-party broadcast realized previously. Using pairwise quantum channels and entangled qubits, the detection between these players also can be accomplished. Finally, the feasibility of the protocol and the analysis of security are illustrated

  5. Adaptive Mesh Redistibution Method Based on Godunov's Scheme

    OpenAIRE

    Azarenok, Boris N.; Ivanenko, Sergey A.; Tang, Tao

    2003-01-01

    In this work, a detailed description for an efficent adaptive mesh redistribution algorithm based on the Godunov's scheme is presented. After each mesh iteration a second-order finite-volume flow solver is used to update the flow parameters at the new time level directly without using interpolation. Numerical experiments are perfomed to demonstrate the efficency and robustness of the proposed adaptive mesh algorithm in one and two dimensions.

  6. Functions and Design Scheme of Tibet High Altitude Test Base

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The functional orientation of the Tibet High Altitude Test Base, subordinated to the State Grid Corporation of China (SGCC), is to serve power transmission projects in high altitude areas, especially to provide technical support for southwestern hydropower delivery projects by UHVDC transmission and Qinghai-Tibet grid interconnection project. This paper presents the matters concerned during siting and planning, functions, design scheme, the main performances and parameters of the test facilities, as well as...

  7. Revisiting Quantum Authentication Scheme Based on Entanglement Swapping

    Science.gov (United States)

    Naseri, Mosayeb

    2016-05-01

    The crucial issue of quantum communication protocol is its security. In this paper, the security of the Quantum Authentication Scheme Based on Entanglement Swapping proposed by Penghao et al. (Int J Theor Phys., doi: 10.1007/s10773-015-2662-7) is reanalyzed. It is shown that the original does not complete the task of quantum authentication and communication securely. Furthermore a simple improvement on the protocol is proposed.

  8. Experimental encryption multiplexing based on a JTC scheme

    OpenAIRE

    Tebaldi, Myriam C.; Vargas, Carlos; Bolognini, Néstor Alberto; Torroba, Roberto

    2010-01-01

    We present an alternative scheme to perform a multiple encrypting technique based on the use of a Joint Transform Correlator architecture. The basic approach relies on using an extra random phase mask placed before the correlator input plane, where we select different disjoint regions to encode each input object. In this way we avoid the cross talking when reconstructing the encoded objects. We experimentally validated the procedure using a photorefractive crystal as a storing medium.

  9. Problems with a probabilistic encryption scheme based on chaotic systems

    OpenAIRE

    Li, SJ; Mou, XQ; Yang, BL; Ji, Z.; Zhang, JH

    2003-01-01

    Recently S. Papadimitriou et al. have proposed a new probabilistic encryption scheme based on chaotic systems. In this letter, we point out some problems with Papadimitriou et al.'s chaotic cryptosystem: (1) the size of the ciphertext and the plaintext cannot simultaneously ensure practical implementation and high security; (2) the estimated number of all possible virtual states is wrong; (3) the practical security to exhaustive attack is overestimated; (4) the fast encryption speed is depend...

  10. Importance Sampling Based Decision Trees for Security Assessment and the Corresponding Preventive Control Schemes: the Danish Case Study

    OpenAIRE

    Liu, Leo; Rather, Zakir Hussain; Chen, Zhe; Bak, Claus Leth; Thøgersen, Paul

    2013-01-01

    Decision Trees (DT) based security assessment helps Power System Operators (PSO) by providing them with the most significant system attributes and guiding them in implementing the corresponding emergency control actions to prevent system insecurity and blackouts. DT is obtained offline from time-domain simulation and the process of data mining, which is then implemented online as guidelines for preventive control schemes. An algorithm named Classification and Regression Trees (CART) is used t...

  11. Opposition-Based Discrete PSO Using Natural Encoding for Classification Rule Discovery

    Directory of Open Access Journals (Sweden)

    Naveed Kazim Khan

    2012-11-01

    Full Text Available In this paper we present a new Discrete Particle Swarm Optimization approach to induce rules from discrete data. The proposed algorithm, called Opposition‐ based Natural Discrete PSO (ONDPSO, initializes its population by taking into account the discrete nature of the data. Particles are encoded using a Natural Encoding scheme. Each member of the population updates its position iteratively on the basis of a newly designed position update rule. Opposition‐based learning is implemented in the optimization process. The encoding scheme and position update rule used by the algorithm allows individual terms corresponding to different attributes within the rule’s antecedent to be a disjunction of the values of those attributes. The performance of the proposed algorithm is evaluated against seven different datasets using a tenfold testing scheme. The achieved median accuracy is compared against various evolutionary and non‐evolutionary classification techniques. The algorithm produces promising results by creating highly accurate and precise rules for each dataset.

  12. Classification of Product Requirements Based on Product Environment

    OpenAIRE

    Chen, Zhen Yu; Zeng, Yong

    2006-01-01

    Abstract Effective management of product requirements is critical for designers to deliver a quality design solution in a reasonable range of cost and time. The management depends on a well-defined classification and a flexible representation of product requirements. This article proposes two classification criteria in terms of different partitions of product environment based on a formal structure of produ...

  13. Transportation Mode Choice Analysis Based on Classification Methods

    OpenAIRE

    Zeņina, N; Borisovs, A

    2011-01-01

    Mode choice analysis has received the most attention among discrete choice problems in travel behavior literature. Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. This paper investigates performance of mode choice analysis with classification methods - decision trees, discriminant analysis and multinomial logit. Experimental results have demonstrated satisfactory quality of classification.

  14. A Curriculum-Based Classification System for Community Colleges.

    Science.gov (United States)

    Schuyler, Gwyer

    2003-01-01

    Proposes and tests a community college classification system based on curricular characteristics and their association with institutional characteristics. Seeks readily available data correlates to represent percentage of a college's course offerings that are in the liberal arts. A simple two-category classification system using total enrollment…

  15. An Object-Based Method for Chinese Landform Types Classification

    Science.gov (United States)

    Ding, Hu; Tao, Fei; Zhao, Wufan; Na, Jiaming; Tang, Guo'an

    2016-06-01

    Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

  16. Knowledge-Based Classification in Automated Soil Mapping

    Institute of Scientific and Technical Information of China (English)

    ZHOU BIN; WANG RENCHAO

    2003-01-01

    A machine-learning approach was developed for automated building of knowledge bases for soil resourcesmapping by using a classification tree to generate knowledge from training data. With this method, buildinga knowledge base for automated soil mapping was easier than using the conventional knowledge acquisitionapproach. The knowledge base built by classification tree was used by the knowledge classifier to perform thesoil type classification of Longyou County, Zhejiang Province, China using Landsat TM bi-temporal imagesand GIS data. To evaluate the performance of the resultant knowledge bases, the classification results werecompared to existing soil map based on a field survey. The accuracy assessment and analysis of the resultantsoil maps suggested that the knowledge bases built by the machine-learning method was of good quality formapping distribution model of soil classes over the study area.

  17. A Novel Land Cover Classification Map Based on a MODIS Time-Series in Xinjiang, China

    Directory of Open Access Journals (Sweden)

    Linlin Lu

    2014-04-01

    Full Text Available Accurate mapping of land cover on a regional scale is useful for climate and environmental modeling. In this study, we present a novel land cover classification product based on spectral and phenological information for the Xinjiang Uygur Autonomous Region (XUAR in China. The product is derived at a 500 m spatial resolution using an innovative approach employing moderate resolution imaging spectroradiometer (MODIS surface reflectance and the enhanced vegetation index (EVI time series. The classification results capture regional scale land cover patterns and small-scale phenomena. By applying a regionally specified classification scheme, an extensive collection of training data, and regionally tuned data processing, the quality and consistency of the phenological maps are significantly improved. With the ability to provide an updated land cover product considering the heterogenic environmental and climatic conditions, the novel land cover map is valuable for research related to environmental change in this region.

  18. A Novel User Authentication Scheme Based on QR-Code

    Directory of Open Access Journals (Sweden)

    Kuan-Chieh Liao

    2010-08-01

    Full Text Available User authentication is one of the fundamental procedures to ensure secure communications and share system resources over an insecure public network channel.  Thus, a simple and efficient authentication mechanism is required for securing the network system in the real environment. In general, the password-based authentication mechanism provides the basic capability to prevent unauthorized access. Especially, the purpose of the one-time password is to make it more difficult to gain unauthorized access to restricted resources. Instead of using the password file as conventional authentication systems, many researchers have devoted to implement various one-time password schemes using smart cards, time-synchronized token or short message service in order to reduce the risk of tampering and maintenance cost.  However, these schemes are impractical because of the far from ubiquitous hardware devices or the infrastructure requirements. To remedy these weaknesses, the attraction of the QR-code technique can be introduced into our one-time password authentication protocol. Not the same as before, the proposed scheme based on QR code not only eliminates the usage of the password verification table, but also is a cost effective solution since most internet users already have mobile phones. For this reason, instead of carrying around a separate hardware token for each security domain, the superiority of handiness benefit from the mobile phone makes our approach more practical and convenient.

  19. Image integrity authentication scheme based on fixed point theory.

    Science.gov (United States)

    Li, Xu; Sun, Xingming; Liu, Quansheng

    2015-02-01

    Based on the fixed point theory, this paper proposes a new scheme for image integrity authentication, which is very different from digital signature and fragile watermarking. By the new scheme, the sender transforms an original image into a fixed point image (very close to the original one) of a well-chosen transform and sends the fixed point image (instead of the original one) to the receiver; using the same transform, the receiver checks the integrity of the received image by testing whether it is a fixed point image and locates the tampered areas if the image has been modified during the transmission. A realization of the new scheme is based on Gaussian convolution and deconvolution (GCD) transform, for which an existence theorem of fixed points is proved. The semifragility is analyzed via commutativity of transforms, and three commutativity theorems are found for the GCD transform. Three iterative algorithms are presented for finding a fixed point image with a few numbers of iterations, and for the whole procedure of image integrity authentication; a fragile authentication system and a semifragile one are separately built. Experiments show that both the systems have good performance in transparence, fragility, security, and tampering localization. In particular, the semifragile system can perfectly resist the rotation by a multiple of 90° flipping and brightness attacks. PMID:25420259

  20. About the Key Escrow Properties of Identity Based Encryption Schemes

    Directory of Open Access Journals (Sweden)

    Ruxandra Olimid

    2012-09-01

    Full Text Available IBE (Identity Based Encryption represents a type of public key encryption that allows a party to encrypt a message using the recipient’s identity as public key. The private keys needed for decryption are generated and distributed to each party by a KGC (Key Generation Center. The existence of such an entity in an IBE scheme allows access to the encrypted information for other parties other than the intended recipient by construction: the KGC or any other entity that receives the cryptographic keys from the KGC may perform decryption. A system that permits other parties to have access to the private keys of the users is said to have key escrow abilities. The paper performs a brief analysis of the key escrow properties of IBE schemes and gives a practical example of communication protocol that improves the key escrow capabilities.

  1. Cell morphology-based classification of red blood cells using holographic imaging informatics.

    Science.gov (United States)

    Yi, Faliu; Moon, Inkyu; Javidi, Bahram

    2016-06-01

    We present methods that automatically select a linear or nonlinear classifier for red blood cell (RBC) classification by analyzing the equality of the covariance matrices in Gabor-filtered holographic images. First, the phase images of the RBCs are numerically reconstructed from their holograms, which are recorded using off-axis digital holographic microscopy (DHM). Second, each RBC is segmented using a marker-controlled watershed transform algorithm and the inner part of the RBC is identified and analyzed. Third, the Gabor wavelet transform is applied to the segmented cells to extract a series of features, which then undergo a multivariate statistical test to evaluate the equality of the covariance matrices of the different shapes of the RBCs using selected features. When these covariance matrices are not equal, a nonlinear classification scheme based on quadratic functions is applied; otherwise, a linear classification is applied. We used the stomatocyte, discocyte, and echinocyte RBC for classifier training and testing. Simulation results demonstrated that 10 of the 14 RBC features are useful in RBC classification. Experimental results also revealed that the covariance matrices of the three main RBC groups are not equal and that a nonlinear classification method has a much lower misclassification rate. The proposed automated RBC classification method has the potential for use in drug testing and the diagnosis of RBC-related diseases. PMID:27375953

  2. Shape classification based on singular value decomposition transform

    Institute of Scientific and Technical Information of China (English)

    SHAABAN Zyad; ARIF Thawar; BABA Sami; KREKOR Lala

    2009-01-01

    In this paper, a new shape classification system based on singular value decomposition (SVD) transform using nearest neighbour classifier was proposed. The gray scale image of the shape object was converted into a black and white image. The squared Euclidean distance transform on binary image was applied to extract the boundary image of the shape. SVD transform features were extracted from the the boundary of the object shapes. In this paper, the proposed classification system based on SVD transform feature extraction method was compared with classifier based on moment invariants using nearest neighbour classifier. The experimental results showed the advantage of our proposed classification system.

  3. Behavior Based Social Dimensions Extraction for Multi-Label Classification.

    Directory of Open Access Journals (Sweden)

    Le Li

    Full Text Available Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes' behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA to model the network generation process, nodes' connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions.

  4. Behavior Based Social Dimensions Extraction for Multi-Label Classification.

    Science.gov (United States)

    Li, Le; Xu, Junyi; Xiao, Weidong; Ge, Bin

    2016-01-01

    Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes' behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes' connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions. PMID:27049849

  5. Multiclass Classification Based on the Analytical Center of Version Space

    Institute of Scientific and Technical Information of China (English)

    ZENGFanzi; QIUZhengding; YUEJianhai; LIXiangqian

    2005-01-01

    Analytical center machine, based on the analytical center of version space, outperforms support vector machine, especially when the version space is elongated or asymmetric. While analytical center machine for binary classification is well understood, little is known about corresponding multiclass classification.Moreover, considering that the current multiclass classification method: “one versus all” needs repeatedly constructing classifiers to separate a single class from all the others, which leads to daunting computation and low efficiency of classification, and that though multiclass support vector machine corresponds to a simple quadratic optimization, it is not very effective when the version spaceis asymmetric or elongated, Thus, the multiclass classification approach based on the analytical center of version space is proposed to address the above problems. Experiments on wine recognition and glass identification dataset demonstrate validity of the approach proposed.

  6. Behavior Based Social Dimensions Extraction for Multi-Label Classification

    Science.gov (United States)

    Li, Le; Xu, Junyi; Xiao, Weidong; Ge, Bin

    2016-01-01

    Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes’ behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes’ connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions. PMID:27049849

  7. Evaluation of a 5-tier scheme proposed for classification of sequence variants using bioinformatic and splicing assay data: inter-reviewer variability and promotion of minimum reporting guidelines.

    Science.gov (United States)

    Walker, Logan C; Whiley, Phillip J; Houdayer, Claude; Hansen, Thomas V O; Vega, Ana; Santamarina, Marta; Blanco, Ana; Fachal, Laura; Southey, Melissa C; Lafferty, Alan; Colombo, Mara; De Vecchi, Giovanna; Radice, Paolo; Spurdle, Amanda B

    2013-10-01

    Splicing assays are commonly undertaken in the clinical setting to assess the clinical relevance of sequence variants in disease predisposition genes. A 5-tier classification system incorporating both bioinformatic and splicing assay information was previously proposed as a method to provide consistent clinical classification of such variants. Members of the ENIGMA Consortium Splicing Working Group undertook a study to assess the applicability of the scheme to published assay results, and the consistency of classifications across multiple reviewers. Splicing assay data were identified for 235 BRCA1 and 176 BRCA2 unique variants, from 77 publications. At least six independent reviewers from research and/or clinical settings comprehensively examined splicing assay methods and data reported for 22 variant assays of 21 variants in four publications, and classified the variants using the 5-tier classification scheme. Inconsistencies in variant classification occurred between reviewers for 17 of the variant assays. These could be attributed to a combination of ambiguity in presentation of the classification criteria, differences in interpretation of the data provided, nonstandardized reporting of results, and the lack of quantitative data for the aberrant transcripts. We propose suggestions for minimum reporting guidelines for splicing assays, and improvements to the 5-tier splicing classification system to allow future evaluation of its performance as a clinical tool. PMID:23893897

  8. Multiresolution image fusion scheme based on fuzzy region feature

    Institute of Scientific and Technical Information of China (English)

    LIU Gang; JING Zhong-liang; SUN Shao-yuan

    2006-01-01

    This paper proposes a novel region based image fusion scheme based on multiresolution analysis. The low frequency band of the image multiresolution representation is segmented into important regions, sub-important regions and background regions. Each feature of the regions is used to determine the region's degree of membership in the multiresolution representation,and then to achieve multiresolution representation of the fusion result. The final image fusion result can be obtained by using the inverse multiresolution transform. Experiments showed that the proposed image fusion method can have better performance than existing image fusion methods.

  9. Program Classification for Performance-Based Budgeting

    OpenAIRE

    Robinson, Marc

    2013-01-01

    This guide provides practical guidance on program classification, that is, on how to define programs and their constituent elements under a program budgeting system. Program budgeting is the most widespread form of performance budgeting as applied to the government budget as a whole. The defining characteristics of program budgeting are: (1) funds are allocated in the budget to results-bas...

  10. A Fuzzy Logic Based Sentiment Classification

    Directory of Open Access Journals (Sweden)

    J.I.Sheeba

    2014-07-01

    Full Text Available Sentiment classification aims to detect information such as opinions, explicit , implicit feelings expressed in text. The most existing approaches are able to detect either explicit expressions or implicit expressions of sentiments in the text separately. In this proposed framework it will detect both Implicit and Explicit expressions available in the meeting transcripts. It will classify the Positive, Negative, Neutral words and also identify the topic of the particular meeting transcripts by using fuzzy logic. This paper aims to add some additional features for improving the classification method. The quality of the sentiment classification is improved using proposed fuzzy logic framework .In this fuzzy logic it includes the features like Fuzzy rules and Fuzzy C-means algorithm.The quality of the output is evaluated using the parameters such as precision, recall, f-measure. Here Fuzzy C-means Clustering technique measured in terms of Purity and Entropy. The data set was validated using 10-fold cross validation method and observed 95% confidence interval between the accuracy values .Finally, the proposed fuzzy logic method produced more than 85 % accurate results and error rate is very less compared to existing sentiment classification techniques.

  11. Classification of idiopathic toe walking based on gait analysis: development and application of the ITW severity classification.

    Science.gov (United States)

    Alvarez, Christine; De Vera, Mary; Beauchamp, Richard; Ward, Valerie; Black, Alec

    2007-09-01

    Idiopathic toe walking (ITW), considered abnormal after the age of 3 years, is a common complaint seen by medical professionals, especially orthopaedic surgeons and physiotherapists. A classification for idiopathic toe walking would be helpful to better understand the condition, delineate true idiopathic toe walkers from patients with other conditions, and allow for assignment of a severity gradation, thereby directing management of ITW. The purpose of this study was to describe idiopathic toe walking and develop a toe walking classification scheme in a large sample of children. Three primary criteria, presence of a first ankle rocker, presence of an early third ankle rocker, and predominant early ankle moment, were used to classify idiopathic toe walking into three severity groups: Type 1 mild; Type 2 moderate; and Type 3 severe. Supporting data, based on ankle range of motion, sagittal joint powers, knee kinematics, and EMG data were also analyzed. Prospectively collected gait analysis data of 133 children (266 feet) with idiopathic toe walking were analyzed. Subjects' age range was from 4.19 to 15.96 years with a mean age of 8.80 years. Pooling right and left foot data, 40 feet were classified as Type 1, 129 were classified as Type 2, and 90 were classified as Type 3. Seven feet were unclassifiable. Statistical analysis of continuous variables comprising the primary criteria showed that the toe walking severity classification was able to differentiate between three levels of toe walking severity. This classification allowed for the quantitative description of the idiopathic toe walking pattern as well as the delineation of three distinct types of ITW patients (mild, moderate, and severe). PMID:17161602

  12. Demand response scheme based on lottery-like rebates

    KAUST Repository

    Schwartz, Galina A.

    2014-08-24

    In this paper, we develop a novel mechanism for reducing volatility of residential demand for electricity. We construct a reward-based (rebate) mechanism that provides consumers with incentives to shift their demand to off-peak time. In contrast to most other mechanisms proposed in the literature, the key feature of our mechanism is its modest requirements on user preferences, i.e., it does not require exact knowledge of user responsiveness to rewards for shifting their demand from the peak to the off-peak time. Specifically, our mechanism utilizes a probabilistic reward structure for users who shift their demand to the off-peak time, and is robust to incomplete information about user demand and/or risk preferences. We approach the problem from the public good perspective, and demonstrate that the mechanism can be implemented via lottery-like schemes. Our mechanism permits to reduce the distribution losses, and thus improve efficiency of electricity distribution. Finally, the mechanism can be readily incorporated into the emerging demand response schemes (e.g., the time-of-day pricing, and critical peak pricing schemes), and has security and privacy-preserving properties.

  13. An improved biometrics-based remote user authentication scheme with user anonymity.

    Science.gov (United States)

    Khan, Muhammad Khurram; Kumari, Saru

    2013-01-01

    The authors review the biometrics-based user authentication scheme proposed by An in 2012. The authors show that there exist loopholes in the scheme which are detrimental for its security. Therefore the authors propose an improved scheme eradicating the flaws of An's scheme. Then a detailed security analysis of the proposed scheme is presented followed by its efficiency comparison. The proposed scheme not only withstands security problems found in An's scheme but also provides some extra features with mere addition of only two hash operations. The proposed scheme allows user to freely change his password and also provides user anonymity with untraceability. PMID:24350272

  14. SVM-based spectrum mobility prediction scheme in mobile cognitive radio networks.

    Science.gov (United States)

    Wang, Yao; Zhang, Zhongzhao; Ma, Lin; Chen, Jiamei

    2014-01-01

    Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements. PMID:25143975

  15. Dynamic Symmetric Key Mobile Commerce Scheme Based on Self-Verified Mechanism

    Directory of Open Access Journals (Sweden)

    Jiachen Yang

    2014-01-01

    Full Text Available In terms of the security and efficiency of mobile e-commerce, the authors summarized the advantages and disadvantages of several related schemes, especially the self-verified mobile payment scheme based on the elliptic curve cryptosystem (ECC and then proposed a new type of dynamic symmetric key mobile commerce scheme based on self-verified mechanism. The authors analyzed the basic algorithm based on self-verified mechanisms and detailed the complete transaction process of the proposed scheme. The authors analyzed the payment scheme based on the security and high efficiency index. The analysis shows that the proposed scheme not only meets the high efficiency of mobile electronic payment premise, but also takes the security into account. The user confirmation mechanism at the end of the proposed scheme further strengthens the security of the proposed scheme. In brief, the proposed scheme is more efficient and practical than most of the existing schemes.

  16. Multiclass cancer classification based on gene expression comparison

    OpenAIRE

    Yang Sitan; Naiman Daniel Q.

    2014-01-01

    As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analyses, microarray-based cancer classification comprising multiple discriminatory molecular markers is an emerging trend. Such multiclass classification problems pose new methodological and computational challenges for developing novel and effective statistical approaches. In this paper, we introduce a new approach for classifying multiple disease states associated with cancer based on gene expre...

  17. Network planning tool based on network classification and load prediction

    OpenAIRE

    Hammami, Seif eddine; Afifi, Hossam; Marot, Michel; Gauthier, Vincent

    2016-01-01

    Real Call Detail Records (CDR) are analyzed and classified based on Support Vector Machine (SVM) algorithm. The daily classification results in three traffic classes. We use two different algorithms, K-means and SVM to check the classification efficiency. A second support vector regression (SVR) based algorithm is built to make an online prediction of traffic load using the history of CDRs. Then, these algorithms will be integrated to a network planning tool which will help cellular operators...

  18. Iris Image Classification Based on Hierarchical Visual Codebook.

    Science.gov (United States)

    Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

    2014-06-01

    Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection. PMID:26353275

  19. Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

    Directory of Open Access Journals (Sweden)

    Xiaolong Liu

    2015-01-01

    Full Text Available Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR data in an object-based image analysis (OBIA paradigm. The aims of this work were the following: (i to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species’ separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI with a 1 m spatial resolution and the Canopy Height Model (CHM data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR, VHR/CHM, and minimum noise fractional transformed data (MNF/CHM were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that

  20. Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network

    Science.gov (United States)

    Naghsh-Nilchi, Ahmad R.; Kadkhodamohammadi, A. Rahim

    2009-12-01

    An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

  1. Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network

    Directory of Open Access Journals (Sweden)

    2009-03-01

    Full Text Available An electrocardiogram (ECG beat classification scheme based on multiple signal classification (MUSIC algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP neural network and a probabilistic neural network (PNN, are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

  2. Text document classification based on mixture models

    Czech Academy of Sciences Publication Activity Database

    Novovičová, Jana; Malík, Antonín

    2004-01-01

    Roč. 40, č. 3 (2004), s. 293-304. ISSN 0023-5954 R&D Projects: GA AV ČR IAA2075302; GA ČR GA102/03/0049; GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : text classification * text categorization * multinomial mixture model Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.224, year: 2004

  3. Fast Wavelet-Based Visual Classification

    OpenAIRE

    Yu, Guoshen; Slotine, Jean-Jacques

    2008-01-01

    We investigate a biologically motivated approach to fast visual classification, directly inspired by the recent work of Serre et al. Specifically, trading-off biological accuracy for computational efficiency, we explore using wavelet and grouplet-like transforms to parallel the tuning of visual cortex V1 and V2 cells, alternated with max operations to achieve scale and translation invariance. A feature selection procedure is applied during learning to accelerate recognition. We introduce a si...

  4. Blurred Image Classification based on Adaptive Dictionary

    OpenAIRE

    Xiaofei Zhou; Guangling Sun; Jie Yin

    2012-01-01

    Two frameworks for blurred image classification bas ed on adaptive dictionary are proposed. Given a blurred image, instead of image deblurring, the sem antic category of the image is determined by blur insensitive sparse coefficients calculated dependin g on an adaptive dictionary. The dictionary is adap tive to an assumed space invariant Point Spread Function (PSF) estimated from the input blurred image. In o ne of th...

  5. A classification-based review recommender

    OpenAIRE

    O'Mahony, Michael P.; Smyth, Barry

    2010-01-01

    Many online stores encourage their users to submit product or service reviews in order to guide future purchasing decisions. These reviews are often listed alongside product recommendations but, to date, limited attention has been paid as to how best to present these reviews to the end-user. In this paper, we describe a supervised classification approach that is designed to identify and recommend the most helpful product reviews. Using the TripAdvisor service as a case study, we compare...

  6. A new iterative speech enhancement scheme based on Kalman filtering

    DEFF Research Database (Denmark)

    Li, Chunjian; Andersen, Søren Vang

    2005-01-01

    A new iterative speech enhancement scheme that can be seen as an approximation to the Expectation-Maximization (EM) algorithm is proposed. The algorithm employs a Kalman filter that models the excitation source as a spectrally white process with a rapidly time-varying variance, which calls for a...... high temporal resolution estimation of this variance. A Local Variance Estimator based on a Prediction Error Kalman Filter is designed for this high temporal resolution variance estimation. To achieve fast convergence and avoid local maxima of the likelihood function, a Weighted Power Spectral...

  7. An Industrial Model Based Disturbance Feedback Control Scheme

    DEFF Research Database (Denmark)

    Kawai, Fukiko; Nakazawa, Chikashi; Vinther, Kasper;

    2014-01-01

    This paper presents a model based disturbance feedback control scheme. Industrial process systems have been traditionally controlled by using relay and PID controller. However these controllers are affected by disturbances and model errors and these effects degrade control performance. The authors...... propose a new control method that can decrease the negative impact of disturbance and model errors. The control method is motivated by industrial practice by Fuji Electric. Simulation tests are examined with a conventional PID controller and the disturbance feedback control. The simulation results...

  8. A " quantum public key " based cryptographic scheme for continuous variables

    CERN Document Server

    Navez, P; Lugiato, L A; Navez, Patrick; Gatti, Alessandra; Lugiato, Luigi A.

    2001-01-01

    By analogy to classical cryptography, we develop a "quantum public key" based cryptographic scheme in which the two public and private keys consist in each of two entangled beams of squeezed light. An analog message is encrypted by modulating the phase of the beam sent in public. The knowledge of the degree of non classical correlation between the beam quadratures measured in private and in public allows only the receiver to decrypt the message. Finally, in a view towards absolute security, we formally prove that any external intervention of an eavesdropper makes him vulnerable to any subsequent detection.

  9. Innovative Network Engineering Practice based on Multimedia Education Scheme

    Directory of Open Access Journals (Sweden)

    Rongbo Zhu

    2014-03-01

    Full Text Available Multimedia techniques have important influence on education of computer science and engineering. In order to improve the innovation ability, this paper proposes an improved multimedia-driven curriculum system of network engineering, which concentrates on the practical training and innovative learning and applications. Project-oriented software, hardware design and development abilities of students are improved based on the proposed multimedia-driven curriculum system, which include major curriculums related to internet of things, computer networks and software engineering. Detailed results show the effectiveness of the proposed scheme.

  10. Defining organic matter quality in sediment systems: a suggested classification scheme

    Science.gov (United States)

    Alderson, Danielle; Evans, Martin; Rothwell, James; Boult, Stephen

    2015-04-01

    The quantity and quality of the mineral component of sediments is a core focus of sedimentological investigation in terrestrial systems. This is not to say that the organic component of collected sediments is simply ignored; the organic component is often scrutinised, but in some fields in a restricted manner, limited to basic characteristics such as the ratio of organic to mineral content derived from loss on ignition. There is no doubt that this information is useful; however, these types of analysis indicate the quantity of organic matter relative to a particular temporal scale or volume, rather than treating the organic fraction as a separate entity worthy of substantial investigation. The quality of the organic component is being increasingly considered in a number of fields, with molecular, thermal, spectroscopic and bulk methods being used. However, models and theories on organic matter processing in a variety of environmental systems, have been developed without clearly defining organic matter quality, because most results do not depend on an outright measure of quality (Bosatta and Agren, 1999). With approaches and techniques varying between fields, there is a need to consider a more systematic approach to the analysis and definition of organic matter quality. The disparities in the definition of the quality of organic matter, and thus how it may be measured have vital implications for the study of carbon cycling, biogeochemical processing, and ultimately ecosystem structure and function. The quality and quantity of organic matter have an influence on the chemistry and biology of systems and may reveal a wealth of past or contemporary environmental information. In this paper we provide a classification of organic matter quality and examples of potential applications and suitable techniques for the analysis of the main classes of organic matter character. A more consistent approach to organic matter characterisation has the potential to aid understanding of

  11. A novel fully automatic scheme for fiducial marker-based alignment in electron tomography.

    Science.gov (United States)

    Han, Renmin; Wang, Liansan; Liu, Zhiyong; Sun, Fei; Zhang, Fa

    2015-12-01

    Although the topic of fiducial marker-based alignment in electron tomography (ET) has been widely discussed for decades, alignment without human intervention remains a difficult problem. Specifically, the emergence of subtomogram averaging has increased the demand for batch processing during tomographic reconstruction; fully automatic fiducial marker-based alignment is the main technique in this process. However, the lack of an accurate method for detecting and tracking fiducial markers precludes fully automatic alignment. In this paper, we present a novel, fully automatic alignment scheme for ET. Our scheme has two main contributions: First, we present a series of algorithms to ensure a high recognition rate and precise localization during the detection of fiducial markers. Our proposed solution reduces fiducial marker detection to a sampling and classification problem and further introduces an algorithm to solve the parameter dependence of marker diameter and marker number. Second, we propose a novel algorithm to solve the tracking of fiducial markers by reducing the tracking problem to an incomplete point set registration problem. Because a global optimization of a point set registration occurs, the result of our tracking is independent of the initial image position in the tilt series, allowing for the robust tracking of fiducial markers without pre-alignment. The experimental results indicate that our method can achieve an accurate tracking, almost identical to the current best one in IMOD with half automatic scheme. Furthermore, our scheme is fully automatic, depends on fewer parameters (only requires a gross value of the marker diameter) and does not require any manual interaction, providing the possibility of automatic batch processing of electron tomographic reconstruction. PMID:26433028

  12. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    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…

  13. A rational function based scheme for solving advection equation

    Energy Technology Data Exchange (ETDEWEB)

    Xiao, Feng [Gunma Univ., Kiryu (Japan). Faculty of Engineering; Yabe, Takashi

    1995-07-01

    A numerical scheme for solving advection equations is presented. The scheme is derived from a rational interpolation function. Some properties of the scheme with respect to convex-concave preserving and monotone preserving are discussed. We find that the scheme is attractive in surpressinging overshoots and undershoots even in the vicinities of discontinuity. The scheme can also be easily swicthed as the CIP (Cubic interpolated Pseudo-Particle) method to get a third-order accuracy in smooth region. Numbers of numerical tests are carried out to show the non-oscillatory and less diffusive nature of the scheme. (author).

  14. Geometrically Invariant Watermarking Scheme Based on Local Feature Points

    Directory of Open Access Journals (Sweden)

    Jing Li

    2012-06-01

    Full Text Available Based on local invariant feature points and cross ratio principle, this paper presents a feature-point-based image watermarking scheme. It is robust to geometric attacks and some signal processes. It extracts local invariant feature points from the image using the improved scale invariant feature transform algorithm. Utilizing these points as vertexes it constructs some quadrilaterals to be as local feature regions. Watermark is inserted these local feature regions repeatedly. In order to get stable local regions it adjusts the number and distribution of extracted feature points. In every chosen local feature region it decides locations to embed watermark bits based on the cross ratio of four collinear points, the cross ratio is invariant to projective transformation. Watermark bits are embedded by quantization modulation, in which the quantization step value is computed with the given PSNR. Experimental results show that the proposed method can strongly fight more geometrical attacks and the compound attacks of geometrical ones.

  15. Support vector classification algorithm based on variable parameter linear programming

    Institute of Scientific and Technical Information of China (English)

    Xiao Jianhua; Lin Jian

    2007-01-01

    To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed.In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model.The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given.An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.

  16. Words semantic orientation classification based on HowNet

    Institute of Scientific and Technical Information of China (English)

    LI Dun; MA Yong-tao; GUO Jian-li

    2009-01-01

    Based on the text orientation classification, a new measurement approach to semantic orientation of words was proposed. According to the integrated and detailed definition of words in HowNet, seed sets including the words with intense orientations were built up. The orientation similarity between the seed words and the given word was then calculated using the sentiment weight priority to recognize the semantic orientation of common words. Finally, the words' semantic orientation and the context were combined to recognize the given words' orientation. The experiments show that the measurement approach achieves better results for common words' orientation classification and contributes particularly to the text orientation classification of large granularities.

  17. Feature Extraction based Face Recognition, Gender and Age Classification

    Directory of Open Access Journals (Sweden)

    Venugopal K R

    2010-01-01

    Full Text Available The face recognition system with large sets of training sets for personal identification normally attains good accuracy. In this paper, we proposed Feature Extraction based Face Recognition, Gender and Age Classification (FEBFRGAC algorithm with only small training sets and it yields good results even with one image per person. This process involves three stages: Pre-processing, Feature Extraction and Classification. The geometric features of facial images like eyes, nose, mouth etc. are located by using Canny edge operator and face recognition is performed. Based on the texture and shape information gender and age classification is done using Posteriori Class Probability and Artificial Neural Network respectively. It is observed that the face recognition is 100%, the gender and age classification is around 98% and 94% respectively.

  18. A Human Gait Classification Method Based on Radar Doppler Spectrograms

    Directory of Open Access Journals (Sweden)

    Fok Hing Chi Tivive

    2010-01-01

    Full Text Available An image classification technique, which has recently been introduced for visual pattern recognition, is successfully applied for human gait classification based on radar Doppler signatures depicted in the time-frequency domain. The proposed method has three processing stages. The first two stages are designed to extract Doppler features that can effectively characterize human motion based on the nature of arm swings, and the third stage performs classification. Three types of arm motion are considered: free-arm swings, one-arm confined swings, and no-arm swings. The last two arm motions can be indicative of a human carrying objects or a person in stressed situations. The paper discusses the different steps of the proposed method for extracting distinctive Doppler features and demonstrates their contributions to the final and desirable classification rates.

  19. A NOVEL RULE-BASED FINGERPRINT CLASSIFICATION APPROACH

    Directory of Open Access Journals (Sweden)

    Faezeh Mirzaei

    2014-03-01

    Full Text Available Fingerprint classification is an important phase in increasing the speed of a fingerprint verification system and narrow down the search of fingerprint database. Fingerprint verification is still a challenging problem due to the difficulty of poor quality images and the need for faster response. The classification gets even harder when just one core has been detected in the input image. This paper has proposed a new classification approach which includes the images with one core. The algorithm extracts singular points (core and deltas from the input image and performs classification based on the number, locations and surrounded area of the detected singular points. The classifier is rule-based, where the rules are generated independent of a given data set. Moreover, shortcomings of a related paper has been reported in detail. The experimental results and comparisons on FVC2002 database have shown the effectiveness and efficiency of the proposed method.

  20. Analysis of Kernel Approach in Fuzzy-Based Image Classifications

    Directory of Open Access Journals (Sweden)

    Mragank Singhal

    2013-03-01

    Full Text Available This paper presents a framework of kernel approach in the field of fuzzy based image classification in remote sensing. The goal of image classification is to separate images according to their visual content into two or more disjoint classes. Fuzzy logic is relatively young theory. Major advantage of this theory is that it allows the natural description, in linguistic terms, of problems that should be solved rather than in terms of relationships between precise numerical values. This paper describes how remote sensing data with uncertainty are handled with fuzzy based classification using Kernel approach for land use/land cover maps generation. The introduction to fuzzification using Kernel approach provides the basis for the development of more robust approaches to the remote sensing classification problem. The kernel explicitly defines a similarity measure between two samples and implicitly represents the mapping of the input space to the feature space.

  1. Bazhenov Fm Classification Based on Wireline Logs

    Science.gov (United States)

    Simonov, D. A.; Baranov, V.; Bukhanov, N.

    2016-03-01

    This paper considers the main aspects of Bazhenov Formation interpretation and application of machine learning algorithms for the Kolpashev type section of the Bazhenov Formation, application of automatic classification algorithms that would change the scale of research from small to large. Machine learning algorithms help interpret the Bazhenov Formation in a reference well and in other wells. During this study, unsupervised and supervised machine learning algorithms were applied to interpret lithology and reservoir properties. This greatly simplifies the routine problem of manual interpretation and has an economic effect on the cost of laboratory analysis.

  2. PLANNING BASED ON CLASSIFICATION BY INDUCTION GRAPH

    Directory of Open Access Journals (Sweden)

    Sofia Benbelkacem

    2013-11-01

    Full Text Available In Artificial Intelligence, planning refers to an area of research that proposes to develop systems that can automatically generate a result set, in the form of an integrated decisionmaking system through a formal procedure, known as plan. Instead of resorting to the scheduling algorithms to generate plans, it is proposed to operate the automatic learning by decision tree to optimize time. In this paper, we propose to build a classification model by induction graph from a learning sample containing plans that have an associated set of descriptors whose values change depending on each plan. This model will then operate for classifying new cases by assigning the appropriate plan.

  3. ID-based authentication scheme combined with identity-based encryption with fingerprint hashing

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Current identity-based (ID) cryptosystem lacks the mechanisms of two-party authentication and user's private key distribution. Some ID-based signcryption schemes and ID-based authenticated key agreement protocols have been presented, but they cannot solve the problem completely. A novel ID-based authentication scheme based on ID-based encryption (IBE) and fingerprint hashing method is proposed to solve the difficulties in the IBE scheme, which includes message receiver authenticating the sender, the trusted authority (TA) authenticating the users and transmitting the private key to them. Furthermore, the scheme extends the application of fingerprint authentication from terminal to network and protects against fingerprint data fabrication. The fingerprint authentication method consists of two factors. This method combines a token key, for example, the USB key, with the user's fingerprint hash by mixing a pseudo-random number with the fingerprint feature. The security and experimental efficiency meet the requirements of practical applications.

  4. Fuzzy-Wavelet Based Double Line Transmission System Protection Scheme in the Presence of SVC

    Science.gov (United States)

    Goli, Ravikumar; Shaik, Abdul Gafoor; Tulasi Ram, Sankara S.

    2014-07-01

    Increasing the power transfer capability and efficient utilization of available transmission lines, improving the power system controllability and stability, power oscillation damping and voltage compensation have made strides and created Flexible AC Transmission (FACTS) devices in recent decades. Shunt FACTS devices can have adverse effects on distance protection both in steady state and transient periods. Severe under reaching is the most important problem of relay which is caused by current injection at the point of connection to the system. Current absorption of compensator leads to overreach of relay. This work presents an efficient method based on wavelet transforms, fault detection, classification and location using Fuzzy logic technique which is almost independent of fault impedance, fault distance and fault inception angle. The proposed protection scheme is found to be fast, reliable and accurate for various types of faults on transmission lines with and without Static Var compensator at different locations and with various incidence angles.

  5. Iris image recognition wavelet filter-banks based iris feature extraction schemes

    CERN Document Server

    Rahulkar, Amol D

    2014-01-01

    This book provides the new results in wavelet filter banks based feature extraction, and the classifier in the field of iris image recognition. It provides the broad treatment on the design of separable, non-separable wavelets filter banks, and the classifier. The design techniques presented in the book are applied on iris image analysis for person authentication. This book also brings together the three strands of research (wavelets, iris image analysis, and classifier). It compares the performance of the presented techniques with state-of-the-art available schemes. This book contains the compilation of basic material on the design of wavelets that avoids reading many different books. Therefore, it provide an easier path for the new-comers, researchers to master the contents. In addition, the designed filter banks and classifier can also be effectively used than existing filter-banks in many signal processing applications like pattern classification, data-compression, watermarking, denoising etc.  that will...

  6. A Lattice-Based Identity-Based Proxy Blind Signature Scheme in the Standard Model

    Directory of Open Access Journals (Sweden)

    Lili Zhang

    2014-01-01

    Full Text Available A proxy blind signature scheme is a special form of blind signature which allowed a designated person called proxy signer to sign on behalf of original signers without knowing the content of the message. It combines the advantages of proxy signature and blind signature. Up to date, most proxy blind signature schemes rely on hard number theory problems, discrete logarithm, and bilinear pairings. Unfortunately, the above underlying number theory problems will be solvable in the postquantum era. Lattice-based cryptography is enjoying great interest these days, due to implementation simplicity and provable security reductions. Moreover, lattice-based cryptography is believed to be hard even for quantum computers. In this paper, we present a new identity-based proxy blind signature scheme from lattices without random oracles. The new scheme is proven to be strongly unforgeable under the standard hardness assumption of the short integer solution problem (SIS and the inhomogeneous small integer solution problem (ISIS. Furthermore, the secret key size and the signature length of our scheme are invariant and much shorter than those of the previous lattice-based proxy blind signature schemes. To the best of our knowledge, our construction is the first short lattice-based identity-based proxy blind signature scheme in the standard model.

  7. Feature Extraction based Face Recognition, Gender and Age Classification

    OpenAIRE

    Venugopal K R2; L M Patnaik; Ramesha K; K B Raja

    2010-01-01

    The face recognition system with large sets of training sets for personal identification normally attains good accuracy. In this paper, we proposed Feature Extraction based Face Recognition, Gender and Age Classification (FEBFRGAC) algorithm with only small training sets and it yields good results even with one image per person. This process involves three stages: Pre-processing, Feature Extraction and Classification. The geometric features of facial images like eyes, nose, mouth etc. are loc...

  8. Triangle-based key management scheme for wireless sensor networks

    Institute of Scientific and Technical Information of China (English)

    Hangyang DAI; Hongbing XU

    2009-01-01

    For security services in wireless sensor net-works, key management is a fundamental building block.In this article, we propose a triangle-based key predis-tribution approach and show that it can improve the effectiveness of key management in wireless sensor networks. This is achieved by using the bivariate polynomial in a triangle deployment system based on deployment information about expected locations of the sensor nodes. The analysis indicates that this scheme can achieve higher probability of both direct key establishment and indirect key establishment. On the other hand, the security analysis shows that its security against node capture would increase with a decrease of the sensor node deployment density and size of the deployment model and an increase of the polynomial degree.

  9. VARIABLE LENGTH KEY BASED VISUAL CRYPTOGRAPHY SCHEME FOR COLOR IMAGE

    Directory of Open Access Journals (Sweden)

    Akhil Anjikar

    2014-11-01

    Full Text Available Visual Cryptography is a special encryption technique that encrypts the secret image into n number of shares to hide information in images in such a way that it can be decrypted by the human visual system. It is imperceptible to reveal the secret information unless a certain number of shares (k or more are superimposed. Simple visual cryptography is very insecure. Variable length key based visual cryptography for color image uses a variable length Symmetric Key based Visual Cryptographic Scheme for color images where a secret key is used to encrypt the image and division of the encrypted image is done using Random Number. Unless the secret key, the original image will not be decrypted. Here secret key ensures the security of image.

  10. Adaptive SPC monitoring scheme for DOE-based APC

    Institute of Scientific and Technical Information of China (English)

    Ye Liang; Pan Ershun; Xi Lifeng

    2008-01-01

    Automatic process control (APC) based on design of experiment (DOE) is a cost-efficient approach for variation reduction. The process changes both in mean and variance owing to online parameter adjustment make it hard to apply traditional SPC charts in such DOE-based APC applied process. An adaptive SPC scheme is developed, which can better track the process transitions and achieve the possible SPC run cost reduction when the process is stable. The control law of SPC parameters is designed by fully utilizing the estimation properties of the process model instead of traditionally using the data collected from the production line. An example is provided to illustrate the proposed adaptive SPC design approach.

  11. Three-dimensional shapelets and an automated classification scheme for dark matter haloes

    OpenAIRE

    Fluke, C. J.; Malec, A.L.; Lasky, P. D.; B. R. Barsdell

    2011-01-01

    We extend the two-dimensional Cartesian shapelet formalism to d-dimensions. Concentrating on the three-dimensional case, we derive shapelet-based equations for the mass, centroid, root-mean-square radius, and components of the quadrupole moment and moment of inertia tensors. Using cosmological N-body simulations as an application domain, we show that three-dimensional shapelets can be used to replicate the complex sub-structure of dark matter halos and demonstrate the basis of an automated cl...

  12. Energy Aware Cluster Based Routing Scheme For Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Roy Sohini

    2015-09-01

    Full Text Available Wireless Sensor Network (WSN has emerged as an important supplement to the modern wireless communication systems due to its wide range of applications. The recent researches are facing the various challenges of the sensor network more gracefully. However, energy efficiency has still remained a matter of concern for the researches. Meeting the countless security needs, timely data delivery and taking a quick action, efficient route selection and multi-path routing etc. can only be achieved at the cost of energy. Hierarchical routing is more useful in this regard. The proposed algorithm Energy Aware Cluster Based Routing Scheme (EACBRS aims at conserving energy with the help of hierarchical routing by calculating the optimum number of cluster heads for the network, selecting energy-efficient route to the sink and by offering congestion control. Simulation results prove that EACBRS performs better than existing hierarchical routing algorithms like Distributed Energy-Efficient Clustering (DEEC algorithm for heterogeneous wireless sensor networks and Energy Efficient Heterogeneous Clustered scheme for Wireless Sensor Network (EEHC.

  13. A Robust Image Watermarking Scheme Based Multiresolution Analysis

    Directory of Open Access Journals (Sweden)

    N. Mohananthini

    2012-10-01

    Full Text Available Digital watermarking has been widely applied to solve copyright protection problems of digital media relating to illegal use of distributions. In digital watermarking, a watermark is embedded into a cover image in such way that the resulting watermarked signal is robust to certain distortion. This paper presents a digital image watermarking based on Discrete Wavelet Transform (DWT. In the proposed method, the watermark as well as the cover image seldom looses the quality in both embedding and extraction process. The embedding process is carried out by tetra-furcating the watermark and embedded into the sub-bands of cover image. Signal to Noise Ratio (SNR and Peak Signal to Noise Ratio (PSNR are computed to measure image quality for the DWT transform. We present traces of host and watermarked images. From the traces we observe that, we get good SNR and PSNR with DWT. Experiment evaluation demonstrates that the proposed scheme is able to withstand a variety of attacks. This scheme shows good performance on different types of cover images in terms of imperceptibility and resist to jpeg compression.

  14. The CLIC positron source based on compton schemes

    CERN Document Server

    Rinolfi, L; Braun, H; Papaphilippou, Y; Schulte, D; Vivoli, A; Zimmermann, F; Dadoun, O; Lepercq, P; Roux, R; Variola, A; Zomer, F; Pogorelski, I; Yakimenko, V; Gai, W; Liu, W; Kamitani, T; Omori, T; Urakawa, J; Kuriki, M; Takahasi, TM; Bulyak, E; Gladkikh, P; Chehab, R; Clarke, J

    2010-01-01

    The CLIC polarized positron source is based on a positron production scheme in which polarized photons are produced by a Compton process. In one option, Compton backscattering takes place in a so-called “Compton ring”, where an electron beam of 1 GeV interacts with circularly-polarized photons in an optical resonator. The resulting circularly-polarized gamma photons are sent on to an amorphous target, producing pairs of longitudinally polarized electrons and positrons. The nominal CLIC bunch population is 4.2x109 particles per bunch at the exit of the Pre-Damping Ring (PDR). Since the photon flux coming out from a "Compton ring" is not sufficient to obtain the requested charge, a stacking process is required in the PDR. Another option is to use a Compton Energy Recovery Linac (ERL) where a quasicontinual stacking in the PDR could be achieved. A third option is to use a "Compton Linac" which would not require stacking. We describe the overall scheme as well as advantages and constraints of the three option...

  15. Normal Vector Based Subdivision Scheme to Generate Fractal Curves

    Directory of Open Access Journals (Sweden)

    Yi Li

    2013-08-01

    Full Text Available In this paper, we firstly devise a new and general p-ary subdivision scheme based on normal vectors with multi-parameters to generate fractals. Rich and colorful fractals including some known fractals and a lot of unknown ones can be generated directly and conveniently by using it uniformly. The method is easy to use and effective in generating fractals since the values of the parameters and the directions of normal vectors can be designed freely to control the shape of generated fractals. Secondly, we illustrate the technique with some design results of fractal generation and the corresponding fractal examples from the point of view of visualization, including the classical Lévy curves, Dragon curves, Sierpiński gasket, Koch curve, Koch-type curves and other fractals. Finally, some fractal properties of the limit of the presented subdivision scheme, including existence, self-similarity, non-rectifiability, and continuity but nowhere differentiability are described from the point of view of theoretical analysis.

  16. A group signature scheme based on quantum teleportation

    Energy Technology Data Exchange (ETDEWEB)

    Wen Xiaojun; Tian Yuan; Ji Liping; Niu Xiamu, E-mail: wxjun36@gmail.co [Information Countermeasure Technique Research Institute, Harbin Institute of Technology, Harbin 150001 (China)

    2010-05-01

    In this paper, we present a group signature scheme using quantum teleportation. Different from classical group signature and current quantum signature schemes, which could only deliver either group signature or unconditional security, our scheme guarantees both by adopting quantum key preparation, quantum encryption algorithm and quantum teleportation. Security analysis proved that our scheme has the characteristics of group signature, non-counterfeit, non-disavowal, blindness and traceability. Our quantum group signature scheme has a foreseeable application in the e-payment system, e-government, e-business, etc.

  17. A group signature scheme based on quantum teleportation

    International Nuclear Information System (INIS)

    In this paper, we present a group signature scheme using quantum teleportation. Different from classical group signature and current quantum signature schemes, which could only deliver either group signature or unconditional security, our scheme guarantees both by adopting quantum key preparation, quantum encryption algorithm and quantum teleportation. Security analysis proved that our scheme has the characteristics of group signature, non-counterfeit, non-disavowal, blindness and traceability. Our quantum group signature scheme has a foreseeable application in the e-payment system, e-government, e-business, etc.

  18. Classification of normal and pathological aging processes based on brain MRI morphology measures

    Science.gov (United States)

    Perez-Gonzalez, J. L.; Yanez-Suarez, O.; Medina-Bañuelos, V.

    2014-03-01

    Reported studies describing normal and abnormal aging based on anatomical MRI analysis do not consider morphological brain changes, but only volumetric measures to distinguish among these processes. This work presents a classification scheme, based both on size and shape features extracted from brain volumes, to determine different aging stages: healthy control (HC) adults, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Three support vector machines were optimized and validated for the pair-wise separation of these three classes, using selected features from a set of 3D discrete compactness measures and normalized volumes of several global and local anatomical structures. Our analysis show classification rates of up to 98.3% between HC and AD; of 85% between HC and MCI and of 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indexes to classify different aging stages.

  19. Classification approach based on association rules mining for unbalanced data

    CERN Document Server

    Ndour, Cheikh

    2012-01-01

    This paper deals with the supervised classification when the response variable is binary and its class distribution is unbalanced. In such situation, it is not possible to build a powerful classifier by using standard methods such as logistic regression, classification tree, discriminant analysis, etc. To overcome this short-coming of these methods that provide classifiers with low sensibility, we tackled the classification problem here through an approach based on the association rules learning because this approach has the advantage of allowing the identification of the patterns that are well correlated with the target class. Association rules learning is a well known method in the area of data-mining. It is used when dealing with large database for unsupervised discovery of local patterns that expresses hidden relationships between variables. In considering association rules from a supervised learning point of view, a relevant set of weak classifiers is obtained from which one derives a classification rule...

  20. Ensemble polarimetric SAR image classification based on contextual sparse representation

    Science.gov (United States)

    Zhang, Lamei; Wang, Xiao; Zou, Bin; Qiao, Zhijun

    2016-05-01

    Polarimetric SAR image interpretation has become one of the most interesting topics, in which the construction of the reasonable and effective technique of image classification is of key importance. Sparse representation represents the data using the most succinct sparse atoms of the over-complete dictionary and the advantages of sparse representation also have been confirmed in the field of PolSAR classification. However, it is not perfect, like the ordinary classifier, at different aspects. So ensemble learning is introduced to improve the issue, which makes a plurality of different learners training and obtained the integrated results by combining the individual learner to get more accurate and ideal learning results. Therefore, this paper presents a polarimetric SAR image classification method based on the ensemble learning of sparse representation to achieve the optimal classification.

  1. Blurred Image Classification Based on Adaptive Dictionary

    Directory of Open Access Journals (Sweden)

    Guangling Sun

    2013-02-01

    Full Text Available Two frameworks for blurred image classification bas ed on adaptive dictionary are proposed. Given a blurred image, instead of image deblurring, the sem antic category of the image is determined by blur insensitive sparse coefficients calculated dependin g on an adaptive dictionary. The dictionary is adap tive to an assumed space invariant Point Spread Function (PSF estimated from the input blurred image. In o ne of the proposed two frameworks, the PSF is inferred separately and in the other, the PSF is updated combined with sparse coefficients calculation in an alternative and iterative manner. The experimental results have evaluated three types of blur namely d efocus blur, simple motion blur and camera shake bl ur. The experiment results confirm the effectiveness of the proposed frameworks.

  2. Classification of LiDAR Data with Point Based Classification Methods

    Science.gov (United States)

    Yastikli, N.; Cetin, Z.

    2016-06-01

    LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features

  3. Pathological Bases for a Robust Application of Cancer Molecular Classification

    Directory of Open Access Journals (Sweden)

    Salvador J. Diaz-Cano

    2015-04-01

    Full Text Available Any robust classification system depends on its purpose and must refer to accepted standards, its strength relying on predictive values and a careful consideration of known factors that can affect its reliability. In this context, a molecular classification of human cancer must refer to the current gold standard (histological classification and try to improve it with key prognosticators for metastatic potential, staging and grading. Although organ-specific examples have been published based on proteomics, transcriptomics and genomics evaluations, the most popular approach uses gene expression analysis as a direct correlate of cellular differentiation, which represents the key feature of the histological classification. RNA is a labile molecule that varies significantly according with the preservation protocol, its transcription reflect the adaptation of the tumor cells to the microenvironment, it can be passed through mechanisms of intercellular transference of genetic information (exosomes, and it is exposed to epigenetic modifications. More robust classifications should be based on stable molecules, at the genetic level represented by DNA to improve reliability, and its analysis must deal with the concept of intratumoral heterogeneity, which is at the origin of tumor progression and is the byproduct of the selection process during the clonal expansion and progression of neoplasms. The simultaneous analysis of multiple DNA targets and next generation sequencing offer the best practical approach for an analytical genomic classification of tumors.

  4. Towards functional classification of neuronal types

    OpenAIRE

    Sharpee, Tatyana O.

    2014-01-01

    How many types of neurons are there in the brain? This basic neuroscience question remains unsettled despite many decades of research. Classification schemes have been proposed based on anatomical, electrophysiological or molecular properties. However, different schemes do not always agree with each other. This raises the question of whether one can classify neurons based on their function directly. For example, among sensory neurons, can a classification scheme be devised that is based on th...

  5. Audio Classification from Time-Frequency Texture

    OpenAIRE

    Yu, Guoshen; Slotine, Jean-Jacques

    2008-01-01

    Time-frequency representations of audio signals often resemble texture images. This paper derives a simple audio classification algorithm based on treating sound spectrograms as texture images. The algorithm is inspired by an earlier visual classification scheme particularly efficient at classifying textures. While solely based on time-frequency texture features, the algorithm achieves surprisingly good performance in musical instrument classification experiments.

  6. ELABORATION OF A VECTOR BASED SEMANTIC CLASSIFICATION OVER THE WORDS AND NOTIONS OF THE NATURAL LANGUAGE

    OpenAIRE

    Safonov, K.; Lichargin, D.

    2009-01-01

    The problem of vector-based semantic classification over the words and notions of the natural language is discussed. A set of generative grammar rules is offered for generating the semantic classification vector. Examples of the classification application and a theorem of optional formal classification incompleteness are presented. The principles of assigning the meaningful phrases functions over the classification word groups are analyzed.

  7. Improving Classification Performance by Integrating Multiple Classifiers Based on Landsat ™ Images: A Primary Study

    Science.gov (United States)

    Li, Xuecao; Liu, Xiaoping; Yu, Le; Gong, Peng

    2014-11-01

    Land use/cover change is crucial to many ecological and environmental issues. In this article, we presented a new approach to improve the classification performance of remotely sensed images based on a classifier ensemble scheme, which can be delineated as two procedures, namely ensemble learning and predictions combination. Bagging algorithm, which is a widely used ensemble approach, was employed in the first procedure through a bootstrapped sampling scheme to stabilize and improve the performance of single classifier. Then, in the second stage, predictions of different classifiers are combined through the scheme of Behaviour Knowledge Space (BKS). This classifier ensemble scheme was examined using a Landsat Thematic Mapper (TM) image acquired at 2 January, 2009 in Dongguan (China). The experimental results illustrate the final output (BKS, OA=90.83% and Kappa=0.881) is outperformed not only the best single classifier (SVM, OA=88.83% and Kappa=0.8624) but also the Bagging CART classifier (OA=90.26% and Kappa=0.8808), although the improvements are varying among them. We think the classifier ensemble scheme can mitigate the limitation of some single models.

  8. A Novel Feature Selection Based on One-Way ANOVA F-Test for E-Mail Spam Classification

    Directory of Open Access Journals (Sweden)

    Nadir Omer Fadl Elssied

    2014-01-01

    Full Text Available Spam is commonly defined as unwanted e-mails and it became a global threat against e-mail users. Although, Support Vector Machine (SVM has been commonly used in e-mail spam classification, yet the problem of high data dimensionality of the feature space due to the massive number of e-mail dataset and features still exist. To improve the limitation of SVM, reduce the computational complexity (efficiency and enhancing the classification accuracy (effectiveness. In this study, feature selection based on one-way ANOVA F-test statistics scheme was applied to determine the most important features contributing to e-mail spam classification. This feature selection based on one-way ANOVA F-test is used to reduce the high data dimensionality of the feature space before the classification process. The experiment of the proposed scheme was carried out using spam base well-known benchmarking dataset to evaluate the feasibility of the proposed method. The comparison is achieved for different datasets, categorization algorithm and success measures. In addition, experimental results on spam base English datasets showed that the enhanced SVM (FSSVM significantly outperforms SVM and many other recent spam classification methods for English dataset in terms of computational complexity and dimension reduction.

  9. Watermarking scheme of colour image based on chaotic sequences

    Institute of Scientific and Technical Information of China (English)

    LIU Nian-sheng; GUO Dong-hui

    2009-01-01

    The proposed perceptual mask is based on the singularity of cover image and matches very well with the properties of the human visual system. The cover colour image is decomposed into several subbands by the wavelet transform. The water-mark composed of chaotic sequence and the covert image is embedded into the subband with the largest energy. The chaos system plays an important role in the security invisibility and robustness of the proposed scheme. The parameter and initial state of chaos system can directly influence the generation of watermark information as a key. Moreover, the watermark information has the property of spread spectrum signal by chaotic sequence to improve the invisibility and security of watermarked image. Experimental results and comparisons with other watermarking techniques prove that the proposed algorithm is effective and feasible, and improves the security, invisibility and robustness of watermarking information.

  10. Optimization algorithm based characterization scheme for tunable semiconductor lasers.

    Science.gov (United States)

    Chen, Quanan; Liu, Gonghai; Lu, Qiaoyin; Guo, Weihua

    2016-09-01

    In this paper, an optimization algorithm based characterization scheme for tunable semiconductor lasers is proposed and demonstrated. In the process of optimization, the ratio between the power of the desired frequency and the power except of the desired frequency is used as the figure of merit, which approximately represents the side-mode suppression ratio. In practice, we use tunable optical band-pass and band-stop filters to obtain the power of the desired frequency and the power except of the desired frequency separately. With the assistance of optimization algorithms, such as the particle swarm optimization (PSO) algorithm, we can get stable operation conditions for tunable lasers at designated frequencies directly and efficiently. PMID:27607701

  11. MIMO transmit scheme based on morphological perceptron with competitive learning.

    Science.gov (United States)

    Valente, Raul Ambrozio; Abrão, Taufik

    2016-08-01

    This paper proposes a new multi-input multi-output (MIMO) transmit scheme aided by artificial neural network (ANN). The morphological perceptron with competitive learning (MP/CL) concept is deployed as a decision rule in the MIMO detection stage. The proposed MIMO transmission scheme is able to achieve double spectral efficiency; hence, in each time-slot the receiver decodes two symbols at a time instead one as Alamouti scheme. Other advantage of the proposed transmit scheme with MP/CL-aided detector is its polynomial complexity according to modulation order, while it becomes linear when the data stream length is greater than modulation order. The performance of the proposed scheme is compared to the traditional MIMO schemes, namely Alamouti scheme and maximum-likelihood MIMO (ML-MIMO) detector. Also, the proposed scheme is evaluated in a scenario with variable channel information along the frame. Numerical results have shown that the diversity gain under space-time coding Alamouti scheme is partially lost, which slightly reduces the bit-error rate (BER) performance of the proposed MP/CL-NN MIMO scheme. PMID:27135805

  12. Dynamic frequency feature selection based approach for classification of motor imageries.

    Science.gov (United States)

    Luo, Jing; Feng, Zuren; Zhang, Jun; Lu, Na

    2016-08-01

    Electroencephalography (EEG) is one of the most popular techniques to record the brain activities such as motor imagery, which is of low signal-to-noise ratio and could lead to high classification error. Therefore, selection of the most discriminative features could be crucial to improve the classification performance. However, the traditional feature selection methods employed in brain-computer interface (BCI) field (e.g. Mutual Information-based Best Individual Feature (MIBIF), Mutual Information-based Rough Set Reduction (MIRSR) and cross-validation) mainly focus on the overall performance on all the trials in the training set, and thus may have very poor performance on some specific samples, which is not acceptable. To address this problem, a novel sequential forward feature selection approach called Dynamic Frequency Feature Selection (DFFS) is proposed in this paper. The DFFS method emphasized the importance of the samples that got misclassified while only pursuing high overall classification performance. In the DFFS based classification scheme, the EEG data was first transformed to frequency domain using Wavelet Packet Decomposition (WPD), which is then employed as the candidate set for further discriminatory feature selection. The features are selected one by one in a boosting manner. After one feature being selected, the importance of the correctly classified samples based on the feature will be decreased, which is equivalent to increasing the importance of the misclassified samples. Therefore, a complement feature to the current features could be selected in the next run. The selected features are then fed to a classifier trained by random forest algorithm. Finally, a time series voting-based method is utilized to improve the classification performance. Comparisons between the DFFS-based approach and state-of-art methods on BCI competition IV data set 2b have been conducted, which have shown the superiority of the proposed algorithm. PMID:27253616

  13. Cryptanalysis and Improvement of a Biometric-Based Multi-Server Authentication and Key Agreement Scheme

    Science.gov (United States)

    Wang, Chengqi; Zhang, Xiao; Zheng, Zhiming

    2016-01-01

    With the security requirements of networks, biometrics authenticated schemes which are applied in the multi-server environment come to be more crucial and widely deployed. In this paper, we propose a novel biometric-based multi-server authentication and key agreement scheme which is based on the cryptanalysis of Mishra et al.’s scheme. The informal and formal security analysis of our scheme are given, which demonstrate that our scheme satisfies the desirable security requirements. The presented scheme provides a variety of significant functionalities, in which some features are not considered in the most of existing authentication schemes, such as, user revocation or re-registration and biometric information protection. Compared with several related schemes, our scheme has more secure properties and lower computation cost. It is obviously more appropriate for practical applications in the remote distributed networks. PMID:26866606

  14. Cryptanalysis and Improvement of a Biometric-Based Multi-Server Authentication and Key Agreement Scheme.

    Science.gov (United States)

    Wang, Chengqi; Zhang, Xiao; Zheng, Zhiming

    2016-01-01

    With the security requirements of networks, biometrics authenticated schemes which are applied in the multi-server environment come to be more crucial and widely deployed. In this paper, we propose a novel biometric-based multi-server authentication and key agreement scheme which is based on the cryptanalysis of Mishra et al.'s scheme. The informal and formal security analysis of our scheme are given, which demonstrate that our scheme satisfies the desirable security requirements. The presented scheme provides a variety of significant functionalities, in which some features are not considered in the most of existing authentication schemes, such as, user revocation or re-registration and biometric information protection. Compared with several related schemes, our scheme has more secure properties and lower computation cost. It is obviously more appropriate for practical applications in the remote distributed networks. PMID:26866606

  15. SAR Imagery Simulation of Ship Based on Electromagnetic Calculations and Sea Clutter Modelling for Classification Applications

    International Nuclear Information System (INIS)

    Ship detection and classification with space-borne SAR has many potential applications within the maritime surveillance, fishery activity management, monitoring ship traffic, and military security. While ship detection techniques with SAR imagery are well established, ship classification is still an open issue. One of the main reasons may be ascribed to the difficulties on acquiring the required quantities of real data of vessels under different observation and environmental conditions with precise ground truth. Therefore, simulation of SAR images with high scenario flexibility and reasonable computation costs is compulsory for ship classification algorithms development. However, the simulation of SAR imagery of ship over sea surface is challenging. Though great efforts have been devoted to tackle this difficult problem, it is far from being conquered. This paper proposes a novel scheme for SAR imagery simulation of ship over sea surface. The simulation is implemented based on high frequency electromagnetic calculations methods of PO, MEC, PTD and GO. SAR imagery of sea clutter is modelled by the representative K-distribution clutter model. Then, the simulated SAR imagery of ship can be produced by inserting the simulated SAR imagery chips of ship into the SAR imagery of sea clutter. The proposed scheme has been validated with canonical and complex ship targets over a typical sea scene

  16. S1 gene-based phylogeny of infectious bronchitis virus: An attempt to harmonize virus classification.

    Science.gov (United States)

    Valastro, Viviana; Holmes, Edward C; Britton, Paul; Fusaro, Alice; Jackwood, Mark W; Cattoli, Giovanni; Monne, Isabella

    2016-04-01

    Infectious bronchitis virus (IBV) is the causative agent of a highly contagious disease that results in severe economic losses to the global poultry industry. The virus exists in a wide variety of genetically distinct viral types, and both phylogenetic analysis and measures of pairwise similarity among nucleotide or amino acid sequences have been used to classify IBV strains. However, there is currently no consensus on the method by which IBV sequences should be compared, and heterogeneous genetic group designations that are inconsistent with phylogenetic history have been adopted, leading to the confusing coexistence of multiple genotyping schemes. Herein, we propose a simple and repeatable phylogeny-based classification system combined with an unambiguous and rationale lineage nomenclature for the assignment of IBV strains. By using complete nucleotide sequences of the S1 gene we determined the phylogenetic structure of IBV, which in turn allowed us to define 6 genotypes that together comprise 32 distinct viral lineages and a number of inter-lineage recombinants. Because of extensive rate variation among IBVs, we suggest that the inference of phylogenetic relationships alone represents a more appropriate criterion for sequence classification than pairwise sequence comparisons. The adoption of an internationally accepted viral nomenclature is crucial for future studies of IBV epidemiology and evolution, and the classification scheme presented here can be updated and revised novel S1 sequences should become available. PMID:26883378

  17. Entropy-based gene ranking without selection bias for the predictive classification of microarray data

    Directory of Open Access Journals (Sweden)

    Serafini Maria

    2003-11-01

    Full Text Available Abstract Background We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process. Results With E-RFE, we speed up the recursive feature elimination (RFE with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

  18. Satellite image classification using convolutional learning

    Science.gov (United States)

    Nguyen, Thao; Han, Jiho; Park, Dong-Chul

    2013-10-01

    A satellite image classification method using Convolutional Neural Network (CNN) architecture is proposed in this paper. As a special case of deep learning, CNN classifies classes of images without any feature extraction step while other existing classification methods utilize rather complex feature extraction processes. Experiments on a set of satellite image data and the preliminary results show that the proposed classification method can be a promising alternative over existing feature extraction-based schemes in terms of classification accuracy and classification speed.

  19. Multivariate Discretization Based on Evolutionary Cut Points Selection for Classification.

    Science.gov (United States)

    Ramirez-Gallego, Sergio; Garcia, Salvador; Benitez, Jose Manuel; Herrera, Francisco

    2016-03-01

    Discretization is one of the most relevant techniques for data preprocessing. The main goal of discretization is to transform numerical attributes into discrete ones to help the experts to understand the data more easily, and it also provides the possibility to use some learning algorithms which require discrete data as input, such as Bayesian or rule learning. We focus our attention on handling multivariate classification problems, where high interactions among multiple attributes exist. In this paper, we propose the use of evolutionary algorithms to select a subset of cut points that defines the best possible discretization scheme of a data set using a wrapper fitness function. We also incorporate a reduction mechanism to successfully manage the multivariate approach on large data sets. Our method has been compared with the best state-of-the-art discretizers on 45 real datasets. The experiments show that our proposed algorithm overcomes the rest of the methods producing competitive discretization schemes in terms of accuracy, for C4.5, Naive Bayes, PART, and PrUning and BuiLding Integrated in Classification classifiers; and obtained far simpler solutions. PMID:25794409

  20. Semi-automatic classification of glaciovolcanic landforms: An object-based mapping approach based on geomorphometry

    Science.gov (United States)

    Pedersen, G. B. M.

    2016-02-01

    A new object-oriented approach is developed to classify glaciovolcanic landforms (Procedure A) and their landform elements boundaries (Procedure B). It utilizes the principle that glaciovolcanic edifices are geomorphometrically distinct from lava shields and plains (Pedersen and Grosse, 2014), and the approach is tested on data from Reykjanes Peninsula, Iceland. The outlined procedures utilize slope and profile curvature attribute maps (20 m/pixel) and the classified results are evaluated quantitatively through error matrix maps (Procedure A) and visual inspection (Procedure B). In procedure A, the highest obtained accuracy is 94.1%, but even simple mapping procedures provide good results (> 90% accuracy). Successful classification of glaciovolcanic landform element boundaries (Procedure B) is also achieved and this technique has the potential to delineate the transition from intraglacial to subaerial volcanic activity in orthographic view. This object-oriented approach based on geomorphometry overcomes issues with vegetation cover, which has been typically problematic for classification schemes utilizing spectral data. Furthermore, it handles complex edifice outlines well and is easily incorporated into a GIS environment, where results can be edited or fused with other mapping results. The approach outlined here is designed to map glaciovolcanic edifices within the Icelandic neovolcanic zone but may also be applied to similar subaerial or submarine volcanic settings, where steep volcanic edifices are surrounded by flat plains.

  1. A new circulation type classification based upon Lagrangian air trajectories

    Science.gov (United States)

    Ramos, Alexandre; Sprenger, Michael; Wernli, Heini; Durán-Quesada, Ana María; Lorenzo, Maria Nieves; Gimeno, Luis

    2014-10-01

    A new classification method of the large-scale circulation characteristic for a specific target area (NW Iberian Peninsula) is presented, based on the analysis of 90-h backward trajectories arriving in this area calculated with the 3-D Lagrangian particle dispersion model FLEXPART. A cluster analysis is applied to separate the backward trajectories in up to five representative air streams for each day. Specific measures are then used to characterise the distinct air streams (e.g., curvature of the trajectories, cyclonic or anticyclonic flow, moisture evolution, origin and length of the trajectories). The robustness of the presented method is demonstrated in comparison with the Eulerian Lamb weather type classification. A case study of the 2003 heatwave is discussed in terms of the new Lagrangian circulation and the Lamb weather type classifications. It is shown that the new classification method adds valuable information about the pertinent meteorological conditions, which are missing in an Eulerian approach. The new method is climatologically evaluated for the five-year time period from December 1999 to November 2004. The ability of the method to capture the inter-seasonal circulation variability in the target region is shown. Furthermore, the multi-dimensional character of the classification is shortly discussed, in particular with respect to inter-seasonal differences. Finally, the relationship between the new Lagrangian classification and the precipitation in the target area is studied.

  2. A new circulation type classification based upon Lagrangian air trajectories

    Directory of Open Access Journals (Sweden)

    Alexandre M. Ramos

    2014-10-01

    Full Text Available A new classification method of the large-scale circulation characteristic for a specific target area (NW Iberian Peninsula is presented, based on the analysis of 90-h backward trajectories arriving in this area calculated with the 3-D Lagrangian particle dispersion model FLEXPART. A cluster analysis is applied to separate the backward trajectories in up to five representative air streams for each day. Specific measures are then used to characterise the distinct air streams (e.g., curvature of the trajectories, cyclonic or anticyclonic flow, moisture evolution, origin and length of the trajectories. The robustness of the presented method is demonstrated in comparison with the Eulerian Lamb weather type classification.A case study of the 2003 heatwave is discussed in terms of the new Lagrangian circulation and the Lamb weather type classifications. It is shown that the new classification method adds valuable information about the pertinent meteorological conditions, which are missing in an Eulerian approach. The new method is climatologically evaluated for the five-year time period from December 1999 to November 2004. The ability of the method to capture the inter-seasonal circulation variability in the target region is shown. Furthermore, the multi-dimensional character of the classification is shortly discussed, in particular with respect to inter-seasonal differences. Finally, the relationship between the new Lagrangian classification and the precipitation in the target area is studied.

  3. D Land Cover Classification Based on Multispectral LIDAR Point Clouds

    Science.gov (United States)

    Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

    2016-06-01

    Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

  4. Super pixel density based clustering automatic image classification method

    Science.gov (United States)

    Xu, Mingxing; Zhang, Chuan; Zhang, Tianxu

    2015-12-01

    The image classification is an important means of image segmentation and data mining, how to achieve rapid automated image classification has been the focus of research. In this paper, based on the super pixel density of cluster centers algorithm for automatic image classification and identify outlier. The use of the image pixel location coordinates and gray value computing density and distance, to achieve automatic image classification and outlier extraction. Due to the increased pixel dramatically increase the computational complexity, consider the method of ultra-pixel image preprocessing, divided into a small number of super-pixel sub-blocks after the density and distance calculations, while the design of a normalized density and distance discrimination law, to achieve automatic classification and clustering center selection, whereby the image automatically classify and identify outlier. After a lot of experiments, our method does not require human intervention, can automatically categorize images computing speed than the density clustering algorithm, the image can be effectively automated classification and outlier extraction.

  5. Failure diagnosis using deep belief learning based health state classification

    International Nuclear Information System (INIS)

    Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach

  6. An ensemble training scheme for machine-learning classification of Hyperion satellite imagery with independent hyperspectral libraries

    Science.gov (United States)

    Friedel, Michael; Buscema, Massimo

    2016-04-01

    A training scheme is proposed for the real-time classification of soil and vegetation (landscape) components in EO-1 Hyperion hyperspectral images. First, an auto-contractive map is used to compute connectivity of reflectance values for spectral bands (N=200) from independent laboratory spectral library components. Second, a minimum spanning tree is used to identify optimal grouping of training components from connectivity values. Third, the reflectance values for optimal landscape component signatures are sorted. Fourth, empirical distribution functions (EDF) are computed for each landscape component. Fifth, the Monte-Carlo technique is used to generate realizations (N=30) for each landscape EDF. The correspondence of component realizations to original signatures validates the stochastic procedure. Presentation of the realizations to the self-organizing map (SOM) is done using three different map sizes: 14x10, 28x20, and 40 x 30. In each case, the SOM training proceeds first with a rough phase (20 iterations using a Gaussian neighborhood with an initial and final radius of 11 units and 3 units) and then fine phase (400 iterations using a Gaussian neighborhood with an initial and final radius of 3 units and 1 unit). The initial and final learning rates of 0.5 and 0.05 decay linearly down to 10-5, and the Gaussian neighborhood function decreases exponentially (decay rate of 10-3 iteration-1) providing reasonable convergence. Following training of the three networks, each corresponding SOM is used to independently classify the original spectral library signatures. In comparing the different SOM networks, the 28x20 map size is chosen for independent reproducibility and processing speed. The corresponding universal distance matrix reveals separation of the seven component classes for this map size thereby supporting it use as a Hyperion classifier.

  7. Classification of Gait Types Based on the Duty-factor

    DEFF Research Database (Denmark)

    Fihl, Preben; Moeslund, Thomas B.

    2007-01-01

    This paper deals with classification of human gait types based on the notion that different gait types are in fact different types of locomotion, i.e., running is not simply walking done faster. We present the duty-factor, which is a descriptor based on this notion. The duty-factor is independent...... with known ground support. Silhouettes are extracted using the Codebook method and represented using Shape Contexts. The matching with database silhouettes is done using the Hungarian method. While manually estimated duty-factors show a clear classification the presented system contains...

  8. Improving PLS-RFE based gene selection for microarray data classification.

    Science.gov (United States)

    Wang, Aiguo; An, Ning; Chen, Guilin; Li, Lian; Alterovitz, Gil

    2015-07-01

    Gene selection plays a crucial role in constructing efficient classifiers for microarray data classification, since microarray data is characterized by high dimensionality and small sample sizes and contains irrelevant and redundant genes. In practical use, partial least squares-based gene selection approaches can obtain gene subsets of good qualities, but are considerably time-consuming. In this paper, we propose to integrate partial least squares based recursive feature elimination (PLS-RFE) with two feature elimination schemes: simulated annealing and square root, respectively, to speed up the feature selection process. Inspired from the strategy of annealing schedule, the two proposed approaches eliminate a number of features rather than one least informative feature during each iteration and the number of removed features decreases as the iteration proceeds. To verify the effectiveness and efficiency of the proposed approaches, we perform extensive experiments on six publicly available microarray data with three typical classifiers, including Naïve Bayes, K-Nearest-Neighbor and Support Vector Machine, and compare our approaches with ReliefF, PLS and PLS-RFE feature selectors in terms of classification accuracy and running time. Experimental results demonstrate that the two proposed approaches accelerate the feature selection process impressively without degrading the classification accuracy and obtain more compact feature subsets for both two-category and multi-category problems. Further experimental comparisons in feature subset consistency show that the proposed approach with simulated annealing scheme not only has better time performance, but also obtains slightly better feature subset consistency than the one with square root scheme. PMID:25912984

  9. An efficient entire chaos-based scheme for deniable authentication

    International Nuclear Information System (INIS)

    By using a chaotic encryption-hash parallel algorithm and the semi-group property of Chebyshev chaotic map, we propose a secure and efficient scheme for the deniable authentication. The scheme is efficient, practicable and reliable, with high potential to be adopted for e-commerce

  10. A novel chaotic encryption scheme based on arithmetic coding

    International Nuclear Information System (INIS)

    In this paper, under the combination of arithmetic coding and logistic map, a novel chaotic encryption scheme is presented. The plaintexts are encrypted and compressed by using an arithmetic coder whose mapping intervals are changed irregularly according to a keystream derived from chaotic map and plaintext. Performance and security of the scheme are also studied experimentally and theoretically in detail

  11. Directional wavelet based features for colonic polyp classification.

    Science.gov (United States)

    Wimmer, Georg; Tamaki, Toru; Tischendorf, J J W; Häfner, Michael; Yoshida, Shigeto; Tanaka, Shinji; Uhl, Andreas

    2016-07-01

    In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state

  12. An Efficient Semantic Model For Concept Based Clustering And Classification

    Directory of Open Access Journals (Sweden)

    SaiSindhu Bandaru

    2012-03-01

    Full Text Available Usually in text mining techniques the basic measures like term frequency of a term (word or phrase is computed to compute the importance of the term in the document. But with statistical analysis, the original semantics of the term may not carry the exact meaning of the term. To overcome this problem, a new framework has been introduced which relies on concept based model and synonym based approach. The proposed model can efficiently find significant matching and related concepts between documents according to concept based and synonym based approaches. Large sets of experiments using the proposed model on different set in clustering and classification are conducted. Experimental results demonstrate the substantialenhancement of the clustering quality using sentence based, document based, corpus based and combined approach concept analysis. A new similarity measure has been proposed to find the similarity between adocument and the existing clusters, which can be used in classification of the document with existing clusters.

  13. Comparative Analysis of Lossless Image Compression Based On Row By Row Classifier and Various Encoding Schemes on Color Images

    Directory of Open Access Journals (Sweden)

    Ramandeep Kaur,

    2014-08-01

    Full Text Available Lossless image compression is needed in many fields like medical imaging, telemetry, geophysics, remote sensing and other applications, which require exact replica of original image and loss of information is not tolerable. In this paper, a near lossless image compression algorithm based on row by row classifier with encoding schemes like Lempel Ziv Welch (LZW, Huffman and Run Length Encoding (RLE on color images is proposed. The algorithm divides the image into three parts R, G and B, apply row by row classification on each part and result of this classification is records in the mask image. After classification the image data is decomposed into two sequences each for R, G and B and mask image is hidden in them. These sequences are encoded using different encoding schemes like LZW, Huffman and RLE. An exhaustive comparative analysis is performed to evaluate these techniques, which reveals that the proposed algorithm have smaller bits per pixel (bpp than simple LZW, Huffman and RLE encoding techniques.

  14. A Quantum Multi-proxy Blind Signature Scheme Based on Genuine Four-Qubit Entangled State

    Science.gov (United States)

    Tian, Juan-Hong; Zhang, Jian-Zhong; Li, Yan-Ping

    2016-02-01

    In this paper, we propose a multi-proxy blind signature scheme based on controlled teleportation. Genuine four-qubit entangled state functions as quantum channel. The scheme uses the physical characteristics of quantum mechanics to implement delegation, signature and verification. The security analysis shows the scheme satisfies the security features of multi-proxy signature, unforgeability, undeniability, blindness and unconditional security.

  15. A GOST-like Blind Signature Scheme Based on Elliptic Curve Discrete Logarithm Problem

    OpenAIRE

    HOSSEINI, Hossein; Bahrak, Behnam; Hessar, Farzad

    2013-01-01

    In this paper, we propose a blind signature scheme and three practical educed schemes based on elliptic curve discrete logarithm problem. The proposed schemes impart the GOST signature structure and utilize the inherent advantage of elliptic curve cryptosystems in terms of smaller key size and lower computational overhead to its counterpart public key cryptosystems such as RSA and ElGamal. The proposed schemes are proved to be secure and have less time complexity in comparison with the existi...

  16. An Efficient ECDSA-Based Signature Scheme for Wireless Networks

    Institute of Scientific and Technical Information of China (English)

    XU Zhong; DAI Guanzhong; YANG Deming

    2006-01-01

    Wired equivalent security is difficult to provide in wireless networks due to high dynamics, wireless link vulnerability, and decentralization. The Elliptic Curve Digital Signature Algorithm(ECDSA) has been applied to wireless networks because of its low computational cost and short key size, which reduces the overheads in a wireless environment. This study improves the ECDSA scheme by reducing its time complexity. The significant advantage of the algorithm is that our new scheme needs not to calculate modular inverse operation in the phases of signature generation and signature verification. Such an improvement makes the proposed scheme more efficient and secure.

  17. Movie Popularity Classification based on Inherent Movie Attributes using C4.5, PART and Correlation Coefficient

    DEFF Research Database (Denmark)

    Ibnal Asad, Khalid; Ahmed, Tanvir; Rahman, Md. Saiedur

    2012-01-01

    Abundance of movie data across the internet makes it an obvious candidate for machine learning and knowledge discovery. But most researches are directed towards bi-polar classification of movie or generation of a movie recommendation system based on reviews given by viewers on various internet...... sites. Classification of movie popularity based solely on attributes of a movie i.e. actor, actress, director rating, language, country and budget etc. has been less highlighted due to large number of attributes that are associated with each movie and their differences in dimensions. In this paper, we...... propose classification scheme of pre-release movie popularity based on inherent attributes using C4.S and PART classifier algorithm and define the relation between attributes of post release movies using correlation coefficient....

  18. Classification and Target Group Selection Based Upon Frequent Patterns

    NARCIS (Netherlands)

    W.H.L.M. Pijls (Wim); R. Potharst (Rob)

    2000-01-01

    textabstractIn this technical report , two new algorithms based upon frequent patterns are proposed. One algorithm is a classification method. The other one is an algorithm for target group selection. In both algorithms, first of all, the collection of frequent patterns in the training set is constr

  19. Time Series Classification by Class-Based Mahalanobis Distances

    CERN Document Server

    Prekopcsák, Zoltán

    2010-01-01

    To classify time series by nearest neighbor, we need to specify or learn a distance. We consider several variations of the Mahalanobis distance and the related Large Margin Nearest Neighbor Classification (LMNN). We find that the conventional Mahalanobis distance is counterproductive. However, both LMNN and the class-based diagonal Mahalanobis distance are competitive.

  20. Classification-Based Method of Linear Multicriteria Optimization

    OpenAIRE

    Vassilev, Vassil; Genova, Krassimira; Vassileva, Mariyana; Narula, Subhash

    2003-01-01

    The paper describes a classification-based learning-oriented interactive method for solving linear multicriteria optimization problems. The method allows the decision makers describe their preferences with greater flexibility, accuracy and reliability. The method is realized in an experimental software system supporting the solution of multicriteria optimization problems.

  1. Hierarchical Real-time Network Traffic Classification Based on ECOC

    Directory of Open Access Journals (Sweden)

    Yaou Zhao

    2013-09-01

    Full Text Available Classification of network traffic is basic and essential for manynetwork researches and managements. With the rapid development ofpeer-to-peer (P2P application using dynamic port disguisingtechniques and encryption to avoid detection, port-based and simplepayload-based network traffic classification methods were diminished.An alternative method based on statistics and machine learning hadattracted researchers' attention in recent years. However, most ofthe proposed algorithms were off-line and usually used a single classifier.In this paper a new hierarchical real-time model was proposed which comprised of a three tuple (source ip, destination ip and destination portlook up table(TT-LUT part and layered milestone part. TT-LUT was used to quickly classify short flows whichneed not to pass the layered milestone part, and milestones in layered milestone partcould classify the other flows in real-time with the real-time feature selection and statistics.Every milestone was a ECOC(Error-Correcting Output Codes based model which was usedto improve classification performance. Experiments showed that the proposedmodel can improve the efficiency of real-time to 80%, and themulti-class classification accuracy encouragingly to 91.4% on the datasets which had been captured from the backbone router in our campus through a week.

  2. Optimizing Mining Association Rules for Artificial Immune System based Classification

    Directory of Open Access Journals (Sweden)

    SAMEER DIXIT

    2011-08-01

    Full Text Available The primary function of a biological immune system is to protect the body from foreign molecules known as antigens. It has great pattern recognition capability that may be used to distinguish between foreigncells entering the body (non-self or antigen and the body cells (self. Immune systems have many characteristics such as uniqueness, autonomous, recognition of foreigners, distributed detection, and noise tolerance . Inspired by biological immune systems, Artificial Immune Systems have emerged during the last decade. They are incited by many researchers to design and build immune-based models for a variety of application domains. Artificial immune systems can be defined as a computational paradigm that is inspired by theoretical immunology, observed immune functions, principles and mechanisms. Association rule mining is one of the most important and well researched techniques of data mining. The goal of association rules is to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in thetransaction databases or other data repositories. Association rules are widely used in various areas such as inventory control, telecommunication networks, intelligent decision making, market analysis and risk management etc. Apriori is the most widely used algorithm for mining the association rules. Other popular association rule mining algorithms are frequent pattern (FP growth, Eclat, dynamic itemset counting (DIC etc. Associative classification uses association rule mining in the rule discovery process to predict the class labels of the data. This technique has shown great promise over many other classification techniques. Associative classification also integrates the process of rule discovery and classification to build the classifier for the purpose of prediction. The main problem with the associative classification approach is the discovery of highquality association rules in a very large space of

  3. TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

    OpenAIRE

    Li, N.; Liu, C; Pfeifer, N; Yin, J. F.; Liao, Z.Y.; Zhou, Y.

    2016-01-01

    Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could kee...

  4. Pulse frequency classification based on BP neural network

    Institute of Scientific and Technical Information of China (English)

    WANG Rui; WANG Xu; YANG Dan; FU Rong

    2006-01-01

    In Traditional Chinese Medicine (TCM), it is an important parameter of the clinic disease diagnosis to analysis the pulse frequency. This article accords to pulse eight major essentials to identify pulse type of the pulse frequency classification based on back-propagation neural networks (BPNN). The pulse frequency classification includes slow pulse, moderate pulse, rapid pulse etc. By feature parameter of the pulse frequency analysis research and establish to identify system of pulse frequency features. The pulse signal from detecting system extracts period, frequency etc feature parameter to compare with standard feature value of pulse type. The result shows that identify-rate attains 92.5% above.

  5. Classification of CT-brain slices based on local histograms

    Science.gov (United States)

    Avrunin, Oleg G.; Tymkovych, Maksym Y.; Pavlov, Sergii V.; Timchik, Sergii V.; Kisała, Piotr; Orakbaev, Yerbol

    2015-12-01

    Neurosurgical intervention is a very complicated process. Modern operating procedures based on data such as CT, MRI, etc. Automated analysis of these data is an important task for researchers. Some modern methods of brain-slice segmentation use additional data to process these images. Classification can be used to obtain this information. To classify the CT images of the brain, we suggest using local histogram and features extracted from them. The paper shows the process of feature extraction and classification CT-slices of the brain. The process of feature extraction is specialized for axial cross-section of the brain. The work can be applied to medical neurosurgical systems.

  6. AN EFFICIENT CLASSIFICATION OF GENOMES BASED ON CLASSES AND SUBCLASSES

    Directory of Open Access Journals (Sweden)

    B.V. DHANDRA,

    2010-08-01

    Full Text Available The grass family has been the subject of intense research over the past. Reliable and fast classification / sub-classification of large sequences which are rapidly gaining importance due to genome sequencing projects all over the world is contributing large amount of genome sequences to public gene bank . Hence sequence classification has gained importance for predicting the genome function, structure, evolutionary relationships and also gives the insight into the features associated with the biological role of the class. Thus, classification of functional genome is an important andchallenging task to both computer scientists and biologists. The presence of motifs in grass genome chains predicts the functional behavior of the grass genome. The correlation between grass genome properties and their motifs is not always obvious since more than one motif may exist within a genome chain. Due to the complexity of this association most of the data mining algorithms are either non efficient or time consuming. Hence, in this paper we proposed an efficient method for main classes based on classes to reduce the time complexity for the classification of large sequences of grass genomes dataset. The proposed approaches classify the given dataset into classes with conserved threshold and again reclassify the class relaxed threshold into major classes. Experimental results indicate that the proposed method reduces the time complexity keepingclassification accuracy level as that compared with general NNCalgorithm.

  7. Online Network Traffic Classification Algorithm Based on RVM

    Directory of Open Access Journals (Sweden)

    Zhang Qunhui

    2013-06-01

    Full Text Available Since compared with the Support Vector Machine (SVM, the Relevance Vector Machine (RVM not only has the advantage of avoiding the over- learn which is the characteristic of the SVM, but also greatly reduces the amount of computation of the kernel function and avoids the defects of the SVM that the scarcity is not strong, the large amount of calculation as well as the kernel function must satisfy the Mercer's condition and that human empirically determined parameters, so we proposed a new online traffic classification algorithm base on the RVM for this purpose. Through the analysis of the basic principles of RVM and the steps of the modeling, we made use of the training traffic classification model of the RVM to identify the network traffic in the real time through this model and the “port number+ DPI”. When the RVM predicts that the probability is in the query interval, we jointly used the "port number" and "DPI". Finally, we made a detailed experimental validation which shows that: compared with the Support Vector Machine (SVM network traffic classification algorithm, this algorithm can achieve the online network traffic classification, and the classification predication probability is greatly improved.

  8. A rhythm-based authentication scheme for smart media devices.

    Science.gov (United States)

    Lee, Jae Dong; Jeong, Young-Sik; Park, Jong Hyuk

    2014-01-01

    In recent years, ubiquitous computing has been rapidly emerged in our lives and extensive studies have been conducted in a variety of areas related to smart devices, such as tablets, smartphones, smart TVs, smart refrigerators, and smart media devices, as a measure for realizing the ubiquitous computing. In particular, smartphones have significantly evolved from the traditional feature phones. Increasingly higher-end smartphone models that can perform a range of functions are now available. Smart devices have become widely popular since they provide high efficiency and great convenience for not only private daily activities but also business endeavors. Rapid advancements have been achieved in smart device technologies to improve the end users' convenience. Consequently, many people increasingly rely on smart devices to store their valuable and important data. With this increasing dependence, an important aspect that must be addressed is security issues. Leaking of private information or sensitive business data due to loss or theft of smart devices could result in exorbitant damage. To mitigate these security threats, basic embedded locking features are provided in smart devices. However, these locking features are vulnerable. In this paper, an original security-locking scheme using a rhythm-based locking system (RLS) is proposed to overcome the existing security problems of smart devices. RLS is a user-authenticated system that addresses vulnerability issues in the existing locking features and provides secure confidentiality in addition to convenience. PMID:25110743

  9. A Rhythm-Based Authentication Scheme for Smart Media Devices

    Directory of Open Access Journals (Sweden)

    Jae Dong Lee

    2014-01-01

    Full Text Available In recent years, ubiquitous computing has been rapidly emerged in our lives and extensive studies have been conducted in a variety of areas related to smart devices, such as tablets, smartphones, smart TVs, smart refrigerators, and smart media devices, as a measure for realizing the ubiquitous computing. In particular, smartphones have significantly evolved from the traditional feature phones. Increasingly higher-end smartphone models that can perform a range of functions are now available. Smart devices have become widely popular since they provide high efficiency and great convenience for not only private daily activities but also business endeavors. Rapid advancements have been achieved in smart device technologies to improve the end users’ convenience. Consequently, many people increasingly rely on smart devices to store their valuable and important data. With this increasing dependence, an important aspect that must be addressed is security issues. Leaking of private information or sensitive business data due to loss or theft of smart devices could result in exorbitant damage. To mitigate these security threats, basic embedded locking features are provided in smart devices. However, these locking features are vulnerable. In this paper, an original security-locking scheme using a rhythm-based locking system (RLS is proposed to overcome the existing security problems of smart devices. RLS is a user-authenticated system that addresses vulnerability issues in the existing locking features and provides secure confidentiality in addition to convenience.

  10. Digital Signature Scheme Based on a New Hard Problem

    Directory of Open Access Journals (Sweden)

    Nikolay A. Moldovyan

    2008-07-01

    Full Text Available Factorizing composite number n=qr, where q and r are two large primes, and finding discrete logarithm modulo large prime number p are two difficult computational problems which are usually put into the base of different digital signature schemes (DSSes. This paper introduces a new hard computational problem that consists in finding the k th roots modulo large prime p=Nk2+1 where N is an even number and k is a prime with the length |k|≥160. Difficulty of the last problem is estimated as O(√k. It is proposed a new DSS with the public key xkmodp, where x is the private key. The signature corresponding to some message M represents a pair of the |p|$-bit numbers S and R calculated as follows: R=tk mod p and S=txf(R,Mmodp, where f(R, M is a compression function. The verification equation is Sk mod p=yf(R, MRmodp. The DSS is used to implement an efficient protocol for generating collective digital signatures.

  11. Torrent classification - Base of rational management of erosive regions

    Energy Technology Data Exchange (ETDEWEB)

    Gavrilovic, Zoran; Stefanovic, Milutin; Milovanovic, Irina; Cotric, Jelena; Milojevic, Mileta [Institute for the Development of Water Resources ' Jaroslav Cerni' , 11226 Beograd (Pinosava), Jaroslava Cernog 80 (Serbia)], E-mail: gavrilovicz@sbb.rs

    2008-11-01

    A complex methodology for torrents and erosion and the associated calculations was developed during the second half of the twentieth century in Serbia. It was the 'Erosion Potential Method'. One of the modules of that complex method was focused on torrent classification. The module enables the identification of hydro graphic, climate and erosion characteristics. The method makes it possible for each torrent, regardless of its magnitude, to be simply and recognizably described by the 'Formula of torrentially'. The above torrent classification is the base on which a set of optimisation calculations is developed for the required scope of erosion-control works and measures, the application of which enables the management of significantly larger erosion and torrential regions compared to the previous period. This paper will present the procedure and the method of torrent classification.

  12. Torrent classification - Base of rational management of erosive regions

    International Nuclear Information System (INIS)

    A complex methodology for torrents and erosion and the associated calculations was developed during the second half of the twentieth century in Serbia. It was the 'Erosion Potential Method'. One of the modules of that complex method was focused on torrent classification. The module enables the identification of hydro graphic, climate and erosion characteristics. The method makes it possible for each torrent, regardless of its magnitude, to be simply and recognizably described by the 'Formula of torrentially'. The above torrent classification is the base on which a set of optimisation calculations is developed for the required scope of erosion-control works and measures, the application of which enables the management of significantly larger erosion and torrential regions compared to the previous period. This paper will present the procedure and the method of torrent classification.

  13. Experimental quantum-cryptography scheme based on orthogonal states

    International Nuclear Information System (INIS)

    Since, in general, nonorthogonal states cannot be cloned, any eavesdropping attempt in a quantum-communication scheme using nonorthogonal states as carriers of information introduces some errors in the transmission, leading to the possibility of detecting the spy. Usually, orthogonal states are not used in quantum-cryptography schemes since they can be faithfully cloned without altering the transmitted data. Nevertheless, L. Goldberg and L. Vaidman [Phys. Rev. Lett. 75, 1239 (1995)] proposed a protocol in which, even if the data exchange is realized using two orthogonal states, any attempt to eavesdrop is detectable by the legal users. In this scheme the orthogonal states are superpositions of two localized wave packets traveling along separate channels. Here we present an experiment realizing this scheme.

  14. Arbitrated quantum signature scheme based on cluster states

    Science.gov (United States)

    Yang, Yu-Guang; Lei, He; Liu, Zhi-Chao; Zhou, Yi-Hua; Shi, Wei-Min

    2016-03-01

    Cluster states can be exploited for some tasks such as topological one-way computation, quantum error correction, teleportation and dense coding. In this paper, we investigate and propose an arbitrated quantum signature scheme with cluster states. The cluster states are used for quantum key distribution and quantum signature. The proposed scheme can achieve an efficiency of 100 %. Finally, we also discuss its security against various attacks.

  15. Arbitrated quantum signature scheme based on cluster states

    Science.gov (United States)

    Yang, Yu-Guang; Lei, He; Liu, Zhi-Chao; Zhou, Yi-Hua; Shi, Wei-Min

    2016-06-01

    Cluster states can be exploited for some tasks such as topological one-way computation, quantum error correction, teleportation and dense coding. In this paper, we investigate and propose an arbitrated quantum signature scheme with cluster states. The cluster states are used for quantum key distribution and quantum signature. The proposed scheme can achieve an efficiency of 100 %. Finally, we also discuss its security against various attacks.

  16. Fast rule-based bioactivity prediction using associative classification mining

    Directory of Open Access Journals (Sweden)

    Yu Pulan

    2012-11-01

    Full Text Available Abstract Relating chemical features to bioactivities is critical in molecular design and is used extensively in the lead discovery and optimization process. A variety of techniques from statistics, data mining and machine learning have been applied to this process. In this study, we utilize a collection of methods, called associative classification mining (ACM, which are popular in the data mining community, but so far have not been applied widely in cheminformatics. More specifically, classification based on predictive association rules (CPAR, classification based on multiple association rules (CMAR and classification based on association rules (CBA are employed on three datasets using various descriptor sets. Experimental evaluations on anti-tuberculosis (antiTB, mutagenicity and hERG (the human Ether-a-go-go-Related Gene blocker datasets show that these three methods are computationally scalable and appropriate for high speed mining. Additionally, they provide comparable accuracy and efficiency to the commonly used Bayesian and support vector machines (SVM methods, and produce highly interpretable models.

  17. Index-based reactive power compensation scheme for voltage regulation

    Science.gov (United States)

    Dike, Damian Obioma

    2008-10-01

    Increasing demand for electrical power arising from deregulation and the restrictions posed to the construction of new transmission lines by environment, socioeconomic, and political issues had led to higher grid loading. Consequently, voltage instability has become a major concern, and reactive power support is vital to enhance transmission grid performance. Improved reactive power support to distressed grid is possible through the application of relatively unfamiliar emerging technologies of "Flexible AC Transmission Systems (FACTS)" devices and "Distributed Energy Resources (DERS)." In addition to these infrastructure issues, a lack of situational awareness by system operators can cause major power outages as evidenced by the August 14, 2003 widespread North American blackout. This and many other recent major outages have highlighted the inadequacies of existing power system indexes. In this work, a novel "Index-based reactive compensation scheme" appropriate for both on-line and off-line computation of grid status has been developed. A new voltage stability index (Ls-index) suitable for long transmission lines was developed, simulated, and compared to the existing two-machine modeled L-index. This showed the effect of long distance power wheeling amongst regional transmission organizations. The dissertation further provided models for index modulated voltage source converters (VSC) and index-based load flow analysis of both FACTS and microgrid interconnected power systems using the Newton-Raphson's load flow model incorporated with multi-FACTS devices. The developed package has been made user-friendly through the embodiment of interactive graphical user interface and implemented on the IEEE 14, 30, and 300 bus systems. The results showed reactive compensation has system wide-effect, provided readily accessible system status indicators, ensured seamless DERs interconnection through new islanding modes and enhanced VSC utilization. These outcomes may contribute

  18. The normalization of citation counts based on classification systems

    CERN Document Server

    Bornmann, Lutz; Barth, Andreas

    2013-01-01

    If we want to assess whether the paper in question has had a particularly high or low citation impact compared to other papers, the standard practice in bibliometrics is to normalize citations in respect of the subject category and publication year. A number of proposals for an improved procedure in the normalization of citation impact have been put forward in recent years. Against the background of these proposals this study describes an ideal solution for the normalization of citation impact: in a first step, the reference set for the publication in question is collated by means of a classification scheme, where every publication is associated with a single principal research field or subfield entry (e. g. via Chemical Abstracts sections) and a publication year. In a second step, percentiles of citation counts are calculated for this set and used to assign the normalized citation impact score to the publications (and also to the publication in question).

  19. The Normalization of Citation Counts Based on Classification Systems

    Directory of Open Access Journals (Sweden)

    Andreas Barth

    2013-08-01

    Full Text Available If we want to assess whether the paper in question has had a particularly high or low citation impact compared to other papers, the standard practice in bibliometrics is to normalize citations in respect of the subject category and publication year. A number of proposals for an improved procedure in the normalization of citation impact have been put forward in recent years. Against the background of these proposals, this study describes an ideal solution for the normalization of citation impact: in a first step, the reference set for the publication in question is collated by means of a classification scheme, where every publication is associated with a single principal research field or subfield entry (e.g., via Chemical Abstracts sections and a publication year. In a second step, percentiles of citation counts are calculated for this set and used to assign the normalized citation impact score to the publications (and also to the publication in question.

  20. Dynamic Symmetric Key Mobile Commerce Scheme Based on Self-Verified Mechanism

    OpenAIRE

    2014-01-01

    In terms of the security and efficiency of mobile e-commerce, the authors summarized the advantages and disadvantages of several related schemes, especially the self-verified mobile payment scheme based on the elliptic curve cryptosystem (ECC) and then proposed a new type of dynamic symmetric key mobile commerce scheme based on self-verified mechanism. The authors analyzed the basic algorithm based on self-verified mechanisms and detailed the complete transaction process of the proposed schem...

  1. CONSTRUCTION OF PROXY BLIND SIGNATURE SCHEME BASED ON MULTI-LINEAR TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Zhao Zemao; Liu Fengyu

    2004-01-01

    A general method of constructing proxy blind signature is proposed based on multilinear transform. Based on this method, the four proxy blind signature schemes are correspondently generated with four different signature equations, and each of them has four forms of variations of signs. Hence there are sixteen signatures in all, and all of them are proxy stronglyblind signature schemes. Furthermore, the two degenerated situations of multi-linear transform are discussed. Their corresponding proxy blind signature schemes are shown, too. But some schemes come from one of these degenerate situations are proxy weakly-blind signature scheme.The security for proposed scheme is analyzed in details. The results indicate that these signature schemes have many good properties such as unforgeability, distinguish-ability of proxy signature,non-repudiation and extensive value of application etc.

  2. A secure biometrics-based authentication scheme for telecare medicine information systems.

    Science.gov (United States)

    Yan, Xiaopeng; Li, Weiheng; Li, Ping; Wang, Jiantao; Hao, Xinhong; Gong, Peng

    2013-10-01

    The telecare medicine information system (TMIS) allows patients and doctors to access medical services or medical information at remote sites. Therefore, it could bring us very big convenient. To safeguard patients' privacy, authentication schemes for the TMIS attracted wide attention. Recently, Tan proposed an efficient biometrics-based authentication scheme for the TMIS and claimed their scheme could withstand various attacks. However, in this paper, we point out that Tan's scheme is vulnerable to the Denial-of-Service attack. To enhance security, we also propose an improved scheme based on Tan's work. Security and performance analysis shows our scheme not only could overcome weakness in Tan's scheme but also has better performance. PMID:23996083

  3. DESIGN OF A DIGITAL SIGNATURE SCHEME BASED ON FACTORING AND DISCRETE LOGARITHMS

    Institute of Scientific and Technical Information of China (English)

    杨利英; 覃征; 胡广伍; 王志敏

    2004-01-01

    Objective Focusing on the security problem of authentication and confidentiality in the context of computer networks, a digital signature scheme was proposed based on the public key cryptosystem. Methods Firstly, the course of digital signature based on the public key cryptosystem was given. Then, RSA and ELGamal schemes were described respectively. They were the basis of the proposed scheme. Generalized ELGamal type signature schemes were listed. After comparing with each other, one scheme, whose Signature equation was (m+r)x=j+s modΦ(p) , was adopted in the designing. Results Based on two well-known cryptographic assumptions, the factorization and the discrete logarithms, a digital signature scheme was presented. It must be required that s' was not equal to p'q' in the signing procedure, because attackers could forge the signatures with high probabilities if the discrete logarithms modulo a large prime were solvable. The variable public key "e" is used instead of the invariable parameter "3" in Harns signature scheme to enhance the security. One generalized ELGamal type scheme made the proposed scheme escape one multiplicative inverse operation in the signing procedure and one modular exponentiation in the verification procedure. Conclusion The presented scheme obtains the security that Harn's scheme was originally claimed. It is secure if the factorization and the discrete logarithms are simultaneously unsolvable.

  4. Linear Models Based on Noisy Data and the Frisch Scheme*

    Science.gov (United States)

    Ning, Lipeng; Georgiou, Tryphon T.; Tannenbaum, Allen; Boyd, Stephen P.

    2016-01-01

    We address the problem of identifying linear relations among variables based on noisy measurements. This is a central question in the search for structure in large data sets. Often a key assumption is that measurement errors in each variable are independent. This basic formulation has its roots in the work of Charles Spearman in 1904 and of Ragnar Frisch in the 1930s. Various topics such as errors-in-variables, factor analysis, and instrumental variables all refer to alternative viewpoints on this problem and on ways to account for the anticipated way that noise enters the data. In the present paper we begin by describing certain fundamental contributions by the founders of the field and provide alternative modern proofs to certain key results. We then go on to consider a modern viewpoint and novel numerical techniques to the problem. The central theme is expressed by the Frisch–Kalman dictum, which calls for identifying a noise contribution that allows a maximal number of simultaneous linear relations among the noise-free variables—a rank minimization problem. In the years since Frisch’s original formulation, there have been several insights, including trace minimization as a convenient heuristic to replace rank minimization. We discuss convex relaxations and theoretical bounds on the rank that, when met, provide guarantees for global optimality. A complementary point of view to this minimum-rank dictum is presented in which models are sought leading to a uniformly optimal quadratic estimation error for the error-free variables. Points of contact between these formalisms are discussed, and alternative regularization schemes are presented. PMID:27168672

  5. Automatic classification of eclipsing binaries light curves using neural networks

    CERN Document Server

    Sarro, L M; Giménez, A

    2005-01-01

    In this work we present a system for the automatic classification of the light curves of eclipsing binaries. This system is based on a classification scheme that aims to separate eclipsing binary sistems according to their geometrical configuration in a modified version of the traditional classification scheme. The classification is performed by a Bayesian ensemble of neural networks trained with {\\em Hipparcos} data of seven different categories including eccentric binary systems and two types of pulsating light curve morphologies.

  6. An arbitrated quantum signature scheme based on entanglement swapping with signer anonymity

    International Nuclear Information System (INIS)

    In this paper an arbitrated quantum signature scheme based on entanglement swapping is proposed. In this scheme a message to be signed is coded with unitary operators. Combining quantum measurement with quantum encryption, the signer can generate the signature for a given message. Combining the entangled states generated by the TTP's Bell measurement with the signature information, the verifier can verify the authentication of a signature through a single quantum state measurement. Compared with previous schemes, our scheme is more efficient and less complex, furthermore, our scheme can ensure the anonymity of the signer. (general)

  7. An arbitrated quantum signature scheme based on entanglement swapping with signer anonymity

    Science.gov (United States)

    Li, Wei; Fan, Ming-Yu; Wang, Guang-Wei

    2012-12-01

    In this paper an arbitrated quantum signature scheme based on entanglement swapping is proposed. In this scheme a message to be signed is coded with unitary operators. Combining quantum measurement with quantum encryption, the signer can generate the signature for a given message. Combining the entangled states generated by the TTP's Bell measurement with the signature information, the verifier can verify the authentication of a signature through a single quantum state measurement. Compared with previous schemes, our scheme is more efficient and less complex, furthermore, our scheme can ensure the anonymity of the signer.

  8. Vertical diffuse attenuation coefficient () based optical classification of IRS-P3 MOS-B satellite ocean colour data

    Indian Academy of Sciences (India)

    R K Sarangi; Prakash Chauhan; S R Nayak

    2002-09-01

    The optical classification of the different water types provides vital input for studies related to primary productivity, water clarity and determination of euphotic depth. Image data of the IRS- P3 MOS-B, for Path 90 of 27th February, 1998 was used for deriving vertical diffuse attenuation Coeffcient () and an optical classification based on values was performed. An atmospheric correction scheme was used for retrieving water leaving radiances in blue and green channels of 412, 443, 490 and 550 nm. The upwelling radiances from 443nm and 550nm spectral channels were used for computation of vertical diffuse attenuation coeffcient at 490 nm. The waters off the Gujarat coast were classified into different water types based on Jerlov classification scheme. The oceanic water type IA ( range 0.035-0.040m-1), type IB (0.042-0.065m-1), type II (0.07-0.1m-1) and type III (0.115-0.14m-1) were identified. For the coastal waters along Gujarat coast and Gulf of Kachchh, (490) values ranged between 0.15m-1 and 0.35m-1. The depth of 1% of surface light for water type IA, IB, II and III corresponds to 88, 68, 58 and 34 meters respectively. Classification of oceanic and coastal waters based on is useful in understanding the light transmission characteristics for sub-marine navigation and under-water imaging.

  9. Novel Sequence Number Based Secure Authentication Scheme for Wireless LANs

    Institute of Scientific and Technical Information of China (English)

    Rajeev Singh; Teek Parval Sharma

    2015-01-01

    Authentication per frame is an implicit necessity for security in wireless local area networks (WLANs). We propose a novel per frame secure authentication scheme which provides authentication to data frames in WLANs. The scheme involves no cryptographic overheads for authentication of frames. It utilizes the sequence number of the frame along with the authentication stream generators for authentication. Hence, it requires no extra bits or messages for the authentication purpose and also no change in the existing frame format is required. The scheme provides authentication by modifying the sequence number of the frame at the sender, and that the modification is verified at the receiver. The modified sequence number is protected by using the XOR operation with a random number selected from the random stream. The authentication is lightweight due to the fact that it requires only trivial arithmetic operations like the subtraction and XOR operation.

  10. Classification of Regional Ionospheric Disturbances Based on Support Vector Machines

    Science.gov (United States)

    Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil

    2016-07-01

    Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification

  11. Upper limit for context based crop classification

    DEFF Research Database (Denmark)

    Midtiby, Henrik; Åstrand, Björn; Jørgensen, Rasmus Nyholm;

    2012-01-01

    Mechanical in-row weed control of crops like sugarbeet require precise knowledge of where individual crop plants are located. If crop plants are placed in known pattern, information about plant locations can be used to discriminate between crop and weed plants. The success rate of such a classifier...... depends on the weed pressure, the position uncertainty of the crop plants and the crop upgrowth percentage. The first two measures can be combined to a normalized weed pressure, \\lambda. Given the normalized weed pressure an upper bound on the positive predictive value is shown to be 1/(1+\\lambda). If the...... weed pressure is \\rho = 400/m^2 and the crop position uncertainty is \\sigma_x = 0.0148m along the row and \\sigma_y = 0.0108m perpendicular to the row, the normalized weed pressure is \\lambda ~ 0.40$; the upper bound on the positive predictive value is then 0.71. This means that when a position based...

  12. Object-Based Classification and Change Detection of Hokkaido, Japan

    Science.gov (United States)

    Park, J. G.; Harada, I.; Kwak, Y.

    2016-06-01

    Topography and geology are factors to characterize the distribution of natural vegetation. Topographic contour is particularly influential on the living conditions of plants such as soil moisture, sunlight, and windiness. Vegetation associations having similar characteristics are present in locations having similar topographic conditions unless natural disturbances such as landslides and forest fires or artificial disturbances such as deforestation and man-made plantation bring about changes in such conditions. We developed a vegetation map of Japan using an object-based segmentation approach with topographic information (elevation, slope, slope direction) that is closely related to the distribution of vegetation. The results found that the object-based classification is more effective to produce a vegetation map than the pixel-based classification.

  13. A secure quantum group signature scheme based on Bell states

    International Nuclear Information System (INIS)

    In this paper, we propose a new secure quantum group signature with Bell states, which may have applications in e-payment system, e-government, e-business, etc. Compared with the recent quantum group signature protocols, our scheme is focused on the most general situation in practice, i.e. only the arbitrator is trusted and no intermediate information needs to be stored in the signing phase to ensure the security. Furthermore, our scheme has achieved all the characteristics of group signature—anonymity, verifiability, traceability, unforgetability and undeniability, by using some current developed quantum and classical technologies. Finally, a feasible security analysis model for quantum group signature is presented. (paper)

  14. New Steganographic Scheme Based Of Reed- Solomon Codes

    Directory of Open Access Journals (Sweden)

    DIOP

    2012-04-01

    Full Text Available Modern steganography [1] is a new science that makes a secret communication. Using the technique of Matrix Embedding in steganography schemes tends to reduce distortion during insertion. Recently, Fontaine and Galand [2] showed that the Reed-Solomon codes can be good tools for the design of a Steganographic scheme. In this paper, we present an implementation of the technique Matrix Embedding using the Reed-Solomon codes. The advantage of these codes is that they allow easy way to solve the problem of bounded syndrome decoding, a problem which is the basis of the technique of embedding matrix.

  15. A New Digital Signature Scheme Based on Factoring and Discrete Logarithms

    Directory of Open Access Journals (Sweden)

    E. S. Ismail

    2008-01-01

    Full Text Available Problem statement: A digital signature scheme allows one to sign an electronic message and later the produced signature can be validated by the owner of the message or by any verifier. Most of the existing digital signature schemes were developed based on a single hard problem like factoring, discrete logarithm, residuosity or elliptic curve discrete logarithm problems. Although these schemes appear secure, one day in a near future they may be exploded if one finds a solution of the single hard problem. Approach: To overcome this problem, in this study, we proposed a new signature scheme based on multiple hard problems namely factoring and discrete logarithms. We combined the two problems into both signing and verifying equations such that the former depends on two secret keys whereas the latter depends on two corresponding public keys. Results: The new scheme was shown to be secure against the most five considering attacks for signature schemes. The efficiency performance of our scheme only requires 1203Tmul+Th time complexity for signature generation and 1202Tmul+Th time complexity for verification generation and this magnitude of complexity is considered minimal for multiple hard problems-like signature schemes. Conclusions: The new signature scheme based on multiple hard problems provides longer and higher security level than that scheme based on one problem. This is because no enemy can solve multiple hard problems simultaneously.

  16. A training-based scheme for communicating over unknown channels with feedback

    CERN Document Server

    Mahajan, Aditya

    2009-01-01

    We consider communication with noiseless feedback over a channel that is either BSC(p) or BSC(1-p); neither the transmitter nor the receiver know which one. The parameter $p \\in [0, 1/2]$ is known to both. We propose a variable length training-based scheme for this channel. The error exponent of this scheme is within a constant fraction of the best possible error exponent. Thus, contrary to popular belief, variable length training-based schemes need not have poor error exponents. Moreover, training-based schemes can preserve the main advantage of feedback -- an error exponent with non-zero slope at rates close to capacity.

  17. A New Loss-Tolerant Image Encryption Scheme Based on Secret Sharing and Two Chaotic Systems

    OpenAIRE

    Li Li; Ahmed A. Abd El-Latif; Zhenfeng Shi and Xiamu Niu

    2012-01-01

    In this study, we propose an efficient loss-tolerant image encryption scheme that protects both confidentiality and loss-tolerance simultaneously in shadow images. In this scheme, we generate the key sequence based on two chaotic maps and then encrypt the image during the sharing phase based on Shamir’s method. Experimental results show a better performance of the proposed scheme for different images than other methods from human vision. Security analysis confirms a high probability to resist...

  18. Metagenome fragment classification based on multiple motif-occurrence profiles

    Directory of Open Access Journals (Sweden)

    Naoki Matsushita

    2014-09-01

    Full Text Available A vast amount of metagenomic data has been obtained by extracting multiple genomes simultaneously from microbial communities, including genomes from uncultivable microbes. By analyzing these metagenomic data, novel microbes are discovered and new microbial functions are elucidated. The first step in analyzing these data is sequenced-read classification into reference genomes from which each read can be derived. The Naïve Bayes Classifier is a method for this classification. To identify the derivation of the reads, this method calculates a score based on the occurrence of a DNA sequence motif in each reference genome. However, large differences in the sizes of the reference genomes can bias the scoring of the reads. This bias might cause erroneous classification and decrease the classification accuracy. To address this issue, we have updated the Naïve Bayes Classifier method using multiple sets of occurrence profiles for each reference genome by normalizing the genome sizes, dividing each genome sequence into a set of subsequences of similar length and generating profiles for each subsequence. This multiple profile strategy improves the accuracy of the results generated by the Naïve Bayes Classifier method for simulated and Sargasso Sea datasets.

  19. Comparison Of Power Quality Disturbances Classification Based On Neural Network

    Directory of Open Access Journals (Sweden)

    Nway Nway Kyaw Win

    2015-07-01

    Full Text Available Abstract Power quality disturbances PQDs result serious problems in the reliability safety and economy of power system network. In order to improve electric power quality events the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis MRA algorithm and feed forward neural network probabilistic and multilayer feed forward neural network based methodology for automatic classification of eight types of PQ signals flicker harmonics sag swell impulse fluctuation notch and oscillatory will be presented. The wavelet family Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore it can be seen that PNN has better classification accuracy than MLFF.

  20. Deflection routing scheme for GMPLS-based OBS networks

    DEFF Research Database (Denmark)

    Eid, Arafat; Mahmood, Waqar; Alomar, Anwar;

    2010-01-01

    Integrating the Generalized Multi-Protocol Label Switching (GMPLS) framework into an Optical Burst Switching (OBS) Control Plane is a promising solution to alleviating most of OBS performance and design issues. However, implementing the already proposed OBS deflection routing schemes is not appli...

  1. An AERONET-based aerosol classification using the Mahalanobis distance

    Science.gov (United States)

    Hamill, Patrick; Giordano, Marco; Ward, Carolyne; Giles, David; Holben, Brent

    2016-09-01

    We present an aerosol classification based on AERONET aerosol data from 1993 to 2012. We used the AERONET Level 2.0 almucantar aerosol retrieval products to define several reference aerosol clusters which are characteristic of the following general aerosol types: Urban-Industrial, Biomass Burning, Mixed Aerosol, Dust, and Maritime. The classification of a particular aerosol observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance to each reference cluster. We have calculated the fractional aerosol type distribution at 190 AERONET sites, as well as the monthly variation in aerosol type at those locations. The results are presented on a global map and individually in the supplementary material. Our aerosol typing is based on recognizing that different geographic regions exhibit characteristic aerosol types. To generate reference clusters we only keep data points that lie within a Mahalanobis distance of 2 from the centroid. Our aerosol characterization is based on the AERONET retrieved quantities, therefore it does not include low optical depth values. The analysis is based on "point sources" (the AERONET sites) rather than globally distributed values. The classifications obtained will be useful in interpreting aerosol retrievals from satellite borne instruments.

  2. A New Digital Signature Scheme Based on Mandelbrot and Julia Fractal Sets

    Directory of Open Access Journals (Sweden)

    M. A. Alia

    2007-01-01

    Full Text Available This paper describes a new cryptographic digital signature scheme based on Mandelbrot and Julia fractal sets. Having fractal based digital signature scheme is possible due to the strong connection between the Mandelbrot and Julia fractal sets. The link between the two fractal sets used for the conversion of the private key to the public key. Mandelbrot fractal function takes the chosen private key as the input parameter and generates the corresponding public-key. Julia fractal function then used to sign the message with receiver's public key and verify the received message based on the receiver's private key. The propose scheme was resistant against attacks, utilizes small key size and performs comparatively faster than the existing DSA, RSA digital signature scheme. Fractal digital signature scheme was an attractive alternative to the traditional number theory digital signature scheme.

  3. Intra-generational Redistribution under Public Pension Planning Based on Generation-based Funding Scheme

    Science.gov (United States)

    Banjo, Daisuke; Tamura, Hiroyuki; Murata, Tadahiko

    In this paper, we propose a method of determining the pension in the generation-based funding scheme. In this proposal, we include two types of pensions in the scheme. One is the payment-amount related pension and the other is the payment-frequency related pension. We set the ratio of the total amount of payment-amount related pension to the total amount of both pensions, and simulate income gaps and the relationship between contributions and benefits for each individual when the proposed method is applied.

  4. A soft-hard combination-based cooperative spectrum sensing scheme for cognitive radio networks.

    Science.gov (United States)

    Do, Nhu Tri; An, Beongku

    2015-01-01

    In this paper we propose a soft-hard combination scheme, called SHC scheme, for cooperative spectrum sensing in cognitive radio networks. The SHC scheme deploys a cluster based network in which Likelihood Ratio Test (LRT)-based soft combination is applied at each cluster, and weighted decision fusion rule-based hard combination is utilized at the fusion center. The novelties of the SHC scheme are as follows: the structure of the SHC scheme reduces the complexity of cooperative detection which is an inherent limitation of soft combination schemes. By using the LRT, we can detect primary signals in a low signal-to-noise ratio regime (around an average of -15 dB). In addition, the computational complexity of the LRT is reduced since we derive the closed-form expression of the probability density function of LRT value. The SHC scheme also takes into account the different effects of large scale fading on different users in the wide area network. The simulation results show that the SHC scheme not only provides the better sensing performance compared to the conventional hard combination schemes, but also reduces sensing overhead in terms of reporting time compared to the conventional soft combination scheme using the LRT. PMID:25688589

  5. A Provably-Secure ECC-Based Authentication Scheme for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Junghyun Nam

    2014-11-01

    Full Text Available A smart-card-based user authentication scheme for wireless sensor networks (in short, a SUA-WSN scheme is designed to restrict access to the sensor data only to users who are in possession of both a smart card and the corresponding password. While a significant number of SUA-WSN schemes have been suggested in recent years, their intended security properties lack formal definitions and proofs in a widely-accepted model. One consequence is that SUA-WSN schemes insecure against various attacks have proliferated. In this paper, we devise a security model for the analysis of SUA-WSN schemes by extending the widely-accepted model of Bellare, Pointcheval and Rogaway (2000. Our model provides formal definitions of authenticated key exchange and user anonymity while capturing side-channel attacks, as well as other common attacks. We also propose a new SUA-WSN scheme based on elliptic curve cryptography (ECC, and prove its security properties in our extended model. To the best of our knowledge, our proposed scheme is the first SUA-WSN scheme that provably achieves both authenticated key exchange and user anonymity. Our scheme is also computationally competitive with other ECC-based (non-provably secure schemes.

  6. Active Dictionary Learning in Sparse Representation Based Classification

    OpenAIRE

    Xu, Jin; He, Haibo; Man, Hong

    2014-01-01

    Sparse representation, which uses dictionary atoms to reconstruct input vectors, has been studied intensively in recent years. A proper dictionary is a key for the success of sparse representation. In this paper, an active dictionary learning (ADL) method is introduced, in which classification error and reconstruction error are considered as the active learning criteria in selection of the atoms for dictionary construction. The learned dictionaries are caculated in sparse representation based...

  7. Understanding Acupuncture Based on ZHENG Classification from System Perspective

    OpenAIRE

    Junwei Fang; Ningning Zheng; Yang Wang; Huijuan Cao; Shujun Sun; Jianye Dai; Qianhua Li; Yongyu Zhang

    2013-01-01

    Acupuncture is an efficient therapy method originated in ancient China, the study of which based on ZHENG classification is a systematic research on understanding its complexity. The system perspective is contributed to understand the essence of phenomena, and, as the coming of the system biology era, broader technology platforms such as omics technologies were established for the objective study of traditional chinese medicine (TCM). Omics technologies could dynamically determine molecular c...

  8. BCI Signal Classification using a Riemannian-based kernel

    OpenAIRE

    Barachant, Alexandre; Bonnet, Stéphane; Congedo, Marco; Jutten, Christian

    2012-01-01

    The use of spatial covariance matrix as feature is investigated for motor imagery EEG-based classification. A new kernel is derived by establishing a connection with the Riemannian geometry of symmetric positive definite matrices. Different kernels are tested, in combination with support vector machines, on a past BCI competition dataset. We demonstrate that this new approach outperforms significantly state of the art results without the need for spatial filtering.

  9. DATA MINING BASED TECHNIQUE FOR IDS ALERT CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    Hany Nashat Gabra

    2015-06-01

    Full Text Available Intrusion detection systems (IDSs have become a widely used measure for security systems. The main problem for such systems is the irrelevant alerts. We propose a data mining based method for classification to distinguish serious and irrelevant alerts with a performance of 99.9%, which is better in comparison with the other recent data mining methods that achieved 97%. A ranked alerts list is also created according to the alert’s importance to minimize human interventions.

  10. DATA MINING BASED TECHNIQUE FOR IDS ALERT CLASSIFICATION

    OpenAIRE

    Hany Nashat Gabra; Bahaa-Eldin, Ayman M.; Hoda Korashy Mohammed

    2015-01-01

    Intrusion detection systems (IDSs) have become a widely used measure for security systems. The main problem for such systems is the irrelevant alerts. We propose a data mining based method for classification to distinguish serious and irrelevant alerts with a performance of 99.9%, which is better in comparison with the other recent data mining methods that achieved 97%. A ranked alerts list is also created according to the alert’s importance to minimize human interventions.

  11. Data Mining Based Technique for IDS Alerts Classification

    OpenAIRE

    Gabra, Hany N.; Bahaa-Eldin, Ayman M.; Mohamed, Hoda K.

    2012-01-01

    Intrusion detection systems (IDSs) have become a widely used measure for security systems. The main problem for those systems results is the irrelevant alerts on those results. We will propose a data mining based method for classification to distinguish serious alerts and irrelevant one with a performance of 99.9% which is better in comparison with the other recent data mining methods that have reached the performance of 97%. A ranked alerts list also created according to alerts importance to...

  12. Classification of objects in images based on various object representations

    OpenAIRE

    Cichocki, Radoslaw

    2006-01-01

    Object recognition is a hugely researched domain that employs methods derived from mathematics, physics and biology. This thesis combines the approaches for object classification that base on two features – color and shape. Color is represented by color histograms and shape by skeletal graphs. Four hybrids are proposed which combine those approaches in different manners and the hybrids are then tested to find out which of them gives best results.

  13. A Cluster Based Approach for Classification of Web Results

    OpenAIRE

    Apeksha Khabia; M. B. Chandak

    2014-01-01

    Nowadays significant amount of information from web is present in the form of text, e.g., reviews, forum postings, blogs, news articles, email messages, web pages. It becomes difficult to classify documents in predefined categories as the number of document grows. Clustering is the classification of a data into clusters, so that the data in each cluster share some common trait – often vicinity according to some defined measure. Underlying distribution of data set can somewhat be depicted base...

  14. Expected energy-based restricted Boltzmann machine for classification.

    Science.gov (United States)

    Elfwing, S; Uchibe, E; Doya, K

    2015-04-01

    In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach to provide a self-contained RBM method for classification, inspired by free-energy based function approximation (FE-RBM), originally proposed for reinforcement learning. For classification, the FE-RBM method computes the output for an input vector and a class vector by the negative free energy of an RBM. Learning is achieved by stochastic gradient-descent using a mean-squared error training objective. In an earlier study, we demonstrated that the performance and the robustness of FE-RBM function approximation can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that the learning performance of RBM function approximation can be further improved by computing the output by the negative expected energy (EE-RBM), instead of the negative free energy. To create a deep learning architecture, we stack several RBMs on top of each other. We also connect the class nodes to all hidden layers to try to improve the performance even further. We validate the classification performance of EE-RBM using the MNIST data set and the NORB data set, achieving competitive performance compared with other classifiers such as standard neural networks, deep belief networks, classification RBMs, and support vector machines. The purpose of using the NORB data set is to demonstrate that EE-RBM with binary input nodes can achieve high performance in the continuous input domain. PMID:25318375

  15. A Topic Space Oriented User Group Discovering Scheme in Social Network: A Trust Chain Based Interest Measuring Perspective

    Directory of Open Access Journals (Sweden)

    Wang Dong

    2016-01-01

    Full Text Available Currently, user group has become an effective platform for information sharing and communicating among users in social network sites. In present work, we propose a single topic user group discovering scheme, which includes three phases: topic impact evaluation, interest degree measurement, and trust chain based discovering, to enable selecting influential topic and discovering users into a topic oriented group. Our main works include (1 an overview of proposed scheme and its related definitions; (2 topic space construction method based on topic relatedness clustering and its impact (influence degree and popularity degree evaluation; (3 a trust chain model to take user relation network topological information into account with a strength classification perspective; (4 an interest degree (user explicit and implicit interest degree evaluation method based on trust chain among users; and (5 a topic space oriented user group discovering method to group core users according to their explicit interest degrees and to predict ordinary users under implicit interest and user trust chain. Finally, experimental results are given to explain effectiveness and feasibility of our scheme.

  16. Vascular bone tumors: a proposal of a classification based on clinicopathological, radiographic and genetic features

    Energy Technology Data Exchange (ETDEWEB)

    Errani, Costantino [Istituto Ortopedico Rizzoli, Ortopedia Generale, Orthopaedic Service, Bagheria (Italy); Struttura Complessa Ortopedia Generale, Dipartimento Rizzoli-Sicilia, Bagheria, PA (Italy); Vanel, Daniel; Gambarotti, Marco; Alberghini, Marco [Istituto Ortopedico Rizzoli, Pathology Service, Bologna (Italy); Picci, Piero [Istituto Ortopedico Rizzoli, Laboratory for Cancer Research, Bologna (Italy); Faldini, Cesare [Istituto Ortopedico Rizzoli, Ortopedia Generale, Orthopaedic Service, Bagheria (Italy)

    2012-12-15

    The classification of vascular bone tumors remains challenging, with considerable morphological overlap spanning across benign to malignant categories. The vast majority of both benign and malignant vascular tumors are readily diagnosed based on their characteristic histological features, such as the formation of vascular spaces and the expression of endothelial markers. However, some vascular tumors have atypical histological features, such as a solid growth pattern, epithelioid change, or spindle cell morphology, which complicates their diagnosis. Pathologically, these tumors are remarkably similar, which makes differentiating them from each other very difficult. For this rare subset of vascular bone tumors, there remains considerable controversy with regard to the terminology and the classification that should be used. Moreover, one of the most confusing issues related to vascular bone tumors is the myriad of names that are used to describe them. Because the clinical behavior and, consequently, treatment and prognosis of vascular bone tumors can vary significantly, it is important to effectively and accurately distinguish them from each other. Upon review of the nomenclature and the characteristic clinicopathological, radiographic and genetic features of vascular bone tumors, we propose a classification scheme that includes hemangioma, hemangioendothelioma, angiosarcoma, and their epithelioid variants. (orig.)

  17. Vascular bone tumors: a proposal of a classification based on clinicopathological, radiographic and genetic features

    International Nuclear Information System (INIS)

    The classification of vascular bone tumors remains challenging, with considerable morphological overlap spanning across benign to malignant categories. The vast majority of both benign and malignant vascular tumors are readily diagnosed based on their characteristic histological features, such as the formation of vascular spaces and the expression of endothelial markers. However, some vascular tumors have atypical histological features, such as a solid growth pattern, epithelioid change, or spindle cell morphology, which complicates their diagnosis. Pathologically, these tumors are remarkably similar, which makes differentiating them from each other very difficult. For this rare subset of vascular bone tumors, there remains considerable controversy with regard to the terminology and the classification that should be used. Moreover, one of the most confusing issues related to vascular bone tumors is the myriad of names that are used to describe them. Because the clinical behavior and, consequently, treatment and prognosis of vascular bone tumors can vary significantly, it is important to effectively and accurately distinguish them from each other. Upon review of the nomenclature and the characteristic clinicopathological, radiographic and genetic features of vascular bone tumors, we propose a classification scheme that includes hemangioma, hemangioendothelioma, angiosarcoma, and their epithelioid variants. (orig.)

  18. Design and implementation based on the classification protection vulnerability scanning system

    International Nuclear Information System (INIS)

    With the application and spread of the classification protection, Network Security Vulnerability Scanning should consider the efficiency and the function expansion. It proposes a kind of a system vulnerability from classification protection, and elaborates the design and implementation of a vulnerability scanning system based on vulnerability classification plug-in technology and oriented classification protection. According to the experiment, the application of classification protection has good adaptability and salability with the system, and it also approves the efficiency of scanning. (authors)

  19. ECC Based Threshold Decryption Scheme and Its Application in Web Security

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xian-feng; ZHANG Feng; QIN Zhi-guang; LIU Jin-de

    2004-01-01

    The threshold cryptography provides a new approach to building intrusion tolerance applications. In this paper, a threshold decryption scheme based elliptic curve cryptography is presented. A zero-knowledge test approach based on elliptic curve cryptography is designed. The application of these techniques in Web security is studied. Performance analysis shows that our scheme is characterized by excellent security as well as high efficiency.

  20. A new gammagraphic and functional-based classification for hyperthyroidism

    International Nuclear Information System (INIS)

    The absence of an universal classification for hyperthyroidism's (HT), give rise to inadequate interpretation of series and trials, and prevents decision making. We offer a tentative classification based on gammagraphic and functional findings. Clinical records from patients who underwent thyroidectomy in our Department since 1967 to 1997 were reviewed. Those with functional measurements of hyperthyroidism were considered. All were managed according to the same preestablished guidelines. HT was the surgical indication in 694 (27,1%) of the 2559 thyroidectomy. Based on gammagraphic studies, we classified HTs in: parenchymatous increased-uptake, which could be diffuse, diffuse with cold nodules or diffuse with at least one nodule, and nodular increased-uptake (Autonomous Functioning Thyroid Nodes-AFTN), divided into solitary AFTN or toxic adenoma and multiple AFTN o toxic multi-nodular goiter. This gammagraphic-based classification in useful and has high sensitivity to detect these nodules assessing their activity, allowing us to make therapeutic decision making and, in some cases, to choose surgical technique. (authors)

  1. Performance Analysis of Virtual MIMO Relaying Schemes Based on Detect–Split–Forward

    KAUST Repository

    Al-Basit, Suhaib M.

    2014-10-29

    © 2014, Springer Science+Business Media New York. Virtual multi-input multi-output (vMIMO) schemes in wireless communication systems improve coverage, throughput, capacity, and quality of service. In this paper, we propose three uplink vMIMO relaying schemes based on detect–split–forward (DSF). In addition, we investigate the effect of several physical parameters such as distance, modulation type and number of relays. Furthermore, an adaptive vMIMO DSF scheme based on VBLAST and STBC is proposed. In order to do that, we provide analytical tools to evaluate the performance of the propose vMIMO relaying scheme.

  2. UNIFIED COMPUTATION OF FLOW WITH COMPRESSIBLE AND INCOMPRESSIBLE FLUID BASED ON ROE'S SCHEME

    Institute of Scientific and Technical Information of China (English)

    HUANG Dian-gui

    2006-01-01

    A unified numerical scheme for the solutions of the compressible and incompressible Navier-Stokes equations is investigated based on a time-derivative preconditioning algorithm. The primitive variables are pressure, velocities and temperature. The time integration scheme is used in conjunction with a finite volume discretization. The preconditioning is coupled with a high order implicit upwind scheme based on the definition of a Roe's type matrix. Computational capabilities are demonstrated through computations of high Mach number, middle Mach number, very low Mach number, and incompressible flow. It has also been demonstrated that the discontinuous surface in flow field can be captured for the implementation Roe's scheme.

  3. Establishment of Virtual Policy Based Network Management Scheme By Load Experiments in Virtual Environment

    Directory of Open Access Journals (Sweden)

    Kazuya Odagiri

    2016-05-01

    Full Text Available In the current Internet-based systems, there are many problems using anonymity of the network communication such as personal information leak and crimes using the Internet systems. This is because the TCP/IP protocol used in Internet systems does not have the user identification information on the communication data, and it is difficult to supervise the user performing the above acts immediately. As a solution for solving the above problem, there is the approach of Policy-based Network Management (PBNM. This is the scheme for managing a whole Local Area Network (LAN through communication control of every user. In this PBNM, two types of schemes exist. The first is the scheme for managing the whole LAN by locating the communication control mechanisms on the course between network servers and clients. The second is the scheme of managing the whole LAN by locating the communication control mechanisms on clients. As the second scheme, we have been studied theoretically about the Destination Addressing Control System (DACS Scheme. By applying this DACS Scheme to Internet system management, we intend to realize the policy-based Internet system management finally. In the DACS Scheme, the inspection is not done about compatibility to cloud environment with virtualization technology that spreads explosively. As the result, the coverage of the DACS Scheme is limited only in physical environment now. In this study, we inspect compatibility of the DACS Scheme for the cloud environment with virtualization technology, and enlarge coverage of this scheme. With it, the Virtual DACS Scheme (vDACS Scheme is established.

  4. Twitter content classification

    OpenAIRE

    Dann, Stephen

    2010-01-01

    This paper delivers a new Twitter content classification framework based sixteen existing Twitter studies and a grounded theory analysis of a personal Twitter history. It expands the existing understanding of Twitter as a multifunction tool for personal, profession, commercial and phatic communications with a split level classification scheme that offers broad categorization and specific sub categories for deeper insight into the real world application of the service.

  5. DWT-Based Robust Color Image Watermarking Scheme

    Institute of Scientific and Technical Information of China (English)

    Liu Lianshan; Li Renhou; Gao Qi

    2005-01-01

    A scheme of embedding an encrypted watermark into the green component of a color image is proposed. The embedding process is implemented in the discrete wavelet transformation (DWT) domain. The original binary watermark image is firstly encrypted through scrambling technique, and then spread with two orthogonal pseudo-random sequences whose mean values are equal to zero, and finally embedded into the DWT low frequency sub-band of green components. The coefficients whose energies are larger than the others are selected to hide watermark, and the hidden watermark strength is determined by the energy ratio between the selected coefficients energies and the mean energy of the subband. The experiment results demonstrate that the proposed watermarking scheme is very robust against the attacks such as additive noise, low-pass filtering, scaling, cropping image, row ( or column ) deleting, and JPEG compression.

  6. A unified classification of alien species based on the magnitude of their environmental impacts.

    Directory of Open Access Journals (Sweden)

    Tim M Blackburn

    2014-05-01

    Full Text Available Species moved by human activities beyond the limits of their native geographic ranges into areas in which they do not naturally occur (termed aliens can cause a broad range of significant changes to recipient ecosystems; however, their impacts vary greatly across species and the ecosystems into which they are introduced. There is therefore a critical need for a standardised method to evaluate, compare, and eventually predict the magnitudes of these different impacts. Here, we propose a straightforward system for classifying alien species according to the magnitude of their environmental impacts, based on the mechanisms of impact used to code species in the International Union for Conservation of Nature (IUCN Global Invasive Species Database, which are presented here for the first time. The classification system uses five semi-quantitative scenarios describing impacts under each mechanism to assign species to different levels of impact-ranging from Minimal to Massive-with assignment corresponding to the highest level of deleterious impact associated with any of the mechanisms. The scheme also includes categories for species that are Not Evaluated, have No Alien Population, or are Data Deficient, and a method for assigning uncertainty to all the classifications. We show how this classification system is applicable at different levels of ecological complexity and different spatial and temporal scales, and embraces existing impact metrics. In fact, the scheme is analogous to the already widely adopted and accepted Red List approach to categorising extinction risk, and so could conceivably be readily integrated with existing practices and policies in many regions.

  7. A continuous and prognostic convection scheme based on buoyancy, PCMT

    Science.gov (United States)

    Guérémy, Jean-François; Piriou, Jean-Marcel

    2016-04-01

    A new and consistent convection scheme (PCMT: Prognostic Condensates Microphysics and Transport), providing a continuous and prognostic treatment of this atmospheric process, is described. The main concept ensuring the consistency of the whole system is the buoyancy, key element of any vertical motion. The buoyancy constitutes the forcing term of the convective vertical velocity, which is then used to define the triggering condition, the mass flux, and the rates of entrainment-detrainment. The buoyancy is also used in its vertically integrated form (CAPE) to determine the closure condition. The continuous treatment of convection, from dry thermals to deep precipitating convection, is achieved with the help of a continuous formulation of the entrainment-detrainment rates (depending on the convective vertical velocity) and of the CAPE relaxation time (depending on the convective over-turning time). The convective tendencies are directly expressed in terms of condensation and transport. Finally, the convective vertical velocity and condensates are fully prognostic, the latter being treated using the same microphysics scheme as for the resolved condensates but considering the convective environment. A Single Column Model (SCM) validation of this scheme is shown, allowing detailed comparisons with observed and explicitly simulated data. Four cases covering the convective spectrum are considered: over ocean, sensitivity to environmental moisture (S. Derbyshire) non precipitating shallow convection to deep precipitating convection, trade wind shallow convection (BOMEX) and strato-cumulus (FIRE), together with an entire continental diurnal cycle of convection (ARM). The emphasis is put on the characteristics of the scheme which enable a continuous treatment of convection. Then, a 3D LAM validation is presented considering an AMMA case with both observations and a CRM simulation using the same initial and lateral conditions as for the parameterized one. Finally, global

  8. Market-based support schemes for renewable energy sources

    OpenAIRE

    Fagiani, R.

    2014-01-01

    The European Union set ambitious goals regarding the production of electricity from renewable energy sources and the majority of European governments have implemented policies stimulating investments in such technologies. Support schemes differ in many aspects, not only in their effectivity and efficiency but also in the long-term incentives provided and in the financial risk involved for investors. This research compares the performance of different policy mechanisms analyzing the interactio...

  9. Secure communication scheme based on asymptotic model of deterministic randomness

    International Nuclear Information System (INIS)

    In this Letter, we introduce a new cryptosystem by integrating the asymptotic model of deterministic randomness with the one-way coupled map lattice (OCML) system. The key space, the encryption efficiency, and the security under various attacks are investigated. With the properties of deterministic randomness and spatiotemporal dynamics, the new scheme can improve the security to the order of computational precision, even when the lattice size is three only. Meanwhile, all the lattices can be fully utilized to increase the encryption speed

  10. Identity based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing

    DEFF Research Database (Denmark)

    Cheng, Hongbing; Rong, Chunming; Tan, Zheng-Hua;

    2012-01-01

    access scheme based on identity-based encryption and biometric authentication for cloud computing. Firstly, we describe the security concern of cloud computing and then propose an integrated data access scheme for cloud computing, the procedure of the proposed scheme include parameter setup, key...... distribution, feature template creation, cloud data processing and secure data access control. Finally, we compare the proposed scheme with other schemes through comprehensive analysis and simulation. The results show that the proposed data access scheme is feasible and secure for cloud computing.......Cloud computing will be a main information infrastructure in the future; it consists of many large datacenters which are usually geographically distributed and heterogeneous. How to design a secure data access for cloud computing platform is a big challenge. In this paper, we propose a secure data...

  11. A Quantum Proxy Weak Blind Signature Scheme Based on Controlled Quantum Teleportation

    Science.gov (United States)

    Cao, Hai-Jing; Yu, Yao-Feng; Song, Qin; Gao, Lan-Xiang

    2015-04-01

    Proxy blind signature is applied to the electronic paying system, electronic voting system, mobile agent system, security of internet, etc. A quantum proxy weak blind signature scheme is proposed in this paper. It is based on controlled quantum teleportation. Five-qubit entangled state functions as quantum channel. The scheme uses the physical characteristics of quantum mechanics to implement message blinding, so it could guarantee not only the unconditional security of the scheme but also the anonymity of the messages owner.

  12. Error Robust H.264 Video Transmission Schemes Based on Multi-frame

    Institute of Scientific and Technical Information of China (English)

    余红斌; 余松煜; 王慈

    2004-01-01

    Multi-frame coding is supported by the emerging H. 264. It is important for the enhancement of both coding efficiency and error robustness. In this paper, error resilient schemes for H. 264 based on multi-frame were investigated. Error robust H. 264 video transmission schemes were introduced for the applications with and without a feedback channel. The experimental results demonstrate the effectiveness of the proposed schemes.

  13. Changing Histopathological Diagnostics by Genome-Based Tumor Classification

    Directory of Open Access Journals (Sweden)

    Michael Kloth

    2014-05-01

    Full Text Available Traditionally, tumors are classified by histopathological criteria, i.e., based on their specific morphological appearances. Consequently, current therapeutic decisions in oncology are strongly influenced by histology rather than underlying molecular or genomic aberrations. The increase of information on molecular changes however, enabled by the Human Genome Project and the International Cancer Genome Consortium as well as the manifold advances in molecular biology and high-throughput sequencing techniques, inaugurated the integration of genomic information into disease classification. Furthermore, in some cases it became evident that former classifications needed major revision and adaption. Such adaptations are often required by understanding the pathogenesis of a disease from a specific molecular alteration, using this molecular driver for targeted and highly effective therapies. Altogether, reclassifications should lead to higher information content of the underlying diagnoses, reflecting their molecular pathogenesis and resulting in optimized and individual therapeutic decisions. The objective of this article is to summarize some particularly important examples of genome-based classification approaches and associated therapeutic concepts. In addition to reviewing disease specific markers, we focus on potentially therapeutic or predictive markers and the relevance of molecular diagnostics in disease monitoring.

  14. A Fuzzy Similarity Based Concept Mining Model for Text Classification

    CERN Document Server

    Puri, Shalini

    2012-01-01

    Text Classification is a challenging and a red hot field in the current scenario and has great importance in text categorization applications. A lot of research work has been done in this field but there is a need to categorize a collection of text documents into mutually exclusive categories by extracting the concepts or features using supervised learning paradigm and different classification algorithms. In this paper, a new Fuzzy Similarity Based Concept Mining Model (FSCMM) is proposed to classify a set of text documents into pre - defined Category Groups (CG) by providing them training and preparing on the sentence, document and integrated corpora levels along with feature reduction, ambiguity removal on each level to achieve high system performance. Fuzzy Feature Category Similarity Analyzer (FFCSA) is used to analyze each extracted feature of Integrated Corpora Feature Vector (ICFV) with the corresponding categories or classes. This model uses Support Vector Machine Classifier (SVMC) to classify correct...

  15. SPEECH/MUSIC CLASSIFICATION USING WAVELET BASED FEATURE EXTRACTION TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Thiruvengatanadhan Ramalingam

    2014-01-01

    Full Text Available Audio classification serves as the fundamental step towards the rapid growth in audio data volume. Due to the increasing size of the multimedia sources speech and music classification is one of the most important issues for multimedia information retrieval. In this work a speech/music discrimination system is developed which utilizes the Discrete Wavelet Transform (DWT as the acoustic feature. Multi resolution analysis is the most significant statistical way to extract the features from the input signal and in this study, a method is deployed to model the extracted wavelet feature. Support Vector Machines (SVM are based on the principle of structural risk minimization. SVM is applied to classify audio into their classes namely speech and music, by learning from training data. Then the proposed method extends the application of Gaussian Mixture Models (GMM to estimate the probability density function using maximum likelihood decision methods. The system shows significant results with an accuracy of 94.5%.

  16. Histological image classification using biologically interpretable shape-based features

    International Nuclear Information System (INIS)

    Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

  17. MPSK Symbol-based Soft-Information-Forwarding Scheme in Rayleigh Fading Channels

    Directory of Open Access Journals (Sweden)

    Huamei Xin

    2014-06-01

    Full Text Available In this paper, we proposed a symbol-based multiple phase shift keying (MPSK soft-information-forwarding (SIF scheme for a two-hop parallel relay wireless network in Rayleigh fading channel. First the binary information streams at the source are mapped into MPSK symbols, and the relays construct the relay processing function by passing the intermediate soft decisions. Then the relays broadcast the processed symbols to the destination. After the maximum ratio combination, the received symbols at the destination can be decided by maximum-likelihood (ML decision. Four MPSK symbol-based forwarding schemes are investigated and the simulation results show that the bit error rate (BER performance of soft information forwarding scheme has better BER performance than the existing memoryless forwarding scheme based on MPSK modulation, and it is more practical than the SIF scheme based on BPSK modulation

  18. Rule based fuzzy logic approach for classification of fibromyalgia syndrome.

    Science.gov (United States)

    Arslan, Evren; Yildiz, Sedat; Albayrak, Yalcin; Koklukaya, Etem

    2016-06-01

    Fibromyalgia syndrome (FMS) is a chronic muscle and skeletal system disease observed generally in women, manifesting itself with a widespread pain and impairing the individual's quality of life. FMS diagnosis is made based on the American College of Rheumatology (ACR) criteria. However, recently the employability and sufficiency of ACR criteria are under debate. In this context, several evaluation methods, including clinical evaluation methods were proposed by researchers. Accordingly, ACR had to update their criteria announced back in 1990, 2010 and 2011. Proposed rule based fuzzy logic method aims to evaluate FMS at a different angle as well. This method contains a rule base derived from the 1990 ACR criteria and the individual experiences of specialists. The study was conducted using the data collected from 60 inpatient and 30 healthy volunteers. Several tests and physical examination were administered to the participants. The fuzzy logic rule base was structured using the parameters of tender point count, chronic widespread pain period, pain severity, fatigue severity and sleep disturbance level, which were deemed important in FMS diagnosis. It has been observed that generally fuzzy predictor was 95.56 % consistent with at least of the specialists, who are not a creator of the fuzzy rule base. Thus, in diagnosis classification where the severity of FMS was classified as well, consistent findings were obtained from the comparison of interpretations and experiences of specialists and the fuzzy logic approach. The study proposes a rule base, which could eliminate the shortcomings of 1990 ACR criteria during the FMS evaluation process. Furthermore, the proposed method presents a classification on the severity of the disease, which was not available with the ACR criteria. The study was not limited to only disease classification but at the same time the probability of occurrence and severity was classified. In addition, those who were not suffering from FMS were

  19. Network Traffic Anomalies Identification Based on Classification Methods

    Directory of Open Access Journals (Sweden)

    Donatas Račys

    2015-07-01

    Full Text Available A problem of network traffic anomalies detection in the computer networks is analyzed. Overview of anomalies detection methods is given then advantages and disadvantages of the different methods are analyzed. Model for the traffic anomalies detection was developed based on IBM SPSS Modeler and is used to analyze SNMP data of the router. Investigation of the traffic anomalies was done using three classification methods and different sets of the learning data. Based on the results of investigation it was determined that C5.1 decision tree method has the largest accuracy and performance and can be successfully used for identification of the network traffic anomalies.

  20. Scheme of adaptive polarization filtering based on Kalman model

    Institute of Scientific and Technical Information of China (English)

    Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande

    2006-01-01

    A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.

  1. Remodulation scheme based on a two-section reflective SOA

    International Nuclear Information System (INIS)

    A simple and cost-effective remodulation scheme using a two-section reflective semiconductor optical amplifier (RSOA) is proposed for a colorless optical network unit (ONU). Under proper injection currents, the front section functions as a modulator to upload the upstream signal while the rear section serves as a data eraser for efficient suppression of the downstream data. The dependences of the upstream transmission performance on the lengths and driven currents of the RSOA, the injection optical power and extinction ratio of the downstream are investigated. By optimizing these parameters, the downstream data can be more completely suppressed and the upstream transmission performance can be greatly improved. (semiconductor devices)

  2. Gene function classification using Bayesian models with hierarchy-based priors

    Directory of Open Access Journals (Sweden)

    Neal Radford M

    2006-10-01

    Full Text Available Abstract Background We investigate whether annotation of gene function can be improved using a classification scheme that is aware that functional classes are organized in a hierarchy. The classifiers look at phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL model, a hierarchical model based on a set of nested MNL models, and an MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs from the E. coli genome. Results The results from all three models show substantial improvement over previous methods, which were based on the C5 decision tree algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining the three sources of information in this dataset, our new approach to combining data sources produces a higher accuracy rate than applying our models to each data source alone. Conclusion Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information.

  3. Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification

    International Nuclear Information System (INIS)

    Purpose: To develop a computer-aided detection (CADe) scheme for nodules in chest radiographs (CXRs) with a high sensitivity and a low false-positive (FP) rate. Methods: The authors developed a CADe scheme consisting of five major steps, which were developed for improving the overall performance of CADe schemes. First, to segment the lung fields accurately, the authors developed a multisegment active shape model. Then, a two-stage nodule-enhancement technique was developed for improving the conspicuity of nodules. Initial nodule candidates were detected and segmented by using the clustering watershed algorithm. Thirty-one shape-, gray-level-, surface-, and gradient-based features were extracted from each segmented candidate for determining the feature space, including one of the new features based on the Canny edge detector to eliminate a major FP source caused by rib crossings. Finally, a nonlinear support vector machine (SVM) with a Gaussian kernel was employed for classification of the nodule candidates. Results: To evaluate and compare the scheme to other published CADe schemes, the authors used a publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs. The CADe scheme based on the SVM classifier achieved sensitivities of 78.6% (110/140) and 71.4% (100/140) with averages of 5.0 (1165/233) FPs/image and 2.0 (466/233) FPs/image, respectively, in a leave-one-out cross-validation test, whereas the CADe scheme based on a linear discriminant analysis classifier had a sensitivity of 60.7% (85/140) at an FP rate of 5.0 FPs/image. For nodules classified as ''very subtle'' and ''extremely subtle,'' a sensitivity of 57.1% (24/42) was achieved at an FP rate of 5.0 FPs/image. When the authors used a database developed at the University of Chicago, the sensitivities was 83.3% (40/48) and 77.1% (37/48) at an FP rate of 5.0 (240/48) FPs/image and 2.0 (96/48) FPs /image, respectively. Conclusions: These results compare favorably to those described for

  4. Cost-based droop scheme with lower generation costs for microgrids

    DEFF Research Database (Denmark)

    Nutkani, I. U.; Loh, Poh Chiang; Blaabjerg, Frede

    2013-01-01

    on the DG kVA ratings. Other operating characteristics like generation costs, efficiencies and emission penalties at different loadings have not been considered. This makes existing droop schemes not too well-suited for standalone microgrids without central management system, where different types of...... DGs usually exist. As an alternative, this paper proposes a cost-based droop scheme, whose objective is to reduce a generation cost realized with various DG operating characteristics taken into consideration. The proposed droop scheme therefore retains all advantages of the traditional droop schemes......, while at the same time keep its generation cost low. These findings have been validated through simulation and scaled down lab experiment....

  5. MULTIMEDIA DATA TRANSMISSION THROUGH TCP/IP USING HASH BASED FEC WITH AUTO-XOR SCHEME

    Directory of Open Access Journals (Sweden)

    R. Shalin

    2012-09-01

    Full Text Available The most preferred mode for communication of multimedia data is through the TCP/IP protocol. But on the other hand the TCP/IP protocol produces huge packet loss unavoidable due to network traffic and congestion. In order to provide a efficient communication it is necessary to recover the loss of packets. The proposed scheme implements Hash based FEC with auto XOR scheme for this purpose. The scheme is implemented through Forward error correction, MD5 and XOR for providing efficient transmission of multimedia data. The proposed scheme provides transmission high accuracy, throughput and low latency and loss.

  6. PSO Based Optimized Security Scheme for Image Authentication and Tamper Proofing

    Directory of Open Access Journals (Sweden)

    K. Kuppusamy

    2013-05-01

    Full Text Available The hash function offers an authentication and an i ntegrity to digital images. In this paper an innovative optimized security scheme based on Parti cle swarm optimization (PSO for image authentication and tamper proofing is proposed. Thi s scheme provide solutions to the issues such as robustness, security and tamper detection with precise localization. The features are extracted in Daubechies4 wavelet transform domain w ith help of PSO to generate the image hash. This scheme is moderately robust against atta cks and to detect and locate the tampered areas in an image. The experimental results are pre sented to exhibit the effectiveness of the proposed scheme.

  7. Transonic inviscid/turbulent airfoil flow simulations using a pressure based method with high order schemes

    Science.gov (United States)

    Zhou, Gang; Davidson, Lars; Olsson, Erik

    This paper presents computations of transonic aerodynamic flow simulations using a pressure-based Euler/Navier-Stokes solver. In this work emphasis is focused on the implementation of higher-order schemes such as QUICK, LUDS and MUSCL. A new scheme CHARM is proposed for convection approximation. Inviscid flow simulations are carried out for the airfoil NACA 0012. The CHARM scheme gives better resolution for the present inviscid case. The turbulent flow computations are carried out for the airfoil RAE 2822. Good results were obtained using QUICK scheme for mean motion equation combined with the MUSCL scheme for k and ɛ equations. No unphysical oscillations were observed. The results also show that the second-order and thir-dorder schemes yielded a comparable accuracy compared with the experimental data.

  8. Bloom Filter-Based Advanced Traceback Scheme in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Seoksoo Kim

    2012-11-01

    Full Text Available Many recent studies have focused on traceback schemes with the aim of finding the source of spoofed malicious packets and tracing the path of denial-of-service attacks. Although such schemes are academically well known, most exhibit some critical points in wireless sensor networks, which could become a significant issue as they are increasingly used for IP networks. This paper suggests an advanced traceback scheme based on existing Bloom filter methods. The proposed traceback scheme extends the basic Bloom filter design, enabling it to identify which entity added a given element, albeit with the incursion of false positives. However, our scheme allows only specific tracebacks, which can reduce the false positive rate of a node near a sink. Performance results show that the scheme can perform efficient tracebacks with very few false positives.

  9. A Data Gathering Scheme in Wireless Sensor Networks Based on Synchronization of Chaotic Spiking Oscillator Networks

    International Nuclear Information System (INIS)

    This paper studies chaos-based data gathering scheme in multiple sink wireless sensor networks. In the proposed scheme, each wireless sensor node has a simple chaotic oscillator. The oscillators generate spike signals with chaotic interspike intervals, and are impulsively coupled by the signals via wireless communication. Each wireless sensor node transmits and receives sensor information only in the timing of the couplings. The proposed scheme can exhibit various chaos synchronous phenomena and their breakdown phenomena, and can effectively gather sensor information with the significantly small number of transmissions and receptions compared with the conventional scheme. Also, the proposed scheme can flexibly adapt various wireless sensor networks not only with a single sink node but also with multiple sink nodes. This paper introduces our previous works. Through simulation experiments, we show effectiveness of the proposed scheme and discuss its development potential.

  10. Evaluation of Scheme Design of Blast Furnace Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    TANG Hong; LI Jing-min; YAO Bi-qiang; LIAO Hong-fu; YAO Jin

    2008-01-01

    Blast furnace scheme design is very important, since it directly affects the performance, cost and configuration of the blast furnace. An evaluation approach to furnace scheme design was brought forward based on artificial neural network. Ten independent parameters which determined a scheme design were proposed. The improved threelayer BP network algorithm was used to build the evaluation model in which the 10 independent parameters were taken as input evaluation indexes and the degree to which the scheme design satisfies the requirements of the blast furnace as output. It was trained by the existing samples of the scheme design and the experts' experience, and then tested by the other samples so as to develop the evaluation model. As an example, it is found that a good scheme design of blast furnace can be chosen by using the evaluation model proposed.

  11. A Data Gathering Scheme in Wireless Sensor Networks Based on Synchronization of Chaotic Spiking Oscillator Networks

    Science.gov (United States)

    Nakano, Hidehiro; Utani, Akihide; Miyauchi, Arata; Yamamoto, Hisao

    2011-04-01

    This paper studies chaos-based data gathering scheme in multiple sink wireless sensor networks. In the proposed scheme, each wireless sensor node has a simple chaotic oscillator. The oscillators generate spike signals with chaotic interspike intervals, and are impulsively coupled by the signals via wireless communication. Each wireless sensor node transmits and receives sensor information only in the timing of the couplings. The proposed scheme can exhibit various chaos synchronous phenomena and their breakdown phenomena, and can effectively gather sensor information with the significantly small number of transmissions and receptions compared with the conventional scheme. Also, the proposed scheme can flexibly adapt various wireless sensor networks not only with a single sink node but also with multiple sink nodes. This paper introduces our previous works. Through simulation experiments, we show effectiveness of the proposed scheme and discuss its development potential.

  12. An efficient authentication scheme based on one-way key chain for sensor network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    To strike a tradeoff between the security and the consumption of energy, computing and communication resources in the nodes, this paper presents an efficient authentication scheme based on one-way key chain for sensor network. The scheme can provide immediate authentication to fulfill the latency and the storage requirements and defends against various attacks such as replay, impersonation and denial of service. Meanwhile,our scheme possesses low overhead and scalability to large networks. Furthermore, the simple related protocols or algorithms in the scheme and inexpensive public-key operation required in view of resource-starved sensor nodes minimize the storage, computation and communication overhead, and improve the efficiency of our scheme. In addition, the proposed scheme also supports source authentication without precluding in-network processing and passive participation.

  13. A User Authentication Scheme Based on Elliptic Curves Cryptography for Wireless Ad Hoc Networks.

    Science.gov (United States)

    Chen, Huifang; Ge, Linlin; Xie, Lei

    2015-01-01

    The feature of non-infrastructure support in a wireless ad hoc network (WANET) makes it suffer from various attacks. Moreover, user authentication is the first safety barrier in a network. A mutual trust is achieved by a protocol which enables communicating parties to authenticate each other at the same time and to exchange session keys. For the resource-constrained WANET, an efficient and lightweight user authentication scheme is necessary. In this paper, we propose a user authentication scheme based on the self-certified public key system and elliptic curves cryptography for a WANET. Using the proposed scheme, an efficient two-way user authentication and secure session key agreement can be achieved. Security analysis shows that our proposed scheme is resilient to common known attacks. In addition, the performance analysis shows that our proposed scheme performs similar or better compared with some existing user authentication schemes. PMID:26184224

  14. Comment Fail-Stop Blind Signature Scheme Design Based on Pairings

    Institute of Scientific and Technical Information of China (English)

    HU Xiaoming; HUANG Shangteng

    2006-01-01

    Fail-stop signature schemes provide security for a signer against forgeries of an enemy with unlimited computational power by enabling the signer to provide a proof of forgery when a forgery happens. Chang et al proposed a robust fail-stop blind signature scheme based on bilinear pairings. However, in this paper, it will be found that there are several mistakes in Chang et al' fail-stop blind signature scheme. Moreover, it will be pointed out that this scheme doesn' meet the property of a fail-stop signature: unconditionally secure for a signer. In Chang et al' scheme, a forger can forge a valid signature that can' be proved by a signer using the "proof of forgery". The scheme also doesn' possess the unlinkability property of a blind signature.

  15. Evaluation of a 5-tier scheme proposed for classification of sequence variants using bioinformatic and splicing assay data

    DEFF Research Database (Denmark)

    Walker, Logan C; Whiley, Phillip J; Houdayer, Claude;

    2013-01-01

    Splicing assays are commonly undertaken in the clinical setting to assess the clinical relevance of sequence variants in disease predisposition genes. A 5-tier classification system incorporating both bioinformatic and splicing assay information was previously proposed as a method to provide...

  16. Spectral classification of stars based on LAMOST spectra

    CERN Document Server

    Liu, Chao; Zhang, Bo; Wan, Jun-Chen; Deng, Li-Cai; Hou, Yonghui; Wang, Yuefei; Yang, Ming; Zhang, Yong

    2015-01-01

    In this work, we select the high signal-to-noise ratio spectra of stars from the LAMOST data andmap theirMK classes to the spectral features. The equivalentwidths of the prominent spectral lines, playing the similar role as the multi-color photometry, form a clean stellar locus well ordered by MK classes. The advantage of the stellar locus in line indices is that it gives a natural and continuous classification of stars consistent with either the broadly used MK classes or the stellar astrophysical parameters. We also employ a SVM-based classification algorithm to assignMK classes to the LAMOST stellar spectra. We find that the completenesses of the classification are up to 90% for A and G type stars, while it is down to about 50% for OB and K type stars. About 40% of the OB and K type stars are mis-classified as A and G type stars, respectively. This is likely owe to the difference of the spectral features between the late B type and early A type stars or between the late G and early K type stars are very we...

  17. Risk Classification and Risk-based Safety and Mission Assurance

    Science.gov (United States)

    Leitner, Jesse A.

    2014-01-01

    Recent activities to revamp and emphasize the need to streamline processes and activities for Class D missions across the agency have led to various interpretations of Class D, including the lumping of a variety of low-cost projects into Class D. Sometimes terms such as Class D minus are used. In this presentation, mission risk classifications will be traced to official requirements and definitions as a measure to ensure that projects and programs align with the guidance and requirements that are commensurate for their defined risk posture. As part of this, the full suite of risk classifications, formal and informal will be defined, followed by an introduction to the new GPR 8705.4 that is currently under review.GPR 8705.4 lays out guidance for the mission success activities performed at the Classes A-D for NPR 7120.5 projects as well as for projects not under NPR 7120.5. Furthermore, the trends in stepping from Class A into higher risk posture classifications will be discussed. The talk will conclude with a discussion about risk-based safety and mission assuranceat GSFC.

  18. MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

    Science.gov (United States)

    Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

  19. Content-based image retrieval applied to BI-RADS tissue classification in screening mammography

    OpenAIRE

    2011-01-01

    AIM: To present a content-based image retrieval (CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification.

  20. A Hybrid Classification Approach based on FCA and Emerging Patterns - An application for the classification of biological inhibitors

    OpenAIRE

    Asses, Yasmine; Buzmakov, Aleksey; Bourquard, Thomas; Kuznetsov, Sergei O.; Napoli, Amedeo

    2012-01-01

    Classification is an important task in data analysis and learning. Classification can be performed using supervised or unsupervised methods. From the unsupervised point of view, Formal Concept Analysis (FCA) can be used for such a task in an efficient and well-founded way. From the supervised point of view, emerging patterns rely on pattern mining and can be used to characterize classes of objects w.r.t. a priori labels. In this paper, we present a hybrid classification method which is based ...

  1. Counter-based Traffic Management Scheme for Vehicular Networks

    Directory of Open Access Journals (Sweden)

    Tarun Prakash

    2011-06-01

    Full Text Available Vehicles traffic congestion on the road is reflected as delays while traveling. This congestion has a number of negative effects such as energy consumption, wastage of time and increased tailpipes emission of idling vehicles probably bad for our health. Vehicular congestion has become the serious problem and it is getting worse day by day as the growth of the vehicles significantly increased. In this paper, we proposed a novel counter approach to avoid such vehicular congestion on the road. We have also proposed a path selection algorithm that ensures best path suggestion to vehicles in terms of reduction in trip time and less fuel consumption during whole trip. The whole traffic management solution is combination of "stochastic turn" (i.e. vehicles choose a new direction at each intersection or any other way point and path planning (i.e. origin and destination of the vehicle required in advance that ensured by suggested path selection algorithm. In the later part of this paper simulation results prove the effectiveness of our traffic management scheme in terms of reducing traffic congestion on the road. In addition, this scheme utilizes best of the resources and characteristics of vehicular networks to provide less congested path prediction and also smoothed flow of traffic for vehicles in high density vehicular traffic conditions.

  2. A new Identity Based Encryption (IBE) scheme using extended Chebyshev polynomial over finite fields Zp

    International Nuclear Information System (INIS)

    We present a method to extract key pairs needed for the Identity Based Encryption (IBE) scheme from extended Chebyshev polynomial over finite fields Zp. Our proposed scheme relies on the hard problem and the bilinear property of the extended Chebyshev polynomial over Zp. The proposed system is applicable, secure, and reliable.

  3. A dispersion minimizing scheme for the 3-D Helmholtz equation based on ray theory

    NARCIS (Netherlands)

    C.C. Stolk

    2016-01-01

    We develop a new dispersion minimizing compact finite difference scheme for the Helmholtz equation in 2 and 3 dimensions. The scheme is based on a newly developed ray theory for difference equations. A discrete Helmholtz operator and a discrete operator to be applied to the source and the wavefields

  4. PERFORMANCE COMPARISON OF CELL-BASED AND PACKET-BASED SWITCHING SCHEMES FOR SHARED MEMORY SWITCHES

    Institute of Scientific and Technical Information of China (English)

    Xi Kang; Ge Ning; Feng Chongxi

    2004-01-01

    Shared Memory (SM) switches are widely used for its high throughput, low delay and efficient use of memory. This paper compares the performance of two prominent switching schemes of SM packet switches: Cell-Based Switching (CBS) and Packet-Based Switching (PBS).Theoretical analysis is carried out to draw qualitative conclusion on the memory requirement,throughput and packet delay of the two schemes. Furthermore, simulations are carried out to get quantitative results of the performance comparison under various system load, traffic patterns,and memory sizes. Simulation results show that PBS has the advantage of shorter time delay while CBS has lower memory requirement and outperforms in throughput when the memory size is limited. The comparison can be used for tradeoff between performance and complexity in switch design.

  5. Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples

    Science.gov (United States)

    Azad, Ariful; Rajwa, Bartek; Pothen, Alex

    2016-01-01

    We describe algorithms for discovering immunophenotypes from large collections of flow cytometry samples and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations’ characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters), a template consists of generic meta-populations (a group of homogeneous cell populations obtained from the samples in a class) that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples while ignoring noise and small sample-specific variations. We have applied the template-based scheme to analyze several datasets, including one representing a healthy immune system and one of acute myeloid leukemia (AML) samples. The last task is challenging due to the phenotypic heterogeneity of the several subtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML and were able to distinguish acute promyelocytic leukemia (APL) samples with the markers provided. Clinically, this is helpful since APL has a different treatment regimen from other subtypes of AML. Core algorithms used in our data analysis are

  6. Content Based Image Retrieval : Classification Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Shereena V.B

    2014-11-01

    Full Text Available In a content-based image retrieval system (CBIR, the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity measures based on which matches are made and images are retrieved are also discussed. For effective indexing and fast searching of images based on visual features, neural network based pattern learning can be used to achieve effective classification.

  7. Content Based Image Retrieval : Classification Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Shereena V.B

    2014-10-01

    Full Text Available In a content-based image retrieval system (CBIR, the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity measures based on which matches are made and images are retrieved are also discussed. For effective indexing and fast searching of images based on visual features, neural network based pattern learning can be used to achieve effective classification.

  8. Intrusion Awareness Based on Data Fusion and SVM Classification

    Directory of Open Access Journals (Sweden)

    Ramnaresh Sharma

    2012-06-01

    Full Text Available Network intrusion awareness is important factor forrisk analysis of network security. In the currentdecade various method and framework are availablefor intrusion detection and security awareness.Some method based on knowledge discovery processand some framework based on neural network.These entire model take rule based decision for thegeneration of security alerts. In this paper weproposed a novel method for intrusion awarenessusing data fusion and SVM classification. Datafusion work on the biases of features gathering ofevent. Support vector machine is super classifier ofdata. Here we used SVM for the detection of closeditem of ruled based technique. Our proposedmethod simulate on KDD1999 DARPA data set andget better empirical evaluation result in comparisonof rule based technique and neural network model.

  9. Intrusion Awareness Based on Data Fusion and SVM Classification

    Directory of Open Access Journals (Sweden)

    Ramnaresh Sharma

    2012-06-01

    Full Text Available Network intrusion awareness is important factor for risk analysis of network security. In the current decade various method and framework are available for intrusion detection and security awareness. Some method based on knowledge discovery process and some framework based on neural network. These entire model take rule based decision for the generation of security alerts. In this paper we proposed a novel method for intrusion awareness using data fusion and SVM classification. Data fusion work on the biases of features gathering of event. Support vector machine is super classifier of data. Here we used SVM for the detection of closed item of ruled based technique. Our proposed method simulate on KDD1999 DARPA data set and get better empirical evaluation result in comparison of rule based technique and neural network model.

  10. Texton Based Shape Features on Local Binary Pattern for Age Classification

    OpenAIRE

    V. Vijaya Kumar; B. Eswara Reddy; P. Chandra Sekhar Reddy

    2012-01-01

    Classification and recognition of objects is interest of many researchers. Shape is a significant feature of objects and it plays a crucial role in image classification and recognition. The present paper assumes that the features that drastically affect the adulthood classification system are the Shape features (SF) of face. Based on this, the present paper proposes a new technique of adulthood classification by extracting feature parameters of face on Integrated Texton based LBP (IT-LBP) ima...

  11. Land Cover - Minnesota Land Cover Classification System

    Data.gov (United States)

    Minnesota Department of Natural Resources — Land cover data set based on the Minnesota Land Cover Classification System (MLCCS) coding scheme. This data was produced using a combination of aerial photograph...

  12. Content-based and Algorithmic Classifications of Journals: Perspectives on the Dynamics of Scientific Communication and Indexer Effects

    CERN Document Server

    Rafols, Ismael

    2008-01-01

    The aggregated journal-journal citation matrix -based on the Journal Citation Reports (JCR) of the Science Citation Index- can be decomposed by indexers and/or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two content-based classifications of journals: the ISI Subject Categories and the field/subfield classification of Glaenzel & Schubert (2003). The content-based schemes allow for the attribution of more than a single category to a journal, whereas the algorithms maximize the ratio of within-category citations over between-category citations in the aggregated category-category citation matrix. By adding categories, indexers generate between-category citations, which may enrich the database, for example, in the case of inter-disciplinary developments. The consequent indexer effects are significant in sparse areas of the matrix more than in denser ones. Algorithmic decompositions, on the other hand, are more heavily ...

  13. An Effective Capacity Estimation Scheme in IEEE802.11-based Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    H. Zafar

    2012-11-01

    Full Text Available Capacity estimation is a key component of any admission control scheme required to support quality of serviceprovision in mobile ad hoc networks. A range of schemes have been previously proposed to estimate residualcapacity that is derived from window-based measurements of channel estimation. In this paper a simple and improvedmechanism to estimate residual capacity in IEEE802.11-based ad hoc networks is presented. The scheme proposesthe use of a ‘forgiveness’ factor to weight these previous measurements and is shown through simulation-basedevaluation to provide accurate utilizations estimation and improved residual capacity based admission control.

  14. Pairing-Free ID-Based Key-Insulated Signature Scheme

    Institute of Scientific and Technical Information of China (English)

    Guo-Bin Zhu; Hu Xiong; Zhi-Guang Qin

    2015-01-01

    Abstract⎯Without the assumption that the private keys are kept secure perfectly, cryptographic primitives cannot be deployed in the insecure environments where the key leakage is inevitable. In order to reduce the damage caused by the key exposure in the identity-based (ID-based) signature scenarios efficiently, we propose an ID-based key-insulated signature scheme in this paper, which eliminates the expensive bilinear pairing operations. Compared with the previous work, our scheme minimizes the computation cost without any extra cost. Under the discrete logarithm (DL) assumption, a security proof of our scheme in the random oracle model has also been given.

  15. Security Encryption Scheme for Communication of Web Based Control Systems

    Science.gov (United States)

    Robles, Rosslin John; Kim, Tai-Hoon

    A control system is a device or set of devices to manage, command, direct or regulate the behavior of other devices or systems. The trend in most systems is that they are connected through the Internet. Traditional Supervisory Control and Data Acquisition Systems (SCADA) is connected only in a limited private network Since the internet Supervisory Control and Data Acquisition Systems (SCADA) facility has brought a lot of advantages in terms of control, data viewing and generation. Along with these advantages, are security issues regarding web SCADA, operators are pushed to connect Control Systems through the internet. Because of this, many issues regarding security surfaced. In this paper, we discuss web SCADA and the issues regarding security. As a countermeasure, a web SCADA security solution using crossed-crypto-scheme is proposed to be used in the communication of SCADA components.

  16. TOKEN BASED KEY MANAGEMENT SCHEME FOR SCADA COMMUNICATION

    Directory of Open Access Journals (Sweden)

    Anupam Saxena

    2011-08-01

    Full Text Available Security of SCADA (supervisory Control and Data Acquisition has become a challenging issue todaybecause of its connectivity with the outside world and remote access to the system. One major challengein the SCADA systems is securing the data over the communication channel.PKI (public key infrastructure is a well known framework for securing the communication. In SCADAsystem, due to limited bandwidth and rare communications among some nodes (Remote Terminal Units,there is a need of customization of general PKI which can reduce the openness of Public Key, frequenttransfer of certificates and reduction in DOS (Denial of Service attacks at MTUs (Master TerminalUnits and other nodes.This paper intends to address the issues of securing data over communication channel in the constrainedenvironment and presents the novel solutions pivoted on key distribution and key management schemes.

  17. Dynamic Task Scheduling Algorithm based on Ant Colony Scheme

    Directory of Open Access Journals (Sweden)

    Kamolov Nizomiddin Baxodirjonovich

    2015-08-01

    Full Text Available Many scientific applications running in Cloud Computing system are workflow applications that contains large number of tasks and in which tasks are connected by precedence relations. Efficient scheduling the workflow tasks become a challenging issue in Cloud Computing environments because the scheduling decides performance of the applications. Unfortunately, finding the optimal scheduling is known as NP-hard. Ant Colony Optimization algorithm can be applied to design efficient scheduling algorithms. Previous scheduling algorithms that use Ant Colony mechanism lack rapid adaptivity. This paper proposes a task scheduling algorithm that uses a modified Ant Colony Optimization. The modified version uses probability in order for ants to decide target machine. The proposed task scheduling algorithm is implemented in WorkflowSim in order to measure performance. The experimental results show that the proposed scheduling algorithm reduce average makespan to about 6.4% compared to a scheduling algorithm that uses basic Ant Colony Optimization scheme.

  18. AN AGENT BASED TRANSACTION PROCESSING SCHEME FOR DISCONNECTED MOBILE NODES

    Directory of Open Access Journals (Sweden)

    J.L. Walter Jeyakumar

    2010-12-01

    Full Text Available We present a mobile transaction framework in which mobile users can share data which is stored in the cache of a mobile agent. This mobile agent is a special mobile node which coordinates the sharing process. The proposed framework allows mobile affiliation work groups to be formed dynamically with a mobile agent and mobile hosts. Using short range wireless communication technology, mobile users can simultaneously access the data from the cache of the mobile agent. The data Access Manager module at the mobile agent enforces concurrency control using cache invalidation technique. This model supports disconnected mobile computing allowing mobile agent to move along with the Mobile Hosts. The proposed Transaction frame work has been simulated in Java 2 and performance of this scheme is compared with existing frame works.

  19. The role of catchment classification in rainfall-runoff modeling

    OpenAIRE

    He, Y.; A. Bárdossy; E. Zehe

    2011-01-01

    A sound catchment classification scheme is a fundamental step towards improved catchment hydrology science and prediction in ungauged basins. Two categories of catchment classification methods are presented in the paper. The first one is based directly on physiographic properties and climatic conditions over a catchment and regarded as a Linnaean type or natural classification scheme. The second one is based on numerical clustering and regionalization methods and considered as a statistical o...

  20. Security enhancement of a biometric based authentication scheme for telecare medicine information systems with nonce.

    Science.gov (United States)

    Mishra, Dheerendra; Mukhopadhyay, Sourav; Kumari, Saru; Khan, Muhammad Khurram; Chaturvedi, Ankita

    2014-05-01

    Telecare medicine information systems (TMIS) present the platform to deliver clinical service door to door. The technological advances in mobile computing are enhancing the quality of healthcare and a user can access these services using its mobile device. However, user and Telecare system communicate via public channels in these online services which increase the security risk. Therefore, it is required to ensure that only authorized user is accessing the system and user is interacting with the correct system. The mutual authentication provides the way to achieve this. Although existing schemes are either vulnerable to attacks or they have higher computational cost while an scalable authentication scheme for mobile devices should be secure and efficient. Recently, Awasthi and Srivastava presented a biometric based authentication scheme for TMIS with nonce. Their scheme only requires the computation of the hash and XOR functions.pagebreak Thus, this scheme fits for TMIS. However, we observe that Awasthi and Srivastava's scheme does not achieve efficient password change phase. Moreover, their scheme does not resist off-line password guessing attack. Further, we propose an improvement of Awasthi and Srivastava's scheme with the aim to remove the drawbacks of their scheme. PMID:24771484

  1. A multihop key agreement scheme for wireless ad hoc networks based on channel characteristics.

    Science.gov (United States)

    Hao, Zhuo; Zhong, Sheng; Yu, Nenghai

    2013-01-01

    A number of key agreement schemes based on wireless channel characteristics have been proposed recently. However, previous key agreement schemes require that two nodes which need to agree on a key are within the communication range of each other. Hence, they are not suitable for multihop wireless networks, in which nodes do not always have direct connections with each other. In this paper, we first propose a basic multihop key agreement scheme for wireless ad hoc networks. The proposed basic scheme is resistant to external eavesdroppers. Nevertheless, this basic scheme is not secure when there exist internal eavesdroppers or Man-in-the-Middle (MITM) adversaries. In order to cope with these adversaries, we propose an improved multihop key agreement scheme. We show that the improved scheme is secure against internal eavesdroppers and MITM adversaries in a single path. Both performance analysis and simulation results demonstrate that the improved scheme is efficient. Consequently, the improved key agreement scheme is suitable for multihop wireless ad hoc networks. PMID:23766725

  2. Generalization performance of graph-based semisupervised classification

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    Semi-supervised learning has been of growing interest over the past few years and many methods have been proposed. Although various algorithms are provided to implement semi-supervised learning,there are still gaps in our understanding of the dependence of generalization error on the numbers of labeled and unlabeled data. In this paper,we consider a graph-based semi-supervised classification algorithm and establish its generalization error bounds. Our results show the close relations between the generalization performance and the structural invariants of data graph.

  3. Hydrophobicity classification of polymeric materials based on fractal dimension

    Directory of Open Access Journals (Sweden)

    Daniel Thomazini

    2008-12-01

    Full Text Available This study proposes a new method to obtain hydrophobicity classification (HC in high voltage polymer insulators. In the method mentioned, the HC was analyzed by fractal dimension (fd and its processing time was evaluated having as a goal the application in mobile devices. Texture images were created from spraying solutions produced of mixtures of isopropyl alcohol and distilled water in proportions, which ranged from 0 to 100% volume of alcohol (%AIA. Based on these solutions, the contact angles of the drops were measured and the textures were used as patterns for fractal dimension calculations.

  4. An AIS-Based E-mail Classification Method

    Science.gov (United States)

    Qing, Jinjian; Mao, Ruilong; Bie, Rongfang; Gao, Xiao-Zhi

    This paper proposes a new e-mail classification method based on the Artificial Immune System (AIS), which is endowed with good diversity and self-adaptive ability by using the immune learning, immune memory, and immune recognition. In our method, the features of spam and non-spam extracted from the training sets are combined together, and the number of false positives (non-spam messages that are incorrectly classified as spam) can be reduced. The experimental results demonstrate that this method is effective in reducing the false rate.

  5. A kind of signature scheme based on class groups of quadratic fields

    Institute of Scientific and Technical Information of China (English)

    董晓蕾; 曹珍富

    2004-01-01

    Quadratic-field cryptosystem is a cryptosystem built from discrete logarithm problem in ideal class groups of quadratic fields(CL-DLP). The problem on digital signature scheme based on ideal class groups of quadratic fields remained open, because of the difficulty of computing class numbers of quadratic fields. In this paper, according to our researches on quadratic fields, we construct the first digital signature scheme in ideal class groups of quadratic fields, using q as modulus, which denotes the prime divisors of ideal class numbers of quadratic fields. Security of the new signature scheme is based fully on CL-DLP. This paper also investigates realization of the scheme, and proposes the concrete technique. In addition, the technique introduced in the paper can be utilized to realize signature schemes of other kinds.

  6. A Fair Off-Line Electronic Cash Scheme Based on Restrictive Partially Blind Signature

    Institute of Scientific and Technical Information of China (English)

    王常吉; 吴建平; 段海新

    2004-01-01

    A fair off-line electronic cash scheme was presented based on a provable secure restrictive partially blind signature.The scheme is more efficient than those of previous works as the expiry date and denomination information are embedded in the electronic cash,which alleviates the storage pressure for the bank to check double spending,and the bank need not use different public keys for different coin values,shops and users need not carry a list of bank's public keys to verify in their electronic wallet.The modular exponentiations are reduced for both the user and the bank by letting the trustee publish the public values with different structure as those of previous electronic cash schemes.The scheme security is based on the random oracle model and the decision Diffie-Hellman assumption.The scheme can be easily extended to multi-trustees and multi-banks using threshold cryptography.

  7. A Method for Data Classification Based on Discernibility Matrix and Discernibility Function

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A method for data classification will influence the efficiency of classification. Attributes reduction based on discernibility matrix and discernibility function in rough sets can use in data classification, so we put forward a method for data classification. Namely, firstly, we use discernibility matrix and discernibility function to delete superfluous attributes in formation system and get a necessary attribute set. Secondly, we delete superfluous attribute values and get decision rules. Finally, we classify data by means of decision rules. The experiments show that data classification using this method is simpler in the structure, and can improve the efficiency of classification.

  8. Semi-Supervised Classification based on Gaussian Mixture Model for remote imagery

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Semi-Supervised Classification (SSC),which makes use of both labeled and unlabeled data to determine classification borders in feature space,has great advantages in extracting classification information from mass data.In this paper,a novel SSC method based on Gaussian Mixture Model (GMM) is proposed,in which each class’s feature space is described by one GMM.Experiments show the proposed method can achieve high classification accuracy with small amount of labeled data.However,for the same accuracy,supervised classification methods such as Support Vector Machine,Object Oriented Classification,etc.should be provided with much more labeled data.

  9. Decommissioning technology development for research reactors; establishment on the classification scheme of the decommissioning information and data

    Energy Technology Data Exchange (ETDEWEB)

    Ser, J. S.; Jang, Se Kyu; Kim, Young Do [Chungchong Institute of Regional Information System, Taejeon (Korea)

    2002-04-01

    The establishment of the decommissioning DB is the first thing in KOREA. It has never been decided the standardization in relation to the decommissioning DB all over the world and many countries has been constructed their decommissioning DB which serve their purpose. Owing to get the classification of the decommissioning information and data, it is used a prototyping design that is needed the DB construction as a basic data and applied to a nuclear facilities in the future. 10 refs. (Author)

  10. Bacteriological investigations of the irradiation of fresh fish using a new classification scheme for bacteria of the fish skin

    International Nuclear Information System (INIS)

    Investigations were made on the effect of irradiation with 36, 72, 108 and 144 kr, carried out once and twice, on the bacterial flora of the skin of red fish on the day of catching and after 9, 16 and 23 days of storage in ice. With an initial total count of the fish of 13,700 bacteria/cm2 of skin surface irradiation with 36 kr (108 kr) on the day of catching caused a reduction of the total count to 11% (1.3%) of the nonirradiated fish on the 9th day of storage. Nearly all differences disappeared by the 16th day. A second irradiation with 36 kr and 108 kr on the 9th day reduced the bacterial count on a large scale by which on the 16th day the total count of these fishes was lower than that of the nonirradiated fish on the 9th day. Later on the differentiation disappeared quickly but there were small differences unlike the nonirradiated fish on the 23rd day. The rapid equalization during the last storage period is possibly only typical of the storage in boxes. A scheme for the characterization of types of spoilage bacteria recently established and based on the bacterial attack on leucine, β-alanine, creatine, creatinine and cystine yielded the following results: The different Pseudomonas types were reduced much more than Achromobacter types. The irradiation effect does not only consist in a reduction of the general total count of bacteria but also in the selective destruction of the most active spoilage bacteria with a very extensive enzymatic pattern which concerns many organic nitrogen compounds in the tissue of fish. By means of a sub-group of Pseudomonas and several maturity stages of the bacterial populations a 7 days delay of the bacterial evolution, caused by the second irradiation with 36 kr, could be observed. The useful effect of irradiation carried out twice with doses about 50 kr was discussed and estimated at a 10-12 days delay of the bacterial spoilage. (orig./MG)

  11. Feature selection gait-based gender classification under different circumstances

    Science.gov (United States)

    Sabir, Azhin; Al-Jawad, Naseer; Jassim, Sabah

    2014-05-01

    This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.

  12. Forest Classification Based on Forest texture in Northwest Yunnan Province

    International Nuclear Information System (INIS)

    Forest texture is an intrinsic characteristic and an important visual feature of a forest ecological system. Full utilization of forest texture will be a great help in increasing the accuracy of forest classification based on remote sensed data. Taking Shangri-La as a study area, forest classification has been based on the texture. The results show that: (1) From the texture abundance, texture boundary, entropy as well as visual interpretation, the combination of Grayscale-gradient co-occurrence matrix and wavelet transformation is much better than either one of both ways of forest texture information extraction; (2) During the forest texture information extraction, the size of the texture-suitable window determined by the semi-variogram method depends on the forest type (evergreen broadleaf forest is 3×3, deciduous broadleaf forest is 5×5, etc.). (3)While classifying forest based on forest texture information, the texture factor assembly differs among forests: Variance Heterogeneity and Correlation should be selected when the window is between 3×3 and 5×5; Mean, Correlation, and Entropy should be used when the window in the range of 7×7 to 19×19; and Correlation, Second Moment, and Variance should be used when the range is larger than 21×21

  13. Classification Based on Hierarchical Linear Models: The Need for Incorporation of Social Contexts in Classification Analysis

    Science.gov (United States)

    Vaughn, Brandon K.; Wang, Qui

    2009-01-01

    Many areas in educational and psychological research involve the use of classification statistical analysis. For example, school districts might be interested in attaining variables that provide optimal prediction of school dropouts. In psychology, a researcher might be interested in the classification of a subject into a particular psychological…

  14. Dense Iterative Contextual Pixel Classification using Kriging

    DEFF Research Database (Denmark)

    Ganz, Melanie; Loog, Marco; Brandt, Sami;

    2009-01-01

    In medical applications, segmentation has become an ever more important task. One of the competitive schemes to perform such segmentation is by means of pixel classification. Simple pixel-based classification schemes can be improved by incorporating contextual label information. Various methods h...... relatively long range interactions may play a role. We propose a new method based on Kriging that makes it possible to include such long range interactions, while keeping the computations manageable when dealing with large medical images....

  15. Joint Probability-Based Neuronal Spike Train Classification

    Directory of Open Access Journals (Sweden)

    Yan Chen

    2009-01-01

    Full Text Available Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs. The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

  16. FINGERPRINT-BASED KEY BINDING/RECOVERING SCHEME BASED ON FUZZY VAULT

    Institute of Scientific and Technical Information of China (English)

    Feng Quan; Su Fei; Cai Anni

    2008-01-01

    This letter proposes fingerprint-based key binding/recovering with fuzzy vault. Fingerprint minutiae data and the cryptographic key are merged together by a multivariable linear function. First,the minutiae data are bound by a set of random data through the linear function. The number of the function's variables is determined by the required number of matched minutiae. Then, a new key derived from the random data is used to encrypt the cryptographic key. Lastly, the binding data are protected using fuzzy vault scheme. The proposed scheme provides the system with the flexibility to use changeable number of minutiae to bind/recover the protected key and a unified method regardless of the length of the key.

  17. Evaluation of Superimposed Sequence Components of Currents based Islanding Detection Scheme during DG Interconnections

    Science.gov (United States)

    Sareen, Karan; Bhalja, Bhavesh R.; Maheshwari, Rudra Prakash

    2016-02-01

    A new islanding detection scheme for distribution network containing different types of distributed generations (DGs) is presented in this paper. The proposed scheme is based on acquiring three phase current samples for full cycle duration of each simulation case of islanding/non-islanding conditions at the point of common coupling (PCC) of the targeted DG. Afterwards, superimposed positive & negative sequence components of current are calculated and continuously compared with pre-determined threshold values. Performance of the proposed scheme has been evaluated on diversified islanding and non-islanding events which were generated by modeling standard IEEE 34-bus system using PSCAD/EMTDC software package. The proposed scheme is capable to detect islanding condition rapidly even for perfect power balance situation for both synchronous and inverter based DGs. Furthermore, it remains stable during non-islanding events such as tripping of multiple DGs and different DG interconnection operating conditions. Therefore, the proposed scheme avoids nuisance tripping during diversified non-islanding events. At the end, comparison of the proposed scheme with the existing scheme clearly indicates its advantage over the existing scheme.

  18. A Novel Graph Based Fuzzy Clustering Technique For Unsupervised Classification Of Remote Sensing Images

    Science.gov (United States)

    Banerjee, B.; Krishna Moohan, B.

    2014-11-01

    This paper addresses the problem of unsupervised land-cover classification of multi-spectral remotely sensed images in the context of self-learning by exploring different graph based clustering techniques hierarchically. The only assumption used here is that the number of land-cover classes is known a priori. Object based image analysis paradigm which processes a given image at different levels, has emerged as a popular alternative to the pixel based approaches for remote sensing image segmentation considering the high spatial resolution of the images. A graph based fuzzy clustering technique is proposed here to obtain a better merging of an initially oversegmented image in the spectral domain compared to conventional clustering techniques. Instead of using Euclidean distance measure, the cumulative graph edge weight is used to find the distance between a pair of points to better cope with the topology of the feature space. In order to handle uncertainty in assigning class labels to pixels, which is not always a crisp allocation for remote sensing data, fuzzy set theoretic technique is incorporated to the graph based clustering. Minimum Spanning Tree (MST) based clustering technique is used to over-segment the image at the first level. Furthermore, considering that the spectral signature of different land-cover classes may overlap significantly, a self-learning based Maximum Likelihood (ML) classifier coupled with the Expectation Maximization (EM) based iterative unsupervised parameter retraining scheme is used to generate the final land-cover classification map. Results on two medium resolution images establish the superior performance of the proposed technique in comparison to the traditional fuzzy c-means clustering technique.

  19. Knowledge-Based Trajectory Error Pattern Method Applied to an Active Force Control Scheme

    Directory of Open Access Journals (Sweden)

    Endra Pitowarno, Musa Mailah, Hishamuddin Jamaluddin

    2012-08-01

    Full Text Available The active force control (AFC method is known as a robust control scheme that dramatically enhances the performance of a robot arm particularly in compensating the disturbance effects. The main task of the AFC method is to estimate the inertia matrix in the feedback loop to provide the correct (motor torque required to cancel out these disturbances. Several intelligent control schemes have already been introduced to enhance the estimation methods of acquiring the inertia matrix such as those using neural network, iterative learning and fuzzy logic. In this paper, we propose an alternative scheme called Knowledge-Based Trajectory Error Pattern Method (KBTEPM to suppress the trajectory track error of the AFC scheme. The knowledge is developed from the trajectory track error characteristic based on the previous experimental results of the crude approximation method. It produces a unique, new and desirable error pattern when a trajectory command is forced. An experimental study was performed using simulation work on the AFC scheme with KBTEPM applied to a two-planar manipulator in which a set of rule-based algorithm is derived. A number of previous AFC schemes are also reviewed as benchmark. The simulation results show that the AFC-KBTEPM scheme successfully reduces the trajectory track error significantly even in the presence of the introduced disturbances.Key Words:  Active force control, estimated inertia matrix, robot arm, trajectory error pattern, knowledge-based.

  20. DISTRIBUTED CERTIFICATE AUTHORITY IN CLUSTER-BASED MANET USING MULTI SECRET SHARING SCHEME

    Directory of Open Access Journals (Sweden)

    Mohammed Azza

    2015-11-01

    Full Text Available Providing secure communications in mobile ad hoc networks (MANET is an important and difficult problem, due to a lack of a key management infrastructure. The authentication is an important security service in (MANETs. To provide a node authentication service we use a fully distributed certificate authorities (FDCA based on the threshold cryptography. In this paper we propose an efficient and verifiable multi secret sharing scheme in cluster-based MANET with a low computation system. Our scheme is based on the overdetermined linear system equation in Galois fields GF(2r. We have analyzed our scheme based on security and performance criteria, and compared with existing approaches. The efficiency of our proposed schemes was verified and evaluated by simulation. Simulation results show that this approach is scalable.

  1. An Improved MAC Scheme of HORNET Based on Node Structure with Variable Optical Buffer

    Institute of Scientific and Technical Information of China (English)

    Nian Fang; Lutang Wang; Zhaoming Huang

    2003-01-01

    An improved unslotted CSMA/CA MAC scheme of HORNET based on the node structure with variable optical buffer is reported. It can be used for transmitting high effectively all variable IP packets in the WDM network.

  2. A New Classification Analysis of Customer Requirement Information Based on Quantitative Standardization for Product Configuration

    OpenAIRE

    Zheng Xiao; Zude Zhou; Buyun Sheng

    2016-01-01

    Traditional methods used for the classification of customer requirement information are typically based on specific indicators, hierarchical structures, and data formats and involve a qualitative analysis in terms of stationary patterns. Because these methods neither consider the scalability of classification results nor do they regard subsequent application to product configuration, their classification becomes an isolated operation. However, the transformation of customer requirement inform...

  3. A New Loss-Tolerant Image Encryption Scheme Based on Secret Sharing and Two Chaotic Systems

    Directory of Open Access Journals (Sweden)

    Li Li

    2012-04-01

    Full Text Available In this study, we propose an efficient loss-tolerant image encryption scheme that protects both confidentiality and loss-tolerance simultaneously in shadow images. In this scheme, we generate the key sequence based on two chaotic maps and then encrypt the image during the sharing phase based on Shamir’s method. Experimental results show a better performance of the proposed scheme for different images than other methods from human vision. Security analysis confirms a high probability to resist both brute-force and collusion attacks.

  4. Communication-based fault handling scheme for ungrounded distribution systems

    International Nuclear Information System (INIS)

    The requirement for high quality and highly reliable power supplies has been increasing as a result of increasing demand for power. At the time of a fault occurrence in a distribution system, some protection method would be dedicated to fault section isolation and service restoration. However, if there are many outage areas when the protection method is performed, it is an inconvenience to the customer. A conventional method to determine a fault section in ungrounded systems requires many successive outage invocations. This paper proposed an efficient fault section isolation method and service restoration method for single line-to-ground fault in an ungrounded distribution system that was faster than the conventional one using the information exchange between connected feeders. The proposed algorithm could be performed without any power supply interruption and could decrease the number of switching operations, so that customers would not experience outages very frequently. The method involved the use of an intelligent communication method and a sequential switching control scheme. The proposed algorithm was also applied in both a single-tie and multi-tie distribution system. This proposed algorithm has been verified through fault simulations in a simple model of ungrounded multi-tie distribution system. The method proposed in this paper was proven to offer more efficient fault identification and much less outage time than the conventional method. The proposed method could contribute to a system design since it is valid in multi-tie systems. 5 refs., 2 tabs., 8 figs

  5. Style-based classification of Chinese ink and wash paintings

    Science.gov (United States)

    Sheng, Jiachuan; Jiang, Jianmin

    2013-09-01

    Following the fact that a large collection of ink and wash paintings (IWP) is being digitized and made available on the Internet, their automated content description, analysis, and management are attracting attention across research communities. While existing research in relevant areas is primarily focused on image processing approaches, a style-based algorithm is proposed to classify IWPs automatically by their authors. As IWPs do not have colors or even tones, the proposed algorithm applies edge detection to locate the local region and detect painting strokes to enable histogram-based feature extraction and capture of important cues to reflect the styles of different artists. Such features are then applied to drive a number of neural networks in parallel to complete the classification, and an information entropy balanced fusion is proposed to make an integrated decision for the multiple neural network classification results in which the entropy is used as a pointer to combine the global and local features. Evaluations via experiments support that the proposed algorithm achieves good performances, providing excellent potential for computerized analysis and management of IWPs.

  6. ECG-based heartbeat classification for arrhythmia detection: A survey.

    Science.gov (United States)

    Luz, Eduardo José da S; Schwartz, William Robson; Cámara-Chávez, Guillermo; Menotti, David

    2016-04-01

    An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works. PMID:26775139

  7. Proposed classification of medial maxillary labial frenum based on morphology

    Directory of Open Access Journals (Sweden)

    Ranjana Mohan

    2014-01-01

    Full Text Available Objectives: To propose a new classification of median maxillary labial frenum (MMLF based on the morphology in permanent dentition, conducting a cross-sectional survey. Materials and Methods: Unicentric study was conducted on 2,400 adults (1,414 males, 986 females, aged between 18 and 76 years, with mean age = 38.62, standard deviation (SD = 12.53. Male mean age = 38.533 years and male SD = 12.498. Female mean age = 38.71 and female SD = 12.5750 for a period of 6 months at Teerthanker Mahaveer University, Moradabad, Northern India. The frenum morphology was determined by using the direct visual method under natural light and categorized. Results: Diverse frenum morphologies were observed. Several variations found in the study have not been documented in the past literature and were named and classified according to their morphology. Discussion: The MMLF presents a diverse array of morphological variations. Several other undocumented types of frena were observed and revised, detailed classification has been proposed based on cross-sectional survey.

  8. A Cluster Based Approach for Classification of Web Results

    Directory of Open Access Journals (Sweden)

    Apeksha Khabia

    2014-12-01

    Full Text Available Nowadays significant amount of information from web is present in the form of text, e.g., reviews, forum postings, blogs, news articles, email messages, web pages. It becomes difficult to classify documents in predefined categories as the number of document grows. Clustering is the classification of a data into clusters, so that the data in each cluster share some common trait – often vicinity according to some defined measure. Underlying distribution of data set can somewhat be depicted based on the learned clusters under the guidance of initial data set. Thus, clusters of documents can be employed to train the classifier by using defined features of those clusters. One of the important issues is also to classify the text data from web into different clusters by mining the knowledge. Conforming to that, this paper presents a review on most of document clustering technique and cluster based classification techniques used so far. Also pre-processing on text dataset and document clustering method is explained in brief.

  9. Understanding Acupuncture Based on ZHENG Classification from System Perspective

    Directory of Open Access Journals (Sweden)

    Junwei Fang

    2013-01-01

    Full Text Available Acupuncture is an efficient therapy method originated in ancient China, the study of which based on ZHENG classification is a systematic research on understanding its complexity. The system perspective is contributed to understand the essence of phenomena, and, as the coming of the system biology era, broader technology platforms such as omics technologies were established for the objective study of traditional chinese medicine (TCM. Omics technologies could dynamically determine molecular components of various levels, which could achieve a systematic understanding of acupuncture by finding out the relationships of various response parts. After reviewing the literature of acupuncture studied by omics approaches, the following points were found. Firstly, with the help of omics approaches, acupuncture was found to be able to treat diseases by regulating the neuroendocrine immune (NEI network and the change of which could reflect the global effect of acupuncture. Secondly, the global effect of acupuncture could reflect ZHENG information at certain structure and function levels, which might reveal the mechanism of Meridian and Acupoint Specificity. Furthermore, based on comprehensive ZHENG classification, omics researches could help us understand the action characteristics of acupoints and the molecular mechanisms of their synergistic effect.

  10. Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

    Science.gov (United States)

    Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

    2016-01-01

    Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

  11. A physiologically-inspired model of numerical classification based on graded stimulus coding

    Directory of Open Access Journals (Sweden)

    John Pearson

    2010-01-01

    Full Text Available In most natural decision contexts, the process of selecting among competing actions takes place in the presence of informative, but potentially ambiguous, stimuli. Decisions about magnitudes—quantities like time, length, and brightness that are linearly ordered—constitute an important subclass of such decisions. It has long been known that perceptual judgments about such quantities obey Weber’s Law, wherein the just-noticeable difference in a magnitude is proportional to the magnitude itself. Current physiologically inspired models of numerical classification assume discriminations are made via a labeled line code of neurons selectively tuned for numerosity, a pattern observed in the firing rates of neurons in the ventral intraparietal area (VIP of the macaque. By contrast, neurons in the contiguous lateral intraparietal area (LIP signal numerosity in a graded fashion, suggesting the possibility that numerical classification could be achieved in the absence of neurons tuned for number. Here, we consider the performance of a decision model based on this analog coding scheme in a paradigmatic discrimination task—numerosity bisection. We demonstrate that a basic two-neuron classifier model, derived from experimentally measured monotonic responses of LIP neurons, is sufficient to reproduce the numerosity bisection behavior of monkeys, and that the threshold of the classifier can be set by reward maximization via a simple learning rule. In addition, our model predicts deviations from Weber Law scaling of choice behavior at high numerosity. Together, these results suggest both a generic neuronal framework for magnitude-based decisions and a role for reward contingency in the classification of such stimuli.

  12. Target Image Classification through Encryption Algorithm Based on the Biological Features

    OpenAIRE

    Zhiwu Chen; Qing E. Wu; Weidong Yang

    2014-01-01

    In order to effectively make biological image classification and identification, this paper studies the biological owned characteristics, gives an encryption algorithm, and presents a biological classification algorithm based on the encryption process. Through studying the composition characteristics of palm, this paper uses the biological classification algorithm to carry out the classification or recognition of palm, improves the accuracy and efficiency of the existing biological classifica...

  13. Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Classification

    OpenAIRE

    Rajendra Palange,; Nishikant Pachpute

    2015-01-01

    This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accu...

  14. An Assessment of Case Base Reasoning for Short Text Message Classification

    OpenAIRE

    Healy, Matt, (Thesis); Delany, Sarah Jane; Zamolotskikh, Anton

    2004-01-01

    Message classification is a text classification task that has provoked much interest in machine learning. One aspect of message classification that presents a particular challenge is the classification of short text messages. This paper presents an assessment of applying a case based approach that was developed for long text messages (specifically spam filtering) to short text messages. The evaluation involves determining the most appropriate feature types and feature representation for short...

  15. Comparison of Irrigation Performance Based on the Basin, Crop Pattern, and Scheme Sizes Using External Indicators

    OpenAIRE

    Merdun, Hasan

    2004-01-01

    A comparative assessment allows screening of irrigation systems based on the key issues relative to performance and indicates where improvements should be made, such as in type of management, infrastructure, crop pattern and intensity, and system size. The objective of this study was to assess the performance of 239 irrigation schemes (57 DSI-operated and 182 transferred to Irrigation Associations) based on the basin, crop pattern, and scheme sizes using 6 external indicators for 2001. The ba...

  16. A Novel Spectrum Detection Scheme Based on Dynamic Threshold in Cognitive Radio Systems

    OpenAIRE

    Guicai Yu; Chengzhi Long; Mantian Xiang

    2012-01-01

    In cognitive radio networks, nodes should have the capability to decide whether a signal from a primary transmitter is locally present or not in a certain spectrum in short detection period. This study presents a new spectrum detection algorithm based on dynamic threshold. Spectrum detection schemes based on fixed threshold are sensitive to noise uncertainty, the proposed scheme can improve the antagonism of noise uncertainty, get a good performance of detection while without increasing the c...

  17. Optimal Scheme Selection of Agricultural Production Structure Adjustment - Based on DEA Model; Punjab (Pakistan)

    Institute of Scientific and Technical Information of China (English)

    Zeeshan Ahmad; Meng Jun; Muhammad Abdullah; Mazhar Nadeem Ishaq; Majid Lateef; Imran Khan

    2015-01-01

    This paper used the modern evaluation method of DEA (Data Envelopment Analysis) to assess the comparative efficiency and then on the basis of this among multiple schemes chose the optimal scheme of agricultural production structure adjustment. Based on the results of DEA model, we dissected scale advantages of each discretionary scheme or plan. We examined scale advantages of each discretionary scheme, tested profoundly a definitive purpose behind not-DEA efficient, which elucidated the system and methodology to enhance these discretionary plans. At the end, another method had been proposed to rank and select the optimal scheme. The research was important to guide the practice if the modification of agricultural production industrial structure was carried on.

  18. Asynchronous error-correcting secure communication scheme based on fractional-order shifting chaotic system

    Science.gov (United States)

    Chao, Luo

    2015-11-01

    In this paper, a novel digital secure communication scheme is firstly proposed. Different from the usual secure communication schemes based on chaotic synchronization, the proposed scheme employs asynchronous communication which avoids the weakness of synchronous systems and is susceptible to environmental interference. Moreover, as to the transmission errors and data loss in the process of communication, the proposed scheme has the ability to be error-checking and error-correcting in real time. In order to guarantee security, the fractional-order complex chaotic system with the shifting of order is utilized to modulate the transmitted signal, which has high nonlinearity and complexity in both frequency and time domains. The corresponding numerical simulations demonstrate the effectiveness and feasibility of the scheme.

  19. A Non-symmetric Digital Image Secure Communication Scheme Based on Generalized Chaos Synchronization System

    International Nuclear Information System (INIS)

    Based on a generalized chaos synchronization system and a discrete Sinai map, a non-symmetric true color (RGB) digital image secure communication scheme is proposed. The scheme first changes an ordinary RGB digital image with 8 bits into unrecognizable disorder codes and then transforms the disorder codes into an RGB digital image with 16 bits for transmitting. A receiver uses a non-symmetric key to verify the authentication of the received data origin, and decrypts the ciphertext. The scheme can encrypt and decrypt most formatted digital RGB images recognized by computers, and recover the plaintext almost without any errors. The scheme is suitable to be applied in network image communications. The analysis of the key space, sensitivity of key parameters, and correlation of encrypted images imply that this scheme has sound security.

  20. A Non-symmetric Digital Image Secure Communication Scheme Based on Generalized Chaos Synchronization System

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xiao-Hong; MIN Le-Quan

    2005-01-01

    Based on a generalized chaos synchronization system and a discrete Sinai map, a non-symmetric true color(RGB) digital image secur e communication scheme is proposed. The scheme first changes an ordinary RGB digital image with 8 bits into unrecognizable disorder codes and then transforms the disorder codes into an RGB digital image with 16 bits for transmitting. A receiver uses a non-symmetric key to verify the authentication of the received data origin,and decrypts the ciphertext. The scheme can encrypt and decrypt most formatted digital RGB images recognized by computers, and recover the plaintext almost without any errors. The scheme is suitable to be applied in network image communications. The analysis of the key space, sensitivity of key parameters, and correlation of encrypted images imply that this scheme has sound security.