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SAR Target Classification Using Bayesian Compressive Sensing with Scattering Centers Features
Xinzheng Zhang;Jianhong Qin;Guojun Li
PIER , 2013, DOI: 10.2528/PIER12120705
Abstract: The emerging field of compressed sensing provides sparse reconstruction, which has demonstrated promising results in the areas of signal processing and pattern recognition. In this paper, a new approach for synthetic aperture radar (SAR) target classification is proposed based on Bayesian compressive sensing (BCS) with scattering centers features. Scattering centers features are extracted as a -norm sparse problem on the basis of the SAR observation physical model, which can improve discrimination ability compared with original SAR image. Using an overcomplete dictionary constructed of training samples, BCS is utilized to design targets classifier. For target classification performance evaluation, the proposed method is compared with several state-of-art methods through experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public release database. Experimental results illustrate the effectiveness and robustness of the proposed approach.
Target Classification with Low-Resolution Surveillance Radars Based on Multifractal Features
Qiusheng Li;Weixin Xie
PIER B , 2012, DOI: 10.2528/PIERB12091509
Abstract: The multifractal characteristics of return signals from aircraft targets in conventional radars offer a fine description of dynamic characteristics which induce the targets’ echo structure; therefore they can provide a new way for aircraft target classification and recognition with low-resolution surveillance radars. On basis of introducing the mathematical model of return signals from aircraft targets in conventional radars, the paper analyzes the multifractal characteristics of the return signals as well as the extraction method of their multifractal features by means of the multifractal analysis of measures, and puts forward a multifractal-feature-based classification method for three types of aircraft targets (including jet aircrafts, propeller aircrafts and helicopters) from the viewpoint of pattern classification. The analysis shows that the conventional radar return signals from the three types of aircraft targets have significantly different multifractal characteristics, and the defined characteristic parameters can be used as effective features for aircraft target classification and recognition. The results of classification experiments validate the proposed method.
Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features  [PDF]
Zhisong He,Jian Zhang,Xiao-He Shi,Le-Le Hu,Xiangyin Kong,Yu-Dong Cai,Kuo-Chen Chou
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0009603
Abstract: Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.
The Classification of Quantum Symmetric-Key Encryption Protocols  [PDF]
Chong Xiang,Li Yang,Yong Peng,Dongqing Chen
Computer Science , 2015,
Abstract: The classification of quantum symmetric-key encryption protocol is presented. According to five elements of a quantum symmetric-key encryption protocol: plaintext, ciphertext, key, encryption algorithm and decryption algorithm, there are 32 different kinds of them. Among them, 5 kinds of protocols have already been constructed and studied, and 21 kinds of them are proved to be impossible to construct, the last 6 kinds of them are not yet presented effectively. That means the research on quantum symmetric-key encryption protocol only needs to consider with 5 kinds of them nowadays.
Target classification by surveillance radar based on multifractal features
基于多重分形特征的防空雷达目标分类方法

LI Qiu-sheng,XIE Wei-xin,
李秋生
,谢维信

计算机应用研究 , 2013,
Abstract: On basis of introducing the mathematical model of aircraft returns in the conventional radar, by means of the multifractal measure analysis, this paper analyzed the multifractal characteristic of the aircraft returns as well as the extraction method of their multifractal signatures, and proposed the classification method for three types of aircraft containing jets, propeller aircrafts and helicopters from the angle of pattern recognition. The experimental analysis shows, the conventional radar returns from three types of aircraft targets, containing jets, propeller aircrafts and helicopters, have significantly different multifractal characteristic curves, and the defined multifractal characteristic parameters can be used as effective features for aircraft target classification and recognition. The simulation validated the validity of the proposed method.
A complete Classification of Quantum Public-key Encryption Protocols  [PDF]
Chenmiao Wu,Li Yang
Computer Science , 2015,
Abstract: We present a classification of quantum public-key encryption protocols. There are six elements in quantum public-key encryption: plaintext, ciphertext, public-key, private-key, encryption algorithm and decryption algorithm. According to the property of each element which is either quantum or classical, the quantum public-key encryption protocols can be divided into 64 kinds. Among 64 kinds of protocols, 8 kinds have already been constructed, 52 kinds can be proved to be impossible to construct and the remaining 4 kinds have not been presented effectively yet. This indicates that the research on quantum public-key encryption protocol should be focus on the existed kinds and the unproposed kinds.
Texture Classification Based on Texton Features  [cached]
U Ravi Babu,V Vijay Kumar,B Sujatha
International Journal of Image, Graphics and Signal Processing , 2012,
Abstract: 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.
Facilitating the Automatic Characterisation, Classification and Description of Biological Images with the VisionBioShape Package for R
Biel Stela, Antonio Monleón-Getino
Open Access Library Journal (OALib Journal) , 2016, DOI: 10.4236/oalib.1103108
Abstract:
Here, we present the VisioBioshapeR package for R [R Core, 2014]. This new library is a comprehensive, multifunctional toolbox designed to automatically analyse biological images. The package extends other common libraries (Momocs, ShapeR) used for biological shape analysis by allowing the user to extract closed contour outlines automatically from reading binary images. Current functionalities of VisioBioshapeR include: random extraction of image coordinates, analysis of the shape of a biological image by the elliptic Fourier descriptor (EFD) method, extraction of an image characteristic vector using multivariate principal component analysis (PCA) and geometrical analysis. The image vector of characteristics can be directly exported to a wide range of statistical packages in R and can be used to perform classification or other types of analysis in order to sort new images into classes. The package could prove useful in studies of any two-dimensional images and is presented with three examples of its application in ecology. The library is useful when multiple images are processed at a time and we wish to automate their analysis for example for recognition of images from patterns.
Discriminative Topological Features Reveal Biological Network Mechanisms  [PDF]
Manuel Middendorf,Etay Ziv,Carter Adams,Jen Hom,Robin Koytcheff,Chaya Levovitz,Gregory Woods,Linda Chen,Chris Wiggins
Quantitative Biology , 2004,
Abstract: Recent genomic and bioinformatic advances have motivated the development of numerous random network models purporting to describe graphs of biological, technological, and sociological origin. The success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional) ``word space.'' This map defines an input space for classification schemes which allow us for the first time to state unambiguously which models are most descriptive of the networks they purport to describe. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work, source code for which is freely available. We anticipate that this new approach to network analysis will be of broad impact to a number of communities.
Feature Extraction in Radar Target Classification
J. Kurty,F. Nebus,Z. Kus
Radioengineering , 1999,
Abstract: This paper presents experimental results of extracting features in the Radar Target Classification process using the J frequency band pulse radar. The feature extraction is based on frequency analysis methods, the discrete-time Fourier Transform (DFT) and Multiple Signal Characterisation (MUSIC), based on the detection of Doppler effect. The analysis has turned to the preference of DFT with implemented Hanning windowing function. We assumed to classify targets-vehicles into two classes, the wheeled vehicle and tracked vehicle. The results show that it is possible to classify them only while moving. The feature of the class results from a movement of moving parts of the vehicle. However, we have not found any feature to classify the wheeled and tracked vehicles while non-moving, although their engines are on.
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