An improved two-channel Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) method based on eigen-decomposition of the covariance matrix is investigated. Based on the joint Probability Density Function...An improved two-channel Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) method based on eigen-decomposition of the covariance matrix is investigated. Based on the joint Probability Density Function (PDF) of the Along-Track Interferometric (ATI) phase and the similarity between the two SAR complex images, a novel ellipse detector is presented and is applied to the indication of ground moving targets. We derive its statistics and analyze the performance of detection process in detail. Compared with the approach using the ATI phase, the ellipse detector has a better performance of detection in homogenous clutter. Numerical experiments on simulated data are presented to validate the improved performance of the ellipse detector with respect to the ATI phase approach. Finally, the detection capability of the proposed method is demonstrated by measured SAR data.展开更多
Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning m...Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.展开更多
The present paper develops a novel way of reducing a protein sequence of any length to a real symmetric condensed 20 × 20 matrix. This condensed matrix can be nicely applied as a protein sequence descriptor. In f...The present paper develops a novel way of reducing a protein sequence of any length to a real symmetric condensed 20 × 20 matrix. This condensed matrix can be nicely applied as a protein sequence descriptor. In fact, with such a condensed representation, comparison of two protein sequences is reduced to a comparison of two such 20 × 20 matrices. As each square matrix has a unique Alley Index/normalized Alley Index, such index is conveniently used in getting distance matrix to construct Phylogenetic trees of different protein sequences. Finally protein sequence comparison is made based on these Phylogenetic trees. In this paper three types viz., NADH dehydrogenase subunit 3 (ND3), subunit 4 (ND4) and subunit 5 (ND5) of protein sequences of nine species, Human, Gorilla, Common Chimpanzee, Pygmy Chimpanzee, Fin Whale, Blue Whale, Rat, Mouse and Opossum are used for comparison.展开更多
基金Supported by the Aviation Science Fund (No. 20080152004)China Postdoctoral Foundation (No. 20090461119)
文摘An improved two-channel Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) method based on eigen-decomposition of the covariance matrix is investigated. Based on the joint Probability Density Function (PDF) of the Along-Track Interferometric (ATI) phase and the similarity between the two SAR complex images, a novel ellipse detector is presented and is applied to the indication of ground moving targets. We derive its statistics and analyze the performance of detection process in detail. Compared with the approach using the ATI phase, the ellipse detector has a better performance of detection in homogenous clutter. Numerical experiments on simulated data are presented to validate the improved performance of the ellipse detector with respect to the ATI phase approach. Finally, the detection capability of the proposed method is demonstrated by measured SAR data.
基金supported by Research Grants Council of Hong Kong under Grant No.17301214HKU CERG Grants,Fundamental Research Funds for the Central Universities+2 种基金the Research Funds of Renmin University of ChinaHung Hing Ying Physical Research Grantthe Natural Science Foundation of China under Grant No.11271144
文摘Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.
文摘The present paper develops a novel way of reducing a protein sequence of any length to a real symmetric condensed 20 × 20 matrix. This condensed matrix can be nicely applied as a protein sequence descriptor. In fact, with such a condensed representation, comparison of two protein sequences is reduced to a comparison of two such 20 × 20 matrices. As each square matrix has a unique Alley Index/normalized Alley Index, such index is conveniently used in getting distance matrix to construct Phylogenetic trees of different protein sequences. Finally protein sequence comparison is made based on these Phylogenetic trees. In this paper three types viz., NADH dehydrogenase subunit 3 (ND3), subunit 4 (ND4) and subunit 5 (ND5) of protein sequences of nine species, Human, Gorilla, Common Chimpanzee, Pygmy Chimpanzee, Fin Whale, Blue Whale, Rat, Mouse and Opossum are used for comparison.
