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作者： Hori, Gen Tanaka, Toshihisa Faculty of Business Administration Asia University Tokyo 180-8629 Japan Dept. of Electrical and Electronic Eng. Tokyo Univ. of Agriculture and Technology Tokyo 184-8588 Japan Lab. for Advanced Brain Signal Processing Brain Science Institute RIKEN Saitama 351-0198 Japan

Matrix optimization problems with orthogonal constraints appear in a variety of application fields including signal and image processing. Several researchers have developed algorithms for orthogonal optimization problems using the Cayley transform that parameterizes the group of orthogonal matrices by the space of skew-symmetric matrices. However those algorithms sometimes have experienced extremely slow progress in their convergence. This paper shows that the slow progress is related to the singular points of the Cayley transform and introduces a simple trick called "pivoting" to circumvent the slow progress.

关键词： Optimization

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Teaching Reform and Innovation of the Course - Digital Image processing Experiments

Teaching Reform and Innovation of the Course - Digital Image...

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The 5th International Conference on computer Science & Education(第五届国际计算机新技术与教育学术研讨会 ICCSE'10)

作者： Haifeng Zhao Jin Tang Bin Luo Key Lab of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230039P. R. China

Digital image processing is an interdisciplinary course, which needs students have a strong background in mathematics. To help them change from passive learning to active learning, teaching reform and innovation of the course "Digital Image processing Experiments" is discussed in this paper. The combined platform of experiments includes TI DSP experimental box and three different kinds of programming languages. The six experimental projects cover the basic theories of digital image processing. The result can offer a significant reference for the teaching innovations in the other related specialties.

关键词： digital image processing:experimental teaching matlab.DSP

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A method of multiple parameters estimation for high speed moving targets in multi-carrier-frequency MIMO radar

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2010年第12期38卷 2763-2768页

作者： Qin, Guo-Dong Chen, Bai-Xiao Chen, Duo-Fang Zhang, Shou-Hong School of Electronic Engineering Xidian University Xi'an Shaanxi 710071 China National Key Lab. of Radar Signal Processing School of Electronic Engineering Xidian University Xi'an Shaanxi 710071 China School of Life Sciences and Technology Xidian University Xi'an Shaanxi 710071 China

In multi-carrier-frequency Multiple-Input Multiple-Output (MIMO) radar, the separated echoes of a target with high speed locate in neither the same range bin nor the same Doppler bin. As a result, the parameters (including range, azimuth and elevation) of the target can't be estimated using the traditional supper-resolution algorithms. A method based on cascaded Keystone transformation is presented to estimate the parameters of the targets moving at a high speed. Keystone transformation is used to correct range migration in the fast-time dimension, and then the Doppler migration is corrected in the slow-time dimension. Finally the Multiple signal Classification (MUSIC) algorithm is applied on the corrected data to obtain the range, azimuth and elevation of targets. The simulation results indicate the feasibility and validity of the methods presented.

关键词： Multiple signal classification

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The participation payoff: Challenges and opportunities for multimedia access in networked communities

The participation payoff: Challenges and opportunities for m...

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2010 ACM SIGMM International Conference on Multimedia Information Retrieval, MIR 2010

作者： Ramzan, Naeem Larson, Martha Dufaux, Frédéric Clüver, Kai Multimedia and Vision Group School of Electronic Eng. and Computer Science Queen Mary University of London United Kingdom Multimedia Information Retrieval Lab Elec. Eng. Mathematics and Computer Science Delft University of Technology Netherlands Multimedia Signal Processing Group Institute of Electrical Engineering Ecole Polytechnique Fédérale de Lausanne Switzerland Communication Systems Group Technische Universität Berlin Germany

ISBN: (纸本)9781605588155

Increasingly, multimedia collections are associated with networked communities consisting of interconnected groups of users who create, annotate, browse, search, share, view, critique and remix collection content. Information arises within networked communities via connections among users and in the course of interactions between users and content. Community-derived information can be exploited to improve user access to multimedia. This paper provides a survey of techniques that make use of a combination of three information sources: community-contributed information (e.g., tags and ratings), network structure and techniques for multimedia content analysis. This triple synergy offers a wide range of opportunities for improving access to multimedia in networked communities. We focus our survey on three areas important for multimedia access: annotation, distribution and retrieval. The picture that emerges is promising: information derived from the social community is remarkably effective in improving access to multimedia content, and participation in networked communities has a high payoff. Copyright 2010 ACM.

关键词： Surveys

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A HSC-based sample selection method for Support Vector Machine

A HSC-based sample selection method for Support Vector Machi...

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International Conference on Machine Learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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Information theory related learning

Information theory related learning

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作者： Villmann, T. Cichocki, A. Principe, J. University of Applied Sciences Mittweida Dep. of Mathematics/Natural and Computer Sciences Computational Intelligence Group Technikumplatz 17 09648 Mittweida Germany Riken Brain Sience Institute Lab. for Advanced Brain Signal Processing 2-1 Hirosawa Wako City Saitama 351-0198 Japan University of Florida Computational NeuroEngineering Laboratory Gainesville FL 32611 United States

ISBN: (纸本)9782874190445

This is the introduction paper to a special session held on ESANN conference 2011. It reviews and highlights recent developments and new direction in information related learning, which is a fastly developing research area. These algorithms are based on the fundamental principles of information theory and relate them implicitly or explicitly to learning algoithms and strategies.

关键词： Information theory

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Bit-plane based contour information extraction for digital image

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Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of technology 2010年第SUPPL. 2期42卷 110-113页

作者： Wu, Song-Lin Cui, Rong-Yi Intelligent Information Processing Lab. Department of Computer Science and Technology Yanbian University Yanji 133002 China

A bit-plane-based image contour information extracting method for histogram equalized image was proposed in this paper. Firstly, the influence of histogram equalization on bit planes was analyzed and the stability of Gray-code-based bit planes was proved. Furthermore, by calculating information entropy, the set of bit planes concluding the main contour information of images was determined. Finally, the procedure and algorithm for extracting effective contour information was described. The experimental results show that the proposed method can provide effective information for rough classification of images.

关键词： Graphic methods

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Subspace selection using semi-supervised harmonic mean of Kullback-Leibler divergences

Subspace selection using semi-supervised harmonic mean of Ku...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Si-Bao Chen Hai-Xian Wang Bin Luo Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Lab of Child Development and Learning Science of Ministry of Education Research Center of Learning Sciences South-East University Nanjing Jiangsu China

In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), can significantly reduce the class separation problem. Furthermore, maximizing the harmonic mean of Kullback-Leibler (KL) divergences of class pairs (MHMD) emphasizes smaller divergences more than MGMD, and deals with the class separation problem more effectively. However, in many applications, lab.led data are very limited while unlab.led data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate lab.led data. To take advantage of unlab.led data for subspace selection, semi-supervised MHMD (SSMHMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMHMD.

关键词： Harmonic analysis Training Laplace equations Manifolds Covariance matrix Machine learning Symmetric matrices

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Shot Transition Detection Based on Locally Linear Embedding

Shot Transition Detection Based on Locally Linear Embedding

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2010 International Conference on Measuring technology and Mechatronics Automation(ICMTMA 2010)(2010年检测技术与机电自动化国际会议)

作者： MaoSheng Fu DaMing Zhang Min Kong Bin Luo Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui University Hefei230039. P. R. China Department of Computer Science and Technology in West Anhui University Anhui Lu'an 237012. P. R. China

ISBN: (纸本)9781424450015

Detection of shot transitions servers as the preliminary step to video indexing and retrieval. Locally linear embedding (LLE) algorithm fails when it is applied to video with multi-shot. In this paper, we present a novel framework of shot transitions detection. The method involves two processes: First we extract the manifold feature of shot transition using LLE through addition of virtual frames on an enriched set, and then they are classified by *** show that the recognition rate of shot transition is reached over 90％.

关键词： shot transition detection locally linear embedding feature extraction

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Cooperation Reliability Based on Reinforcement Learning for Cognitive Radio Networks

Cooperation Reliability Based on Reinforcement Learning for ...

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IEEE Workshop on Networking Technologies for Software Defined Radio Networks (SDR)

作者： Nemanja Vučević Ian F. Akyildiz Jordi Pérez-Romero Signal Theory and Communications Dpt. Universitat Politècnica de Catalunya Barcelona Spain Broadband Wireless Networking Lab. Georgia Institute of Technology Atlanta GA USA Broadband Wireless Networking Laboratory School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

The primary objective of cooperation in Cognitive Radio (CR) networks is to increase the efficiency and improve the network performance. However, CR users may act destructively and decrease both their own and others'' performances. This can be due to Byzantine adversaries or unintentional erroneous conduct in cooperation. This work presents an autonomous cooperation solution for each CR user, i.e., each CR user decides with whom to cooperate. The objective of the proposed solution is to increase the spectrum access in cooperative CR networks. To realize this, a Reinforcement Learning (RL) algorithm is utilized to determine the suitability of the availab.e cooperators and select the appropriate set of cooperators. In addition, the proposed so-lution determines the most appropriate number of cooperators to achieve the highest efficiency for spectrum access. Accordingly, the control communication overhead is reduced. The simulation results demonstrate the learning capabilities of the proposed to achieve reliable behavior under highly unreliable conditions.

关键词： Learning Cognitive radio Chromium computer network reliability Telecommunication network reliability Reliability engineering Reliability theory Communication system control Wireless networks Frequency

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