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Joint estimative method of three-dimensional parameters for near-field sources based on cumulant

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2010年第10期32卷 2041-2044页

作者： Ma, Ke-Jiang Li, Jun Wu, Yun-Tao Liao, Gui-Sheng National Lab of Radar Signal Processing Xidian Univ. Xi'an 710071 China School of Computer Science and Engineering Wuhan Inst. of Technology Wuhan 430073 China

A new joint estimative method of three-dimensional parameters for near-field sources without pairing parameters is presented. A new matrix is obtained from the transformation of the constructed four-order cumulant matrix of array output signals. The parameters are directly calculated by the eigenvalues and eigenvactors of the new matrix and just once eigen-decomposition. It does not requre searching spectral peak and can be used to arbitrary Gaussian noise environment, the result of the parameters estimation can achieved automatic pairing. The loss of array aperture is efficiently avoided because the method needs not to use the center of symmetry of the array stracture. Finally, the simulation results verify the effectiveness of the proposed method.

关键词： Eigenvalues and eigenfunctions

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Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition

Dynamic weighted discrimination power analysis in DCT domain...

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2010 International Conference on Information and Communication technology Convergence, ICTC 2010

作者： Leng, Lu Zhang, Jiashu Xu, Jing Khan, Muhammad Khurram Alghathbar, Khaled Sichuan Province Key Lab. of Signal and Information Processing Southwest Jiaotong University Chengdu China Center of Excellence in Information Assurance King Saud University Riyadh Saudi Arabia Information Systems Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9781424498062

Discrimination power analysis (DPA) is a statistical analysis combining discrimination concept with discrete cosine transform coefficients (DCTCs) properties. Unfortunately there is not a uniform and effective criterion to optimize the shape and size of premasking window on which the effect of DPA excessively relies. Proper premasking is an auxiliary process to select the feature coefficients that have more discrimination power (DP). Dynamic weighted DPA (DWDPA) is proposed in this paper to enhance the DP of the selected DCTCs without premasking window, in other words, it does not need to optimize the shape and size of premasking window. The DCTCs are adaptively selected according to their discrimination power values (DPVs). More DCTCs with higher DP are preserved. The selected coefficients are normalized and dynamic weighted according to their DPVs. Normalization assures that the DCTCs with large absolute value don't destroy the DP of the other DCTCs that have less absolute value but high DPVs. Dynamic weighting gives larger weights to the DCTCs with larger DPVs which optimizes and enhances the recognition performance. The experimental results on ORL, Yale and PolyU databases show that DWDPA outperforms DPA obviously. © 2010 IEEE.

关键词： Discrete cosine transforms

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Auditory speech processing for scale-shift covariance and its evaluation in automatic speech recognition

Auditory speech processing for scale-shift covariance and it...

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2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010

作者： Patterson, Roy D. Walters, Thomas C. Monaghan, Jessica Feldbauer, Christian Irino, Toshio Department of Physiology Development and Neuroscience University of Cambridge Cambridge United Kingdom MRC Institute of Hearing Research Nottingham United Kingdom Signal Processing and Speech Communication Lab. Graz University of Technology Graz Austria Faculty of Systems Engineering Wakayama University Wakayama Japan

ISBN: (纸本)9781424453085

The syllab.es of speech contain information about the vocal tract length (VTL) of the speaker as well as the phonetic message. Ideally, the pre-processor used for automatic speech recognition (ASR) should segregate the phonetic message from the VTL information. This paper describes a method to calculate VTL-invariant auditory feature vectors from speech, using a method in which the message and the VTL are segregated. Spectra produced by an auditory filterbank are summarized by a Gaussian mixture model (GMM) to produce a low-dimensional feature vector. These features are evaluated for robustness in comparison with conventional mel-frequency cepstral coefficients (MFCCs) using a hidden-Markov-model (HMM) recognizer. A dynamic, compressive gammachirp (dcGC) auditory filterbank is also introduced. The dcGC provides a leveldependent spectral analysis, with near instantaneous compression, and two-tone suppression. ©2010 IEEE.

关键词： Hidden Markov models

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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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2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Chen, Si-Bao Wang, Hai-Xian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui 230039 China Key Lab of Child Development and Learning Science of Ministry of Education Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9781424459346

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. ©2010 IEEE.

关键词： Discriminant analysis

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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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5th IEEE Workshop on Networking Technologies for Software-Defined Radio , SDR 2010

作者： Vučević, Nemanja Akyildiz, Ian F. Pérez-Romero, Jordi Signal Theory and Communications Dpt. Universitat Politècnica de Catalunya Barcelona 08034 Spain Broadband Wireless Networking Lab. School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

ISBN: (纸本)9781424472130

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 solution 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. © 2010 IEEE.

关键词： Cognitive radio

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Improved track-before-detect algorithm for weak targets based on particle filter

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Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2010年第9期32卷 1875-1879页

作者： Wu, Sun-Yong Liao, Gui-Sheng Yang, Zhi-Wei Li, Cai-Cai National Lab. of Radar Signal Processing Xidian Univ. Xi'an 710071 China Dept. of Computational Science and Mathematics Guilin Univ. of Electronic Technology Guilin 541004 China

An improved track-before-detect (TBD) algorithm based on the particle filter is proposed for high-speed weak target detection. This method uses a unique measurement model for radar range-Doppler compression, which can effectively reduce the model error of the traditional sensor point spread function. An updating strategy is proposed that newborn particles are uniformly distributed within the set with high-intensity bins and that the existing particles with low weights are replaced by new particles with probability. This strategy can improve the diversity among the particles and mitigate the effects of degeneracy. Simulation results show that the proposed algorithm has an improved performance of detecting and tracking dim targets compared with the standard particle filter.

关键词： Optical transfer function

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User-centric context-aware mobile applications for embodied music listening

User-centric context-aware mobile applications for embodied ...

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1st International Conference on User Centric Media, UCMedia 2009

作者： Camurri, Antonio Volpe, Gualtiero Vinet, Hugues Bresin, Roberto Fabiani, Marco Dubus, Gaël Maestre, Esteban Llop, Jordi Kleimola, Jari Oksanen, Sami Välimäki, Vesa Seppanen, Jarno Casa Paganini - InfoMus Lab. DIST - University of Genova Genova Italy IRCAM Paris France KTH School of Computer Science and Communication Stockholm Sweden Music Technology Group UPF - Universitat Pompeu Fabra Barcelona Spain TKK Department of Signal Processing and Acoustics Espoo Finland Nokia Research Center Helsinki Finland Viale Causa 13 I-16145 Genova Italy

ISBN: (纸本)3642126294

This paper surveys a collection of sample applications for networked user-centric context-aware embodied music listening. The applications have been designed and developed in the framework of the EU-ICT Project SAME (***) and have been presented at Agora Festival (IRCAM, Paris, France) in June 2009. All of them address in different ways the concept of embodied, active listening to music, i.e., enabling listeners to interactively operate in real-time on the music content by means of their movements and gestures as captured by mobile devices. In the occasion of the Agora Festival the applications have also been evaluated by both expert and non-expert users. © Institute for computer Sciences, Social-Informatics and Telecommunications Engineering 2010.

关键词： computers

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Robust speech recognition using compression of Mel sub-band energies and temporal filtering

2010 5th International Symposium on Telecommunications, IST ...

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2010 5th International Symposium on Telecommunications, IST 2010 2010年 760-763页

作者： Moradi, Naghmeh Nasersharif, Babak Akbari, Ahmad Faculty of Engineering University of Guilan Rasht Iran Audio and Speech Processing Lab. Computer Engineering Department Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781424481835

The Mel-frequency cepstral coefficients (MFCC) are commonly used in speech recognition systems. But, they are highly sensitive to presence of external noise. In this paper, we propose a two-step method to compensate noise effects on MFCC. In the first step, we propose a sub-band SNR-dependent compression function for Mel sub-band energies to give higher weights to sub-bands less contaminated with noise and give lower weights to sub-bands more contaminated with noise. In the second step, we apply temporal filters to the weighted MFCCs in order to improve their temporal characteristics. Our results on Aurora2 databases show that the proposed method has higher performance than both of conventional temporal filtering methods and weighted MFCC. © 2010 IEEE.

关键词： signal to noise ratio

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A Network-based Approach for Protein Functions Prediction Using Locally Linear Embedding

A Network-based Approach for Protein Functions Prediction Us...

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The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Haifeng Zhao Dengdi Sun Rifeng Wang Bin Luo Key Lab of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 P.R.China

Inferring protein functions from different data sources is a challenging task in the post-genomic era, as a large number of crude protein structures from structural genomics project are now solved without their biochemical functions characterized. Recently, many different methods have been used to predict protein functions including those based on Protein-Protein Interaction (PPI), structure, sequence relationship, gene expression data, etc. Among these approaches, methods based on protein interaction data are very promising. In this paper, we studied a network-based method using locally linear embedding (LLE). LLE is a robust learning algorithm that manipulates dimensionality reduction, neighborhood-preserving embedding for high-dimensional data. We first embed both annotated and unannotated proteins in a low dimensional Euclidean space;then, we apply semi-supervised learning techniques to classify unannotated proteins into different functional groups. Finally, we made predictions to the unknown functional proteins in yeast. 5-fold cross validation is then applied to the GO terms to compare the performance of different approaches, and the proposed method performs significantly better than the others.

关键词： Bioinformatics Semisupervised learning Genomics Protein sequence Gene expression Learning systems Sun Intelligent networks Intelligent structures computer networks

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Inferring Protein Annotation from Topological Structural Analysis on Protein Interaction Network

Inferring Protein Annotation from Topological Structural Ana...

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The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Dengdi Sun Jin Fan Haifeng Zhao Bin Luo Key Lab of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 P.R.China

As more and more high-throughput protein-protein interactions data are collected, a large fraction of newly discovered proteins have an unknown functional role. A challenge to the scientific community is to assign these newly proteins with a biological function that can be verified by experiment. On the basis of thorough analysis of existing protein function prediction, we take double direction enumeration method combined with the distance constrains to find protein pathway, and propose a new predicting model based on topological structural of protein-protein interaction (PPI) network and protein pathway, which greatly increases the speed of the algorithm. To validate the method, the Yeast Saccharomyces cerevisiae protein-protein interaction network and corresponding protein pathways are analyzed. Comparing with other methods, our results can substantially improve the accuracy and robustness of functional annotation.

关键词： Proteins Fungi Databases computer networks Biological system modeling Predictive models Organisms signal analysis Sun Intelligent networks

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