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2009 ACM International Wireless communications and Mobile Computing Conference, IWCMC 2009

作者： Hamza, Abdelkrim Chitroub, Salim Benmimoune, Moncef Touhami, Rachida Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB P. O. Box 32 El-Alia Bab-Ezzouar 16111 Algiers Algeria Instrumentation Laboratory Electronics and Computer Science Faculty USTHB P. O. Box 32 El-Alia Bab-Ezzouar 16111 Algiers Algeria

ISBN: (纸本)9781605585697

For increasing the capacity of the wireless channel, the Space - Time Block Coding (STBC) has been proposed in the literature. The problem with such scheme is that the accurate channel state information is required. The channel is then estimated by transmitting the training sequences. Such channel estimation causes the spectral efficiency problem, i.e. the useful data rate is reduced. Likewise, the noise presence in the data affects the STBC performances. In this paper, we try to overcome these drawbacks by detecting and separating the transmitted symbols without channel estimation and by including the noise in the global model. So, the noisy compound PCA - ICA model for STBC is proposed here. Using the Bit Error Rate (BER) and signal to Noise Ration (SNR) as criteria, the obtained simulation results show that these methods are very suitable for transmitted symbols detection and separation in the STBC context. Copyright © 2009 ACM.

关键词： Channel estimation

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A comparative study on excitation extension algorithms for artificial bandwidth extension

A comparative study on excitation extension algorithms for a...

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5th IASTED International Conference on signal processing, Pattern Recognition, and Applications, SPPRA 2008

作者： Schafer, Friedrich Van Pham, Tuan Kubin, Gemot Chien, Tang Tan Signal Processing and Speech Communication Laboratory Graz University of Technology Inffeldgasse 16 Graz Styria Austria Faculty of Electronics and Telecommunications Danang University of Technology 54 Nguyen Luong Bang Danang Viet Nam

ISBN: (纸本)9780889867178

The most common approach for artificial bandwidth extension is based on the source-filter model such that the estimation of missing signal components is done in two stages: excitation extension and spectral envelope extension. In this paper we address an objective comparison between different excitation extension algorithms while the wideband spectral envelope is assumed to be known priorly. A variety of methods have been implemented and subjected to several objective quality measures. All methods are tested with the NTIMIT corpus and the bandpass filtered TIMIT corpus. The results show that: 1) different extension methods provide roughly the same speech quality which implicitly means the choice of the method can be made on account of the efficient implementation;2) underestimation of the extended components is to be preferred over an overestimation of these;3) the PESQ-MOS is a most suitable objective measure for evaluating bandwidth extended speech signal.

关键词： Bandwidth

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Seasonal vegetation change detection using independent component analysis 21

Seasonal vegetation change detection using independent compo...

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21st Congress of the International Society for Photogrammetry and Remote Sensing, ISPRS 2008

作者： Chitroub, S. Al Sultan, S. Signal and Images Processing Laboratory Electronics and Computer Science Faculty U. S. T. H. B. P. O. Box 32 El - Alia Bab - Ezzouar Algiers16111 Algeria Al Madinah Remote Sensing Center AlMadinah Rd P.O. Box 242 Riaydh Alkhabra City Saudi Arabia

The seasonal analysis of vegetation can be considered as looking for fundamental redundant information and detecting, at the same time, the natural changes of the vegetative cover undergone by the observed scene. From the statistical point of view, the redundant information can be quantified by the correlation coefficients between the multi-temporal images while the natural changes can be considered as the mutual information between the transition zones of the observed scene. For detecting and emerging the zones of transition and preserving at the same time the zones of vegetation temporal evolution stability, it is interesting to create new images in which the correlation between the images is vanished and the mutual information is minimized. To reach such purpose, we have developed a new approach for seasonal vegetation analysis based on a new statistical multi-variate method called independent component analysis (ICA).

关键词： Independent component analysis

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Independent Component Analysis of POLSAR Images. Relative Newton-Based Approach

Independent Component Analysis of POLSAR Images. Relative Ne...

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IEEE International Conference on signal processing and communications, ICSPC

作者： Salim Chitroub Rafik Hachemi Electronics and Computer Science Faculty U. S. T. H. B Signal Image Processing Laboratory Algiers Algeria National Institute of Computer Science Algiers Algeria

We propose here a new method for POLSAR image analysis. The method is based on a new PCA-ICA model in which the relative Newton-based approach for performing ICA is developed. The basic idea of ICA with relative Newton method consists in approximating the negentropy by taking account of the orthogonality constraint of the extracted components. This concept is recognized for its robustness and gives consequently very good theoretical results. The approach is well justified from the mathematical point of view. However, its implementation requires being more flexible because of the number of the estimated parameters. The purpose of this paper is to try to open new issues, in future research, in the concern of working out a new method for SAR image analysis that accumulate the advantages of the proposed method while avoiding its disadvantages.

关键词： Independent component analysis Speckle Integrated circuit noise Image analysis Newton method Integrated circuit modeling signal processing Laboratories computer science Principal component analysis

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

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Asian Journal of Control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Evidential Reasoning Based Classifier Combination for an Optimal Remote Sensing Image Classification

Evidential Reasoning Based Classifier Combination for an Opt...

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International Conference on Information and communication Technologies: From Theory to Applications, ICTTA

作者： S. Chitroub Electronics and Computer Science Faculty U.S.T.H.B. Signal Image Processing Laboratory

The work presented here addresses the problem of the enhancement of the remote sensing image classification. For that, an evidential reasoning based classifier combination method is proposed. The originality of the work lies in the fact that the method treats the outputs of the classifiers by completely ignoring the internal characteristics of the latter. The method is thus general and applicable to any type of classifier. It cumulates the advantages of each classifier without cumulating the disadvantages of them. It overcomes the disadvantages of the remote sensing image classification methods developed in the literature. Thus, it constitutes a powerful tool for several remote sensing image processing applications

关键词： Remote sensing Image classification Image processing Classification algorithms signal processing Laboratories computer science Layout Pattern classification Merging

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Statistical method based on simultaneous diagonalisation for POLSAR images analysis

Statistical method based on simultaneous diagonalisation for...

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14th European signal processing Conference, EUSIPCO 2006

作者： Chitroub, Salim Signal and Image Processing Laboratory Electronics and Computer Science Faculty U. S. T. H. B. P. O. Box 32 El-Alia Bab-Ezzouar 16111 Algiers Algeria

In [1], we have proposed a PCA-ICA neural network model for POLSAR image analysis. We propose here a new method that is full based on an algebraic statistical formulation and that is well justified from the mathematical point of view. Its advantage is that it is easy in its implementation that requires certain subroutines of the inverse matrix and the eigenvalues/ eigenvectors decomposition. While the PCA-ICA neural network model is very sensible to both the probabilistic model of the data [2], [3] and the power of the noise that corrupts the input data [1]. In addition, it requires more computation times in its learning process. Thus, the goal of this paper is to arise the power of each method and by this way we try to open new issues in the concern of working out new methods that accumulate the advantages of each method while avoiding their disadvantages.

关键词： Image analysis

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Special Issue on Variable Structure System Control — New Designs and Applications

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Asian Journal of Control 2008年第4期5卷

作者： Prof. Jian-Xin Xu Prof. Leonid Fridman Department of Electrical and Computer Eng. National University of Singapore 4 Engineering Drive 3 Singapore 117576 Tel +65 6874-2566 Fax +65 6779-1103 Dr Jian-Xin Xu received his Bachelor degree from Zhejiang University China in 1982. He attended the University of Tokyo Japan where he received his Master's and Ph.D. degrees in 1986 and 1989 respectively. All his degrees are in Electrical Engineering. He worked for one year in the Hitachi research Laboratory Japan and for more than one year in Ohio State University U.S.A. as a Visiting Scholar. In 1991 he joined the National University of Singapore and is currently an associate professor in the Department of Electrical Engineering. His research interests lie in the fields of learning control variable structure control fuzzy logic control discontinuous signal processing and applications to motion control and process control problems. He is the associate editor of Asian Journal of Control member of TC on variable structure systems and sliding mode control of IEEE Control Systems Society and a senior member of IEEE. He has produced more than 90 peer-refereed journal papers near 160 technical papers in conference proceedings and authored/edited 4 books. Division de Estudios de Posgrado Facultad de Ingenieria National Autonomous University of Mexico DEP-FI UNAM Edificio “A” Circuito Exterior Ciudad Universitaria A. P. 70–256 C.P.04510 Mexico D.F. Mexico Tel +52 55 56223014 Fax +52 55 56161719 Dr. Leonid M. Fridman received his M.S in mathematics from Kuibyshev (Samara) State University Russia Ph.D. in Applied Mathematics from Institute of Control Science (Moscow) and Dr. of Science degrees in Control Science from Moscow State University of Mathematics and Electronics in 1976 1988 and 1998 respectively. In 1976–1999 Dr. Fridman was with the Department of Mathematics at the Samara State Architecture and Civil Engineering Academy Samara Russia. In 2000–2002 he was with the Department of Postgraduate Study and Investigations at the Chihuahu

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Multi-Modal Human Verification Using Face and speech

Multi-Modal Human Verification Using Face and Speech

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IEEE International Conference on computer Vision Systems (ICVS)

作者： Changhan Park Joonki Paik Taewoong Choi Soonhyob Kim Youngouk Kim J. Namkung Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Multimedia Signal Processing Laboratory Kwangwoon University Seoul South Korea Precision Machinery Center Korea Electronics and Technology Institute Puchon Gyeonggi South Korea Intelligent Image Communication Laboratory Department of Computer Engineering Kwangwoon University Seoul South Korea

In this paper, we propose a personal verification method using both face and speech to improve the rate of single biometric verification. False acceptance rate (FAR) and false rejection rate (FRR) have been a fundamental bottleneck of real-time personal verification. The proposed multimodal biometric method is to improve both verification rate and reliability in real-time by overcoming technical limitations of single biometric verification methods. The proposed method uses principal component analysis (PCA) for face recognition and hidden markov model (HMM) for speech recognition. It also uses fuzzy logic for the final decision of personal verification. Based on experimental results, the proposed system can reduce FAR down to 0.0001%, which provides that the proposed method overcomes the limitation of single biometric system and provides stable personal verification in real-time.

关键词： Humans speech Face detection Face recognition Biometrics Principal component analysis Feature extraction Hidden Markov models Image edge detection Laboratories

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Multi-modal human verification using face and speech

Multi-modal human verification using face and speech

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Fourth IEEE International Conference on computer Vision Systems, ICVS'06

作者： Park, Changhan Choi, Taewoong Kim, Youngouk Kim, Soonhyob Namkung, Jaechan Paik, Joonki Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University 221 Huksuk-dong Tongjak-Ku Seoul 156-756 Korea Republic of Multimedia Signal Processing Laboratory Department of Computer Engineering Kwangwoon University 447-1 Wolge-dong Nowon-Gu Seoul 139-701 Korea Republic of Intelligent Image Communication Laboratory Department of Computer Engineering Kwangwoon University 447-1 Wolge-dong Nowon-Gu Seoul 139-701 Korea Republic of Precision Machinery Center Korea Electronics Technology Institute 401-402 B/D 193 Yakdae-dong Wonmi-Gu Puchon Kyunggido 420-140 Korea Republic of

ISBN: (纸本)0769525067

关键词： Face recognition

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