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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sofiane Ouanezar Frédéric Jean Bertrand Tondu Marc A. Maier Christian Darlot Selim Eskiizmirliler Signal and Image Processing Department TELECOM ParisTech Paris France Applied Mathematics Department ENSTA Paris France LATTIS Laboratory INSA Toulouse France CESeM Laboratory CNRS UMR 8194 Université Paris Descartes and Université Paris Diderot Paris France

This study focuses on biomimetic sensory motor control of a robotic arm. We have developed a command circuit that was mathematically deduced from physical and mathematical constraints describing the function of cerebellar pathways. The control circuit contains an internal predictive model of the direct biomechanical function of the limb placed in a closed loop, so that the circuit computes an approximate inverse function. The structure of the model resembles the anatomic connectivity of the cerebellar pathways. In this paper, we present an application of this model to the control of a 2-link robotic arm actuated by four single-joint McKibben muscles and report the results obtained by simulation and real-time learning of 2 degrees of freedom pointing movements.

关键词： Muscles Biological system modeling Robot sensing systems Brain modeling Joints Computational modeling

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signal recovery in PDM optical communication systems employing independent component analysis

Signal recovery in PDM optical communication systems employi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： E. S. Rosa L. T. Duarte J. M. T. Romano R. Suyama DSPCom-Laboratory of Signal Processing for Communications School of Electrical and Computer Engineering (FEEC) State University of Campinas-UNICAMP Sao Paulo Brazil Engineering Modeling and Applied Social Sciences-CECS Federal University of ABC UFABC Sao Paulo Brazil

There is a growing interest in employing digital signal processing methods to compensate the distortions typical of the next generation optical systems. For instance, several works considered the multi-user constant modulus algorithm (MU-CMA) to recover the transmitted signals in Polarization Division Multiplexing (PDM) systems. In this work, the same problem is tackled through independent component analysis methods. The obtained results point out that, differently from the MU-CMA, our proposal is robust against sources loss even when polarization dependent loss is present.

关键词： Optical fibers Optical distortion Optical polarization Optical fiber dispersion Integrated optics Chromatic dispersion

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Vector space embedding of undirected graphs with fixed-cardinality vertex sequences for classification

Vector space embedding of undirected graphs with fixed-cardi...

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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Richiardi, Jonas Van De Ville, Dimitri Riesen, Kaspar Bunke, Horst Medical Image Processing Laboratory Ecole Polytechnique Fédérale de Lausanne Switzerland Medical Image Processing Laboratory University of Geneva Switzerland Institute of Computer Science and Applied Mathematics University of Bern

ISBN: (纸本)9780769541099

Simple weighted undirected graphs with a fixed number of vertices and fixed vertex orderings can be used to represent data and patterns in a wide variety of scientific and engineering domains. Classification of such graphs by existing graph matching methods perform rather poorly because they do not exploit their specificity. As an alternative, methods relying on vector-space embedding hold promising potential. We propose two such techniques that can be deployed as a front-end for any pattern recognition classifiers: one has low computational cost but generates high-dimensional spaces, while the other is more computationally demanding but can yield relatively low-dimensional vector space representations. We show experimental results on an fMRI brain state decoding task and discuss the shortfalls of graph edit distance for the type of graph under consideration. © 2010 IEEE.

关键词： Vector spaces

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Group successive interference cancelator based on minimum mean-square error detection

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2010年第5期45卷 739-744页

作者： Zhang, Donghong Liao, Guisheng National Key Laboratory for Radar Signal Processing Xidian University Xi'an 710071 China Department of Applied Mathematics Ankang University Ankang 725000 China

In order to reduce the demodulation delay of the conventional successive interference cancelator (SIC) and improve the BER (bit error rate) performance of each stage, an improved SIC was proposed based on the diagonal loading method and the interval grouping method. This improved SIC loads the correlation matrix of each stage diagonally. Through the selection of loading parameters, the output signal interference ratio of grouping users is maximized so that the minimum mean-square error (MMSE) detection can be realized and the update of the linear operator in each stage is only related to the diagonal loading parameters. Theoretic analysis and simulation results show that compared with the conventional SIC and the MMSE detector, the improved SIC reduces demodulation delays and achieves a better BER performance.

关键词： Bit error rate

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Distributed classification of multiple observations by consensus

Distributed classification of multiple observations by conse...

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IEEE International Conference on Image processing

作者： Effrosyni Kokiopoulou Pascal Frossard Seminar of Applied Mathematics ETH Zurich Zurich Switzerland Signal Processing Laboratory (LTS4) University of Electronic and Technology Lausanne Switzerland

We consider the problem of distributed classification of multiple observations of the same object that are collected in an ad-hoc network of vision sensors. Assuming that each sensor captures a different observation of the same object, the problem is to classify this object by distributed processing from the sensors. We present a graph-based problem formulation whose objective function captures the smoothness of candidate labels on the data manifold. We design a distributed average consensus algorithm for estimating the unknown object class by computing the value of the above smoothness objective function for different class hypotheses. It initially estimates the objective function locally, based on the observation of each sensor. All the observations are then progressively taken into account in the estimation of the objective function, along the iterations of the distributed consensus algorithm. We illustrate the performance of the distributed classification algorithm by simulation of multi-view face recognition in an ad-hoc network of vision sensors. When the training set is sufficiently large, the simulation results show that the consensus classification decision is equivalent to the decision of a centralized system that would have access to all observations.

关键词： Sensors Training Ad hoc networks Face recognition Error analysis signal processing algorithms Nearest neighbor searches

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Wireless tomography, part I: A novel approach to remote sensing

Wireless tomography, part I: A novel approach to remote sens...

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International Waveform Diversity and Design Conference

作者： Qiu, R.C. Wicks, M.C. Li, L. Hu, Z. Hou, S.J. Chen, P. Browning, J.P. Cognitive Radio Insitute Department of Electrical and Computer Engineering Tennessee Technological University Cookeville TN United States Air Force Research Laboratory Sensors Directorate Rome NY United States Department of Mathematics Syracuse University Syracuse NY United States Air Force Research Laboratory Sensors Directorate Radar Signal Processing Branch Dayton OH United States

ISBN: (纸本)9781424482009

Wireless tomography, a novel approach to remote sensing, is proposed in Part I of this series. The methodology, literature review, related work, and system engineering are presented. Concrete algorithms and hardware platforms are implemented to demonstrate this concept. Self-cohering tomography is studied in depth. More research will be reported, following this initiative.

关键词： Tomography

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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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Vector Space Embedding of Undirected Graphs with Fixed-cardinality Vertex Sequences for Classification

Vector Space Embedding of Undirected Graphs with Fixed-cardi...

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International Conference on Pattern Recognition

作者： Jonas Richiardi Dimitri Van De Ville Kaspar Riesen Horst Bunke Medical Image Processing Laboratory Ecole Polytechnique Fédérale de Lausanne Switzerland Medical Image Processing Laboratory University of Genova Italy Institute of Computer Science and Applied Mathematics University of Bern Switzerland

ISBN: (纸本)9781424475421

关键词： Prototypes Pattern recognition Support vector machines Accuracy Covariance matrix Motion pictures Pattern analysis

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POLYNOMIAL FILTER DESIGN FOR QUANTIZED CONSENSUS

POLYNOMIAL FILTER DESIGN FOR QUANTIZED CONSENSUS

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European signal processing Conference

作者： Dorina Thanou Hyunggon Park Effrosyni Kokiopoulou Pascal Frossard Signal Processing Laboratory (LTS4) Swiss Federal Institute of Technology Lausanne (EPFL) Multimedia Communications and Networking Laboratory Ewha Womans University Seminar for Applied Mathematics (SAM) Swiss Federal Institute of Technology

We consider the problem of distributed average consensus where sensors exchange quantized data with their neighbors. We deploy a polynomial filtering approach in the network nodes in order to accelerate the convergence of the consensus problem. The quantization of the values computed by the sensors however imposes a careful design of the polynomial filter. We first study the impact of the quantization noise in the performance of accelerated consensus based on polynomial filtering. It occurs that the performance is clearly penalized by the quantization noise, whose impact directly depends on the filter coefficients. We then formulate a convex optimization problem for determining the coefficients of a polynomial filter, which is able to control the quantization noise while accelerating the convergence rate. The simulation results show that the proposed solution is robust to quantization noise while assuring a high convergence speed to the average value in the network.

关键词： Distributed averaging Distributed consensus Polynomial filtering Uniform quantization

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Wireless tomography, Part I: A novel approach to remote sensing

Wireless tomography, Part I: A novel approach to remote sens...

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International Waveform Diversity and Design Conference

作者： R. C. Qiu M. C. Wicks L. Li Z. Hu S. J. Hou P. Chen J. P. Browning Cognitive Radio Institute Department of Electrical and Computer Engineering Center for Manufacturing Research Tennessee Technological University Cookeville TN USA Air Force Research Laboratory Rome NY USA Department of Mathematics Syracuse University Syracuse NY USA Radar Signal Processing Branch Air Force Research Laboratory Dayton OH USA

关键词： Tomography Wireless communication Wireless sensor networks Sensors Image reconstruction Radar imaging

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