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Recovery of cosparse signals with Greedy Analysis Pursuit in the presence of noise

Recovery of cosparse signals with Greedy Analysis Pursuit in...

引用

ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Sangnam Nam Michael E. Davies Michael Elad Rémi Gribonval Centre de Recherche INRIA Rennes - Bretagne Atlantique Rennes France School of Engineering and Electronics The University of Edinburgh Edinburgh UK Department of Computer Science The Technion Haifa Israel

the sparse synthesis signal model has enjoyed much success and popularity in the recent decade. Much progress ranging from clear theoretical foundations to appealing applications has been made in this field. Alongside the synthesis approach, an analysis counterpart has been used over the years. Despite the similarity, markedly different nature of the two approaches has been observed. In a recent work, the analysis model was formally formulated and the nature of the model was discussed extensively. Furthermore, a new greedy algorithm (GAP) for recovering the signals satisfying the model was proposed and its effectiveness was demonstrated. While the understanding of the analysis model and the new algorithm has been broadened, the stability and the robustness against noise of the model and the algorithm have been mostly left out. In this work, we adapt and propose a new GAP algorithm in order to deal with the presence of noise. Empirical evidence for the algorithm is also provided.

关键词： Algorithm design and analysis Noise Analytical models Image reconstruction signal processing algorithms Data models Approximation algorithms

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A parallel resampling scheme and its application to distributed particle filtering in wireless networks

A parallel resampling scheme and its application to distribu...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Katrin Achutegui Joaquín Míguez Department of Signal Theory and Communications Universidad Carlos III de Madrid Leganes Spain

We address the design of a particle filter (PF) that can be implemented in a distributed manner over a network of wireless sensor nodes, each of them collecting their own local data. this is a problem that has received considerable attention lately and several methods based on consensus, the transmission of likelihood information, the truncation and/or the quantization of data have been proposed. However, all existing schemes suffer from limitations related either to the amount of required communications among the nodes or the accuracy of the filter outputs. In this work we propose a novel distributed PF that is built around the distributed resampling with non-proportional allocation (DRNA) algorithm. this scheme guarantees the properness of the particle approximations produced by the filter and has been shown to be both efficient and accurate when compared with centralized PFs. the standard DRNA technique, however, places stringent demands on the communications among nodes that turn out impractical for a typical wireless sensor network (WSN). In this paper we investigate how to reduce this communication load by using (i) a random model for the spread of data over the WSN and (ii) methods that enable the out-of-sequence processing of sensor observations. A simple numerical illustration of the performance of the new algorithm compared with a centralized PF is provided.

关键词： Wireless sensor networks Artificial neural networks Approximation algorithms signal processing Markov processes Vectors Approximation methods

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A sensor interface based on sparse NMF for piano musical transcription

A sensor interface based on sparse NMF for piano musical tra...

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International workshop on Advances in sensors and Interface (IWASI)

作者： Giovanni Costantini Massimiliano Todisco Giovanni Saggio Department of Electronic Engineering University of Roma Tor Vergata Rome Italy Institute of Acoustics O. M. Corbino Rome Italy

A new sensor interface for detecting notes in the musical audio signals is proposed. Our sensor interface is able to transform acoustic pressure, caused by a sound wave of polyphonic piano music, into a musical score, that is a symbolic representation of musical notes. We focuses on note attack instant (onset) and note name (pitch). signal processing techniques based on the Constant-Q Transform (CQT) are used to create a time-frequency representation of the signal. In particular, we propose a supervised classification methods based on sparse Non-Negative Matrix Factorization (NMF) to detect note pitch. We present a collection of experiments using the Inventions and Sinfonias BWV 772-801 of the composer J. S. Bach.

关键词： Technological innovation Transforms Training Music Detection algorithms Accuracy Feature extraction

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Exploiting spatial diversity in multiagent reinforcement learning based spectrum sensing

Exploiting spatial diversity in multiagent reinforcement lea...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Jarmo Lundén Visa Koivunen Sanjeev R. Kulkarni H. Vincent Poor SMARAD CoE Department of Signal Processing and Acoustics Aalto University Finland Department of Electrical Engineering Princeton University Princeton NJ USA

In this paper a multiband, multiagent reinforcement learning based distributed sensing policy for cognitive radio networks is proposed. In the proposed sensing policy the secondary users (SUs) collaborate with neighboring users by exchanging information locally. the objective is to maximize the amount of free spectrum found for secondary use while guaranteeing a certain probability of detection. the SUs employ spatial diversity through collaborative sensing to control the false alarm rate and thus the probability of finding available spectrum opportunities. the SUs in the cognitive radio network make local decisions based on their own and their neighbors' local test statistics to identify unused spectrum locally. Simulation results show that the proposed sensing policy provides a straightforward approach for obtaining a good tradeoff between sensing more spectrum and the reliability of the sensing results.

关键词： sensors Cognitive radio Learning Collaboration Probability Vectors Time frequency analysis

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Joint robust beamformer of space-frequency-polarization

Joint robust beamformer of space-frequency-polarization

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International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)

作者： Guibao Wang Haihong Tao Lanmei Wang Xiaojie Jiang Jiping Lin Key Laboratory For radar signal processing Xidian University Xian China School of Science Xidian University Xian China

ISBN: (纸本)9781424482658

the space-frequency-polarization (SFP) beam-former using an electromagnetic vector sensor was studied in this paper. An improved robust beamformer algorithm was put forward which was based on Capon and joint constraint. As the three domains joint information was used, the SFP beamformer substantially outperforms any two-domain joint beamformer. Finally, Simulation results verified the effectiveness of the SFP beamformer and the SFP robust beamformer.

关键词： Vectors Robustness Interference Joints Estimation array signal processing signal processing algorithms

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An interior point method for a semidefinite relaxation based equalizer incorporating prior information

An interior point method for a semidefinite relaxation based...

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ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Jake Gunther Todd Moon Department of Electrical & Computer Engineering Utah State University Logan UT USA

the problem of maximum likelihood estimation of digital data transmitted over an intersymbol interference channel may be cast as a quadratically constrained quadratic program (QCQP). this problem may be solved approximately but efficiently using a semidefinite relaxation (SDR) technique in which the quadratic objective and constraints are converted into linear functions of a matrix variable. Recently the authors extended the basic SDR technique using maximum a posteriori probability (MAP) estimation to incorporate prior probabilities on the bits. the resulting estimator is a soft-input soft-output equalizer that can be used in iterative (turbo) equalization in situations where true optimal MAP equalization (implemented via the BCJR algorithm) is impractical because of its exponential complexity. this paper develops a custom interior point algorithm using the barrier method to solve the extended SDR problem which is convex. this custom solver is more computationally efficient than a general purpose solver because it can exploit the structure inherent in the equalization problem. Simulation experiments are provided that compare the running times of the new algorithm and a general purpose code (CVX). the new algorithm is more computationally efficient than the more general purpose solver and delivers results with equal accuracy. Refinements in initialization strategies and stopping criteria can improve the computational efficiency of the new algorithm.

关键词： Equalizers Convex functions signal processing algorithms Optimization Programming Prediction algorithms OFDM

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Wearable sensor systems for healthcare and lifestyle

Wearable sensor systems for healthcare and lifestyle

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International workshop on Advances in sensors and Interface (IWASI)

作者： Julien Penders IMEC-NL/Holst Centre Netherlands

Summary form only given. Recent advances in micro-electronics and sensors open new perspectives in healthcare and lifestyle. New sensors and read-out circuits make recording of physiological signals accessible outside the hospital, and more comfortable than ever. Advanced signal processing algorithms and circuits allow monitoring of patients on-the-move. Ultra-low-power electronics enables a continued miniaturization of sensor systems, facilitating their integration in patch devices. In this talk we show how technology advancements materialize in new product concepts that radically change the way we look at healthcare and lifestyle. A wearable ECG patch monitors cardiac activity 24/7 for over a week, allowing early detection of cardiovascular disorders. A wireless EEG headset allows monitoring brain activity from home, and may soon provide the means to directly control a computer using brain waves. A network of wearable sensors, distributed on the body, measures physiological responses correlated to a particular mental or emotional state. What if your wearable sensors could tell you how you sleep, how you feel or how healthy is your lifestyle? the new era of healthcare is near: personal, wearable and digital.

关键词： Biomedical monitoring Medical services Monitoring Wearable sensors Brain sensor systems

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Passive radar imaging of moving targets with sparsely distributed receivers

Passive radar imaging of moving targets with sparsely distri...

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ieee sensor array and multichannel signal processing workshop

作者： Wang, Ling Yazici, Birsen College of Information Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute 110 8th Street Troy NY 12180 United States

ISBN: (纸本)9781424489770

We develop a novel passive image formation method for moving targets using measurements from a sparse array of receivers that rely on illumination sources of opportunity. We use a physics-based approach to model the wave propagation and develop a passive measurement model that expresses the measurement at each receiver in terms of the measurement at a different receiver. this model eliminates the need for knowledge about the transmitter locations and waveforms in the proposed image formation method. We formulate the image formation problem as a Generalized Likelihood Ratio Test (GLRT) for unknown target location and velocity using the proposed passive measurement model. We form the image in spatial and velocity space using the space- and velocity-resolved test-statistics. © 2010 ieee.

关键词： Wave propagation

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Effects of Compression and Window Size on Remote Acoustic Identification using sensor Networks

Effects of Compression and Window Size on Remote Acoustic Id...

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4th International Conference on signal processing and Communication Systems (ICSPCS)/12th International Symposium on DSP and Communication Systems (DSPCS)/9th workshop on the Internet, Telecommunications and signal processing (WTSP)

作者： Gonzalez, Ruben Griffith Univ Inst Integrated & Intelligent Syst Gold Coast Qld 4217 Australia

ISBN: (纸本)9781424479078

Recently the cost-benefits of automated sensing over traditional field surveys for population management of fauna has been recognised [1,2]. Remote monitoring through automatic identification based on sensor networks has followed one of two approaches [3,7,18];using the sensor nodes to perform data analysis within the network or alternatively using the sensor network as a means for collecting data to be centrally processed. In either case a key goal is minimising power consumption in sensor nodes which imposes constraints on both processing and communication capabilities. While the first approach aims to minimise communication requirements the other aims to reduce processing requirements. In the context of sensor networks for remote monitoring utilising centralised processing, this paper considers the impact on two different strategies for reducing communication requirements on the overall system performance.

关键词： acoustic signal processing data analysis data compression population management remote acoustic identification remote monitoring sensor networks sensor nodes window size

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Estimating the parameters of a moving target in MIMO radar with widely separated antennas

Estimating the parameters of a moving target in MIMO radar w...

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ieee sensor array and multichannel signal processing workshop

作者： Hassanein, Abdulnasr Vorobyov, Sergiy A. Gershman, Alex B. Rübsamen, Michael University of Alberta Electrical and Computer Engineering Edmonton AB T6G 2V4 Canada Darmstadt University of Technology Communication Systems Group 64283 Darmstadt Germany

ISBN: (纸本)9781424489770

In this paper, we develop a new maximum likelihood (ML) moving target parameter estimation technique for multipleinput multiple-output (MIMO) radar. It is required for this technique that different receive antennas have the same time reference, but no synchronization of initial phases of the receive antennas is needed and, therefore, the estimation process is noncoherent. the target motion within a certain processing interval is modeled as a second-order polynomial whose coefficients are given by the initial location, velocity, and acceleration of the target. the proposed ML estimator is able to jointly process the data collected from multiple consecutive radar pulses. It is shown that the considered ML problem simplifies to the classic "overdetermined" nonlinear least-squares problem. the proposed ML estimator requires multi-dimensional search over the unknown location, velocity, and acceleration parameters. the performance of the proposed estimator is validated by simulation results. © 2010 ieee.

关键词： Receiving antennas

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