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    Low-Complexity Iterative Reconstruction Algorithms in Compressed Sensing

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    Author
    Danjean, Ludovic
    Advisor
    Vasić, Bane
    Marcellin, Michael W.
    Declercq, David
    Affiliation
    University of Arizona
    Issue Date
    2013-10
    
    Metadata
    Show full item record
    Rights
    Copyright © held by the author; distribution rights International Foundation for Telemetering
    Collection Information
    Proceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.
    Publisher
    International Foundation for Telemetering
    Journal
    International Telemetering Conference Proceedings
    Abstract
    In this paper we focus on two low-complexity iterative reconstruction algorithms in compressed sensing. These algorithms, called the approximate message-passing algorithm and the interval-passing algorithm, are suitable to recover sparse signals from a small set of measurements. Depending on the type of measurement matrix (sparse or random) used to acquire the samples of the signal, one or the other reconstruction algorithm can be used. We present the reconstruction results of these two reconstruction algorithms in terms of proportion of correct reconstructions in the noise free case. We also report in this paper possible practical applications of compressed sensing where the choice of the measurement matrix and the reconstruction algorithm are often governed by the constraint of the considered application.
    Sponsors
    International Foundation for Telemetering
    ISSN
    0884-5123
    0074-9079
    Additional Links
    http://www.telemetry.org/
    Collections
    International Telemetering Conference Proceedings, Volume 49 (2013)

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