Universal Distributed Sensing via Random Projections

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Title: Universal Distributed Sensing via Random Projections
Author: Wakin, Michael; Duarte, Marco F.; Baraniuk, Richard G.; Baron, Dror
Type: Conference Paper
Keywords: linear programming; compressed sensing; Sparsity; greedy algorithms; sensor networks; correlation
Citation: M. Wakin, M. F. Duarte, R. G. Baraniuk and D. Baron,"Universal Distributed Sensing via Random Projections," in International Symposium on Integrated Processing in Sensor Networks,, pp. 177-185.
Abstract: This paper develops a new framework for distributed coding and compression in sensor networks based on distributed compressed sensing (DCS). DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity; just a few measurements of a jointly sparse signal ensemble contain enough information for reconstruction. DCS is well-suited for sensor network applications, thanks to its simplicity, universality, computational asymmetry, tolerance to quantization and noise, robustness to measurement loss, and scalability. It also requires absolutely no inter- sensor collaboration. We apply our framework to several real world datasets to validate the framework.
Date Published: 2006-04-01

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  • ECE Publications [1032 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.