Texas hold 'em algorithms for distributed compressive sensing

Stephen R. Schnelle; Jason N. Laska; Chinmay Hegde; Marco F. Duarte; Mark A. Davenport; Richard G. Baraniuk

doi:10.1109/ICASSP.2010.5496168

Texas hold 'em algorithms for distributed compressive sensing

Stephen R. Schnelle, Jason N. Laska, Chinmay Hegde, Marco F. Duarte, Mark A. Davenport, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Scopus citations

Abstract

This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity to further reduce the number of measurements required for recovery. DCS is well-suited for sensor network applications due to its universality, computational asymmetry, tolerance to quantization and noise, and robustness to measurement loss. In this paper we propose recovery algorithms for the sparse common and innovation joint sparsity model. Our approach leads to a class of efficient algorithms, the Texas Hold 'Em algorithms, which are scalable both in terms of communication bandwidth and computational complexity.

Original language	English (US)
Title of host publication	2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages	2886-2889
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2010.5496168
State	Published - Nov 8 2010
Event	2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States Duration: Mar 14 2010 → Mar 19 2010

Other

Other	2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/Territory	United States
City	Dallas, TX
Period	3/14/10 → 3/19/10

Keywords

Data compression
Multisensor systems
Signal reconstruction

ASJC Scopus subject areas

Signal Processing
Software
Electrical and Electronic Engineering

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10.1109/ICASSP.2010.5496168

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Texas hold 'em algorithms for distributed compressive sensing. / Schnelle, Stephen R.; Laska, Jason N.; Hegde, Chinmay; Duarte, Marco F.; Davenport, Mark A.; Baraniuk, Richard G.

2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings. 2010. p. 2886-2889 5496168.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Schnelle, SR, Laska, JN, Hegde, C, Duarte, MF, Davenport, MA & Baraniuk, RG 2010, Texas hold 'em algorithms for distributed compressive sensing. in 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings., 5496168, pp. 2886-2889, 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, Dallas, TX, United States, 3/14/10. https://doi.org/10.1109/ICASSP.2010.5496168

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abstract = "This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity to further reduce the number of measurements required for recovery. DCS is well-suited for sensor network applications due to its universality, computational asymmetry, tolerance to quantization and noise, and robustness to measurement loss. In this paper we propose recovery algorithms for the sparse common and innovation joint sparsity model. Our approach leads to a class of efficient algorithms, the Texas Hold 'Em algorithms, which are scalable both in terms of communication bandwidth and computational complexity.",

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AU - Schnelle, Stephen R.

AU - Laska, Jason N.

AU - Hegde, Chinmay

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AU - Davenport, Mark A.

AU - Baraniuk, Richard G.

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AB - This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity to further reduce the number of measurements required for recovery. DCS is well-suited for sensor network applications due to its universality, computational asymmetry, tolerance to quantization and noise, and robustness to measurement loss. In this paper we propose recovery algorithms for the sparse common and innovation joint sparsity model. Our approach leads to a class of efficient algorithms, the Texas Hold 'Em algorithms, which are scalable both in terms of communication bandwidth and computational complexity.

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Texas hold 'em algorithms for distributed compressive sensing

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Keywords

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