Distributed compressed sensing for biomedical signals

Qun Wang*, Zhiwen Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a novel iterative greedy algorithm for Distributed Compressed Sensing (DCS) scenario based on backtracking technique, which is denoted by DCS-SAMP. The algorithm can reconstruct several input signals simultaneously, even when the measurements are contaminated with noise and without any prior information of their sparseness. It can provide a fast runtime while also offers comparably theoretical guarantees as the best optimization-based approach. This makes it as a promising candidate for many practical applications,such as Tele-Health or Telemedicine. Numerical experiments are performed to demonstrate the validity and high performance of the proposed DCS-SAMP algorithm for multichannel biomedical signals.

Original languageEnglish
Title of host publicationProceedings of 2011 3rd International Conference on Awareness Science and Technology, iCAST 2011
Pages252-255
Number of pages4
DOIs
Publication statusPublished - 2011
Event2011 3rd International Conference on Awareness Science and Technology, iCAST 2011 - Dalian, China
Duration: 27 Sept 201130 Sept 2011

Publication series

NameProceedings of 2011 3rd International Conference on Awareness Science and Technology, iCAST 2011

Conference

Conference2011 3rd International Conference on Awareness Science and Technology, iCAST 2011
Country/TerritoryChina
CityDalian
Period27/09/1130/09/11

Keywords

  • Distributed compressed sensing
  • joint sparse model
  • sparse adaptive matching pursuit
  • sparse signal reconstruction

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