License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license (CC BY-NC-ND 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ICCSW.2012.8
URN: urn:nbn:de:0030-drops-37589
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2012/3758/
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Apostolopoulos, Theofanis

A heuristic for sparse signal reconstruction

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Abstract

Compressive Sampling (CS) is a new method of signal acquisition and reconstruction from frequency data which do not follow the basic principle of the Nyquist-Shannon sampling theory. This new method allows reconstruction of the signal from substantially fewer measurements than those required by conventional sampling methods. We present and discuss a new, swarm based, technique for representing and reconstructing signals, with real values, in a noiseless environment. The method consists of finding an approximation of the l_0-norm based problem, as a combinatorial optimization problem for signal reconstruction. We also present and discuss some experimental results which compare the accuracy and the running time of our heuristic to the IHT and IRLS methods.

BibTeX - Entry

@InProceedings{apostolopoulos:OASIcs:2012:3758,
  author =	{Theofanis Apostolopoulos},
  title =	{{A heuristic for sparse signal reconstruction}},
  booktitle =	{2012 Imperial College Computing Student Workshop},
  pages =	{8--14},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-48-4},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{28},
  editor =	{Andrew V. Jones},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2012/3758},
  URN =		{urn:nbn:de:0030-drops-37589},
  doi =		{10.4230/OASIcs.ICCSW.2012.8},
  annote =	{Keywords: Compressive Sampling, sparse signal representation, l_0 minimisation, non-linear programming, signal recovery}
}

Keywords: Compressive Sampling, sparse signal representation, l_0 minimisation, non-linear programming, signal recovery
Collection: 2012 Imperial College Computing Student Workshop
Issue Date: 2012
Date of publication: 09.11.2012

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