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Recovery of binary sparse signals from compressed linear measurements via polynomial optimization

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The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics. In the compressed sensing framework, tailored methods have been recently proposed to deal with the case of finite-valued sparse signals. In this work, we focus on binary sparse
signals and we propose a novel formulation, based on polynomial
optimization. This approach is analyzed and compared to the
state-of-the-art binary compressed sensing methods.

Paper Details

Authors:
Submitted On:
20 May 2020 - 5:38am
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Type:
Presentation Slides
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Presenter's Name:
Sophie Fosson
Paper Code:
6083
Document Year:
2020
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[1] , "Recovery of binary sparse signals from compressed linear measurements via polynomial optimization", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5405. Accessed: Aug. 03, 2020.
@article{5405-20,
url = {http://sigport.org/5405},
author = { },
publisher = {IEEE SigPort},
title = {Recovery of binary sparse signals from compressed linear measurements via polynomial optimization},
year = {2020} }
TY - EJOUR
T1 - Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5405
ER -
. (2020). Recovery of binary sparse signals from compressed linear measurements via polynomial optimization. IEEE SigPort. http://sigport.org/5405
, 2020. Recovery of binary sparse signals from compressed linear measurements via polynomial optimization. Available at: http://sigport.org/5405.
. (2020). "Recovery of binary sparse signals from compressed linear measurements via polynomial optimization." Web.
1. . Recovery of binary sparse signals from compressed linear measurements via polynomial optimization [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5405