IEICE TRANSACTIONS on Information
Performance Evaluation of Finite Sparse Signals for Compressed Sensing Frameworks
Summary :
In this paper, we consider to develop a recovery algorithm of a sparse signal for a compressed sensing (CS) framework over finite fields. A basic framework of CS for discrete signals rather than continuous signals is established from the linear measurement step to the reconstruction. With predetermined priori distribution of a sparse signal, we reconstruct it by using a message passing algorithm, and evaluate the performance obtained from simulation. We compare our simulation results with the theoretic bounds obtained from probability analysis.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.2 pp.531-534
- Publication Date
- 2018/02/01
- Publicized
- 2017/11/06
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDL8166
- Type of Manuscript
- LETTER
- Category
- Fundamentals of Information Systems
Authors
Jin-Taek SEONG
Honam University
Keyword
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Jin-Taek SEONG, "Performance Evaluation of Finite Sparse Signals for Compressed Sensing Frameworks" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 2, pp. 531-534, February 2018, doi: 10.1587/transinf.2017EDL8166.
Abstract: In this paper, we consider to develop a recovery algorithm of a sparse signal for a compressed sensing (CS) framework over finite fields. A basic framework of CS for discrete signals rather than continuous signals is established from the linear measurement step to the reconstruction. With predetermined priori distribution of a sparse signal, we reconstruct it by using a message passing algorithm, and evaluate the performance obtained from simulation. We compare our simulation results with the theoretic bounds obtained from probability analysis.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8166/_p
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@ARTICLE{e101-d_2_531,
author={Jin-Taek SEONG, },
journal={IEICE TRANSACTIONS on Information},
title={Performance Evaluation of Finite Sparse Signals for Compressed Sensing Frameworks},
year={2018},
volume={E101-D},
number={2},
pages={531-534},
abstract={In this paper, we consider to develop a recovery algorithm of a sparse signal for a compressed sensing (CS) framework over finite fields. A basic framework of CS for discrete signals rather than continuous signals is established from the linear measurement step to the reconstruction. With predetermined priori distribution of a sparse signal, we reconstruct it by using a message passing algorithm, and evaluate the performance obtained from simulation. We compare our simulation results with the theoretic bounds obtained from probability analysis.},
keywords={},
doi={10.1587/transinf.2017EDL8166},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Performance Evaluation of Finite Sparse Signals for Compressed Sensing Frameworks
T2 - IEICE TRANSACTIONS on Information
SP - 531
EP - 534
AU - Jin-Taek SEONG
PY - 2018
DO - 10.1587/transinf.2017EDL8166
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2018
AB - In this paper, we consider to develop a recovery algorithm of a sparse signal for a compressed sensing (CS) framework over finite fields. A basic framework of CS for discrete signals rather than continuous signals is established from the linear measurement step to the reconstruction. With predetermined priori distribution of a sparse signal, we reconstruct it by using a message passing algorithm, and evaluate the performance obtained from simulation. We compare our simulation results with the theoretic bounds obtained from probability analysis.
ER -
English
