Introduction to Compressed Sensing with Python

Masaaki NAGAHARA

doi:10.1587/transcom.2023EBI0002

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Introduction to Compressed Sensing with Python

Masaaki NAGAHARA

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Summary :

Compressed sensing is a rapidly growing research field in signal and image processing, machine learning, statistics, and systems control. In this survey paper, we provide a review of the theoretical foundations of compressed sensing and present state-of-the-art algorithms for solving the corresponding optimization problems. Additionally, we discuss several practical applications of compressed sensing, such as group testing, sparse system identification, and sparse feedback gain design, and demonstrate their effectiveness through Python programs. This survey paper aims to contribute to the advancement of compressed sensing research and its practical applications in various scientific disciplines.

Publication: IEICE TRANSACTIONS on Communications Vol.E107-B No.1 pp.126-138

Publication Date: 2024/01/01

Publicized: 2023/08/15

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2023EBI0002

Type of Manuscript: INVITED PAPER

Category: Fundamental Theories for Communications

Authors

Masaaki NAGAHARA
Hiroshima University

Keyword

compressed sensing, sparse representation, convex optimization, group testing, system identification, feedback control, Python

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Masaaki NAGAHARA, "Introduction to Compressed Sensing with Python" in IEICE TRANSACTIONS on Communications, vol. E107-B, no. 1, pp. 126-138, January 2024, doi: 10.1587/transcom.2023EBI0002.
Abstract: Compressed sensing is a rapidly growing research field in signal and image processing, machine learning, statistics, and systems control. In this survey paper, we provide a review of the theoretical foundations of compressed sensing and present state-of-the-art algorithms for solving the corresponding optimization problems. Additionally, we discuss several practical applications of compressed sensing, such as group testing, sparse system identification, and sparse feedback gain design, and demonstrate their effectiveness through Python programs. This survey paper aims to contribute to the advancement of compressed sensing research and its practical applications in various scientific disciplines.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.2023EBI0002/_p

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@ARTICLE{e107-b_1_126,
author={Masaaki NAGAHARA, },
journal={IEICE TRANSACTIONS on Communications},
title={Introduction to Compressed Sensing with Python},
year={2024},
volume={E107-B},
number={1},
pages={126-138},
abstract={Compressed sensing is a rapidly growing research field in signal and image processing, machine learning, statistics, and systems control. In this survey paper, we provide a review of the theoretical foundations of compressed sensing and present state-of-the-art algorithms for solving the corresponding optimization problems. Additionally, we discuss several practical applications of compressed sensing, such as group testing, sparse system identification, and sparse feedback gain design, and demonstrate their effectiveness through Python programs. This survey paper aims to contribute to the advancement of compressed sensing research and its practical applications in various scientific disciplines.},
keywords={},
doi={10.1587/transcom.2023EBI0002},
ISSN={1745-1345},
month={January},}

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TY - JOUR
TI - Introduction to Compressed Sensing with Python
T2 - IEICE TRANSACTIONS on Communications
SP - 126
EP - 138
AU - Masaaki NAGAHARA
PY - 2024
DO - 10.1587/transcom.2023EBI0002
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E107-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2024
AB - Compressed sensing is a rapidly growing research field in signal and image processing, machine learning, statistics, and systems control. In this survey paper, we provide a review of the theoretical foundations of compressed sensing and present state-of-the-art algorithms for solving the corresponding optimization problems. Additionally, we discuss several practical applications of compressed sensing, such as group testing, sparse system identification, and sparse feedback gain design, and demonstrate their effectiveness through Python programs. This survey paper aims to contribute to the advancement of compressed sensing research and its practical applications in various scientific disciplines.
ER -