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Introduction to Compressed Sensing with Python
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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
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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://global.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},}
Copy
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 -
English
