Soft computing based compressive sensing techniques in signal processing: A comprehensive review

In this modern world, a massive amount of data is processed and broadcasted daily. This includes the use of high energy, massive use of memory space, and increased power use. In a few applications, for example, image processing, signal processing, and possession of data signals, etc., the signals in...

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Autores principales:	Mishra Ishani, Jain Sanjay
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	compressive sensing signal processing data acquisition reconstruction compression ratio reconstruction accuracy mean square error 94a08 Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/bc47923d10cb48399a3cee2b662ab047
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id	oai:doaj.org-article:bc47923d10cb48399a3cee2b662ab047
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spelling	oai:doaj.org-article:bc47923d10cb48399a3cee2b662ab0472021-12-05T14:10:51ZSoft computing based compressive sensing techniques in signal processing: A comprehensive review2191-026X10.1515/jisys-2019-0215https://doaj.org/article/bc47923d10cb48399a3cee2b662ab0472020-09-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0215https://doaj.org/toc/2191-026XIn this modern world, a massive amount of data is processed and broadcasted daily. This includes the use of high energy, massive use of memory space, and increased power use. In a few applications, for example, image processing, signal processing, and possession of data signals, etc., the signals included can be viewed as light in a few spaces. The compressive sensing theory could be an appropriate contender to manage these limitations. “Compressive Sensing theory” preserves extremely helpful while signals are sparse or compressible. It very well may be utilized to recoup light or compressive signals with less estimation than customary strategies. Two issues must be addressed by CS: plan of the estimation framework and advancement of a proficient sparse recovery calculation. The essential intention of this work expects to audit a few ideas and utilizations of compressive sensing and to give an overview of the most significant sparse recovery calculations from every class. The exhibition of acquisition and reconstruction strategies is examined regarding the Compression Ratio, Reconstruction Accuracy, Mean Square Error, and so on.Mishra IshaniJain SanjayDe Gruyterarticlecompressive sensingsignal processingdata acquisitionreconstructioncompression ratioreconstruction accuracymean square error94a08ScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 312-326 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	compressive sensing signal processing data acquisition reconstruction compression ratio reconstruction accuracy mean square error 94a08 Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	compressive sensing signal processing data acquisition reconstruction compression ratio reconstruction accuracy mean square error 94a08 Science Q Electronic computers. Computer science QA75.5-76.95 Mishra Ishani Jain Sanjay Soft computing based compressive sensing techniques in signal processing: A comprehensive review
description	In this modern world, a massive amount of data is processed and broadcasted daily. This includes the use of high energy, massive use of memory space, and increased power use. In a few applications, for example, image processing, signal processing, and possession of data signals, etc., the signals included can be viewed as light in a few spaces. The compressive sensing theory could be an appropriate contender to manage these limitations. “Compressive Sensing theory” preserves extremely helpful while signals are sparse or compressible. It very well may be utilized to recoup light or compressive signals with less estimation than customary strategies. Two issues must be addressed by CS: plan of the estimation framework and advancement of a proficient sparse recovery calculation. The essential intention of this work expects to audit a few ideas and utilizations of compressive sensing and to give an overview of the most significant sparse recovery calculations from every class. The exhibition of acquisition and reconstruction strategies is examined regarding the Compression Ratio, Reconstruction Accuracy, Mean Square Error, and so on.
format	article
author	Mishra Ishani Jain Sanjay
author_facet	Mishra Ishani Jain Sanjay
author_sort	Mishra Ishani
title	Soft computing based compressive sensing techniques in signal processing: A comprehensive review
title_short	Soft computing based compressive sensing techniques in signal processing: A comprehensive review
title_full	Soft computing based compressive sensing techniques in signal processing: A comprehensive review
title_fullStr	Soft computing based compressive sensing techniques in signal processing: A comprehensive review
title_full_unstemmed	Soft computing based compressive sensing techniques in signal processing: A comprehensive review
title_sort	soft computing based compressive sensing techniques in signal processing: a comprehensive review
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/bc47923d10cb48399a3cee2b662ab047
work_keys_str_mv	AT mishraishani softcomputingbasedcompressivesensingtechniquesinsignalprocessingacomprehensivereview AT jainsanjay softcomputingbasedcompressivesensingtechniquesinsignalprocessingacomprehensivereview
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Soft computing based compressive sensing techniques in signal processing: A comprehensive review

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