A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the sign...

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Vydáno v:	IEEE access Ročník 6; s. 4875 - 4894
Hlavní autoři:	Rani, Meenu, Dhok, S. B., Deshmukh, R. B.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Acquisitions & mergers Algorithms Compressed sensing Compressibility Compressive sensing CS acquisition strategies CS applications CS reconstruction algorithms Domains Image reconstruction Magnetic resonance imaging Mathematical model OMP random demodulator Reconstruction Reconstruction algorithms Sensors Sparse matrices sparsity Transforms Ultrawideband Video communication
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.
AbstractList	Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.
Author	Deshmukh, R. B. Dhok, S. B. Rani, Meenu
Author_xml	– sequence: 1 givenname: Meenu orcidid: 0000-0001-7411-7787 surname: Rani fullname: Rani, Meenu email: meenubanait@gmail.com organization: Center for VLSI and Nanotechnology, Visvesvaraya National Institute of Technology, Nagpur, India – sequence: 2 givenname: S. B. surname: Dhok fullname: Dhok, S. B. organization: Center for VLSI and Nanotechnology, Visvesvaraya National Institute of Technology, Nagpur, India – sequence: 3 givenname: R. B. surname: Deshmukh fullname: Deshmukh, R. B. organization: Center for VLSI and Nanotechnology, Visvesvaraya National Institute of Technology, Nagpur, India
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SubjectTerms	Acquisitions & mergers Algorithms Compressed sensing Compressibility Compressive sensing CS acquisition strategies CS applications CS reconstruction algorithms Domains Image reconstruction Magnetic resonance imaging Mathematical model OMP random demodulator Reconstruction Reconstruction algorithms Sensors Sparse matrices sparsity Transforms Ultrawideband Video communication
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Title	A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications
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