Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250872" target="_blank" >RIV/61989100:27240/22:10250872 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9852418" target="_blank" >https://ieeexplore.ieee.org/document/9852418</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2022.3197594" target="_blank" >10.1109/ACCESS.2022.3197594</a>
Alternative languages
Result language
angličtina
Original language name
Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks
Original language description
Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of application areas. The use of an efficient sampling matrix for high-performance recovery algorithms improves the performance of the compressive sensing framework significantly. This paper presents the underlying concepts of compressive sensing as well as previous work done in targeted domains in accordance with the various application areas. To develop prospects within the available functional blocks of compressive sensing frameworks, a diverse range of application areas are investigated. The three fundamental elements of a compressive sensing framework (signal sparsity, subsampling, and reconstruction) are thoroughly reviewed in this work by becoming acquainted with the key research gaps previously identified by the research community. Similarly, the basic mathematical formulation is used to outline some primary performance evaluation metrics for 1D and 2D compressive sensing.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
10
Issue of the periodical within the volume
August 2022
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
85002-85018
UT code for WoS article
000842090100001
EID of the result in the Scopus database
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