Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9852418/ |
| _version_ | 1818486462436343808 |
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| author | Irfan Ahmed Amaad Khalil Ishtiaque Ahmed Jaroslav Frnda |
| author_facet | Irfan Ahmed Amaad Khalil Ishtiaque Ahmed Jaroslav Frnda |
| author_sort | Irfan Ahmed |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-10T16:23:20Z |
| format | Article |
| id | doaj.art-eaea69d903b94ec585937757a491e58d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T16:23:20Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eaea69d903b94ec585937757a491e58d2022-12-22T01:41:46ZengIEEEIEEE Access2169-35362022-01-0110850028501810.1109/ACCESS.2022.31975949852418Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing FrameworksIrfan Ahmed0https://orcid.org/0000-0002-3489-3519Amaad Khalil1Ishtiaque Ahmed2https://orcid.org/0000-0002-2242-7051Jaroslav Frnda3https://orcid.org/0000-0001-6065-3087Department of Electrical Engineering, University of Engineering and Technology Peshawar, Jalozai Campus, Peshawar, PakistanDepartment of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar, PakistanNational Centre of Big Data and Cloud Computing (NCBC), University of Engineering and Technology Peshawar, Peshawar, PakistanDepartment of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Žilina, Žilina, SlovakiaCompressive 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.https://ieeexplore.ieee.org/document/9852418/Compressed sensingcompressive samplingreconstruction algorithmssensing matrix |
| spellingShingle | Irfan Ahmed Amaad Khalil Ishtiaque Ahmed Jaroslav Frnda Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks IEEE Access Compressed sensing compressive sampling reconstruction algorithms sensing matrix |
| title | Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks |
| title_full | Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks |
| title_fullStr | Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks |
| title_full_unstemmed | Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks |
| title_short | Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks |
| title_sort | sparse signal representation sampling and recovery in compressive sensing frameworks |
| topic | Compressed sensing compressive sampling reconstruction algorithms sensing matrix |
| url | https://ieeexplore.ieee.org/document/9852418/ |
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