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Compressed Sensing for Engineers / by Angshul Majumdar.

By: Majumdar, Angshul [author.].
Contributor(s): Taylor and Francis.
Material type: materialTypeLabelBookSeries: Devices, Circuits, and Systems: Publisher: Boca Raton, FL : CRC Press, [2018]Copyright date: ©2019Edition: First edition.Description: 1 online resource (292 pages) : 32 illustrations, text file, PDF.Content type: text Media type: computer Carrier type: online resourceISBN: 9781351261364(e-book : PDF).Subject(s): Compressed sensing (Telecommunication) | Image processing -- Digital techniques | Image compression | Signal processing -- MathematicsGenre/Form: Electronic books.Additional physical formats: Print version: : No titleOnline resources: Click here to view Also available in print format.
Contents:
Introduction. Greedy Algorithms. Sparse Recovery. Co-sparse Recovery. Group Sparsity. Joint Sparsity. Low-rank Matrix Recovery. Combined Sparse and Low-rank Recovery. Dictionary Learning. Medical Imaging. Biomedical Signal Reconstruction. Regression. Classification. Computational Imaging. Denoising.
Abstract: Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations. The topic is of immense practical significance since all naturally occurring signals can be sparsely represented in some domain. In recent years, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) andhas also helped reducethe health hazard in X-Ray Computed CT. This book is a valuable resourcesuitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra. Covers fundamental concepts of compressed sensing Makes subject matter accessible for engineers of various levels Focuses on algorithms including group-sparsity and row-sparsity, as well as applications to computational imaging, medical imaging, biomedical signal processing, and machine learning. Includes MATLAB examples for further development.
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Includes bibliographical references and index.

Introduction. Greedy Algorithms. Sparse Recovery. Co-sparse Recovery. Group Sparsity. Joint Sparsity. Low-rank Matrix Recovery. Combined Sparse and Low-rank Recovery. Dictionary Learning. Medical Imaging. Biomedical Signal Reconstruction. Regression. Classification. Computational Imaging. Denoising.

Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations. The topic is of immense practical significance since all naturally occurring signals can be sparsely represented in some domain. In recent years, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) andhas also helped reducethe health hazard in X-Ray Computed CT. This book is a valuable resourcesuitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra. Covers fundamental concepts of compressed sensing Makes subject matter accessible for engineers of various levels Focuses on algorithms including group-sparsity and row-sparsity, as well as applications to computational imaging, medical imaging, biomedical signal processing, and machine learning. Includes MATLAB examples for further development.

Also available in print format.

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