Hardcover. 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) and has also helped reduce the health hazard in X-Ray Computed CT. This book is a valuable resource suitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra.Covers fundamental concepts of compressed sensingMakes subject matter accessible for engineers of various levelsFocuses on algorithms including group-sparsity and row-sparsity, as well as applications to computational imaging, medical imaging, biomedical signal processing, and machine learningIncludes MATLAB examples for further development The first portion of the book is on the algorithms. The second portion of the book is about applications, covering some of the major areas like computational imaging, medical imaging, biomedical signal processing and machine learning. The concentration of this book is on algorithms and applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Compressed Sensing for Engineers (Hardcover)
Editor: Taylor & Francis Inc, Bosa Roca
Fecha de publicaci�n: 2018
Idioma: Ingl�s
ISBN 10: 081536556X
ISBN 13: 9780815365563
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

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) and has also helped reduce the health hazard in X-Ray Computed CT. This book is a valuable resource suitable 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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Angshul Majumdar is currently an assistant professor in Electronics and Communications Engineering at the Indraprastha Institute of Information Technology, Delhi (IIIT-D). He completed his PhD in 2012 from the University of British Columbia, Canada. His main contribution is in reducing acquisition time for Magnetic Resonance Imaging acquisition. He has around 25 papers on this topic published in top-tier journals and conferences. Angshul also works in other areas of biomedical imaging and signal processing. Previously, he was interested in the problem of classification and has published several papers on robust classification techniques with applications in face recognition, fingerprint recognition and optical character recognition. In all, Angshul has published over 50 papers in top-tier journals and conferences in the last 5 years. Before Angshul started in the academia, he worked in business consulting at the Pricewaterhouse Coopers.

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