Compressed Sensing for Engineers

Angshul Majumdar

ISBN 10: 1032338717 / ISBN 13: 9781032338712

Published by Taylor and Francis Ltd, GB, 2022

About this Item

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.

Seller Inventory # LU-9781032338712

Bibliographic Details

• Title: Compressed Sensing for Engineers
• Publisher: Taylor and Francis Ltd, GB
• Publication Date: 2022
• Language: English
• ISBN 10: 1032338717
• ISBN 13: 9781032338712
• Binding: Paperback
• Condition: New
• Weight: 440 grams

About this title

Synopsis

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

About the Author

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.