Published by A.B.D. Publishers ABD Pub., 2013
ISBN 10: 8183764215 ISBN 13: 9788183764216
Language: English
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Seller: ALLBOOKS1, Direk, SA, Australia
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Published by Taylor & Francis Ltd, 2022
ISBN 10: 1032338717 ISBN 13: 9781032338712
Language: English
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Published by Taylor & Francis Ltd, London, 2022
ISBN 10: 1032338717 ISBN 13: 9781032338712
Language: English
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Paperback. Condition: new. Paperback. 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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Published by Taylor & Francis Ltd, 2023
ISBN 10: 1032653108 ISBN 13: 9781032653105
Language: English
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Published by Taylor & Francis Ltd, London, 2022
ISBN 10: 1032338717 ISBN 13: 9781032338712
Language: English
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Add to basketPaperback. Condition: new. Paperback. 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2012
ISBN 10: 3659266590 ISBN 13: 9783659266591
Language: English
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Published by Springer, India, Private Ltd, New Delhi, 2015
ISBN 10: 8132226240 ISBN 13: 9788132226246
Language: English
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First Edition
Paperback. Condition: new. Paperback. This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intelligence and Signal Processing was one of the few unique events that are focused on the convergence of the two fields. The book is comprised of chapters based on the top presentations at the workshop. This book has three chapters on various topics of biometrics two are on face detection and one on iris recognition; all from top researchers in their field. There are four chapters on different biomedical signal / image processing problems. Two of these are on retinal vesselclassification and extraction; one on biomedical signal acquisition and the fourth one on region detection. There are three chapters on data analysis a topic gaining immense popularity in industry and academia. One of these shows a novel use of compressed sensing in missing sales data interpolation. Another chapter is on spam detection and the third one is on simple one-shot movie rating prediction. Four other chapters cover various cutting edge miscellaneous topics on character recognition, software effort prediction, speech recognition and non-linear sparse recovery. The contents of this book will prove useful to researchers, professionals and students in the domains of machine learning and signal processing. This book comprises chapters on key problems in machine learning and signal processing arenas. Nowadays signal processing research is gravitating towards operator learning instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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