Distributed Source Coding: Theory, Algorithms and Applications

Pier Luigi Dragotti

ISBN 10: 0123744857 / ISBN 13: 9780123744852

Editore: Academic Press, 2009

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Dati bibliografici

• Titolo: Distributed Source Coding: Theory, Algorithms and Applications
• Editore: Academic Press
• Data di pubblicazione: 2009
• Lingua: Inglese
• ISBN 10: 0123744857
• ISBN 13: 9780123744852
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

The advent of wireless sensor technology and ad-hoc networks has made DSC a major field of interest. Edited and written by the leading players in the field, this book presents the latest theory, algorithms and applications, making it the definitive reference on DSC for systems designers and implementers, researchers, and graduate students.

This book gives a clear understanding of the performance limits of distributed source coders for specific classes of sources and presents the design and application of practical algorithms for realistic scenarios. Material covered includes the use of standard channel codes, such as LDPC and Turbo codes, to DSC, and discussion of the suitability of compressed sensing for distributed compression of sparse signals. Extensive applications are presented and include distributed video coding, microphone arrays and securing biometric data.

This book is a great resource covering the breadth and depth of distributed source coding that’s appropriate for everyone from theoreticians to practitioners. – Richard Baraniuk, Rice University

*Clear explanation of the principles of distributed source coding (DSC), a technology that has applications in sensor networks, ad-hoc networks, and distributed wireless video systems for surveillance
*Edited and written by the leading players in the field, providing a complete and authoritative reference
*Contains all the latest theory, practical algorithms for DSC design and the most recently developed applications

Informazioni sugli autori

Pier Luigi Dragotti is currently a Senior Lecturer in the Electrical and Electronic Engineering Department at Imperial College, London. He has worked as a researcher at Bell Labs and EPFL and is a member of the IEEE Image and MultiDimensional Signal Processing (IMDSP) Technical Committee.

Michael Gastpar is currently an Associate Professor at the University of California, Berkeley. His research interests are in network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. He won the 2002 EPFL Best Thesis Award, an NSF CAREER award in 2004, and an Okawa Foundation Research Grant in 2008.