Cooperative and Graph Signal Processing: Principles and Applications

by Petar Djuric, Cédric Richard

Overview

Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed communication, networking and sensing and social networks. Finally, the book shows how the principles are applied to a range of applications, such as Big data, Media and video, Smart grids, Internet of Things, Wireless health and Neuroscience.

With this book readers will learn the basics of adaptation and learning in networks, the essentials of detection, estimation and filtering, Bayesian inference in networks, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the perspective of flow of information, and how to apply signal processing methods in distributed settings.

Product Details

ISBN-13:	9780128136775
Publisher:	Elsevier Science
Publication date:	06/20/2018
Pages:	866
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

Petar M. Djurić received the B.S. and M.S. degrees in electrical engineering from the University of Belgrade, Belgrade, Yugoslavia, respectively, and the Ph.D. degree in electrical engineering from the University of Rhode Island, Kingston, RI, USA. He is currently a Professor with the Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA. His research has been in the area of signal and information processing with primary interests in the theory of signal modeling, detection, and estimation; Monte Carlo-based methods; signal and information processing over networks; machine learning, RFID and the IoT. He has been invited to lecture at many universities in the United States and overseas. Prof. Djurić was a recipient of the IEEE Signal Processing Magazine Best Paper Award in 2007 and the EURASIP Technical Achievement Award in 2012. In 2008, he was the Chair of Excellence of Universidad Carlos III de Madrid-Banco de Santander. From 2008 to 2009, he was a Distinguished Lecturer of the IEEE Signal Processing Society. He has been on numerous committees of the IEEE Signal Processing Society and of many professional conferences and workshops. He is the Editor-in-Chief of the IEEE Transactions on Signal and Information Processing over Networks. Prof. Djurić is a Fellow of IEEE and EURASIP.

Cédric Richard received the Dipl.-Ing. and the M.S. degrees in 1994, and the Ph.D. degree in 1998, from Compiègne University of Technology, France, all in Electrical and Computer Engineering. He is a Full Professor at the Université Nice Sophia Antipolis, France. He was a junior member of the Institut Universitaire de France in 2010-2015. His current research interests include adaptation and learning, statistical signal processing, and network science. Cédric Richard is the author of over 250 papers. He was the General Co-Chair of the IEEE SSP Workshop that was held in Nice, France, in 2011. He was the Technical Co-Chair of EUSIPCO 2015 that was held in Nice, France, and of the IEEE CAMSAP Workshop 2015 that was held in Cancun, Mexico. He serves as a Senior Area Editor of the IEEE Transactions on Signal Processing and as an Associate Editor of the IEEE Transactions on Signal and Information Processing over Networks since 2015. He is also an Associate Editor of Signal Processing Elsevier since 2009. Cédric Richard is a member of the IEEE Machine Learning for Signal Processing (IEEE MLSP TC) Technical Committee, and served as member of the IEEE Signal Processing Theory and Methods (IEEE SPTM TC) Technical Committee in 2009-2014.

Table of Contents

PART 1 BASICS OF INFERENCE OVER NETWORKS
1. Asynchronous Adaptive Networks
2. Estimation and Detection Over Adaptive Networks
3. Multitask Learning Over Adaptive Networks With Grouping Strategies
4. Bayesian Approach to Collaborative Inference in Networks of Agents
5. Multiagent Distributed Optimization
6. Distributed Kalman and Particle Filtering
7. Game Theoretic Learning

PART 2 SIGNAL PROCESSING ON GRAPHS
8. Graph Signal Processing
9. Sampling and Recovery of Graph Signals
10. Bayesian Active Learning on Graphs
11. Design of Graph Filters and Filterbanks
12. Statistical Graph Signal Processing: Stationarity and Spectral Estimation
13. Inference of Graph Topology
14. Partially Absorbing Random Walks: A Unified Framework for Learning on Graphs

PART 3 DISTRIBUTED COMMUNICATIONS, NETWORKING, AND SENSING
15. Methods for Decentralized Signal Processing With Big Data
16. The Edge Cloud: A Holistic View of Communication, Computation, and Caching
17. Applications of Graph Connectivity to Network Security
18. Team Methods for Device Cooperation in Wireless Networks
19. Cooperative Data Exchange in Broadcast Networks
20. Collaborative Spectrum Sensing in the Presence of Byzantine Attack

PART 4 SOCIAL NETWORKS
21. Dynamics of Information Diffusion and Social Sensing
22. Active Sensing of Social Networks: Network Identification From Low-Rank Data
23. Dynamic Social Networks: Search and Data Routing
24. Information Diffusion and Rumor Spreading
25. Multilayer Social Networks
26. Multiagent Systems: Learning, Strategic Behavior, Cooperation, and Network Formation

PART 5 APPLICATIONS
27. Genomics and Systems Biology
28. Diffusion Augmented Complex Extended Kalman Filtering for Adaptive Frequency Estimation in Distributed Power Networks
29. Beacons and the City: Smart Internet of Things
30. Big Data
31. Graph Signal Processing on Neuronal Networks

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From the Publisher

Provides a comprehensive understanding of the principles of cooperative and graph signal processing and how to apply them