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Cooperative and Graph Signal Processing

  • ID: 4455017
  • Book
  • June 2018
  • Region: Global
  • 866 Pages
  • Elsevier Science and Technology
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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.

  • Presents the first book on cooperative signal processing and graph signal processing
  • Provides a range of applications and application areas that are thoroughly covered
  • Includes an editor in chief and associate editor from the IEEE Transactions on Signal Processing and Information Processing over Networks who have recruited top contributors for the book

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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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Djuric, Petar
Petar M. Djuric 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. Djuric 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. Djuric is a Fellow of IEEE and EURASIP.
Richard, Cédric
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.
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