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Cooperative and Graph Signal Processing: Principles and Applications

Editat de Petar Djuric, Cédric Richard
Notă GoodReads:
en Limba Engleză Paperback – 20 Jun 2018
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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Specificații

ISBN-13: 9780128136775
ISBN-10: 0128136774
Pagini: 866
Dimensiuni: 191 x 235 mm
Greutate: 1.46 kg
Editura: ELSEVIER SCIENCE

Public țintă

Researchers and graduate students in signal and information processing over networks

Cuprins

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