Cooperative and Graph Signal Processing Principles and Applications

Petar Djuric , C챕dric Richard 지음

elsevier 2018년 06월 29일 출간

작품소개

책소개

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 coveredIncludes 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

목차

PART 1 BASICS OF INFERENCE OVER NETWORKS CHAPTER 1 Asynchronous Adaptive Networks CHAPTER 2 Estimation and Detection Over Adaptive Networks CHAPTER 3 Multitask Learning Over Adaptive Networks With Grouping Strategies CHAPTER 4 Bayesian Approach to Collaborative Inference in Networks of Agents CHAPTER 5 Multiagent Distributed Optimization CHAPTER 6 Distributed Kalman and Particle Filtering CHAPTER 7 Game Theoretic Learning PART 2 SIGNAL PROCESSING ON GRAPHS CHAPTER 8 Graph Signal Processing CHAPTER 9 Sampling and Recovery of Graph Signals CHAPTER 10 Bayesian Active Learning on Graphs CHAPTER 11 Design of Graph Filters and Filterbanks CHAPTER 12 Statistical Graph Signal Processing: Stationarity and Spectral Estimation CHAPTER 13 Inference of Graph Topology CHAPTER 14 Partially Absorbing Random Walks: A Unified Framework for Learning on Graphs PART 3 DISTRIBUTED COMMUNICATIONS, NETWORKING, AND SENSING CHAPTER 15 Methods for Decentralized Signal Processing With Big Data CHAPTER 16 The Edge Cloud: A Holistic View of Communication, Computation, and Caching CHAPTER 17 Applications of Graph Connectivity to Network Security CHAPTER 18 Team Methods for Device Cooperation in Wireless Networks CHAPTER 19 Cooperative Data Exchange in Broadcast Networks CHAPTER 20 Collaborative Spectrum Sensing in the Presence of Byzantine Attack PART 4 SOCIAL NETWORKS CHAPTER 21 Dynamics of Information Diffusion and Social Sensing CHAPTER 22 Active Sensing of Social Networks: Network Identification From Low-Rank Data CHAPTER 23 Dynamic Social Networks: Search and Data Routing CHAPTER 24 Information Diffusion and Rumor

작가정보

저자(글) Petar Djuric

저자(글) C챕dric Richard