Introduction to graph signal processing
By: Ortega, Antonio.
Publisher: Cambridge Cambridge University Press 2022Description: xvii, 301p.ISBN: 9781108428132.Subject(s): Signal processingDDC classification: 621.3822 | Or8i Summary: An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
![]() |
PK Kelkar Library, IIT Kanpur | General Stacks | 621.3822 Or8i (Browse shelf) | Available | A186349 |
Browsing PK Kelkar Library, IIT Kanpur Shelves , Collection code: General Stacks Close shelf browser
621.3822 N73B NONLINEAR SIGNAL AND IMAGE PROCESSING | 621.3822 Oh1h High-speed signaling | 621.3822 Op7 Optimization in signal and image processing | 621.3822 Or8i Introduction to graph signal processing | 621.3822 Os4d DSP for embedded and real-time systems | 621.3822 OW2P PRACTICAL SIGNAL PROCESSING | 621.3822 P172s Signals and systems laboratory with MATLAB |
An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.
There are no comments for this item.