Fundamental Studies in Graph Signal Processing PDF Download
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Author: Amin Jalili
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Amin Jalili
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Antonio Ortega
Publisher: Cambridge University Press
ISBN: 1108640176
Category : Technology & Engineering
Languages : en
Pages :
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Book Description
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.
Author: Petar Djuric
Publisher: Academic Press
ISBN: 0128136782
Category : Computers
Languages : en
Pages : 868
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Book Description
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
Author: Ljubiša Stanković
Publisher: Springer
ISBN: 3030035743
Category : Technology & Engineering
Languages : en
Pages : 516
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Book Description
This book introduces new methods to analyze vertex-varying graph signals. In many real-world scenarios, the data sensing domain is not a regular grid, but a more complex network that consists of sensing points (vertices) and edges (relating the sensing points). Furthermore, sensing geometry or signal properties define the relation among sensed signal points. Even for the data sensed in the well-defined time or space domain, the introduction of new relationships among the sensing points may produce new insights in the analysis and result in more advanced data processing techniques. The data domain, in these cases and discussed in this book, is defined by a graph. Graphs exploit the fundamental relations among the data points. Processing of signals whose sensing domains are defined by graphs resulted in graph data processing as an emerging field in signal processing. Although signal processing techniques for the analysis of time-varying signals are well established, the corresponding graph signal processing equivalent approaches are still in their infancy. This book presents novel approaches to analyze vertex-varying graph signals. The vertex-frequency analysis methods use the Laplacian or adjacency matrix to establish connections between vertex and spectral (frequency) domain in order to analyze local signal behavior where edge connections are used for graph signal localization. The book applies combined concepts from time-frequency and wavelet analyses of classical signal processing to the analysis of graph signals. Covering analytical tools for vertex-varying applications, this book is of interest to researchers and practitioners in engineering, science, neuroscience, genome processing, just to name a few. It is also a valuable resource for postgraduate students and researchers looking to expand their knowledge of the vertex-frequency analysis theory and its applications. The book consists of 15 chapters contributed by 41 leading researches in the field.
Author: Gene Cheung
Publisher: John Wiley & Sons
ISBN: 1789450284
Category : Computers
Languages : en
Pages : 322
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Book Description
Graph spectral image processing is the study of imaging data from a graph frequency perspective. Modern image sensors capture a wide range of visual data including high spatial resolution/high bit-depth 2D images and videos, hyperspectral images, light field images and 3D point clouds. The field of graph signal processing – extending traditional Fourier analysis tools such as transforms and wavelets to handle data on irregular graph kernels – provides new flexible computational tools to analyze and process these varied types of imaging data. Recent methods combine graph signal processing ideas with deep neural network architectures for enhanced performances, with robustness and smaller memory requirements. The book is divided into two parts. The first is centered on the fundamentals of graph signal processing theories, including graph filtering, graph learning and graph neural networks. The second part details several imaging applications using graph signal processing tools, including image and video compression, 3D image compression, image restoration, point cloud processing, image segmentation and image classification, as well as the use of graph neural networks for image processing.
Author: Allen B. Downey
Publisher: "O'Reilly Media, Inc."
ISBN: 149193851X
Category : Technology & Engineering
Languages : en
Pages : 172
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Book Description
If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. You’ll explore: Periodic signals and their spectrums Harmonic structure of simple waveforms Chirps and other sounds whose spectrum changes over time Noise signals and natural sources of noise The autocorrelation function for estimating pitch The discrete cosine transform (DCT) for compression The Fast Fourier Transform for spectral analysis Relating operations in time to filters in the frequency domain Linear time-invariant (LTI) system theory Amplitude modulation (AM) used in radio Other books in this series include Think Stats and Think Bayes, also by Allen Downey.
Author: Nathanaël Perraudin
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Mots-clés de l'auteur: graph signal processing ; semi-supervised learning ; graph stationarity ; graph uncertainty principle ; local uncertainty principle ; machine learning ; data science ; manifold regularization ; structural clustering ; spectral graph theory.
Author: B. S. Manoj
Publisher: Prentice Hall
ISBN: 0134787129
Category : Computers
Languages : en
Pages : 763
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Book Description
The Up-to-Date Guide to Complex Networks for Students, Researchers, and Practitioners Networks with complex and irregular connectivity patterns appear in biology, chemistry, communications, social networks, transportation systems, power grids, the Internet, and many big data applications. Complex Networks offers a novel engineering perspective on these networks, focusing on their key communications, networking, and signal processing dimensions. Three leading researchers draw on recent advances to illuminate the design and characterization of complex computer networks and graph signal processing systems. The authors cover both the fundamental concepts underlying graph theory and complex networks, as well as current theory and research. They discuss spectra and signal processing in complex networks, graph signal processing approaches for extracting information from structural data, and advanced techniques for multiscale analysis. What makes networks complex, and how to successfully characterize them Graph theory foundations, definitions, and concepts Full chapters on small-world, scale-free, small-world wireless mesh, and small-world wireless sensor networks Complex network spectra and graph signal processing concepts and techniques Multiscale analysis via transforms and wavelets
Author: Erchin Serpedin
Publisher: CRC Press
ISBN: 1466514086
Category : Computers
Languages : en
Pages : 859
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Book Description
Mathematical Foundations for Signal Processing, Communications, and Networking describes mathematical concepts and results important in the design, analysis, and optimization of signal processing algorithms, modern communication systems, and networks. Helping readers master key techniques and comprehend the current research literature, the book offers a comprehensive overview of methods and applications from linear algebra, numerical analysis, statistics, probability, stochastic processes, and optimization. From basic transforms to Monte Carlo simulation to linear programming, the text covers a broad range of mathematical techniques essential to understanding the concepts and results in signal processing, telecommunications, and networking. Along with discussing mathematical theory, each self-contained chapter presents examples that illustrate the use of various mathematical concepts to solve different applications. Each chapter also includes a set of homework exercises and readings for additional study. This text helps readers understand fundamental and advanced results as well as recent research trends in the interrelated fields of signal processing, telecommunications, and networking. It provides all the necessary mathematical background to prepare students for more advanced courses and train specialists working in these areas.
Author: Antonio Ortega
Publisher: Cambridge University Press
ISBN: 1108428134
Category : Computers
Languages : en
Pages : 321
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Book Description
An intuitive, accessible text explaining the fundamentals and applications of signal processing on graphs. It covers basic and advanced topics, includes numerous exercises and Matlab examples, and is accompanied online by a solutions manual for instructors, making it essential reading for graduate students, researchers, and industry professionals.
