Introduction To Graph Signal Processing
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Introduction To Graph Signal Processing
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Author : Antonio Ortega
language : en
Publisher: Cambridge University Press
Release Date : 2022-06-09
Introduction To Graph Signal Processing written by Antonio Ortega and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-09 with Computers categories.
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.
Vertex Frequency Analysis Of Graph Signals
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Author : Ljubiša Stanković
language : en
Publisher: Springer
Release Date : 2018-12-01
Vertex Frequency Analysis Of Graph Signals written by Ljubiša Stanković and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-01 with Technology & Engineering categories.
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.
Graph Representation Learning
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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Cooperative And Graph Signal Processing
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Author : Petar Djuric
language : en
Publisher: Academic Press
Release Date : 2018-07-04
Cooperative And Graph Signal Processing written by Petar Djuric and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-04 with Computers categories.
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
Topological Signal Processing
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Author : Michael Robinson
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-01-07
Topological Signal Processing written by Michael Robinson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-07 with Technology & Engineering categories.
Signal processing is the discipline of extracting information from collections of measurements. To be effective, the measurements must be organized and then filtered, detected, or transformed to expose the desired information. Distortions caused by uncertainty, noise, and clutter degrade the performance of practical signal processing systems. In aggressively uncertain situations, the full truth about an underlying signal cannot be known. This book develops the theory and practice of signal processing systems for these situations that extract useful, qualitative information using the mathematics of topology -- the study of spaces under continuous transformations. Since the collection of continuous transformations is large and varied, tools which are topologically-motivated are automatically insensitive to substantial distortion. The target audience comprises practitioners as well as researchers, but the book may also be beneficial for graduate students.
Graph Algorithms In The Language Of Linear Algebra
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Author : Jeremy Kepner
language : en
Publisher: SIAM
Release Date : 2011-01-01
Graph Algorithms In The Language Of Linear Algebra written by Jeremy Kepner and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-01 with Mathematics categories.
The current exponential growth in graph data has forced a shift to parallel computing for executing graph algorithms. Implementing parallel graph algorithms and achieving good parallel performance have proven difficult. This book addresses these challenges by exploiting the well-known duality between a canonical representation of graphs as abstract collections of vertices and edges and a sparse adjacency matrix representation. This linear algebraic approach is widely accessible to scientists and engineers who may not be formally trained in computer science. The authors show how to leverage existing parallel matrix computation techniques and the large amount of software infrastructure that exists for these computations to implement efficient and scalable parallel graph algorithms. The benefits of this approach are reduced algorithmic complexity, ease of implementation, and improved performance.
An Introduction To Digital Signal Processing
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Author : Stanley Mneney
language : en
Publisher: CRC Press
Release Date : 2022-09-01
An Introduction To Digital Signal Processing written by Stanley Mneney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-01 with Science categories.
An Introduction to Digital Signal Processing aims at undergraduate students who have basic knowledge in C programming, Circuit Theory, Systems and Simulations, and Spectral Analysis. The book is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware in which the candidate is introduced to the basic concepts first before embarking to the practical part which comes in the later chapters. Initially Digital Signal Processing evolved as a postgraduate course which slowly filtered into the undergraduate curriculum as a simplified version of the latter. The goal was to study DSP concepts and to provide a foundation for further research where new and more efficient concepts and algorithms can be developed. Though this was very useful it did not arm the student with all the necessary tools that many industries using DSP technology would require to develop applications. This book is an attempt to bridge the gap. It is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware. The objective is to win the student to use a variety of development tools to develop applications. Contents• Introduction to Digital Signal processing.• The transform domain analysis: the Discrete-Time Fourier Transform• The transform domain analysis: the Discrete Fourier Transform• The transform domain analysis: the z-transform• Review of Analogue Filter• Digital filter design.• Digital Signal Processing Implementation Issues• Digital Signal Processing Hardware and Software• Examples of DSK Filter Implementation
An Introduction To The Theory Of Graph Spectra
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Author : Dragoš Cvetković
language : en
Publisher: Cambridge University Press
Release Date : 2009-10-15
An Introduction To The Theory Of Graph Spectra written by Dragoš Cvetković and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-15 with Mathematics categories.
This introductory text explores the theory of graph spectra: a topic with applications across a wide range of subjects, including computer science, quantum chemistry and electrical engineering. The spectra examined here are those of the adjacency matrix, the Seidel matrix, the Laplacian, the normalized Laplacian and the signless Laplacian of a finite simple graph. The underlying theme of the book is the relation between the eigenvalues and structure of a graph. Designed as an introductory text for graduate students, or anyone using the theory of graph spectra, this self-contained treatment assumes only a little knowledge of graph theory and linear algebra. The authors include many new developments in the field which arise as a result of rapidly expanding interest in the area. Exercises, spectral data and proofs of required results are also provided. The end-of-chapter notes serve as a practical guide to the extensive bibliography of over 500 items.
Point Cloud Compression
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Author : Ge Li
language : en
Publisher: Springer Nature
Release Date : 2024-05-17
Point Cloud Compression written by Ge Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-17 with Computers categories.
3D point clouds have broad applications across various industries and have contributed to advancements in fields such as autonomous driving, immersive media, metaverse, and cultural heritage protection. With the fast growth of 3D point cloud data and its applications, the need for efficient compression technologies has become paramount. This book delves into the forefront of point cloud compression, exploring key technologies, standardization efforts, and future prospects. This comprehensive book uncovers the foundational concepts, data acquisition methods, and datasets associated with point cloud compression. By examining the fundamental compression technologies, readers can obtain a clear understanding of prediction coding, transform coding, quantization techniques, and entropy coding. Through vivid illustrations and examples, the book elucidates how these techniques have evolved over the years and their potentials for the future. To provide a complete picture, the book presents cutting-edge research methods in point cloud compression and facilitates comparisons among them. Readers can be equipped with an in-depth understanding of the latest advancements, and can gain insights into the various approaches employed in this dynamic field. Another distinguishing aspect of this book is its exploration of standardization works for point cloud compression. Notable standards, such as MPEG G-PCC, AVS PCC, and MPEG V-PCC, are thoroughly illustrated. By delving into the methods used in geometry-based, video-based, and deep learning-based compression, readers become familiar with the latest breakthroughs in the standard communities.