Introduction To Graph Signal Processing

Author : Antonio Ortega
Publisher : Cambridge University Press
Page : 128 pages
File Size : 55,9 Mb
Release : 2022-06-09
Category : Technology & Engineering
ISBN : 9781108640176

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Introduction to Graph Signal Processing by Antonio Ortega Pdf

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 : Ljubiša Stanković,Ervin Sejdić
Publisher : Springer
Page : 507 pages
File Size : 52,6 Mb
Release : 2018-12-01
Category : Technology & Engineering
ISBN : 9783030035747

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Vertex-Frequency Analysis of Graph Signals by Ljubiša Stanković,Ervin Sejdić Pdf

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 : Petar Djuric,Cédric Richard
Publisher : Academic Press
Page : 866 pages
File Size : 47,8 Mb
Release : 2018-07-04
Category : Computers
ISBN : 9780128136782

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Cooperative and Graph Signal Processing by Petar Djuric,Cédric Richard Pdf

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 : Ljubisa Stankovic,Danilo Mandic,Milos Dakovic,Bruno Scalzo,Shengxi Li,A. G. Constantinides
Publisher : Unknown
Page : 556 pages
File Size : 49,5 Mb
Release : 2020-12-22
Category : Data mining
ISBN : 1680839829

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Data Analytics on Graphs by Ljubisa Stankovic,Danilo Mandic,Milos Dakovic,Bruno Scalzo,Shengxi Li,A. G. Constantinides Pdf

Aimed at readers with a good grasp of the fundamentals of data analytics, this book sets out the fundamentals of graph theory and the emerging mathematical techniques for the analysis of a wide range of data acquired on graph environments. This book will be a useful friend and a helpful companion to all involved in data gathering and analysis.

Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 48,5 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015885

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Graph Representation Learning by William L. William L. Hamilton Pdf

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.

Author : Ioannis Pitas
Publisher : CRC Press
Page : 436 pages
File Size : 42,8 Mb
Release : 2016-04-19
Category : Computers
ISBN : 9781498719056

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Graph-Based Social Media Analysis by Ioannis Pitas Pdf

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear alge

Author : Gene Cheung,Enrico Magli
Publisher : John Wiley & Sons
Page : 322 pages
File Size : 52,5 Mb
Release : 2021-08-31
Category : Computers
ISBN : 9781789450286

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Graph Spectral Image Processing by Gene Cheung,Enrico Magli Pdf

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."
Page : 168 pages
File Size : 42,6 Mb
Release : 2016-07-12
Category : Technology & Engineering
ISBN : 9781491938515

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Think DSP by Allen B. Downey Pdf

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 : Piet van Mieghem
Publisher : Cambridge University Press
Page : 363 pages
File Size : 51,6 Mb
Release : 2010-12-02
Category : Technology & Engineering
ISBN : 9781139492270

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Graph Spectra for Complex Networks by Piet van Mieghem Pdf

Analyzing the behavior of complex networks is an important element in the design of new man-made structures such as communication systems and biologically engineered molecules. Because any complex network can be represented by a graph, and therefore in turn by a matrix, graph theory has become a powerful tool in the investigation of network performance. This self-contained 2010 book provides a concise introduction to the theory of graph spectra and its applications to the study of complex networks. Covering a range of types of graphs and topics important to the analysis of complex systems, this guide provides the mathematical foundation needed to understand and apply spectral insight to real-world systems. In particular, the general properties of both the adjacency and Laplacian spectrum of graphs are derived and applied to complex networks. An ideal resource for researchers and students in communications networking as well as in physics and mathematics.

Author : B. S. Manoj,Abhishek Chakraborty,Rahul Singh
Publisher : Prentice Hall
Page : 762 pages
File Size : 50,9 Mb
Release : 2018-01-23
Category : Computers
ISBN : 9780134787121

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Complex Networks by B. S. Manoj,Abhishek Chakraborty,Rahul Singh Pdf

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 : XINGCHAO;JI JIAN (FENG;TAY, WEE PENG.),Feng Ji,Wee Peng Tay
Publisher : Unknown
Page : 0 pages
File Size : 40,5 Mb
Release : 2023
Category : TECHNOLOGY & ENGINEERING
ISBN : 1638281513

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Generalizing Graph Signal Processing by XINGCHAO;JI JIAN (FENG;TAY, WEE PENG.),Feng Ji,Wee Peng Tay Pdf

In modern data analysis, massive measurements from a network require novel signal processing techniques, which are expected to be adapted to the network topology, have distributed implementation, and are flexible enough for various applications. Graph signal processing (GSP) theories and techniques are geared towards these goals.GSP has seen rapid developments in recent years. Since its introduction around ten years ago, we have seen numerous new ideas and practical applications related to the field. In this monograph, an overview of recent advances in generalizing GSP is presented, with a focus on the extension to high-dimensional spaces, models, and structures. Alongside new frameworks proposed to tackle such problems, many new mathematical tools are introduced.In the first part of the monograph, traditional GSP is reviewed, challenges that it faces are highlighted, and efforts in overcoming such challenges are motivated. These efforts then become the theme for the rest of the publication. Included are the generalization of GSP to high dimensional vertex signal spaces, the theory of random shift operators and the wide-sense stationary (WSS) statistical signal models, and the treatment of high dimensionality in graph structures and generalized graph-like structures. The monograph concludes with an outline of possible future directions.

Author : Stanley Mneney
Publisher : River Publishers
Page : 284 pages
File Size : 40,7 Mb
Release : 2009-01-10
Category : Signal processing
ISBN : 9788792329127

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An Introduction to Digital Signal Processing by Stanley Mneney Pdf

Mneney's text focuses on basic concepts of digital signal processing, MATLAB simulation, and implementation on selected DSP hardware.

Author : Julius Orion Smith
Publisher : Julius Smith
Page : 481 pages
File Size : 48,5 Mb
Release : 2007
Category : Digital electronics
ISBN : 9780974560717

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Introduction to Digital Filters by Julius Orion Smith Pdf

A digital filter can be pictured as a "black box" that accepts a sequence of numbers and emits a new sequence of numbers. In digital audio signal processing applications, such number sequences usually represent sounds. For example, digital filters are used to implement graphic equalizers and other digital audio effects. This book is a gentle introduction to digital filters, including mathematical theory, illustrative examples, some audio applications, and useful software starting points. The theory treatment begins at the high-school level, and covers fundamental concepts in linear systems theory and digital filter analysis. Various "small" digital filters are analyzed as examples, particularly those commonly used in audio applications. Matlab programming examples are emphasized for illustrating the use and development of digital filters in practice.

Author : Paolo Prandoni,Martin Vetterli
Publisher : Collection Savoir suisse
Page : 392 pages
File Size : 48,5 Mb
Release : 2008-06-17
Category : Signal processing
ISBN : 9782940222209

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Signal Processing for Communications by Paolo Prandoni,Martin Vetterli Pdf

With a novel, less classical approach to the subject, the authors have written a book with the conviction that signal processing should be taught to be fun. The treatment is therefore less focused on the mathematics and more on the conceptual aspects, the idea being to allow the readers to think about the subject at a higher conceptual level, thus building the foundations for more advanced topics. The book remains an engineering text, with the goal of helping students solve real-world problems. In this vein, the last chapter pulls together the individual topics as discussed throughout the book into an in-depth look at the development of an end-to-end communication system, namely, a modem for communicating digital information over an analog channel.

Author : Ge Li
Publisher : Springer Nature
Page : 264 pages
File Size : 40,9 Mb
Release : 2024-06-26
Category : Electronic
ISBN : 9789819719570

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Point Cloud Compression by Ge Li Pdf