Vertex frequency analysis graph signals (21 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2018
Serie: Signals and Communication Technology, Buch 155 von 180. Buch 155 von 180 - Signals and Communication Technology
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Sprache: Englisch
Verlag: Springer, 2018
Serie: Signals and Communication Technology, Buch 155 von 180. Buch 155 von 180 - Signals and Communication Technology
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hardcover. Zustand: Good. Hardcover without dust jacket. Bound in glossy pictorial paper over boards with white lettering on the covers and spine. Minor wear to back cover, but book remains in overall very good condition. Binding is tight and secure. Corners are slightly bumped. Pages are crisp, clean and bright. Springer. No da…te on title page. Copyright page dated 2019. 507 pages. A good copy. We ship everyday from a real neighborhood bookstore. This description is written by an actual person, who is holding the book in front of them to make sure it?s properly described. Please contact us with questions or if you would like to see photographs.
Sprache: Englisch
Verlag: Springer, 2018
Serie: Signals and Communication Technology, Buch 155 von 180. Buch 155 von 180 - Signals and Communication Technology
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Sprache: Englisch
Verlag: Springer, 2018
Serie: Signals and Communication Technology, Buch 155 von 180. Buch 155 von 180 - Signals and Communication Technology
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Hardcover. Zustand: new. Hardcover. 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. 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.This second edition has been revised and updated and has now been expanded to include new chapters on cutting-edge topics relevant to the analysis of graph signals such as machine learning.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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 poin…ts). 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.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.This second edition has been revised and updated and has now been expanded to include new chapters on cutting-edge topics relevant to the analysis of graph signals such as machine learning.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.
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Hardcover. Zustand: Brand New. second edition 2026 edition. 569 pages. 6.48x1.21x9.33 inches. In Stock.
Sprache: Englisch
Verlag: Springer, 2018
Serie: Signals and Communication Technology, Buch 155 von 180. Buch 155 von 180 - Signals and Communication Technology
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Sprache: Englisch
Verlag: Springer, 2018
Serie: Signals and Communication Technology, Buch 155 von 180. Buch 155 von 180 - Signals and Communication Technology
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Sprache: Englisch
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Sprache: Englisch
Verlag: Springer International Publishing Dez 2018, 2018
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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 524 pp. Englisch.
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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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.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.This second edition has been revised and updated and has now been expanded to include new chapters on cutting-edge topics relevant to the analysis of graph signals such as machine learning.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. 555 pp. Englisch.
Sprache: Englisch
Verlag: Springer, 2018
Serie: Signals and Communication Technology, Buch 155 von 180. Buch 155 von 180 - Signals and Communication Technology
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Sprache: Englisch
Verlag: Palgrave Macmillan, 2018
Serie: Signals and Communication Technology, Buch 155 von 180. Buch 155 von 180 - Signals and Communication Technology
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Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 s…ensing 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.
Sprache: Englisch
Verlag: Springer, 2018
Serie: Signals and Communication Technology, Buch 155 von 180. Buch 155 von 180 - Signals and Communication Technology
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Buch. Zustand: Neu. Vertex-Frequency Analysis of Graph Signals | Ljubi¿a Stankovi¿ (u. a.) | Buch | Signals and Communication Technology | xiv | Englisch | 2026 | Springer | EAN 9783032165886 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com |… Anbieter: preigu Print on Demand.
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Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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. 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.This second edition has been revised and updated and has now been expanded to include new chapters on cutting-edge topics relevant to the analysis of graph signals such as machine learning.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.Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg 572 pp. Englisch.
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