Vertex-Frequency Analysis of Graph Signals
暫譯: 圖信號的頂點頻率分析
Stankovic, Ljubisa, Sejdic, Ervin
- 出版商: Springer
- 出版日期: 2026-05-29
- 售價: $8,900
- 貴賓價: 9.5 折 $8,455
- 語言: 英文
- 頁數: 555
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3032165881
- ISBN-13: 9783032165886
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相關分類:
離散數學 Discrete-mathematics
海外代購書籍(需單獨結帳)
相關主題
商品描述
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.
商品描述(中文翻譯)
這本書介紹了分析頂點變化圖信號的新方法。在許多現實世界的場景中，數據感測領域並不是一個規則的網格，而是一個更複雜的網絡，由感測點（頂點）和邊（連接感測點）組成。此外，感測幾何或信號特性定義了感測信號點之間的關係。即使在明確定義的時間或空間領域中感測到的數據，感測點之間新關係的引入也可能在分析中產生新的見解，並導致更先進的數據處理技術。在這些情況下，數據領域由圖定義。圖利用數據點之間的基本關係。
儘管針對時間變化信號的信號處理技術已經相當成熟，但相應的圖信號處理等效方法仍處於起步階段。本書提出了分析頂點變化圖信號的新方法。頂點頻率分析方法使用拉普拉斯（Laplacian）或鄰接矩陣來建立頂點與頻譜（頻率）領域之間的連接，以分析局部信號行為，其中邊連接用於圖信號定位。本書將經典信號處理中的時間-頻率和小波分析的綜合概念應用於圖信號的分析。
本書的第二版已進行修訂和更新，並擴展了新章節，涵蓋與圖信號分析相關的前沿主題，如機器學習。
本書涵蓋了針對頂點變化應用的分析工具，對於工程、科學、神經科學、基因組處理等領域的研究人員和實務工作者都具有興趣。它也是希望擴展對頂點頻率分析理論及其應用知識的研究生和研究人員的寶貴資源。
作者簡介
Prof. Ljubisa Stankovic was born in Montenegro in 1960. He received the B.S. degree in EE from the University of Montenegro (UoM) with the Best Student at the University award, winning twice the EE student competition in mathematics in Yugoslavia. He obtained the M.S. degree in communications from the University of Belgrade and the Ph.D. in theory of electromagnetic waves from the UoM.
As Fulbright Grantee, he spent 1984-1985 academic year at the Worcester Polytechnic Institute, USA. In 1997-1999, he was on leave at the Ruhr University Bochum, Germany, supported by the Alexander von Humboldt Foundation. At the beginning of 2001, he was at the Technische Universiteit Eindhoven, The Netherlands, as Visiting Professor. He was Visiting Academic at the Imperial College, London, in 2012-2013. He was Rector of the UoM 2003-2008 and Ambassador of Montenegro to the United Kingdom, Ireland, and Iceland from 2011 to 2015. Prof. Stankovic is a Life Fellow of IEEE and a recipient of numerous awards, including the Best Paper Award from the European Association for Signal Processing in 2017 for a paper published in the Signal Processing, the IEEE Signal Processing Magazine Best Column Award for 2020, and the Outstanding Paper Award at IEEE ICASSP 2021.
Prof. Ervin Sejdic received B.E.Sc. and Ph.D. degrees in electrical engineering from the University of Western Ontario, London, Ontario, Canada, in 2002 and 2008, respectively. From 2008 to 2010, he was Postdoctoral Fellow at the University of Toronto with a cross-appointment at Bloorview Kids Rehab, Canada's largest children's rehabilitation teaching hospital. From 2010 until 2011, he was Research Fellow at Harvard Medical School with a cross-appointment at Beth Israel Deaconess Medical Center. In 2011, Prof. Sejdic joined the Department of Electrical and Computer Engineering at the University of Pittsburgh (Pittsburgh, PA, USA). In 2021, he joined the University of Toronto as Faculty Member. He is also Research Chair in Artificial Intelligence for Health Outcomes at North York General Hospital in Toronto.
Prof. Sejdic is Senior Member of IEEE and Recipient of many awards. In February 2016, President Obama named Dr. Sejdic as a recipient of the Presidential Early Career Award for Scientists and Engineers. In 2017, Dr. Sejdic was awarded the National Science Foundation CAREER Award. In 2018, he was awarded the Chancellor's Distinguished Research Award at the University of Pittsburgh.
作者簡介(中文翻譯)
教授 Ljubisa Stankovic 於1960年出生於黑山。他獲得黑山大學（UoM）電機工程學士學位，並獲得最佳學生獎，兩次贏得南斯拉夫數學電機工程學生競賽。他在貝爾格萊德大學獲得通訊碩士學位，並在黑山大學獲得電磁波理論博士學位。
作為富布賴特獎學金獲得者，他於1984至1985學年在美國伍斯特理工學院學習。1997至1999年，他在德國魯爾大學博胡姆休假，並獲得亞歷山大·馮·洪堡基金會的支持。2001年初，他在荷蘭埃因霍溫科技大學擔任訪問教授。2012至2013年，他在倫敦帝國學院擔任訪問學者。他於2003至2008年擔任黑山大學校長，並於2011至2015年擔任黑山駐英國、愛爾蘭和冰島大使。Stankovic 教授是 IEEE 的終身會員，並獲得多項獎項，包括2017年歐洲信號處理協會頒發的最佳論文獎，該論文發表於《信號處理》期刊，2020年 IEEE 信號處理雜誌最佳專欄獎，以及2021年 IEEE ICASSP 的傑出論文獎。
教授 Ervin Sejdic 於2002年和2008年分別在加拿大安大略省倫敦的西安大略大學獲得電機工程學士和博士學位。2008至2010年，他在多倫多大學擔任博士後研究員，並在加拿大最大的兒童康復教學醫院 Bloorview Kids Rehab 擔任交叉任命。2010年至2011年，他在哈佛醫學院擔任研究員，並在 Beth Israel Deaconess Medical Center 擔任交叉任命。2011年，Sejdic 教授加入美國賓夕法尼亞州匹茲堡大學的電機與計算機工程系。2021年，他成為多倫多大學的教職員。他同時也是多倫多北約克綜合醫院人工智慧健康結果研究主席。
Sejdic 教授是 IEEE 的高級會員，並獲得多項獎項。2016年2月，歐巴馬總統將 Sejdic 博士列為總統早期科學家與工程師獎的獲得者。2017年，Sejdic 博士獲得國家科學基金會的 CAREER 獎。2018年，他獲得匹茲堡大學的校長傑出研究獎。
