Fr. 200.00

Signal Processing and Networking for Big Data Applications

Inglese · Copertina rigida

Spedizione di solito entro 1 a 3 settimane (non disponibile a breve termine)

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Informationen zum Autor Zhu Han is a Professor in the Department of Electrical and Computer Engineering, University of Houston, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He has co-authored several books, including Wireless Device-to-Device Communications and Networks (with Lingyang Song, Dusit Niyato and Ekram Hossain, Cambridge, 2015) and Game Theory in Wireless and Communication Networks (with Dusit Niyato, Walid Saad, Tamer Baser and Are Hjørungnes, Cambridge, 2011). Mingyi Hong is an Assistant Professor and a Black and Veatch Faculty Fellow in the Department of Industrial and Manufacturing Systems Engineering, Iowa State University. Dan Wang is an Associate Professor in the Department of Computing, Hong Kong Polytechnic University, and a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). Klappentext This unique text helps make sense of big data using signal processing techniques, in applications including machine learning, networking, and energy systems. Zusammenfassung This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. Covering fundamental signal processing theories! software implementations! and techniques for analysis! design! and optimization! it is ideal for researchers! practitioners! and students in signal processing and communications networks. Inhaltsverzeichnis Part I. Overview of Big Data Applications: 1. Introduction; 2. Data parallelism: the supporting architecture; Part II. Methodology and Mathematical Background: 3. First order methods; 4. Sparse optimization; 5. Sublinear algorithms; 6. Tensor for big data; 7. Deep learning and applications; Part III. Big Data Applications: 8. Compressive sensing based big data analysis; 9. Distributed large-scale optimization; 10. Optimization of finite sums; 11. Big data optimization for communication networks; 12. Big data optimization for smart grid systems; 13. Processing large data set in MapReduce; 14. Massive data collection using wireless sensor networks....

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