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Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

Proceedings of MDCWC 2020

Jezik AngleščinaAngleščina
Knjiga Mehka
Knjiga Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication E. S. Gopi
Koda Libristo: 39206189
Založba Springer Verlag, Singapore, maj 2022
This book is a collection of best selected research papers presented at the Conference on Machine Le... Celoten opis
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This book is a collection of best selected research papers presented at the Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2020) held during October 22nd to 24th 2020, at the Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, India. The presented papers are grouped under the following topics (a) Machine Learning, Deep learning and Computational intelligence algorithms (b)Wireless communication systems and (c) Mobile data applications and are included in the book. The topics include  the latest research and results in the areas of network prediction, traffic classification, call detail record mining, mobile health care, mobile pattern recognition, natural language processing, automatic speech processing, mobility analysis, indoor localization, wireless sensor networks (WSN), energy minimization, routing, scheduling, resource allocation, multiple access, power control, malware detection, cyber security, flooding attacks detection, mobile apps sniffing, MIMO detection, signal detection in MIMO-OFDM, modulation recognition, channel estimation, MIMO nonlinear equalization, super-resolution channel and direction-of-arrival estimation. The book is a rich reference material for academia and industry.

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O knjigi

Polni naslov Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication
Avtor E. S. Gopi
Jezik Angleščina
Vezava Knjiga - Mehka
Datum izida 2022
Število strani 643
EAN 9789811602917
Koda Libristo 39206189
Teža 1003
Mere 155 x 235 x 36
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