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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > Laser technology

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Machine Learning for Future Fiber-Optic Communication Systems (Paperback) Loot Price: R2,866
Discovery Miles 28 660
Machine Learning for Future Fiber-Optic Communication Systems (Paperback): Alan Pak Tao Lau, Faisal Nadeem Khan

Machine Learning for Future Fiber-Optic Communication Systems (Paperback)

Alan Pak Tao Lau, Faisal Nadeem Khan

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Loot Price R2,866 Discovery Miles 28 660 | Repayment Terms: R269 pm x 12*

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Machine Learning for Future Fiber-Optic Communication Systems provides a comprehensive and in-depth treatment of machine learning concepts and techniques applied to key areas within optical communications and networking, reflecting the state-of-the-art research and industrial practices. The book gives knowledge and insights into the role machine learning-based mechanisms will soon play in the future realization of intelligent optical network infrastructures that can manage and monitor themselves, diagnose and resolve problems, and provide intelligent and efficient services to the end users. With up-to-date coverage and extensive treatment of various important topics related to machine learning for fiber-optic communication systems, this book is an invaluable reference for photonics researchers and engineers. It is also a very suitable text for graduate students interested in ML-based signal processing and networking.

General

Imprint: Academic Press Inc
Country of origin: United Kingdom
Release date: February 2022
First published: 2022
Editors: Alan Pak Tao Lau • Faisal Nadeem Khan
Dimensions: 235 x 191mm (L x W)
Format: Paperback
Pages: 402
ISBN-13: 978-0-323-85227-2
Categories: Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General
Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > Laser technology
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LSN: 0-323-85227-0
Barcode: 9780323852272

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