Machine Learning Applications in Electromagnetics and Antenna Array Processing
Manel Martínez-Ramón, Arjun Gupta,
José Luis Rojo-Álvarez, Christos Christodoulou
Machine learning (ML) has seen a meteoric rise in research and applications in the past decade owing to the increased computation prowess of modern computers and a vast amount of information captured about various aspects of work and life. Written by experts in the field, this book provides a comprehensive overview of state-of-the-art ML approaches for a range of disciplines in signal processing, communications and electromagnetics. Readers will find detailed explanations and relevant examples of the underpinnings of ML principles and common ML architectures such as support vector machines, Gaussian processes, nonlinear kernels, deep learning architectures such as multilayered perceptrons, convolutional neural networks, recurrent neural network, long short-term memory and autoencoders.
Once the trends and methods in ML are established, the book dives into some of the major applications of these methods in signal processing and electromagnetics. Detailed applications of the algorithms for solving a variety of problems are explored, including antenna array beamforming, angle-of-arrival detection, computational electromagnetics, antenna optimization and reconfigurable antennas for cognitive radio using ML and computer vision. The most recent research methods and algorithms are presented.
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ISBN: 978-1-63081-775-6
436 pages
$189 £164
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