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Machine Learning Applications in Electromagnetics and Antenna Array Processing

Machine Learning Applications in Electromagnetics and Antenna Array Processing

Copyright: 2020
Pages: 436
ISBN: 9781630817756
Coming Soon: Available 10/31/2020
List Price: $179.00

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Hardback $134.00 Qty:
This practical resource provides an overview of machine learning (ML) approaches as applied to electromagnetics and antenna array processing. Detailed coverage of the main trends in ML, including uniform and random array processing (beamforming and detection of angle of arrival), antenna optimization, wave propagation, remote sensing, radar, and other aspects of electromagnetic design are explored. An introduction to machine learning principles and the most common machine learning architectures and algorithms used today in electromagnetics and other applications is presented, including basic neural networks, gaussian processes, support vector machines, kernel methods, deep learning, convolutional neural networks, and generative adversarial networks. Applications in electromagnetics and antenna array processing that are solved using machine learning are discussed, including antennas, remote sensing, and target classification.
Part I: Introduction to Machine Learning: Kernel methods for array processing; Support Vector Machines; Gaussian Processes for signal processing; Neural Networks; Convolutional neural networks; Recursive neural networks for signals; Restricted Boltzmann Machines; Generative Adversarial Networks; Part II: Applications in Electromagnetics and Antenna Array Signal Processing: Antenna Array Signal Processing; Radar and Remote Sensing; Computational Electromagnetics; Reconfigurable Antennas and Cognitive Radio; Design and Optimization of Antennas and RF devices; Wave Propagation and Modelling; Electromagnetics for Medicine and Healthcare.
  • Christos Christodoulou Christos G. Christodoulou is a Fellow member of IEEE and a distinguished professor of electrical and computer engineering at the University of New Mexico. He is the recipient of the 2010 IEEE John Krauss Antenna Award for his work on reconfigurable fractal antennas, the Lawton-Ellis Award and the Gardner Zemke Professorship at the University of New Mexico. He holds a M.S. and a Ph.D. in Electrical Engineering from North Carolina State University. Christos is the series editor of antennas at Artech House.
  • Manel Martínez-Ramón

    is a full professor in the Department of Electrical and Computer Engineering at The University of New Mexico. He received his Ph.D in telecommunications from Technol, Universidad Carlos III.

  • Arjun Gupta

    is a research assistant at the RF and Antennas Lab at the University of New Mexico. He received his Ph.D. in Electrical Engineering from the University of New Mexico.

  • José Luis Rojo-Álvarez

    is a professor at the Universidad Rey Juan Carlos. He received his Ph.D from the Universidad Politécnica de Madrid.

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