Автор: Manel Martínez-Ramon, Arjun Gupta
Издательство: Artech House
Формат: pdf (true)
Размер: 33.0 MB
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 (DL), 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.
Machine Learning (ML) is the study of algorithms that utilize learning from previous experience to enhance accuracy over time and improve decision-making. ML has already been applied in a variety of applications, especially in areas of engineering and computer science that involve data analysis and fields that lack closed-form mathematical solutions. These applications rely mainly on machine learning algorithms to implement functions of particular devices that cannot be achieved otherwise. Today, there are several practical implementations of various ML algorithms in robots, drones, or other autonomous vehicles, in data mining, face recognition, stock market prediction, and target classification for security and surveillance, just to name a few. Moreover, machine learning has been used to optimize the design of a variety of engineering products in an autonomous, reliable, and efficient way.
Classic and state-of-the-art machine learning algorithms have been used practically from the inception of this discipline in signal processing, communication, and ultimately in electromagnetics, namely in antenna array processing and microwave circuit design, remote sensing, and radar. The advancements in machine learning of the last two decades, in particular in kernel methods and Deep Learning, together with the progress in the computational power of commercially available computation devices and their associated software, made many ML algorithms and architectures possible to apply in practice in a plethora of applications.
This book is intended to give a comprehensive overview of the state of the art of known ML approaches in a way that the reader can implement them right away by taking advantage of publicly available MATLAB and Python ML libraries and also by understanding the theoretical background behind these algorithms.
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