Christos Christodoulou – författare

The book introduces the basic foundations of high mathematics and vector algebra. Then, it explains the basic aspects of classical electrodynamics and electromagnetism. Based on such knowledge readers investigate various radio propagation problems related to guiding structures connecting electronic devices with antenna terminals placed at the different radar systems. It explains the role of antennas in process of transmission of radio signals between the terminals. Finally, it shows the relation between the main operational charactistics of each kind of radar and the corresponding knowledge obtained from the previous chapters.

The book introduces the basic foundations of high mathematics and vector algebra. Then, it explains the basic aspects of classical electrodynamics and electromagnetism. Based on such knowledge readers investigate various radio propagation problems related to guiding structures connecting electronic devices with antenna terminals placed at the different radar systems. It explains the role of antennas in process of transmission of radio signals between the terminals. Finally, it shows the relation between the main operational charactistics of each kind of radar and the corresponding knowledge obtained from the previous chapters.

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.

This lecture discusses the use of graph models to represent reconfigurable antennas. These models help designers understand reconfigurable antenna structures and enhance their functionality since they transform antennas from bulky devices into mathematical and software accessible models.