Application of Machine Learning in Electromagnetics: Mini-Review

As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automate...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Md. Samiul Islam Sagar, Hassna Ouassal, Asif I. Omi, Anna Wisniewska, Harikrishnan M. Jalajamony, Renny E. Fernandez, Praveen K. Sekhar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electromagnetics antenna machine-learning DoA object detection 5G technology Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/0cde53e3eb244cffb0f266e4b4e563a3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0cde53e3eb244cffb0f266e4b4e563a3
record_format	dspace
spelling	oai:doaj.org-article:0cde53e3eb244cffb0f266e4b4e563a32021-11-25T17:24:22ZApplication of Machine Learning in Electromagnetics: Mini-Review10.3390/electronics102227522079-9292https://doaj.org/article/0cde53e3eb244cffb0f266e4b4e563a32021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2752https://doaj.org/toc/2079-9292As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.Md. Samiul Islam SagarHassna OuassalAsif I. OmiAnna WisniewskaHarikrishnan M. JalajamonyRenny E. FernandezPraveen K. SekharMDPI AGarticleelectromagneticsantennamachine-learningDoAobject detection5G technologyElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2752, p 2752 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electromagnetics antenna machine-learning DoA object detection 5G technology Electronics TK7800-8360
spellingShingle	electromagnetics antenna machine-learning DoA object detection 5G technology Electronics TK7800-8360 Md. Samiul Islam Sagar Hassna Ouassal Asif I. Omi Anna Wisniewska Harikrishnan M. Jalajamony Renny E. Fernandez Praveen K. Sekhar Application of Machine Learning in Electromagnetics: Mini-Review
description	As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.
format	article
author	Md. Samiul Islam Sagar Hassna Ouassal Asif I. Omi Anna Wisniewska Harikrishnan M. Jalajamony Renny E. Fernandez Praveen K. Sekhar
author_facet	Md. Samiul Islam Sagar Hassna Ouassal Asif I. Omi Anna Wisniewska Harikrishnan M. Jalajamony Renny E. Fernandez Praveen K. Sekhar
author_sort	Md. Samiul Islam Sagar
title	Application of Machine Learning in Electromagnetics: Mini-Review
title_short	Application of Machine Learning in Electromagnetics: Mini-Review
title_full	Application of Machine Learning in Electromagnetics: Mini-Review
title_fullStr	Application of Machine Learning in Electromagnetics: Mini-Review
title_full_unstemmed	Application of Machine Learning in Electromagnetics: Mini-Review
title_sort	application of machine learning in electromagnetics: mini-review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0cde53e3eb244cffb0f266e4b4e563a3
work_keys_str_mv	AT mdsamiulislamsagar applicationofmachinelearninginelectromagneticsminireview AT hassnaouassal applicationofmachinelearninginelectromagneticsminireview AT asifiomi applicationofmachinelearninginelectromagneticsminireview AT annawisniewska applicationofmachinelearninginelectromagneticsminireview AT harikrishnanmjalajamony applicationofmachinelearninginelectromagneticsminireview AT rennyefernandez applicationofmachinelearninginelectromagneticsminireview AT praveenksekhar applicationofmachinelearninginelectromagneticsminireview
_version_	1718412390460030976

Application of Machine Learning in Electromagnetics: Mini-Review

Ejemplares similares