Machine Learning Based Computational Electromagnetic Methods for Intelligence CAD/CAE Application

Dubovitskiy, Maxim A.; Pashaev, Soltanbek Yu.; Жуков, А. О.

doi:10.1109/reepe51337.2021.9388056

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, published literature [159,160] proved the viability of using ML algorithms in the detection of spoofing attacks in multiantenna snapshot receivers by tracking the antenna positions by satellite. ML can also automate the design procedure in different computational EM methods of intelligent CAD and CAE applications [161]. ML is becoming popular in the design of complex antennas for advanced and new applications involving 5G technology and IoT [162].…”

Section: Future Scopementioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Future Scopementioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To find the optimal dimensions of applied stubs and also to solve a variety of engineering problems, several models, artificial intelligence methods [40][41][42][43][44][45][46][47][48][49], and neural networks are used [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66]. Additionally, artificial intelligence methods have been utilized to design microwave devices, such as power dividers and couplers [67,68].…”

Section: Introductionmentioning

confidence: 99%

Design and Fabrication of a Compact Branch-Line Coupler Using Resonators with Wide Harmonics Suppression Band

et al. 2022

View full text Add to dashboard Cite

The branch-line coupler (BLC) is an important device in radio frequency (RF) and microwave (MW) circuits. The main drawbacks of the conventional BLC are as follows: first, the four long quarter-wavelength (λ/4) transmission line sections occupy a large size, especially at the low frequencies, and second, the presence of unwanted harmonics. This research paper presents a compact 750 MHz BLC with harmonics suppression using resonators. The typical BLC consists of four λ/4 branches, two series arms of 35 Ω and two shunt arms of 50 Ω impedances. In the proposed BLC, these long branches are replaced with two types of compact resonators. The proposed resonators have the same responses at the operating frequency of 750 MHz and suppress higher frequencies. The designed BLC is simulated, fabricated and measured. The results show that the proposed BLC has good performance at 750 MHz with a bandwidth of 200 MHz, which provides more than 26% fractional bandwidth (FBW). It has a very compact size, about 84% size reduction, as compared with the typical BLC. Moreover, the fabricated BLC suppresses the 2nd up to 7th unwanted harmonics with a high suppression level.

show abstract