Estimation of the Scattering Parameter at the Resonance Frequency of the UHF Band of the E-Shaped RFID Antenna Using Machine Learning Techniques

Akdag, Ismail; Göçen, Cem; Palandöken, Merih; Kaya, Adnan

doi:10.1109/ismsit50672.2020.9254362

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…In a prior report [88], GPR was used for computing the resonant frequency of a square microstrip patch antenna. ML algorithms perform better in both linear and complex problems [89], where different scattering parameters are estimated in the UHF band of an antenna operating at a resonance frequency. An earlier study [90] provided a predictive model using ANN, which can predict the resonant frequency of a flexible microstrip patch antenna considering an inserted airgap.…”

Section: Antenna Position Direction and Radiation Estimationmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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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.

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Section: Antenna Position Direction and Radiation Estimationmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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“…In the study [5], a large sized circular polarized antenna is proposed for applications that require high gain and simple construction. In reference [6], [7], the performance of RFID antennas is increased by using machine learning-based models and prediction successes are presented. UHF RFID frequency varies by country.…”

Section: Introductionmentioning

confidence: 99%

Circular Polarized Antenna with Rotationally Positioned Microstrip Structures For UHF RFID Applications

Saka,

Palandöken

2023

IJANSER

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With the use of wireless communication and the industry 4.0 revolution, RFID technology integration in production has increased. The large-scale preference of RFID systems is both cost-effective and significantly increasing production. It is an innovation that provides convenience for large production facilities such as product control, in-plant tracking, storage, inventory operations. In this paper, a compact circularly polarized (CP) reader antenna is presented for ultra-high frequency (UHF) radio frequency identification (RFID) applications. The proposed antenna consists of a circularly slotted ground plane and folded plus-shaped structures with vertical and horizontal positions. Microstrip line feeding technique is used in the vertically folded plus-shaped structure positioned on the front surface. The antenna has a 10 dB return loss bandwidth of 80 MHz (840-920) and a 3 dB axial ratio bandwidth of 20 MHz (855-875). Theproposed antenna has dimensions of 75 × 75 × 1.6 mm3 . The size advantage of the single-layer and singlefed structure enables its applicability in portable systems.

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