Analyzing equilateral triangle compact microstrip antennas using Gaussian process regression for telemedicine and mobile biomedical imaging systems

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dc.authoridBICER, Mustafa Berkan/0000-0003-3278-6071
dc.contributor.authorBicer, Mustafa Berkan
dc.contributor.authorAydin, Emine Avsar
dc.date.accessioned2025-01-06T17:36:28Z
dc.date.available2025-01-06T17:36:28Z
dc.date.issued2023
dc.description.abstractAntennas are vital in the internet of things (IoT) for enabling telemedicine and healthcare communication between devices and networks. They receive and transmit signals, extending range, improving efficiency, and reducing power consumption. Antennas are versatile and can be integrated into devices or added as external modules. Their flexibility and adaptability are important in applications involving humans, as they can bend and conform to the shape of the body. Overall, antennas are a crucial and adaptable component of IoT technology. The first thing that needs to be done is to determine the frequency at which the antenna should operate for the problem at hand and design an antenna that can work at those resonant frequencies. In this study, equilateral triangular-shaped compact microstrip antennas (ETMAs) were chosen, and their resonance frequencies were calculated using the Gaussian process regression method (GPR). For this purpose, 630 ETMA were simulated, and a dataset was created utilizing the antenna characteristics and resonant frequencies. Support vector machines (SVM), artificial neural networks (ANN), and GPR models were trained on the obtained data set. To validate the performance of the trained models, two ETMAs with an outer length of 50 mm and an inner slot length of 5 mm were fabricated utilizing polylactic acid (PLA) and felt-based substrates with copper tape as the conducting material. The accuracy of the resonant frequency estimation using the GPR approach for the fabricated antennas is 2.833% and 1.706% for the PLA- and felt-based antennas, respectively, when compared to the measurement results. The GPR model trained in this study has an accuracy of 0.470% and 0.662% when compared to simulations in the literature and measurement results, respectively. In addition, one of the designed antennas is in wearable form, and the other is PLA, produced with a low-cost 3D printer, allowing continuous monitoring of patients with high cancer risk. In this article, an easier and cheaper microstrip patch antenna that can be used for imaging and telemedicine applications is designed with a copper band on one flexible and one rigid substrate, and its performance is analyzed experimentally.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [122E093]
dc.description.sponsorshipThis study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) with project number of 122E093.
dc.identifier.doi10.1007/s11042-023-16470-y
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.scopus2-s2.0-85168107915
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s11042-023-16470-y
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1894
dc.identifier.wosWOS:001049147100007
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectArtificial neural networks
dc.subjectEquilateral triangle microstrip antennas
dc.subjectFlexible antennas
dc.subjectHealthcare
dc.subjectGaussian process regression
dc.subjectResonant frequency estimation
dc.subjectSupport vector machines
dc.subjectTelemedicine
dc.titleAnalyzing equilateral triangle compact microstrip antennas using Gaussian process regression for telemedicine and mobile biomedical imaging systems
dc.typeArticle

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