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Analysis and synthesis of L- and T-shaped flexible compact microstrip antennas using regression-based machine learning approaches

  • Mustafa Berkan Bicer ORCID logo EMAIL logo
Published/Copyright: November 16, 2022
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Frequenz
From the journal Frequenz Volume 77 Issue 5-6

Abstract

The purpose of this study was to analyze and synthesize L-shaped compact microstrip antennas (LCMA) and T-shaped compact microstrip antennas using regression-based machine learning algorithms (TCMA). This was accomplished by simulating 3808 LCMAs and 900 TCMAs operating at UHF and SHF frequencies with different physical and electrical characteristics. The acquired data was utilized to create a data set containing the antennas’ physical and electrical characteristics, as well as their resonant frequencies in the TM010 mode. Four baseline regression models and seven machine learning models were developed to determine the resonance frequency of antennas and the values of the physical parameters required for a particular frequency. To examine the efficacy of machine learning models, three-dimensional LCMAs and TCMAs were created using polylactic acid (PLA) and felt-based flexible substrates, as well as copper tape. The results illustrate the feasibility of using machine learning models for LCMA and TCMA analysis and synthesis.

Keywords: flexible antennas; LCMA; machine learning; microstrip antennas; regression analysis; TCMA
Corresponding author: Mustafa Berkan Bicer, Department of Electrical and Electronics Engineering, Faculty of Engineering, Tarsus University, Tarsus, Mersin, Turkey, E-mail:

Funding source: The Scientific and Technological Research Council of Turkey (TUBITAK)

Award Identifier / Grant number: 122E093

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This study is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) with project number of 122E093.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

  4. Data availability: The data used to support the findings of this study are available from the corresponding author upon request.

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Received: 2022-04-04
Accepted: 2022-11-04
Published Online: 2022-11-16
Published in Print: 2023-06-27

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/freq-2022-0070
Keywords for this article
flexible antennas; LCMA; machine learning; microstrip antennas; regression analysis; TCMA

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Coaxial resonant cavity for measuring complex permittivity of liquids
  4. Analysis of quad-band polarization- and incident-angle independent low profile metamaterial absorber
  5. Radiation pressure of a hybrid bianisotropic chiral structure
  6. Improving the automatic target recognition algorithm’s accuracy through an examination of the different time-frequency representations and data augmentation
  7. On the effect of array size on the radar cross section reduction bandwidth of checkerboard metasurfaces
  8. Analysis and synthesis of L- and T-shaped flexible compact microstrip antennas using regression-based machine learning approaches
  9. Novel implantable antenna with miniaturized footprint size for wideband biomedical telemetry applications
  10. Circular shape monopole antenna with modified ground plane proclaiming SWB characteristics
  11. Miniaturized wideband implantable slotted loop antenna for biotelemetry applications
  12. A 156 GHz high-power doubler-embedded cross-coupled local oscillator in 55 nm CMOS technology
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