Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, Cilt: 2 Sayı: 2, 87 - 93, 26.12.2022

Öz

Kaynakça

  • R. Gupta and N. Gupta, ―Two compact microstrip patch antennas for 2.4 GHz band – A comparison‖, Microwave Review, vol. 12, no.2, pp. 29-31, Nov, 2006.
  • Akdağ, İ., Göçen, C., Palandöken, M., & Kaya, A. (2020, October). Estimation of the Scattering Parameter at the Resonance Frequency of the UHF Band of the E-Shaped RFID Antenna Using Machine Learning Techniques. In 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-5). IEEE.
  • Kaplan, Y. & Göçen, C. (2022). A Dual-Band Antenna Design for 2.4 and 5 GHz Wi-Fi Applications. Avrupa Bilim ve Teknoloji Dergisi , Ejosat Special Issue 2022 (ICAENS-1) , 685-688 . DOI: 10.31590/ejosat.1084161.
  • Gocen, C., Dulluc, S., & Akdag, I. (2022). 5.8 GHz BAND Wi-Fi AND IoT APPLICATIONS ANTENNA DESIGN. ICONTECH INTERNATIONAL JOURNAL, 6(1), 42-47.
  • BALANIS C.A., Antenna Theory Analysis and Design, John Wiley and Sons , Arizona State University ,pages: 4-6, 1982
  • Maeurer, C., Futter, P., & Gampala, G. (2020, March). Antenna Design Exploration and Optimization using Machine Learning. In 2020 14th European Conference on Antennas and Propagation (EuCAP) (pp. 1-5). IEEE.
  • Kim, Y. (2018, October). Application of machine learning to antenna design and radar signal processing: A review. In 2018 International Symposium on Antennas and Propagation (ISAP) (pp. 1-2). IEEE.
  • Wu, Q., Cao, Y., Wang, H., & Hong, W. (2020). Machine-learning-assisted optimization and its application to antenna designs: Opportunities and challenges. China Communications, 17(4), 152-164.
  • C. Gocen and M. Palandoken, "Machine Learning Assisted Novel Microwave Sensor Design for Dielectric Parameter Characterization of Water–Ethanol Mixture," in IEEE Sensors Journal, vol. 22, no. 3, pp. 2119-2127, 1 Feb.1, 2022, doi: 10.1109/JSEN.2021.3136092.
  • Belen, A., Günes, F., Palandoken, M. et al. 3D EM data driven surrogate based design optimization of traveling wave antennas for beam scanning in X-band: an application example. Wireless Netw 28, 1827–1834 (2022). https://doi.org/10.1007/s11276-022-02937-7
  • M. Ambrosanio, S. Franceschini, F. Baselice and V. Pascazio, "Machine Learning For Microwave Imaging," 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020, pp. 1-4, doi: 10.23919/EuCAP48036.2020.9136081.
  • Kütük, H., Teşneli, A. Y., & Teşneli, N. B. (2000). 3.3 GHz mikroşerit anten tasarımı ve farklı besleme yöntemleri için analizi. Sakarya University Journal of Science, 17(1), 119-124.
  • El Misilmani, H. M., & Naous, T. (2019, July). Machine learning in antenna design: An overview on machine learning concept and algorithms. In 2019 International Conference on High Performance Computing & Simulation (HPCS) (pp. 600-607). IEEE.

Diagonal L-Shaped Slotted Antenna Design for 2.4 GHz Wireless Applications with Machine Learning Based Reflection Coefficient Calculator GUI

Yıl 2022, Cilt: 2 Sayı: 2, 87 - 93, 26.12.2022

Öz

In this study, a machine learning assisted microstrip antenna design for a 2.4 GHz Wi-Fi frequency band has been designed and numerically calculated. The proposed antenna design has been carried out using an electromagnetic field solver CST, different design parameters have been determined and because of parametric calculation, data suitable for machine learning algorithms have been obtained. According to the different values of 4 design parameters, 625 different antenna reflection coefficients at the 2.4 GHz frequency band were obtained in linear and decibel forms for the machine learning-based design. 4 different machine learning regression algorithms (linear regression, support vector regression, decision tree, and random forest) have been used to estimate the reflection coefficient at 2.4 GHz. The machine learning results have been examined, it has been achieved that the best prediction performance model had R2 value of 0.8 and a mean squared error value of 0.2 for the S11 in dB form, and R2 value of 0.98 and a mean squared error value of 0.02 for the linear S11. In addition, a PyQt based graphical user interface is presented, which can instantly estimate the reflection coefficient with different machine learning techniques depending on the design parameters of the proposed antenna.

Kaynakça

  • R. Gupta and N. Gupta, ―Two compact microstrip patch antennas for 2.4 GHz band – A comparison‖, Microwave Review, vol. 12, no.2, pp. 29-31, Nov, 2006.
  • Akdağ, İ., Göçen, C., Palandöken, M., & Kaya, A. (2020, October). Estimation of the Scattering Parameter at the Resonance Frequency of the UHF Band of the E-Shaped RFID Antenna Using Machine Learning Techniques. In 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-5). IEEE.
  • Kaplan, Y. & Göçen, C. (2022). A Dual-Band Antenna Design for 2.4 and 5 GHz Wi-Fi Applications. Avrupa Bilim ve Teknoloji Dergisi , Ejosat Special Issue 2022 (ICAENS-1) , 685-688 . DOI: 10.31590/ejosat.1084161.
  • Gocen, C., Dulluc, S., & Akdag, I. (2022). 5.8 GHz BAND Wi-Fi AND IoT APPLICATIONS ANTENNA DESIGN. ICONTECH INTERNATIONAL JOURNAL, 6(1), 42-47.
  • BALANIS C.A., Antenna Theory Analysis and Design, John Wiley and Sons , Arizona State University ,pages: 4-6, 1982
  • Maeurer, C., Futter, P., & Gampala, G. (2020, March). Antenna Design Exploration and Optimization using Machine Learning. In 2020 14th European Conference on Antennas and Propagation (EuCAP) (pp. 1-5). IEEE.
  • Kim, Y. (2018, October). Application of machine learning to antenna design and radar signal processing: A review. In 2018 International Symposium on Antennas and Propagation (ISAP) (pp. 1-2). IEEE.
  • Wu, Q., Cao, Y., Wang, H., & Hong, W. (2020). Machine-learning-assisted optimization and its application to antenna designs: Opportunities and challenges. China Communications, 17(4), 152-164.
  • C. Gocen and M. Palandoken, "Machine Learning Assisted Novel Microwave Sensor Design for Dielectric Parameter Characterization of Water–Ethanol Mixture," in IEEE Sensors Journal, vol. 22, no. 3, pp. 2119-2127, 1 Feb.1, 2022, doi: 10.1109/JSEN.2021.3136092.
  • Belen, A., Günes, F., Palandoken, M. et al. 3D EM data driven surrogate based design optimization of traveling wave antennas for beam scanning in X-band: an application example. Wireless Netw 28, 1827–1834 (2022). https://doi.org/10.1007/s11276-022-02937-7
  • M. Ambrosanio, S. Franceschini, F. Baselice and V. Pascazio, "Machine Learning For Microwave Imaging," 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020, pp. 1-4, doi: 10.23919/EuCAP48036.2020.9136081.
  • Kütük, H., Teşneli, A. Y., & Teşneli, N. B. (2000). 3.3 GHz mikroşerit anten tasarımı ve farklı besleme yöntemleri için analizi. Sakarya University Journal of Science, 17(1), 119-124.
  • El Misilmani, H. M., & Naous, T. (2019, July). Machine learning in antenna design: An overview on machine learning concept and algorithms. In 2019 International Conference on High Performance Computing & Simulation (HPCS) (pp. 600-607). IEEE.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Research Articles
Yazarlar

Yaşar Kaplan 0000-0002-9566-3282

Cem Göçen 0000-0002-8086-5690

Yayımlanma Tarihi 26 Aralık 2022
Gönderilme Tarihi 4 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 2 Sayı: 2

Kaynak Göster

IEEE Y. Kaplan ve C. Göçen, “Diagonal L-Shaped Slotted Antenna Design for 2.4 GHz Wireless Applications with Machine Learning Based Reflection Coefficient Calculator GUI”, Journal of Artificial Intelligence and Data Science, c. 2, sy. 2, ss. 87–93, 2022.

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