Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter June 29, 2020

Ultra-Wideband (UWB) characteristic estimation of elliptic patch antenna based on machine learning techniques

  • Duygu Nazan Gençoğlan , Mustafa Turan Arslan , Şule Çolak EMAIL logo and Esen Yildirim
From the journal Frequenz

Abstract

In this study, estimation of Ultra-Wideband (UWB) characteristics of microstrip elliptic patch antenna is investigated by means of k-nearest neighborhood algorithm. A total of 16,940 antennas are simulated by changing antenna dimensions and substrate material. Antennas are examined by observing Return Loss and Voltage Standing Wave Ratio (VSWR) characteristics. In the study, classification of antennas in terms of having UWB characteristics results in accuracies higher than 97%. Additionally, Consistency based Feature Selection method is applied to eliminate redundant and irrelevant features. This method yields that substrate material does not affect the UWB characteristics of the antenna. Classification process is repeated for the reduced feature set, reaching to 97.44% accuracy rate. This result is validated by 854 antennas, which are not included in the original antenna set. Antennas are designed for seven different substrate materials keeping all other parameters constant. Computer Simulation Technology Microwave Studio (CST MWS) is used for the design and simulation of the antennas.


Corresponding author: Şule Çolak, Department of Electrical-Electronics Engineering, Adana Alparslan Turkes Science and Technology University, Adana, 01250, Turkey, E-mail:

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

  2. Research funding: None declared.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

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

References

[1] M. Z. Win, and R. A. Scholtz, “Impulse radio: how it works,” IEEE Communi. Lett., vol. 2, no. 2, pp. 36–38, 1998, http://dx.doi.org/10.1109/4234.660796.10.1109/4234.660796Search in Google Scholar

[2] R. S. Kshetrimayum, “An introduction to uwb communication systems,” IEEE Poten., vol. 28, no. 2, pp. 9–13, 2009, http://dx.doi.org/10.1109/MPOT.2009.931847.10.1109/MPOT.2009.931847Search in Google Scholar

[3] R. Aiello, and A. Batra, Ultra-Wideband Systems: Technologies and Applications, Newton, MA, USA, Newnes, 2006.Search in Google Scholar

[4] Federal Communications Commission, Revision of Part 15 of the Commission's Rules Regarding Ultra-Wideband Transmission Systems, First Report and Order, ET Docket 98-153, FCC 02-48, vol. 15, First Report and Order: Revision of Part, 2002. http://www.fcc.gov.Search in Google Scholar

[5] I. Zivkovic, and K. Scheffler, “A new inovative antenna concept for both narrow band and uwb applications,” Progr. Electromagn. Res., vol. 139, pp. 121–131, 2013, https://dx.doi.org/10.2528/PIER13031510.10.2528/PIER13031510Search in Google Scholar

[6] H. A. Majid, M. K. A. Rahim, M. R. Hamid, and M. F. Ismail, “A compact frequency-reconfigurable narrowband microstrip slot antenna,” IEEE Antennas Wireless Propag. Lett., vol. 11, pp. 616–619, 2012, http://dx.doi.org/10.1109/LAWP.2012.2202869.10.1109/LAWP.2012.2202869Search in Google Scholar

[7] D. Colles, and D. Arakaki, “Multi-technique broadband microstrip patch antenna design,” in Antennas and Propagation Society International Symposium (APSURSI), IEEE, 2014, pp. 1879–1880.10.1109/APS.2014.6905266Search in Google Scholar

[8] K. P. Yang, and K. L. Wong, “Dual-band circularly-polarized square microstrip antenna,” IEEE Trans. Antennas Propaga., vol. 49, no. 3, pp. 377–382, 2001, http://dx.doi.org/10.1109/8.918611.10.1109/8.918611Search in Google Scholar

[9] K. L. Wong, and W. H. Hsu, “A broad-band rectangular patch antenna with a pair of wide slits,” IEEE Trans. Antennas Propaga., vol. 49, no. 9, pp. 1345–1347, 2001, http://dx.doi.org/10.1109/8.951507.10.1109/8.951507Search in Google Scholar

[10] A. Khan, and R. Nema, “Analysis of five different dielectric substrates on microstrip patch antenna,” Intern. J. Comp. Applic., vol. 55, no. 14, pp. 40–47, 2012, http://dx.doi.org/10.5120/8826-2905.10.5120/8826-2905Search in Google Scholar

[11] P. J. Conroy, U.S. Patent No. 4, 160,976. Washington, DC, U.S. Patent and Trademark Office, 1979.Search in Google Scholar

[12] L.C. Paul, M. S. Hosain, S. Sarker, M. H. Prio, M. Morshed, and A. K. Sarkar, “The effect of changing substrate material and thickness on the performance of inset feed microstrip patch antenna,” American J. Net. Commun., vol. 4, no. 3, pp. 54–58, 2015, https://doi.org/10.11648/j.ajnc.20150403.16.Search in Google Scholar

[13] S. S. Shukla, R. K. Verma, and G. S. Gohir, “Investigation of the effect of substrate material on the performance of microstrip antenna,”in 4th International Conf. on Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), IEEE, 2015, pp. 1–3.10.1109/ICRITO.2015.7359350Search in Google Scholar

[14] A. Kayabasi, A. Toktas, and A. Akdagli, “Design of an ann model trained by various learning algorithms to compute the operating frequency of e-shaped patch antennas,” Uludağ Univ. J. Fac. Eng., vol. 21, no. 2, pp. 465–472, 2016, https://doi.org/10.17341\gummfd.02944.Search in Google Scholar

[15] A. Kayabasi, “MLP and kNN algorithm model applications for determining the operating frequency of a-shaped patch antennas,” Intern. J. Intellig. Sys. App. Eng., vol. 5, no. 3, pp. 154–157, 2017, https://doi.org/10.18201/ijisae.2017531432.Search in Google Scholar

[16] E. Yigit, “Operating frequency estimation of slot antenna by using adapted kNN algorithm,” Intern. J. Intelli. Sy. App. Eng., vol. 6, no. 1, pp. 29–32, 2018, https://doi.org/10.18201/ijisae.2018637927.Search in Google Scholar

[17] J. Tak, A. Kantemur, Y. Sharma, and H. Xin, “A 3-d-printedw-band slotted waveguide array antenna optimized using machine learning,” IEEE Antennas Wireless Propag. Lett., vol. 17, no. 11, pp. 2008–2012, 2018, https://doi.org/10.1109/LAWP.2018.2857807.Search in Google Scholar

[18] A. I. Hammoodi, F. Al-Azzo, M. Milanova, and H. Khaleel, “Bayesian regularization based ANN for the design of flexible antenna for uwb wireless applications,” in IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), IEEE, 2018, pp. 174–177.10.1109/MIPR.2018.00039Search in Google Scholar

[19] T. Hong, C. Liu, and M. Kadoch, “Machine learning based antenna design for physical layer security in ambient backscatter communication,” Wireless Commun. Mobile Comp., vol. 2019, pp. 1–10, 2019, https://doi.org/10.1155/2019/4870656.Search in Google Scholar

[20] J. F. Lee, Lee, V. Rawat, K. Sertel, and F. L. Teixeira, Frontiers in Antennas: Next Generation Design & Engineering, New York, USA, McGraw-Hill, 2011, Chap. 11.Search in Google Scholar

[21] C. A. Balanis, Antenna Theory: Analysis and Design, New York, USA, John Wiley & Sons, 2016.Search in Google Scholar

[22] J. Ko, S. N. Baldassano, P. -L. Loh, K. Kording, B. Litt, and D. Issadore, “Machine learning to detect signatures of disease in liquid biopsies–a user’s guide,” Lab. Chip, vol. 18, no. 3, pp. 395–405, 2018, https://doi.org/10.1039/C7LC00955K.Search in Google Scholar

[23] E. Monte-Moreno, “Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques,” Artif. Intellig. Med., vol. 53, no. 2, pp. 127–138, 2011, https://doi.org/10.1016/j.artmed.2011.05.001.Search in Google Scholar PubMed

[24] C. Simon, K. Davidsen, C. Neigaard Hansen, E. Seymour, M. B. Barnkob, and L. Rønn, “BioReader: a text mining tool for performing classification of biomedical literature,”BMC Bioinform., vol. 19, no. 13, pp. 57, 2019, https://doi.org/10.1186/s12859-019-2607-x.Search in Google Scholar PubMed PubMed Central

[25] Y. Song, J. W. Lee, and J. Lee, “A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction,” Appl. Intelli., vol. 49, no. 3, pp. 897–911, 2019, https://doi.org/10.1007/s10489-018-1308-x.Search in Google Scholar

[26] B. Pan, K. Hsu, A. AghaKouchak, and S. Sorooshian, “Improving precipitation estimation using convolutional neural network,” Water Resour. Res., vol. 55, no. 3, pp. 2301–2321, 2019, https://doi.org/10.1029/2018WR024090.Search in Google Scholar

[27] A. M. Jiménez-Carvelo, A. González-Casado, M. GraciaBagur-González, and L. Cuadros-RodríguezAlternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity–a review,” Food Res. Intern., 2019, https://doi.org/10.1016/j.foodres.2019.03.063.Search in Google Scholar PubMed

[28] Q. Wang, D. Zhao, Y. Wang, and X. Hou, “Ensemble learning algorithm based on multi-parameters for sleep staging,” Med. Bio. Eng. Comp., vol. 57, no. 8, pp. 1693–1707, 2019, https://doi.org/10.1007/s11517-019-01978-z.Search in Google Scholar PubMed

[29] M. S. Al-Batah, B. M. Zaqaibeh, S. A. Alomari, and M. S. Alzboon, “Gene microarray cancer classification using correlation based feature selection algorithm and rules classifiers,” Intern. J. Online Eng., vol. 15, no. 8, pp. 62–73, 2019, https://doi.org/10.3991/ijoe.v15i08.10617.Search in Google Scholar

[30] S. Begum, D. Chakraborty, and R. Sarkar, “Data classification using feature selection and kNN machine learning approach,” in International Conf. on Computational Intelligence and Communication Networks (CICN), IEEE, 2015, pp. 811–814.10.1109/CICN.2015.165Search in Google Scholar

[31] H. Liu, and R. Setiono, “A probabilistic approach to feature selection: a filter solution,” Proceed. Thirteenth Intern. Conf. Machine Learn., vol. 96, pp. 319–327, 1996.Search in Google Scholar

[32] S. Zhang, and Z. Zhao, “Feature selection filtering methods for emotion recognition in Chinese speech signal,” in 9th International Conf. on Signal Processing, IEEE, 2008, pp. 1699–1702.Search in Google Scholar

[33] M. A Hall, and G. Holmes, “Benchmarking attribute selection techniques for discrete class data mining,” IEEE Trans. Knowled. Data Eng., vol. 15, no. 6, pp. 1437–1447, 2003, https://doi.org/10.1109/TKDE.2003.1245283.Search in Google Scholar

[34] M. T. Arslan, S. G. Eraldemir, and E. Yildirim, “Subject-dependent and subject-independent classification of mental arithmetic and silent reading tasks,” Intern. J. Eng. Research Develop., vol. 9, no. 3, pp. 186–195, 2017, https://doi.org/10.29137/umagd.348871.Search in Google Scholar

[35] D. Arslan, M. E. Ozdemir, and M. T. Arslan, “Diagnosis of pancreatic cancer by pattern recognition methods using gene expression profiles,” in International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, 2017, pp. 1–4.10.1109/IDAP.2017.8090327Search in Google Scholar

[36] B. Hu, X. Li, S. Sun, and M. Ratcliff, “Attention recognition in EEG-based affective learning research using CFS+ KNN algorithm,” IEEE ACM Trans. Comp. Bio. Bioinform., vol. 15, no. 1, pp. 38–45, 2016, https://doi.org/10.1109/TCBB.2016.2616395.Search in Google Scholar PubMed

[37] H. Polat, M. Akin, and M. S. Ozerdem, “The comparison of wavelet and empirical mode decomposition method in prediction of sleep stages from EEG signals,” in International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, 2017, pp. 1–5.10.1109/IDAP.2017.8090253Search in Google Scholar

[38] K. Sabanci, and M. Koklu, “The classification of eye state by using kNN and MLP classification models according to the EEG signals,” Intern. J. Intellig. Sys. App. Eng., vol. 3, no. 4, pp. 127–130, 2015, https://doi.org/10.18201/ijisae.75836.Search in Google Scholar

[39] C. Rahmad, R. Ariyanto, and D. R. Yunianto, “Brain signal classification using genetic algorithm for right-left motion pattern,” Brain, vol. 9, no. 11, pp. 247–251, 2018. https:/doi.org/10.14569/IJACSA.2018.091134.10.14569/IJACSA.2018.091134Search in Google Scholar

[40] P. M. Shanir, K. A. Khan, Y. U. Khan, O. Farroq, and H. Adeli, “Automatic seizure detection based on morphological features using one-dimensional local binary pattern on long-term EEG,” Clini. EEG. Neurosci., vol. 49, no. 5, pp. 351–362, 2018, https://doi.org/10.1177%2F1550059417744890.10.1177/1550059417744890Search in Google Scholar PubMed

Received: 2019-12-11
Accepted: 2020-05-22
Published Online: 2020-06-29
Published in Print: 2020-09-25

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 10.5.2024 from https://www.degruyter.com/document/doi/10.1515/freq-2019-0210/html
Scroll to top button