volume | PIER Journals

Abstract

In this paper, an electronic textile (E-textile) antenna design using machine learning (ML) algorithms such as polynomial regression, k-nearest neighbor (kNN), random forest regression, and deep neural network (DNN) is proposed for achieving the optimized solution. These ML techniques, including DNN, have been implemented on a python framework and support in selecting efficient optimum design parameters for a co-planar waveguide fed textile antenna to attain the maximum impedance bandwidth performance in 3-24 GHz band, respectively. Moreover, the accuracy of the predicted response values obtained by these ML methods has also been validated by verifying with the CST simulation software tool.

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Dr. Arpan H. Shah

Dr. Kalyanbrata Ghosh

Professor Piyush N. Patel