volume | PIER Journals

Abstract

In this paper, machine learning supports a compact electromagnetic band gap structure (EBG) based dual band microwave sensor which is proposed for dielectric characterization of liquids with high sensitivity. Two edges, located via metalized holes, are electrically coupled with a suspended microstrip line. Two channels are placed in the electric field region of each EBG patch. Therefore, the change in frequency shift and quality factor are observed, which will help to describe the dielectric characterization of Liquid Under Test (LUT). A matrix-based mathematical model, and machine learning based prediction model are developed for the calibration and validation of the sensor. The results are experimentally verified through fabricated prototype for the binary mixture of water and ethanol. The proposed sensor achieved a compactness with size of 0.164λ_2.47GHz × 0.164λ_2.47GHz, an average sensitivity of 0.931, 0.243, and a quality factor of 170, 230 for band-1 and band-2, respectively. The calculated dielectric constant of different samples shows good agreement with the values reported in the literature. The machine learning based model is developed using the Support Vector Regression algorithm and achieves the high value of coefficient determination (R²) which is 99.01, and the less root mean square error (RMSE) value is 0.009.

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Mr. Tulshidas R. Mane

Dr. Reena Sonkusare

Dr. Pramod Bhavarthe

Dr. Kompella S. L. Parvathi