volume | PIER Journals

Abstract

This paper introduces a deep learning assisted sensor for material classification based on two adjacent split-ring resonators unit cell sensor. This sensor operates in the frequency range from 7 GHz to 8 GHz. The sensor is designed to differentiate between different dielectric materials based on their reflection and transmission properties. A dielectric container is used to hold different samples. Reflection and transmission coefficients for different materials are used for classification between different dielectric materials. These materials are also characterized by using Dielectric Assessment Kite (DAK) for verification with the proposed method. The Dual Split Ring Resonator (DSRR) unit cell enhanced resonance characteristics facilitate the classification process for distinguishing different dielectric materials. The measured results of the proposed sensor exhibit a broad detection range, accurately identifying various samples based on their unique resonant frequency responses. The proposed sensor finds utility in industrial applications, identifying and categorizing various different dielectric materials. In addition, the proposed design is used to measure a mixture of two different materials with different volume mixing ratios. The measured samples are used to train a convolution neural network to predict the mixing ratio from the measured S-parameters. The combination of this sensor and the trained model is found to be an efficient tool that determines the mixing ratios of different samples in a fast way. This concept can also be useful to be applied on other types of sensors and other sensing parameters.

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Dr. Sherine Ismail Abd El‑Rahman

Dr. Hany Mahmoud Zamel

Dr. Shimaa Ahmed Megahed Soliman