Journal Article

Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning

Gholizadeh, Nastaran, Musilek, Petr, Kazemi, Nazli

2022

Overview

Microwave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.3, and 5 GHz, with the size of only λg−min/8 per resonator. This resonator is deployed to characterize liquid mixtures with one desired MUT (here water) combined with an interfering material (e.g., methanol, ethanol, or acetone) with various concentrations (0%:10%:100%). To achieve a sensor with selectivity to water, a convolutional neural network (CNN) is used to recognize different concentrations of water regardless of the host medium. To obtain a high accuracy of this classification, Style-GAN is utilized to generate a reliable sensor response for concentrations between water and the host medium (methanol, ethanol, and acetone). A high accuracy of 90.7% is achieved using CNN for selectively discriminating water concentrations.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Deep learning / Dielectric properties / generative adversarial network / Machine learning / microwave sensor / Propagation / resonators / selectivity / Sensors