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- W4285802183 abstract "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." @default.
- W4285802183 created "2022-07-19" @default.
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- W4285802183 date "2022-07-18" @default.
- W4285802183 modified "2023-10-16" @default.
- W4285802183 title "Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning" @default.
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- W4285802183 doi "https://doi.org/10.3390/s22145362" @default.
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