Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning

被引:12
作者
Kazemi, Nazli [1 ]
Gholizadeh, Nastaran [1 ]
Musilek, Petr [1 ,2 ]
机构
[1] Univ Alberta, Elect & Comp Engn, Edmonton, AB T6G 1H9, Canada
[2] Univ Hradec Kralove, Appl Cybernet, Hradec Kralove 50003, Czech Republic
基金
加拿大自然科学与工程研究理事会;
关键词
microwave sensor; selectivity; resonators; machine learning; generative adversarial network; DIELECTRIC-SPECTROSCOPY; RELAXATION; MIXTURES;
D O I
10.3390/s22145362
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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 lambda(g-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.
引用
收藏
页数:20
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