2022
DOI: 10.3390/s22145362
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Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning

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) w… Show more

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Cited by 11 publications
(3 citation statements)
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References 48 publications
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“…While practical situations involve a complex matrix, a sensor needs to be selective to the desired MUT. Machine learning has been found to be lucrative in enabling selectivity in microwave sensors, as shown in [123], where a convolutional neural network is used to process the sensor response with respect to its pre-trained repository. Another aspect of environmental noise on the microwave sensor response is explained in this section as the temperature effect.…”
Section: Neutralizing Environmental Impacts With Machine Learningmentioning
confidence: 99%
“…While practical situations involve a complex matrix, a sensor needs to be selective to the desired MUT. Machine learning has been found to be lucrative in enabling selectivity in microwave sensors, as shown in [123], where a convolutional neural network is used to process the sensor response with respect to its pre-trained repository. Another aspect of environmental noise on the microwave sensor response is explained in this section as the temperature effect.…”
Section: Neutralizing Environmental Impacts With Machine Learningmentioning
confidence: 99%
“…It is noteworthy to mention that, in this preliminary experimental stage, the probing loop was positioned in contact with the resonator to avoid measurement errors due to the placement. Future studies will analyze how the reading-sensor relative arrangement affects the outcomes and how this uncertainty source might be minimized, such as by the use of convolutional neural network approaches [55], [56].…”
Section: B Experimental Resultsmentioning
confidence: 99%
“…Microwave sensors have been widely used in conjunction with electrochemical or chemo-resistive sensors for acetone detection, enabling the monitoring and control of the compo-sition and concentration of both liquid and gaseous forms [3], [8]- [10]. They possess the unique capability to absorb and desorb a target analyte without relying on additional energy sources [11]- [14].…”
Section: Introductionmentioning
confidence: 99%