Enhancing Microwave Sensor Performance with Ultra-High Q Features Using CycleGAN
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In this work, a microwave planar sensor is used for liquid material characterization. Two identical complementary split-ring resonators operating at 3 GHz are coupled to create a highly sensitive capacitive region. The moderate quality factor of the sensor ~230 is significantly improved up to ~5040 with loss compensation using a regenerative amplifier. The moderate quality factor restrains the passive mode sensor from distinguishing low concentrations of 1%-4% water in ethanol, while considerably distinct profiles are achievable using the active-mode sensor. The measured passive mode sensor response is then processed using CycleGAN, a machine learning algorithm conventionally used for image-to-image translation. This strongly enhances the quality factor of the responses, effectively translating them to the active domain. This improvement reduces the limit of water detection down to 1% for water-in-ethanol mixture. In addition, the sensor is used for noninvasive monitoring of glucose levels, in both passive and active modes. The resolution of the CycleGAN-boosted response approaches that of the active sensor (~20 mg/dL), showing a considerable enhancement when compared to the resolution of the passive sensor (~70 mg/dL).
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_b1a7d7d4d402bcce http://purl.org/coar/version/c_71e4c1898caa6e32
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en