Enhancing Microwave Sensor Performance With Ultrahigh Q Features Using CycleGAN
Bibliographic record
Abstract
In this work, a microwave planar sensor is used for liquid material characterization. Two identical complementary split ring resonators (CSRRs) operating at 3 GHz are coupled to create a highly sensitive capacitive region. The moderate quality factor of the sensor$\approx 230$is significantly improved up to$\approx 5040$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 the 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 ($\approx 20$mg/dL), showing a considerable enhancement when compared to the resolution of the passive sensor ($\approx 70$mg/dL).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".