Experimental Microwave Imaging System Calibration via Cycle-GAN
Bibliographic record
Abstract
Microwave (or electromagnetic) imaging systems seek a quantitative reconstruction of the target permittivity. Such systems require calibration of the raw$S$-parameter data. This calibration is especially difficult in some systems where known targets cannot be introduced (e.g., grain bin imaging). Herein we present a machine-learning-based method of calibrating such systems that does not require measuring a known target inside of the imaging chamber. Using a cycle-generative adversarial network (Cycle-GAN) machine-learning network, we show that we can calibrate data from a 2-D scalar microwave imaging (MWI) system and successfully reconstruct targets. Cycle-GAN makes use of two sets of data: experimental and synthetic. Unlike traditional calibration, there is no need for the experimental data to be labeled, i.e., the calibration targets do not need to be “known.” The results show that with an experimental calibration set of roughly 150 targets, the Cycle-GAN approach provides comparable results to known-target calibration for the class of targets we used in this 2-D near-field MWI system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".