Microwave Imaging of Subsurface Flaws in Coated Metallic Structures Using Complementary Split-Ring Resonators
Bibliographic record
Abstract
Microwave testing can detect surface and sub-surface flaws and anomalies under coatings, paint, or dirt in metallic structures. A major application of microwave sensors with an increasing interest is microwave imaging of coated metallic surfaces. Microwave imaging has the capabilities to determine the shape, size, and location of buried flaws using scattered field measurements, because microwave signals can penetrate dielectric coatings. Electrically small complementary split-ring resonators etched on printed circuit boards are strong candidates for microwave imaging as they can provide the benefit of one-sided small footprint sensors while resonating at wavelengths much lower than their dimension to provide high lateral resolution. This paper introduces and investigates the use of complementary split-ring resonators in the microwave regime for imaging subsurface flaws in coated metallic structures. Both transmission and reflection coefficients were studied to build images using magnitude and phase information. Aluminum plates with different corrosion-coated regions were studied numerically and experimentally. The results of a raster scan mechanism demonstrate the viability of the proposed imaging 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".