Augmented Reality-Assisted Battery-Less Microwave-Based Sensors for Smart Health Monitoring of Coatings
Bibliographic record
Abstract
Rapid infrastructure expansion and growing emphasis on smart sustainable cities, demand the need for reliable, real-time coating health monitoring (CHM) techniques to ensure mechanical stability and prevent safety hazards. This work presents a microwave-based CHM system consisting of a passive array of split ring resonator (SRR) sensor integrated with augmented reality, for real-time non-destructive coating damage inspection and enhanced visualization capabilities. The developed system operates by monitoring the variations in the resonant response of the SRR array, caused by the loss in coating thickness due to operational and environmental wear. The SRR array is layered with 0.3 mm thick film-like polyethylene-based coating and gradually eroded. The system demonstrates a resonant frequency increase of ~331 MHz upon the erosive wear of the coating. The integration of augmented reality with the developed system provides a real-time and intuitive visualization of the system response and its corresponding damage assessment, providing a significant low-cost solution in areas that are traditionally challenging to monitor. The developed system promises the potential of microwave-based sensing integrated with augmented reality-based visualization, in out-of-sight monitoring for applications including pipelines, aircraft, bridges, and naval infrastructure.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".