AI-Driven Battery-Free Wireless Sensing of Hazardous Liquid Spills via a Frequency-Selective Surface in a Monostatic Antenna Configuration
Why this work is in the frame
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Bibliographic record
Abstract
The detection of spills is paramount in safeguarding safety and mitigating environmental risks in sensitive environments, including laboratories and industrial facilities. Here, the novel artificial intelligence (AI)-driven, battery-free, and wireless sensing methodology are presented for detecting liquid spills using a monostatic wireless sensing system. The system consists of a frequency-selective surface (FSS) serving as the sensor, in conjunction with a horn antenna that functions as the readout equipment. The sensing structure features a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25 \times 25$</tex-math> </inline-formula> resonator array with a 7-mm periodicity, operating at a resonant frequency of 7.2 GHz. The system analyzes the renormalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{11}$</tex-math> </inline-formula> response to quantify variations caused by the presence of liquid on the FSS, demonstrating a high sensitivity to isopropyl alcohol (IPA) spills. Using machine learning techniques, the framework generates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$512 \times 512$</tex-math> </inline-formula> pixel masks delineating the affected area on the FSS, achieving an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}1$</tex-math> </inline-formula>-score exceeding 0.85 for spill localization. This sensing methodology shows potential for integration with augmented reality (AR) systems, enabling enhanced situational awareness and real-time spill localization. Future work aims to enhance the system’s capability to detect more hazardous materials and accurately classify them.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it