AI-Driven Battery-Free Wireless Sensing of Hazardous Liquid Spills via a Frequency-Selective Surface in a Monostatic Antenna Configuration
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$25 \times 25$resonator array with a 7-mm periodicity, operating at a resonant frequency of 7.2 GHz. The system analyzes the renormalized$S_{11}$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$512 \times 512$pixel masks delineating the affected area on the FSS, achieving an${F}1$-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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".