AI-Driven Battery-Free Wireless Sensing of Hazardous Liquid Spills via a Frequency-Selective Surface in a Monostatic Antenna Configuration
Casacuberta Orta, Pau (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Niknahad, Fatemeh (University of British Columbia Okanagan. Omega Laboratory)
Maleki Gargari, Ali (University of British Columbia Okanagan. Omega Laboratory)
Martín, Ferran (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Zarifi, Mohammad H. (University of British Columbia Okanagan. Omega Laboratory)

Data:	2025
Resum:	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 × 25 resonator array with a 7-mm periodicity, operating at a resonant frequency of 7. 2 GHz. The system analyzes the renormalized S11 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 × 512 pixel masks delineating the affected area on the FSS, achieving an F1-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.
Ajuts:	Agencia Estatal de Investigación PID2022-139181OB-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-00192 Ministerio de Ciencia, Innovación y Universidades FPU20/05700
Nota:	Altres ajuts: acords transformatius de la UAB
Nota:	Altres ajuts: Project 2024 (Grant Number: ICREA 00114)
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Artificial intelligence (AI) ; Deep learning ; Frequency-selective surface (FSS) ; Laboratory safety ; Remote monitoring ; Spill detection ; U-Net ; Wireless sensing
Publicat a:	IEEE microwave and wireless technology letters, (April 2025) , ISSN 2771-9588

DOI: 10.1109/LMWT.2025.3556170

4 p, 1.8 MB

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