Spatial Upscaling-Based Algorithm for Detection and Estimation of Hazardous Gases

• Published in: IEEE access Vol. 11; pp. 17731 - 17738
• Main Authors: Srivastava, Sumit, Chaudhri, Shiv Nath, Rajput, Navin Singh, Alsamhi, Saeed Hamood, Shvetsov, Alexey V.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ambience; Artificial intelligence; Artificial neural networks; Convolutional neural networks; convolutional neural networks (CNNs); electronic nose; Error correction; Error detection; Estimation; Gas detectors; gas sensor array; Gas sensors; Gases; Internet of Things; Internet of Things (IoT); Ketones; Lightweight; Mathematical analysis; Metal oxides; Odors; Pattern recognition; Real time; Sensor arrays; Sensors; Spatial upscaling; Steady-state; Tin dioxide; Xylene; Zinc oxide
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3245041

Recently, society/industry is getting smarter and sustainable through artificial intelligence-based solutions. However, this rapid advancement is also polluting our air ambience. Hence real-time detection and estimation of hazardous gases/odors in the air ambiance has become a critical need. In this paper, a convolutional neural network (CNN) based multi-element gas sensor arrays signature response analysis approach has been presented to achieve higher accuracy in detection and estimation of hazardous gases. Accordingly, the real-time gas sensor array responses are spatially upscaled and processed on the edge, using lightweight CNNs. For the verification of our hypothesis, we have used a four-element metal-oxide semi-conductor (MOS)-based thick-film gas sensor array, fabricated by our group, by using SnO2, ZnO, MoO, CdS materials for detection and estimation of four target hazardous gases, viz., acetone, car-bon-tetrachloride, ethyl-methyl-ketone, and xylene. The four-element (<inline-formula> <tex-math notation="LaTeX">2\times 2 </tex-math></inline-formula>) raw sensor responses are first upscaled to <inline-formula> <tex-math notation="LaTeX">6\times 6 </tex-math></inline-formula> responses and a lightweight CNN is trained on 42 samples of <inline-formula> <tex-math notation="LaTeX">6\times 6 </tex-math></inline-formula> input vectors. The trained system is then tested using 16 unknown (not used during training) test samples of the considered gases/odors. All the 16 test samples are detected correctly. The Mean Squared Error (MSEs) of detection has been <inline-formula> <tex-math notation="LaTeX">1.42\times 10^{-14} </tex-math></inline-formula> while the estimation accuracy of <inline-formula> <tex-math notation="LaTeX">2.43\times 10^{-3} </tex-math></inline-formula> were achieved for the considered gases. Our designed system is generic in design and can be extended to other gases/odors of interest.