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

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 (
$2\times 2$
) raw sensor responses are first upscaled to
$6\times 6$
responses and a lightweight CNN is trained on 42 samples of
$6\times 6$
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
$1.42\times 10^{-14}$
while the estimation accuracy of
$2.43\times 10^{-3}$
were achieved for the considered gases. Our designed system is generic in design and can be extended to other gases/odors of interest.