2021 6th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2021)
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Assoc Prof. Sunil Kumar Jha

Dr. Sunil Kr. Jha.png

Assoc Prof. Sunil Kumar Jha

Nanjing University of Information science & Technology, China


Title: Electronic Nose in Chemical Vapor Sensing

Abstract: 

An electronic nose system consists of an array of chemical sensors combined with a pattern recognition system. The chemical identity information is embedded in the sensor array output patterns. The pattern recognition system models the sensor array output patterns as multivariate statistical data, extracts or builds vapor identity features, and assigns class identity labels. An electronic nose system design is inspired by the working paradigm of human smell sensing (olfactory) system. The pattern recognition system creates mathematical signature of vapors from the measured response patterns, which are variously referred to as “vapor prints” or “chemical fingerprints”.  The developments of electronic noses are important for several application domains ranging from the monitoring of hazardous chemicals in commercial and strategic environment, detection of disease through body odor or breathe sensing, monitoring of foods and beverage quality and freshness, detection of hidden explosives and narcotics to the biometric identification and forensics. The detection of hidden explosives is of paramount importance to homeland security and forensics. The present talk is concerned with the signal processing (or the software) part of the electronic nose system based on the steady state responses of the sensor array. The research contributions pertain to the following subtopics in this domain: (i) Preprocessing by data scaling and normalization, (ii) Denoising and outlier detection, (iii) Feature extraction by multivariate statistical analysis, (iv) Pattern classification by statistical and neural network methods, (v) Polymer selection by statistical data mining for sensor array design, and (vi) Quantitative identification by fuzzy clustering and fuzzy inference system.