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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rao, V. Ramgopal | - |
dc.date.accessioned | 2023-10-20T09:15:40Z | - |
dc.date.available | 2023-10-20T09:15:40Z | - |
dc.date.issued | 2021-03 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9306798/authors#authors | - |
dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/12554 | - |
dc.description.abstract | This paper presents a hybrid pattern recognition with temperature compensation (HPR-TC) used within an E-Nose system. HPR-TC with E-nose has the novelty, amongst MEMS sensor platforms, of having two modes of operation i.e., rapid mode of detection to be used in time-critical conditions and comprehensive analysis mode for improved detection accuracy. Two modes of operations in HPR-TC are possible because of the implementation of hybrid PR featuring a combination of two different data analysis techniques for explosive sensing. The first part of the hybrid PR is the binary PR based on threshold-based detection and the second one is the analog PR based on PCA and K-mean. The E-Nose system with proposed HPR-TC is validated with two different highly sensitive MEMS sensor types, i.e., SU8 and Si3Nx piezo-resistive cantilever. These MEMS sensors are coated with surface receptors, 4-MBA, 6-MNA and 4-ATP, to improve the selectivity. The E-Nose system can detect explosive compounds such as TNT, RDX, and PETN, in a controlled environment at a concentration as low as 16ppb of TNT, 56ppb of RDX and 134ppb of PETN. Furthermore, measurements show that E-Nose with temperature compensated binary PR can detect the explosives with a detection accuracy higher than 74% as true positives and higher than 79% as true negatives in a short time, within initial 17 seconds of the experiment. However, the temperature compensated analog PR gives a detailed classification of explosives with a higher detection accuracy of 80% as true positives and 86% as true negatives after approximately 95 seconds. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | EEE | en_US |
dc.subject | Piezoresistive cantilevers | en_US |
dc.subject | Multi-coatings | en_US |
dc.subject | Hybrid pattern recognition | en_US |
dc.subject | Software temperature compensation | en_US |
dc.subject | Rapid sensing | en_US |
dc.subject | Explosive detection | en_US |
dc.title | Hybrid Pattern Recognition for Rapid Explosive Sensing With Comprehensive Analysis | en_US |
dc.type | Article | en_US |
Appears in Collections: | Department of Electrical and Electronics Engineering |
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