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Proceedings Paper

Study of signal process of gas sensor array with nonlinear principal component analysis
Author(s): Guangfen Wei; Zhenan Tang; Jun Yu; Philip C.H. Chan
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Paper Abstract

Gas sensor array is a useful device to enhance the selectivity of gas detectors and to identify the components of gas mixture. The key step for processing signal from a gas sensor array is to extract the signal feature and make pre-classification for tested gases. Conventional Principal Component Analysis (PCA) is widely used for this purpose. However, conventional PCA is a linear and variance-covariance matrix based technique and it is therefore not strictly applicable for processing the gas sensor array signals that exhibit significant non-linear behavior. Thus, in this paper, non-linear PCA (NPCA) algorithm is introduced to process the gas sensor array signals to adapt to the non-linear characteristics. The signals we processed are the responses of a micro-hotplate (MHP) based integrated gas sensor array to a CO and NO2 binary gas mixture. The gas sensor array, consisted of four SnO2 thin-film sensing elements, was fabricated with integrated circuit (IC) technology and micromachining on silicon substrate. The recognition results of NPCA and conventional PCA are compared in this paper.

Paper Details

Date Published: 15 October 2001
PDF: 5 pages
Proc. SPIE 4601, Micromachining and Microfabrication Process Technology and Devices, (15 October 2001); doi: 10.1117/12.444708
Show Author Affiliations
Guangfen Wei, Dalian Univ. of Technology (China)
Zhenan Tang, Dalian Univ. of Technology (China)
Jun Yu, Dalian Univ. of Technology (China)
Philip C.H. Chan, Hong Kong Univ. of Science and Technology (Hong Kong)

Published in SPIE Proceedings Vol. 4601:
Micromachining and Microfabrication Process Technology and Devices
Norman C. Tien; Qing-An Huang, Editor(s)

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