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Journal of Sensors
Volume 2016 (2016), Article ID 7603931, 12 pages
http://dx.doi.org/10.1155/2016/7603931
Research Article

Short-Time Fourier Transform and Decision Tree-Based Pattern Recognition for Gas Identification Using Temperature Modulated Microhotplate Gas Sensors

1School of Electronic Science and Technology, Key Laboratory of Liaoning for Integrated Circuits Technology, Dalian University of Technology, Dalian 116024, China
2School of Information and Electronics Engineering, Shandong Institute of Business and Technology, Yantai 264005, China

Received 19 November 2015; Revised 6 January 2016; Accepted 8 February 2016

Academic Editor: Oleg Lupan

Copyright © 2016 Aixiang He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Because the sensor response is dependent on its operating temperature, modulated temperature operation is usually applied in gas sensors for the identification of different gases. In this paper, the modulated operating temperature of microhotplate gas sensors combined with a feature extraction method based on Short-Time Fourier Transform (STFT) is introduced. Because the gas concentration in the ambient air usually has high fluctuation, STFT is applied to extract transient features from time-frequency domain, and the relationship between the STFT spectrum and sensor response is further explored. Because of the low thermal time constant, the sufficient discriminatory information of different gases is preserved in the envelope of the response curve. Feature information tends to be contained in the lower frequencies, but not at higher frequencies. Therefore, features are extracted from the STFT amplitude values at the frequencies ranging from 0 Hz to the fundamental frequency to accomplish the identification task. These lower frequency features are extracted and further processed by decision tree-based pattern recognition. The proposed method shows high classification capability by the analysis of different concentration of carbon monoxide, methane, and ethanol.