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Sensors 2011, 11(1), 485-505; doi:10.3390/s110100485

A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression

1
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
2
Biochemistry Center, No.49 Institute of China Electronics Technology Group Corporation, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Received: 13 December 2010 / Revised: 27 December 2010 / Accepted: 29 December 2010 / Published: 5 January 2011
(This article belongs to the Section Chemical Sensors)
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Abstract

This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes—a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR) estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process.
Keywords: wireless electronic nose; combustible gas detection; Fe2O3 gas sensor; humidity insensitivity; DSP; least square support vector regression   wireless electronic nose; combustible gas detection; Fe2O3 gas sensor; humidity insensitivity; DSP; least square support vector regression  
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MDPI and ACS Style

Song, K.; Wang, Q.; Liu, Q.; Zhang, H.; Cheng, Y. A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression. Sensors 2011, 11, 485-505.

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