Multicomponent Gas Mixture Analysis Using a Single Tin Oxide Sensor and Dynamic Pattern Recognition

被引：72

作者：

Llobet, Eduard ^{[1
]}

Ionescu, Radu ^{[1
]}

Al-Khalifa, Sherzad ^{[2
]}

Brezmes, Jesus ^{[1
]}

Vilanova, Xavier ^{[1
]}

Correig, Xavier ^{[1
]}

Barsan, Nicolae ^{[3
]}

Gardner, Julian W. ^{[2
]}

机构：

[1] Univ Rovira & Virgili, Dept Elect Engn, Tarragona, Spain

[2] Univ Warwick, Sch Engn, Coventry CV4 7AL, W Midlands, England

[3] Univ Tubingen, Inst Phys Chem, Tubingen, Germany

来源：

IEEE SENSORS JOURNAL | 2001年 / 1卷 / 03期

关键词：

Discrete wavelet transform; feature extraction; multivariate analysis; neural networks; pattern recognition; tin oxide sensor;

D O I：

10.1109/JSEN.2001.954833

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A new method, which is based on the discrete wavelet transform, is presented for extracting important features from the response transients of a micromachined, tin oxide-based gas sensor. It is shown that two components in a mixture can be simultaneously and accurately quantified by processing the response dynamics of a single sensor operated in a temperature-modulated mode. The discrete wavelet transform outperforms the fast Fourier transform (classical approach) because it is more appropriate for the non-linear frequency-time problem encountered here.

引用

页码：207 / 213

页数：7

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