A comparative study of density models for gas identification using microelectronic gas sensor

S. Brahim-Belhouari; A. Bermak; Guangfen Wei; Philip Ching Ho Chan

doi:10.1109/ISSPIT.2003.1341079

A comparative study of density models for gas identification using microelectronic gas sensor

S. Brahim-Belhouari, A. Bermak, Guangfen Wei, Philip Ching Ho Chan

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

The aim of this paper is to compare the accuracy of a range of advanced density models for gas identification from sensor array signals. Density estimation is applied in the construction of classifiers through the use of Bayes rule. Experiments on real sensors' data has proved the effectiveness of the approach with an excellent classification performance. We compare the classification accuracy of four density models, Gaussian mixture models, generative topographic mapping, probabilistic PCA mixture and k nearest neighbors. On our gas sensors data, the best performance was achieved by the Gaussian mixture models.

Original language	English
Title of host publication	Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003
Publisher	IEEE
Pages	138-141
Number of pages	4
ISBN (Electronic)	0780382927, 9780780382923
DOIs	https://doi.org/10.1109/ISSPIT.2003.1341079
Publication status	Published - 1 Jan 2003
Externally published	Yes
Event	3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003 - Maritim Rhein/Main Hotel, Darmstadt, Germany Duration: 14 Dec 2003 → 17 Dec 2003

Conference

Conference	3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003
Country/Territory	Germany
City	Darmstadt
Period	14/12/03 → 17/12/03

Keywords

Brain modeling
Gas detectors
Linear discriminant analysis
Microelectronics
Nearest neighbor searches
Pattern recognition
Principal component analysis
Sensor arrays
Signal processing
Thin film sensors

ASJC Scopus subject areas

Signal Processing

More information

10.1109/ISSPIT.2003.1341079

Cite this

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title = "A comparative study of density models for gas identification using microelectronic gas sensor",

abstract = "The aim of this paper is to compare the accuracy of a range of advanced density models for gas identification from sensor array signals. Density estimation is applied in the construction of classifiers through the use of Bayes rule. Experiments on real sensors' data has proved the effectiveness of the approach with an excellent classification performance. We compare the classification accuracy of four density models, Gaussian mixture models, generative topographic mapping, probabilistic PCA mixture and k nearest neighbors. On our gas sensors data, the best performance was achieved by the Gaussian mixture models.",

keywords = "Brain modeling, Gas detectors, Linear discriminant analysis, Microelectronics, Nearest neighbor searches, Pattern recognition, Principal component analysis, Sensor arrays, Signal processing, Thin film sensors",

author = "S. Brahim-Belhouari and A. Bermak and Guangfen Wei and Chan, {Philip Ching Ho}",

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doi = "10.1109/ISSPIT.2003.1341079",

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Brahim-Belhouari, S, Bermak, A, Wei, G & Chan, PCH 2003, A comparative study of density models for gas identification using microelectronic gas sensor. in Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003., 1341079, IEEE, pp. 138-141, 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003, Darmstadt, Germany, 14/12/03. https://doi.org/10.1109/ISSPIT.2003.1341079

A comparative study of density models for gas identification using microelectronic gas sensor. / Brahim-Belhouari, S.; Bermak, A.; Wei, Guangfen et al.
Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003. IEEE, 2003. p. 138-141 1341079.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Chan, Philip Ching Ho

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AB - The aim of this paper is to compare the accuracy of a range of advanced density models for gas identification from sensor array signals. Density estimation is applied in the construction of classifiers through the use of Bayes rule. Experiments on real sensors' data has proved the effectiveness of the approach with an excellent classification performance. We compare the classification accuracy of four density models, Gaussian mixture models, generative topographic mapping, probabilistic PCA mixture and k nearest neighbors. On our gas sensors data, the best performance was achieved by the Gaussian mixture models.

KW - Brain modeling

KW - Gas detectors

KW - Linear discriminant analysis

KW - Microelectronics

KW - Nearest neighbor searches

KW - Pattern recognition

KW - Principal component analysis

KW - Sensor arrays

KW - Signal processing

KW - Thin film sensors

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BT - Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003

PB - IEEE

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ER -

A comparative study of density models for gas identification using microelectronic gas sensor

Abstract

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Keywords

ASJC Scopus subject areas

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