Gas identification with microelectronic gas sensor in presence of drift using robust GMM

Sofiane Brahim-Belhouari; Amine Bermak; Philip Ching Ho Chan

Gas identification with microelectronic gas sensor in presence of drift using robust GMM

Sofiane Brahim-Belhouari, Amine Bermak, Philip Ching Ho Chan

Research output: Journal article publication › Conference article › Academic research › peer-review

Abstract

The pattern recognition problem for real life applications of gas identification is particularly challenging due to the small amount of data available and the temporal variability of the instrument mainly caused by drift. In this paper we present a gas identification approach based on class-conditional density estimation using Gaussian mixture models (GMM). A drift counteraction approach based on extracting robust feature using a simulated drift is proposed. The performance of the retrained GMM shows the effectiveness of the new approach in improving the classification performance in the presence of artificial drift.

Original language	English
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	5
Publication status	Published - 27 Sept 2004
Externally published	Yes
Event	Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada Duration: 17 May 2004 → 21 May 2004

ASJC Scopus subject areas

Electrical and Electronic Engineering
Signal Processing
Acoustics and Ultrasonics

Cite this

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title = "Gas identification with microelectronic gas sensor in presence of drift using robust GMM",

abstract = "The pattern recognition problem for real life applications of gas identification is particularly challenging due to the small amount of data available and the temporal variability of the instrument mainly caused by drift. In this paper we present a gas identification approach based on class-conditional density estimation using Gaussian mixture models (GMM). A drift counteraction approach based on extracting robust feature using a simulated drift is proposed. The performance of the retrained GMM shows the effectiveness of the new approach in improving the classification performance in the presence of artificial drift.",

author = "Sofiane Brahim-Belhouari and Amine Bermak and Chan, {Philip Ching Ho}",

year = "2004",

month = sep,

day = "27",

language = "English",

volume = "5",

journal = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

issn = "1520-6149",

publisher = "IEEE",

note = "Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing ; Conference date: 17-05-2004 Through 21-05-2004",

}

Gas identification with microelectronic gas sensor in presence of drift using robust GMM. / Brahim-Belhouari, Sofiane; Bermak, Amine; Chan, Philip Ching Ho.
In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Vol. 5, 27.09.2004.

Research output: Journal article publication › Conference article › Academic research › peer-review

TY - JOUR

T1 - Gas identification with microelectronic gas sensor in presence of drift using robust GMM

AU - Brahim-Belhouari, Sofiane

AU - Bermak, Amine

AU - Chan, Philip Ching Ho

PY - 2004/9/27

Y1 - 2004/9/27

N2 - The pattern recognition problem for real life applications of gas identification is particularly challenging due to the small amount of data available and the temporal variability of the instrument mainly caused by drift. In this paper we present a gas identification approach based on class-conditional density estimation using Gaussian mixture models (GMM). A drift counteraction approach based on extracting robust feature using a simulated drift is proposed. The performance of the retrained GMM shows the effectiveness of the new approach in improving the classification performance in the presence of artificial drift.

AB - The pattern recognition problem for real life applications of gas identification is particularly challenging due to the small amount of data available and the temporal variability of the instrument mainly caused by drift. In this paper we present a gas identification approach based on class-conditional density estimation using Gaussian mixture models (GMM). A drift counteraction approach based on extracting robust feature using a simulated drift is proposed. The performance of the retrained GMM shows the effectiveness of the new approach in improving the classification performance in the presence of artificial drift.

UR - http://www.scopus.com/inward/record.url?scp=4544300279&partnerID=8YFLogxK

M3 - Conference article

SN - 1520-6149

VL - 5

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 17 May 2004 through 21 May 2004

ER -

Gas identification with microelectronic gas sensor in presence of drift using robust GMM

Abstract

ASJC Scopus subject areas

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