Numerical Differentiation From Noisy Signals: A Kernel Regularization Method to Improve Transient-State Features for the Electronic Nose

Liu, T; Zhang, W; Wang, L; Ueland, M; Forbes, SL; Zheng, WX; Su, SW

Numerical Differentiation From Noisy Signals: A Kernel Regularization Method to Improve Transient-State Features for the Electronic Nose

Liu, T Zhang, W Wang, L Ueland, M

Forbes, SL Zheng, WX Su, SW

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54, (6), pp. 3497-3511
Issue Date:: 2024-06-01

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Field	Value	Language
dc.contributor.author	Liu, T
dc.contributor.author	Zhang, W
dc.contributor.author	Wang, L
dc.contributor.author	Ueland, M https://orcid.org/0000-0002-9155-3502
dc.contributor.author	Forbes, SL
dc.contributor.author	Zheng, WX
dc.contributor.author	Su, SW
dc.date.accessioned	2024-08-07T04:54:52Z
dc.date.available	2024-08-07T04:54:52Z
dc.date.issued	2024-06-01
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54, (6), pp. 3497-3511
dc.identifier.issn	2168-2216
dc.identifier.issn	2168-2232
dc.identifier.uri	http://hdl.handle.net/10453/180247
dc.description.abstract	As the simplest feature extraction, traditional hand-crafted transient-state features have been widely used in the area of electronic noses (e-noses). However, the influence of noise in the calculation of numerical differentiation leads to inaccuracy and instability in extracting these features. To tackle this issue, a novel numerical differentiation algorithm is proposed, which uses kernel-based regularization. The proposed method can provide accurate and stable transient-state features by directly estimating high-order derivatives from the noise-contaminated sensor's reading. The feature representation is a prerequisite for the good performance of e-noses. Nevertheless, it should be noted that this performance in real applications can still be affected by other factors, such as sensor drift and the disturbance of nontarget odors. These issues can be addressed by applying a framework of domain adaptation and one-class classification. The proposed method and the adopted framework are verified in a field experiment, which identifies the odor of four targets and two disturbance whiskies measured by a self-designed e-nose system. The classification accuracy with traditional features is improved from mathbf {71.90%} to mathbf {86.36%} , showing the good potential of the proposed method for application in the area of e-noses.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DE210100494
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems
dc.relation.isbasedon	10.1109/TSMC.2024.3362067
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Numerical Differentiation From Noisy Signals: A Kernel Regularization Method to Improve Transient-State Features for the Electronic Nose
dc.type	Journal Article
utslib.citation.volume	54
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Forensic Science (CFS)
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Health Technologies (CHT)
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T04:54:49Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	54
utslib.citation.issue	6

Abstract:

As the simplest feature extraction, traditional hand-crafted transient-state features have been widely used in the area of electronic noses (e-noses). However, the influence of noise in the calculation of numerical differentiation leads to inaccuracy and instability in extracting these features. To tackle this issue, a novel numerical differentiation algorithm is proposed, which uses kernel-based regularization. The proposed method can provide accurate and stable transient-state features by directly estimating high-order derivatives from the noise-contaminated sensor's reading. The feature representation is a prerequisite for the good performance of e-noses. Nevertheless, it should be noted that this performance in real applications can still be affected by other factors, such as sensor drift and the disturbance of nontarget odors. These issues can be addressed by applying a framework of domain adaptation and one-class classification. The proposed method and the adopted framework are verified in a field experiment, which identifies the odor of four targets and two disturbance whiskies measured by a self-designed e-nose system. The classification accuracy with traditional features is improved from mathbf {71.90%} to mathbf {86.36%} , showing the good potential of the proposed method for application in the area of e-noses.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180247