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Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa

Our understanding of the human olfactory system, particularly with respect to the phenomenon of nasal chromatography, has led us to develop a new generation of novel odour-sensitive instruments (or electronic noses). This novel instrument is in need of new approaches to data processing so that the i...

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Main Authors:	Taylor, James, Che Harun, F. K., Covington, J. A., Gardner, J. W.
Format:	Conference or Workshop Item
Published:	2009
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/14842/
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id	my.utm.14842
record_format	eprints
spelling	my.utm.148422020-06-30T08:39:04Z http://eprints.utm.my/id/eprint/14842/ Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa Taylor, James Che Harun, F. K. Covington, J. A. Gardner, J. W. TK Electrical engineering. Electronics Nuclear engineering Our understanding of the human olfactory system, particularly with respect to the phenomenon of nasal chromatography, has led us to develop a new generation of novel odour-sensitive instruments (or electronic noses). This novel instrument is in need of new approaches to data processing so that the information rich signals can be fully exploited; here, we apply a novel time-series based technique for processing such data. The dual-channel, large array artificial olfactory mucosa consists of 3 arrays of 300 sensors each. The sensors are divided into 24 groups, with each group made from a particular type of polymer. The first array is connected to the other two arrays by a pair of retentive columns. One channel is coated with Carbowax 20M, and the other with OV-1. This configuration partly mimics the nasal chromatography effect, and partly augments it by utilizing not only polar (mucus layer) but also non-polar (artificial) coatings. Such a device presents several challenges to multi-variate data processing: a large, redundant dataset, spatio-temporal output, and small sample space. By applying a novel convolution approach to this problem, it has been demonstrated that these problems can be overcome. The artificial mucosa signals have been classified using a probabilistic neural network and gave an accuracy of 85%, Even better results should be possible through the selection of other sensors with lower correlation. 2009 Conference or Workshop Item PeerReviewed Taylor, James and Che Harun, F. K. and Covington, J. A. and Gardner, J. W. (2009) Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa. In: ISOEN 13, 2009., 2009, Brescia, Italy.
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Taylor, James Che Harun, F. K. Covington, J. A. Gardner, J. W. Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa
description	Our understanding of the human olfactory system, particularly with respect to the phenomenon of nasal chromatography, has led us to develop a new generation of novel odour-sensitive instruments (or electronic noses). This novel instrument is in need of new approaches to data processing so that the information rich signals can be fully exploited; here, we apply a novel time-series based technique for processing such data. The dual-channel, large array artificial olfactory mucosa consists of 3 arrays of 300 sensors each. The sensors are divided into 24 groups, with each group made from a particular type of polymer. The first array is connected to the other two arrays by a pair of retentive columns. One channel is coated with Carbowax 20M, and the other with OV-1. This configuration partly mimics the nasal chromatography effect, and partly augments it by utilizing not only polar (mucus layer) but also non-polar (artificial) coatings. Such a device presents several challenges to multi-variate data processing: a large, redundant dataset, spatio-temporal output, and small sample space. By applying a novel convolution approach to this problem, it has been demonstrated that these problems can be overcome. The artificial mucosa signals have been classified using a probabilistic neural network and gave an accuracy of 85%, Even better results should be possible through the selection of other sensors with lower correlation.
format	Conference or Workshop Item
author	Taylor, James Che Harun, F. K. Covington, J. A. Gardner, J. W.
author_facet	Taylor, James Che Harun, F. K. Covington, J. A. Gardner, J. W.
author_sort	Taylor, James
title	Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa
title_short	Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa
title_full	Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa
title_fullStr	Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa
title_full_unstemmed	Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa
title_sort	applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa
publishDate	2009
url	http://eprints.utm.my/id/eprint/14842/
_version_	1672610425988972544
score	13.211869

Applying convolution based processing methods to a dual channel, large array artificial olfactory mucosa

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