Taylor, J. E. (James E.) (2010) Novel convolution-based processing techniques for application in chemical sensing. PhD thesis, University of Warwick.

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Abstract

The electronic nose is a device developed to mimic the human olfactory system.
Despite raising interest from applications in the field of medicine, quality control,
environmental control and security, such devices remain inferior to their
biological counterparts. As the biological system is explored further, new
discoveries generate new ways of thinking in creating electronic nose devices.
This has led to a large variety of sensors and devices, all of which produce data
that requires processing. The data are processed to extract information that can
be used to classify or quantify the input to the electronic nose. However, as the
devices have advanced, the data processing techniques have remained relatively
static, refinements of established statistical methods.
Recently, investigation into the phenomenon of nasal chromatography has
brought about the development of a new class of electronic nose device; the
artificial olfactory mucosa. Taking advantage of a retentive effect, inspired by
the aqueous mucous layer covering the olfactory epithelium, this new device
produces data whose spatio-temporal properties have not been seen in the field of
chemical sensing before. Thus there is a need to develop new processing
approaches to obtain the information being produced by these new devices.
In this thesis, a new processing approach is presented, centred on the use of
convolution to produce characteristic signals which contain information arising
from a sensor space that is separated both spatially and temporally, realised in the
form of multiple sensor arrays separated by retentive columns or channels. This
combined signal is then used to extract an information rich feature set that can be
passed on to classifiers or quantifiers to make practical use of the data.
This method is simulated on data collected during the development of the
artificial olfactory mucosa to validate its use, and then applied to several sets of
real world data, collected from a variety of devices; from current e-nose
technologies to newly developed artificial olfactory mucosa devices. The
simulations put the device in very noisy conditions and the processing approach
deals well with a high level of noise in most circumstances, its performance only
deteriorating in the presence of extremely high levels of sensor drift. However, it
is shown that this method not only has validity when dealing with the advanced
devices for which it is intended, but also shows an improvement over standard
processing approaches when utilised in conjunction with current technologies.
Utilising convolution on data collected from current devices, methods are
developed where the characteristic signal can be generated internally from a
single array, and when applied, produce improvements over standard processing
approaches.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics T Technology > TP Chemical technology
Library of Congress Subject Headings (LCSH):	Electrochemical sensors -- Data processing, Convolutions (Mathematics)
Official Date:	September 2010
Dates:	Date Event September 2010 Submitted
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Gardner, Julian
Sponsors:	Engineering and Physical Sciences Research Council (EPSRC)
Extent:	xvi, 153 leaves : ill., charts
Language:	eng

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