THESIS
2016
xx, 143 pages : illustrations ; 30 cm
Abstract
Artificial olfactory systems, referred to as electronic nose systems, have been developed to
mimic the functionality of the mammalian olfactory system, in which odorant receptors play a
key role to transform odor molecules into electrical spikes, through olfactory transduction. Odor
information is encoded in these spike patterns, which are processed by the brain to identify and
quantify tens of thousands of odors. Research efforts to obtain a similar performance between
electronic olfaction and its biological counterpart, have been focused on two fronts. The first
deals with the fabrication of miniaturized sensor arrays to replicate the functionality of odorant
receptors, while the second targets the development of algorithms with a potentially equivalent
level of odor identific...[
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Artificial olfactory systems, referred to as electronic nose systems, have been developed to
mimic the functionality of the mammalian olfactory system, in which odorant receptors play a
key role to transform odor molecules into electrical spikes, through olfactory transduction. Odor
information is encoded in these spike patterns, which are processed by the brain to identify and
quantify tens of thousands of odors. Research efforts to obtain a similar performance between
electronic olfaction and its biological counterpart, have been focused on two fronts. The first
deals with the fabrication of miniaturized sensor arrays to replicate the functionality of odorant
receptors, while the second targets the development of algorithms with a potentially equivalent
level of odor identification performance to that of the brain.
Miniaturized sensor arrays are now feasible due to the great advancement in fabrication
and characterization techniques of sensing materials in recent decade. But, state-of-the-art pattern
recognition algorithms, on the other hand, are mostly investigated for odor classification.
Although, these algorithms perform well, a low power and portable electronic nose system remains
a challenge due to the algorithmics complexity and its computationally intensive nature.
Moreover, field deployment of electronic nose systems is another issue given the fact that these
algorithms require manually tuning of many parameters as well as the requirement for sensors’
calibration.
We follow two different approaches to dealing with the challenges of developing odor identification
algorithms with high accuracy, namely (i) a closed-form solution for electronic nose
systems and (ii) quantitative feedback and hardware friendly implementation. One utilizes recent
experimental findings about odor identification in the mammalian olfactory system, and the other focuses on some state-of-art pattern recognition algorithms to meet these challenges.
In all these classifiers, we transform the multi-gas identification problem into pair-wise classification
problems and a quantitative feedback is integrated in each binary classifier to avoid the
misclassifications at the cost of rejection of uncertain predictions.
Regarding our first approach, we propose three classifiers. Firstly, we search pairs of those
sensors’ features whose difference results in opposite signs for the two gases in each binary
classification problem, and these are later used for classification of the test feature vectors. In
the second classifier, we propose probabilistic rank scoring classifier that ranks the sensors features
to form rank codes and then a simple probabilistic approach is used to identify the test
vectors. The performance of these two classifiers may be limited when discriminatory information
is not found in the ranks or no signed pair is found due to limited number of sensors
and overlapping features in the electronic nose systems. To handle this challenge, we propose
multivariate Bayesian classifier by overcoming its inherent limitation of poor estimation of covariance
matrix through applications of random matrix theory. By using statistical principles,
we introduce a computationally efficient classifier by using the mean values of the sensors’
features with their weights based on the capability to discriminate the gases. The performance
of this classifier is limited when mean values of the sensors features do not contain discriminatory
information then we propose a cluster k-nearest neighbors classifier by reducing its
inherent computational complexity through clustering the data into subclasses and using representatives
of the subclasses instead of the whole data set for the classification of a new test
vector. The performance of all these classifiers is evaluated by developing three sensor arrays,
containing commercial gas sensors, and acquiring data of multiple gases in the laboratory under
controlled operating conditions. Our classifiers perform comparable to computations-intensive
pattern recognition algorithms despite their hardware friendly implementation and that is further
increased to around 100 % at the cost of rejection of uncertain predictions.
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