Diss. (sammanfattning) Linköping : Linköpings universitet, 2008. / Härtill 5 uppsatser.

Diss. Västerås : Mälardalens högskola, 2004.

Diss. (sammanfattning) Umeå : Umeå universitet, 2005. / Härtill 4 uppsatser.

Diss. Västerås : Mälardalens högskola, 2006. / S. 61-66: Bibliografi.

Thesis (M. App. Sc.)--Carleton University, 2005. / Includes bibliographical references (p. 120-124). Also available in electronic format on the Internet.

Thesis (M.C.S.) Carleton University, 2005. / Includes bibliographical references (p. 257-260). Also available in electronic format on the Internet.

Thesis (m.s.)--University of Kentucky, 2004. / Title from document title page (viewed Jan. 7, 2005). Document formatted into pages; contains vii, p. 103 : ill. Includes abstract and vita. Includes bibliographical references (p. 99-101).

Thesis (M. Sc. (Computer Science))--Rhodes University, 2004.

We present a component-based approach for recognizing objectsunder large pose changes. From a set of training images of a givenobject we extract a large number of components which are clusteredbased on the similarity of their image features and their locations withinthe object image. The cluster centers build an initial set of componenttemplates from which we select a subset for the final recognizer.In experiments we evaluate different sizes and types of components andthree standard techniques for component selection. The component classifiersare finally compared to global classifiers on a database of fourobjects.

The human olfactory system can classify new odors in a dynamic environment with varying odor complexity and concentration, while simultaneously reducing the influence of stable background odors. Replication of this capability has remained an active area of research over the past 3 decades and has great potential to advance medical diagnostics, environmental monitoring and industrial monitoring, among others. New methods for rapid dynamic temporal evaluation of chemical sensor arrays for the monitoring of analytes is explored in this work. One such method is high and low bandpass filtering of changing sensor responses; this is applied to reduce the effects of background noise and sensor drift over time. Processed sensor array responses, coupled with principal component analysis (PCA), will be used to develop a novel approach to classify odors in the presence of changing sensor responses associated with evolving odor concentrations. These methods will enable the removal of noise and drift, as well as facilitating the normalization to decouple classification patterns from intensity; lastly, PCA and artificial neural networks (ANNs) will be used to demonstrate the capability of this approach to function under dynamic conditions, where concentration is changing temporally. / February 2016