Thesis (Ph. D.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 73-75). Also available on the World Wide Web.

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Thesis (Ph.D.)--University of Western Australia, 2004.

Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 74-76). Also available on the World Wide Web.

Thesis (M.S.)--Oregon State University, 2005. / Printout. Includes bibliographical references (leaves 53-54). Also available on the World Wide Web.

Thesis (M.S. in Electrical Engineering and M.S. in Systems Engineering)--Naval Postgraduate School, Sept. 2004. / Thesis Advisor(s): R. Clark Robertson. Includes bibliographical references (p. 67-68). Also available online.

Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Swaminathan Madhavan; Committee Member: Chatterjee Abhijit; Committee Member: Davis Jeffrey.

Thesis (M.S.)--Oregon State University, 2006. / Printout. Includes bibliographical references (leaves 42-43). Also available on the World Wide Web.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 61-62).

Initial investigations of the Outlier Processing Method (OPM), first introduced by Birch [1][2][3] in 1988, have demonstrated a promising ability to extract a special class of signals, called highly variable events (HVEs), from coloured noise processes. The term HVE is introduced in this thesis to identify a finite-duration signal whose shape and latency vary dramatically from trial to trial and typically has a very low signal-to-noise ratio (SNR). This thesis presents a modified version of the original OPM algorithm, which can generate an estimate of the HVE with significantly less estimation noise than the original OPM algorithm. Simulation experiments are used to identify the strengths and limitations of this modified OPM algorithm for linear and stationary processes and to compare the modified algorithm's performance to the performance of the original algorithm and to the performance of a minimum mean-square-error (MMSE) filter. The results of these experiments verify that the modified algorithm can extract an HVE with less estimation noise than the original algorithm. The results also show, that the MMSE filter is unsuitable for extracting HVEs and that its performance is generally inferior to the modified algorithm's performance. The experiments indicate that the modified algorithm can extract HVEs from a linear and stationary process for SNR levels above -2.5dB and can work effectively above -7.5dB for HVEs with certain characteristics. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate