Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 71-78).

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 216-229). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 216-229). Also available on the Internet.

Thesis (M.S.)--OGI School of Science & Engineering at OHSU, Oct. 2005. / Includes bibliographical references (leave 22).

Thesis (M.Eng.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 71-74. Also available online.

Thesis (Ph. D.)--University of Rhode Island, 2006. / Includes bibliographical references (leaves 99-100).

No description available.

The remember-know paradigm is widely used in recognition memory research to explore the mechanisms underlying recognition judgments. The most intriguing question about the paradigm that needs to be answered is: Are the processes that underlie “remember” and “know” responses the same or different? The extant remember-know models provide different answers. The dual-process model (Yonelinas, 1994) assumes that “remember” and “know” judgments are made with qualitatively different underlying processes. The one-dimensional Signal Detection Theory (SDT) model (Donaldson, 1996; Hirshman & Master, 1997) and the Sum-difference Theory of Remembering and Knowing (STREAK) model assume that “remember” and “know” judgments are made with same underlying processes but different response criteria. In this thesis, three experiments were conducted to evaluate these models. The remember-know models were fit to the accuracy data to see which model provides the best account for the ROC data. In addition, the reaction time data were fit with ex-Gaussian distributions and the best-fit skew parameters were used to reveal whether the underlying strategic processes for “remember” and “know” judgments are same or not. The results of the remember-know model fit were mixed: In the first experiment with list length manipulation, 6 out of 8 cases were best fit with the one-dimensional models and the other 2 cases were best fit with the dual-process models; in the second experiment with list strength manipulation, 11 out of 18 cases were best fit with the one-dimensional models, another 6 cases were best fit with the dual-process models and the rest one case were best fit with the STREAK model; in the third experiment with response bias manipulation, 6 out of 16 cases were best fit with the one-dimensional models and the other 10 cases were best fit with the dual-process models. The results of ex-Gaussian fit to RT data supported the one-dimensional model better: for the subjects who provide enough overlapping data in comparison of the distributions of hits followed by “remember” and “know” judgments, the values of skew parameter did not differ for “remember” and “know” responses in 7 out of 8 cases. This indicates that the same process underlies “remember” and “know” responses.

Yes / Probability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense. / De Montfort University - DMU HEIF’18 project, Natural Science Foundation of Shanxi Province [grant number 201701D221112], National Natural Science Foundation of China [grant numbers 61503271 and 61603136]

An experimental and a theoretical analysis for a laser beam propagating through multiple phase screens was performed. The theoretical analysis showed that the statistics for the intensity fluctuations, which can be predicted by the HK and the I-K distributions, could be derived from a multiplicative process using statistical distributions derived from Gaussian statistics. For the single phase screen experiment, the experimental normalized moments were compared with the normalized moments of both the HK and I-K distributions . In addition, the intensity data was lowpass filtered to yield moments that are predicted by the gamma distribution. The single phase screen data was segmented into small time intervals, and all time segments with approximately the same variance were grouped together into bins to yield normalized moments for each bin that are predicted by the Rician distribution. Also, the normalized moments for two and three phase screen experiments were measured. Finally, a computer program was written to simulate K distributed noise from two independent Gaussian noise sources.