Thesis (Ph.D.)--Rochester Institute of Technology, 2005. / Typescript. Includes bibliographical references (leaves 196-201).

Thesis (M.E.)--University of Canterbury, 2009. / Typescript (photocopy). "December 2009." Includes bibliographical references (p. [95)-102). Also available via the World Wide Web.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2005. / Thesis Advisor(s): R. Clark Robertson. Includes bibliographical references (p. 37-38). Also available online.

Thesis (Ph.D.)--Washington State University, December 2005. / Includes bibliographical references (p. 79-85).

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

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

Thesis (Ph. D.)--Missouri University of Science and Technology, 2009. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed November 23, 2009) Includes bibliographical references.

Encoding behaviorally relevant stimuli in a noisy background is critical for animals to survive in their natural environment. We identify core biophysical and synaptic mechanisms that permit the encoding of low frequency signals in pyramidal neurons of the weakly electric fish Apteronotus leptorhynchus, an animal that can accurately encode miniscule (0.1%) amplitude modulations of its self-generated electric field. We demonstrate that slow NMDA-R mediated EPSPs are able to summate over many interspike intervals of the primary electrosensory afferents (EAs), effectively eliminating the EA spike train serial correlations from the pyramidal cell input. This permits stimulus-evoked changes in EA spiking to be transmitted efficiently to downstream ELL pyramidal cells, where a dynamic balance of NMDA-R and GABA-A-R currents is critical for encoding low frequency signals. Interestingly, AMPA-R activity is depressed and plays a negligible role in the generation of action potentials; instead, cell intrinsic membrane noise implements voltage-dependent stochastic resonance to amplify weak sensory input and appears to drive a significant proportion of pyramidal cell spikes. Together, these mechanisms may be sufficient for the ELL to encode signals near the threshold of behavioral detection.

This project is about different methods in radar signal detection. It covers different types of used signals and detection in a radar system. Chosen basic methods are implemented and simulated in Matlab. The results of the simulation are presented and discussed.

The goal underlying educational testing is to measure psychological constructs in a particular domain and to produce valid inferences about examinees’ ability. To achieve this goal of getting a precise ability evaluation, test developers construct questions with different formats, such as multiple-choice (MC) items, and open-ended questions or constructed response (CR) test items, for example, essay items. In recent years, large-scale assessments have implemented CR items in addition to MC items as an essential component of the educational assessment landscape. However, utilizing CR items in testing involves two main challenges, including rater effects and rater correlations. One challenge is the error added by human raters’ subjective judgments, such as rater severity and rater central tendency. Rater severity effect refers to the effect that raters may tend to give consistently low or high ratings that cause biased ability evaluation (Leckie & Baird, 2011). Central tendency describes when raters tend to use middle categories in the scoring rubric and avoid using extreme criteria (Saal et al., 1980). The second challenge is that multiple raters usually grade an examinee’s essay for quality control purposes; however, ratings based on the same item are correlated and need to be handled carefully by appropriate statistical procedures (Eckes, 2011; Kim, 2009). To solve these problems, DeCarlo (2010) proposed an HRM-SDT model that extended the traditional signal detection theory (SDT) model used in the first level of HRM. The HRM-SDT model not only considers the hierarchical structure of rating data but also deals with various rater effects beyond rater severity. This research examined to what extent the HRM-SDT separates rater effects (i.e., rater severity and rater central tendency) from item effects (i.e., item difficulty). Accordingly, one goal of this study was to simulate various rater effects and item effects to investigate the performance of the HRM-SDT model with respect to separating these effects. The other goal was to compare the fit of the HRM-SDT model with one commonly used model in language assessments, the Rasch model, in different simulation conditions and to examine the difference between these two models in terms of segregating rater and item effects. To answer these questions, Simulation A and Simulation B were conducted. In Simulation A, seven sets of parameters were varied in the first set of simulations. Simulation B addressed some questions of particular interest using another four sets of parameters, where both the rater and item parameters were simultaneously varied. This study found the HRM-SDT accurately recovered parameters, and clearly detected and separated changes in rater severity, rater central tendency, and item difficulty in most conditions.