We use the polyhedral process network (PPN) model of computation to program and map streaming applications onto embedded Multi-Processor Systems on Chip (MPSoCs) platforms. The PPNs, which can be... Show moreWe use the polyhedral process network (PPN) model of computation to program and map streaming applications onto embedded Multi-Processor Systems on Chip (MPSoCs) platforms. The PPNs, which can be automatically derived from sequential program applications, do not necessarily meet the performance/resource constraints. A designer can therefore apply the process splitting transformations to increase program performance, and the process merging transformation to reduce the number of processes in a PPN. These transformations were defined, but a designer had many possibilities to apply a particular transformation, and these transformations can also be ordered in many different ways. In this dissertation, we define compile-time solution approaches that assist the designer in evaluating and applying process splitting and merging transformations in the most effective way. Show less
The central topic of this thesis is the CATREG approach to nonlinear regression. This approach finds optimal quantifications for categorical variables and/or nonlinear transformations for numerical... Show moreThe central topic of this thesis is the CATREG approach to nonlinear regression. This approach finds optimal quantifications for categorical variables and/or nonlinear transformations for numerical variables in regression analysis. (CATREG is implemented in SPSS Categories by the author of the thesis; the relevant parts of the Categories manual are included in the appendix.) The first chapter of the thesis provides a non-technical introduction to the CATREG approach, illustrated with graphs. The more technical part of the thesis includes (1) a solution to the local minima problem for monotone transformations, as well as a study of the effect of several data conditions on the incidence and severeness of local minima, (2) the incorporation into CATREG of a particular resampling method (the .632 bootstrap) for assessing prediction accuracy, and (3) the incorporation into CATREG of several regularization methods (Ridge Regression, the Lasso, and the Elastic Net) for stabilizing the estimates of the regression coefficients and transformations. The technical part is followed by a chapter describing a bulimia nervosa study in which the CATREG-Lasso and the .632 bootstrap are applied. Show less
