In this research, parametric software cost estimation models and their related calibration methods have been analyzed, especially for the COCOMO model and the Bayesian calibration approach. This research combines machine learning techniques and statistical techniques. With this approach, the prediction powers of the COCOMO parametric software cost model are shown to be significantly improved while the variability is decreased with respect to the dataset being analyzed. This research studies not only the accuracy but also the variances of the model and the variables. It can improve the...

The main objective of the dissertation is to present work describing theoretical studies of planar structures with erbium doped regions and characterization of erbium doped particles. Numerical models of erbium doped Al2O3 were formulated, taking into account the up-conversion from the metastable Er3+ levels and the cross-relaxation process as a mechanism of energy transfer between Er3+ ions. The influence of amplified spontaneous emission (ASE) is also fully considered in the numerical model. The first part of this research investigates the effects of design geometries and nonlinear losses...