**Ph.D. Dissertation** – QoS-Constrained Information Theoretic Capacity Maximization in CDMA Systems (pdf, url), Candidacy Report: (pdf), Candidacy Report+Appendix (pdf), Candidacy Presentation (pdf).

Code Division Multiple Access (CDMA) has proved to be an efficient and stable means of communication between a group of users which share the same physical medium. Therefore, with the rising demand for high–bandwidth multimedia services on mobile stations, it has become necessary to devise methods for more rigorous management of capacity in these systems. While one of the substantial techniques for regulating capacity in CDMA systems is through power control, the mathematical complexity of the regarding model inhibits useful generalizations and extensions.

In this research, the classical problem of capacity optimization in the reverse link is analyzed. It is shown that the classical formulation is solvable through examination of a finite set of transmission powers, for which closed forms are given. Although, this method leads to a more accurate and faster solution to the classical problem, it is noted that the classical problem is very prone to yielding partial solutions in which the calculated system capacity is not realizable in a practical setting. The developed mathematical model, however, is shown to be applicable to more general definitions of the problem.

The bulk of this research is the analysis of the capacity optimization problem equipped with increasing sets of constraints and utility functions, incorporated into the problem in order to produce solutions deployable in practical systems. Cases of multiple–class systems are also analyzed and directions for future work on multiple–cell systems are given.

**M.Sc. Dissertation** – Color Image Processing using Principal Component Analysis, English (pdf), Persian (pdf).

It is known since 1988 that utilizing linear dimension reduction gives appropriate lower dimensional representation of homogenous swatches of color images of the nature. Though, it is common to see serious research projects, even dated 2005, which are based on the fixed color space paradigm. In this thesis, first experimental evaluations are performed to compare the principal component analysis (PCA)-based representation of color images of the nature with respective color space-based representations. According to the promising results of the experiments and the theoretical anticipation of the properness of local PCA-based approach, we turn into applications. The PCA model for the color vectors of homogenous swatches defines one principal direction and two less important ones. This framework is utilized for designing outperforming color image processing algorithms such as colorizing, recoloring, compression, segmentation, watermarking, deliberate distortion, and so on. For every algorithm numerous experiments are performed and the results are discussed. Also, wherever possible, the proposed methods are compared to the literature. All algorithms are incorporated into a unique color image processing toolbox for MATLAB.

**Last update**: April 2015