The Institute of Informatics and Telecommunications congratulates the three members of staff who have recently been awarded their Doctorates of Philosophy (PhDs).
Dr. Denia Kanellopoulou, SKEL’s collaborating researcher concluded her PhD thesis in July entitled Performance Evaluation Techniques for Modern Wireless Telecommunication Systems.
The increasing number of wireless/mobile users and respective applications impose high QoS expectations on wireless systems design, which needs to concurrently account for fading, interference, and frequency spectrum scarcity. Such systems require extensive performance analysis before adoption, which implies cumbersome (if not impossible) computations, a challenge that this thesis addressed. For a system that operates over generalized fading channels under delay constraints, a generic Lp-norm diversity combining structure was proposed and novel frameworks for the effective capacity evaluation were presented, considering different adaptive transmission policies. Moreover, for a two-tier VFDM cognitive system operating over frequency-selective Rayleigh fading channels, an analytical precise approximation of the SINR statistics was presented and simple, yet accurate, closed-form expressions for evaluating the ergodic capacity and the ABEP were derived using MLE and non-linear regression. For both systems, the mathematical formalism was validated with numerical and computer simulation results, confirming the correctness of the proposed methodologies.
Dr. Dora Katsamori, SKEL’s administrative assistant, completed also her PhD in mid-July. Her thesis is about Citizenship Education and Social Disadvantaged Groups: The case of Second Chance Schools in the prisons.
The study aims to investigate citizenship’s education contribution toward prison-inmates, regarding citizenship’s construction and their preparation in view of their re-integration into society. The research’s questions are posed within the confinement of a qualitative paradigm, with characteristics of an ethnographic study and an action research, which was conducted included three different approaches regarding to the data collection. Research highlights students’ special educational needs and expectations and the educational ‘traumas’ of the past, due to dropping out. Furthermore, education seems to encourage students’ engagement in a procedure of self-reflection and critical thought, regarding to the actions and attitudes of the past, and their entrance in a transformative procedure, which aims to the revision of their self-image and the improvement of their self-esteem and self-confidence. The participants take on the role of the researchers of themselves and through critical thought and reflection, they are trying to achieve the change.
Dr. Dimitris Kouremenos, IIT’s collaborating scientific personnel, received recently his PhD degree on Language Modeling of the Greek Sign Language for Statistical Machine Translation Systems.
This thesis is located in the framework of Automatic Machine Translation and in the human and machine software interface for hearing disabled people using the Greek Sign Language. In this work we present a novel prototype Rule Based Machine Translation (RBMT) system for the creation of large quality written Greek Sign Language (GSL) glossed corpora. In particular, the proposed RBMT system supports the professional translator of GSL to produce different kinds of GSL glossed corpus. Then the glossed corpus is used as training data for the production/creation of Language Model (LM) n-gram. With the GSL glossed corpus and for any domain, we can build, test and evaluate different kinds of Language Models for different kinds of glossed GSL corpus, even if there is no real written GSL large corpus. These GSL parallel corpus and languages models also will be used as training data by the Statistical Machine Translation (SMT) MOSES application system. It should be noted that the whole process is robust and flexible, since it does not demand deep grammar knowledge of GSL. By using the BiLingual Evaluation Understudy (BLEU) metric score, our prototyped MT system achieves a very promising performance, and in particular an average score of 60,53% και 85,1% / 65,5% / 53,8% / 44,8% για 1-gram / 2 -gram / 3-gram / 4-gram.