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Tripoli, Greece

The University of Peloponnese is a university located in the Peloponnese, Greece. It was founded in 2002 and comprises five schools in Tripoli, Corinth, Kalamata, Nafplion, and Sparta.It was established as a public university with the Presidential Decree 13/2000. Its seat is Tripolis and is being developed at the level of complete Schools in the five capitals of the Prefectures of the Region of Peloponnese.The University was inaugurated in the 20th of September 2002 with the beginning of operation of the Department of Computer Science and Technology and the Department of Telecommunication Science and Technology of the School of Science and Technology. The purpose of the establishment and operation of the University of Peloponnese is its creative contribution to the development of the tertiary education in Greece, with high quality standards in the study programme, research and teaching, which will meet the demands of a modern University of national, European and international impact. The University of Peloponnese refers to Greece and the Greeks all over the world with the ambition to create strong ties with the Greek colonies and become a centre of cooperation and mental creativity for all Greeks. Wikipedia.


Kopsinis Y.,National and Kapodistrian University of Athens | Slavakis K.,University of Peloponnese | Theodoridis S.,National and Kapodistrian University of Athens
IEEE Transactions on Signal Processing | Year: 2011

This paper presents a novel projection-based adaptive algorithm for sparse signal and system identification. The sequentially observed data are used to generate an equivalent sequence of closed convex sets, namely hyperslabs. Each hyperslab is the geometric equivalent of a cost criterion, that quantifies "data mismatch". Sparsity is imposed by the introduction of appropriately designed weighted ℓ1 balls and the related projection operator is also derived. The algorithm develops around projections onto the sequence of the generated hyperslabs as well as the weighted ℓ1 balls. The resulting scheme exhibits linear dependence, with respect to the unknown system's order, on the number of multiplications/ additions and an Ο(Llog2L) dependence on sorting operations, where $L$ is the length of the system/signal to be estimated. Numerical results are also given to validate the performance of the proposed method against the Least-Absolute Shrinkage and Selection Operator (LASSO) algorithm and two very recently developed adaptive sparse schemes that fuse arguments from the LMS/RLS adaptation mechanisms with those imposed by the lasso rational. © 2010 IEEE. Source


Glentis G.-O.,University of Peloponnese | Jakobsson A.,Lund University
IEEE Transactions on Signal Processing | Year: 2011

This paper presents computationally efficient implementations for several recent algorithms based on the iterative adaptive approach (IAA) for uniformly sampled one- and two-dimensional data sets, considering both the complete data case, and the cases when the data sets are missing samples, either lacking arbitrary locations, or having gaps or periodically reoccurring gaps. By exploiting the method's inherent low displacement rank, together with the development of suitable Gohberg-Semencul representations, and the use of data dependent trigonometric polynomials, the proposed implementations are shown to offer a reduction of the necessary computational complexity by at least one order of magnitude. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain. © 2011 IEEE. Source


Glentis G.-O.,University of Peloponnese
IEEE Transactions on Signal Processing | Year: 2010

In this paper fast algorithms for adaptive Capon and amplitude and phase estimation (APES) methods for spectral analysis of time varying signals, are derived. Fast, stable, nonrecursive formulae are derived, based on time shifting properties of the pertinent variables. As a consequence, efficient frequency domain recursive least squares (RLS) based, as well as fast RLS based algorithms for the adaptive estimation of the power spectra are developed. Stability issues of the frequency domain estimators are considered, and stabilization procedures are proposed. The computational complexity of the proposed algorithms is lower than relevant existing methods. The performance of the proposed algorithms is demonstrated through extensive simulations. © 2009 IEEE. Source


Chouvardas S.,National and Kapodistrian University of Athens | Slavakis K.,University of Peloponnese | Theodoridis S.,National and Kapodistrian University of Athens
IEEE Transactions on Signal Processing | Year: 2011

In this paper, the problem of adaptive distributed learning in diffusion networks is considered. The algorithms are developed within the convex set theoretic framework. More specifically, they are based on computationally simple geometric projections onto closed convex sets. The paper suggests a novel combine-project-adapt protocol for cooperation among the nodes of the network; such a protocol fits naturally with the philosophy that underlies the projection-based rationale. Moreover, the possibility that some of the nodes may fail is also considered and it is addressed by employing robust statistics loss functions. Such loss functions can easily be accommodated in the adopted algorithmic framework; all that is required from a loss function is convexity. Under some mild assumptions, the proposed algorithms enjoy monotonicity, asymptotic optimality, asymptotic consensus, strong convergence and linear complexity with respect to the number of unknown parameters. Finally, experiments in the context of the system-identification task verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been developed for adaptive distributed learning. © 2011 IEEE. Source


This paper reports on the design and the implementation of the Technological Pedagogical Science Knowledge (TPASK), a new model for science teachers professional development built on an integrated framework determined by the Technological Pedagogical Content Knowledge (TPACK) model and the authentic learning approach. The TPASK curriculum dimensions and the related course sessions are also elaborated and applied in the context of a teacher trainers' preparation program aiming at ICT integration in science classroom practice. A brief description of the project, its accomplishments, and perceptions of the participants, through the lens of TPASK professional development model, are presented. This is followed by the presentation of the evaluation results on the impact of the program which demonstrates that science teachers reported meaningful TPASK knowledge and increased willingness to adopt and apply this framework in their instruction. Finally, we draw on the need to expand TPACK by incorporating a fourth dimension, the Educational Context within Pedagogy, Content and Technology mutually interact, in order to address future policy models concerning teacher preparation to integrate ICT in education. © 2010 Elsevier Ltd. All rights reserved. Source

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