Pattern Recognition Laboratory
University Of Patras
Department of Computer Engineering and Informatics
Computer Science and Engineering


Pattern Recognition Laboratory (PR Lab) was first established in 1979, inside the Department of Electrical Engineering by professor D.G. Lainiotis, who was the first laboratory director. Many national and European projects in Pattern Recognition, Signal Processing and Adaptive Control, were carried out in a Patras since 1989, when professor D. Lainiotis was retired.

PR lab is a traditional name, given to the laboratory in 1999, since it re-consolidated in R&D projects inside the Department of Computer Engineering and Informatics.

Following its strategic research directions and capitalising on previous experience of its key personnel, our department already exhibits substantial research activity, both basic and industry-oriented, as well as technology transfer actions, in the following areas:

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  • Computational Biology and Bioinformatics. Bioinformatics is emerged technology in uncovering the information behind the biological mechanisms. PRLab is active in the research in computational biology and bioinformatics. Also, it is the coordinator of the Computational Biology and Bioinformatics Group of Computer Engineering & Informatics Department (CEID).
  • Cloud Computing and Big Data Analytics. The PR lab has been engaged to the research of novel applications of the emerged technologies of cloud computing and visualization technologies. It is also coordinator of Cloud@Ceid , an initiative of Computer Engineering & Informatics Department (CEID)  for promoting the research on virtualization and cloud computing technologies has access to the HPCLAB and the Computer Center of the Computer Engineering and Informatics Department (CEID).
  • Semantic based management of Digital Information. Semantics covers aspects like interpretation, use of data and rules used by people to convert data into information. As semantics is dependent on humans, it is difficult to catch it in the context of machines. The use of metadata is motivated by two main factors: on the one hand, metadata use has been investigated in order to manage efficiently complex multimedia artefacts, on the other hand, this approach seems promising in order to solve problems in heterogeneous and autonomous systems.
  • Educational and Cultural Technologies, including, Educational Multimedia Software, Educational Multimedia and Hyper-Media Authoring Environments, Open Learning Tools and Platforms for Personalised Collaborative Learning, Flexible Internet-based Distance Education Systems, Tele-Training and Tele-Education Applications, Educational Meta-Data.

The laboratory also supports research and education in the computational intelligence area and specifically in the following:

  • Artificial Neural Network and Applications: Research includes the development of intelligent algorithms for both the optimization of the structure and training of ANN and applications to system structure identification.
  • Genetic Algorithms (GA) and Applications: Research includes the development of new genetic operators and the applications of GAs to real world problems such as non – linear time series prediction, biosignal prediction and chest cancer growth.
  • Evolutionary Algorithms and Applications: Research includes the development of hybrid algorithms, such as advanced signal processing algorithms for the training of ANNs and GAs for the optimization of their structure. The new hybrid algorithms have been applied in many real world problems such as exchange – rate prediction, MEG prediction, seismic signal prediction Computational Finance, e.t.c.
  • Intelligent Agents and Applications: Research includes the design and implementation of intelligent agents for information retrieval over the Internet. The resulting agents have been applied to Electronic Commerce Applications. Currently, this research has been extended to multi-agent systems with application to open and distance learning.
  • Intelligent Algorithms for Advanced Signal Processing: Research includes the design of intelligent algorithms based on the multy-model Partitioning Theory of Lainiotis. Applications include system stucture identifications, linear and non-linear time series prediction, identification of the Direction Of Arrival (DOA), e.t.c.
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2017 © Pattern Recognition Laboratory
Address: University of Patras Campus, Building Β, Rio 26500, Patras, Greece
Phones: +30 2610 996985
FAX: +30 2610 969001

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