Membership Details (Research Institute)
Contact: Massimo Cossentino
Member node: 046
Viale delle Scienze, edificio 11
90128, Palermo (Italy)
Italy
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Description
The Palermo-ICAR research staff comprises research scientists from both the CNR and the University of Palermo. Currently, the total number of researchers involved in the Palermo section activities amounts to 25 units.
The Palermo-ICAR section collaborates extensively with companies, universities and research institutions, in Italy and abroad.
Expertise and experience of Palermo-ICAR section:
Robotic Systems, Artificial Vision and Multi-agent systems design
This group faces with strategies for multi-robot environments, perceptive systems for robotics. Research interests of this group are also in the areas of 3D vision, neural networks for computer vision and multi-agent systems design and implementation techniques
Agent-Oriented Software Engineering
The research activity is mainly directed to the development process of multi-agent systems and related tools
Artificial Intelligence
Research interests of this group are related to hybrid knowledge representation systems and their management. In particular, we are developing decision making techniques for modeling multi-agent systems under uncertainty. Other efforts are related to mobile agents for Internet applications.
Computer Networks
This group is exploring key issues in the recent technology of Active Networks, mainly for the aspects related to a number of new challenges, such as distributed strategies, resource management, application interactions, efficiency and performance.
Image Analysis and Visual Databases
Main experiences of this group are in the areas of automatic feature extraction
Numerical Analysis and Algorithms
The research is devoted to the analysis and development of numerical methods.
Innovative Digital Computer Architecture
The research trusts of the INCA (Innovative Digital Computer Architecture) group are along the following two lines:
(i) definition and design of digital neural architectures for object and feature classification, both supervised and unsupervised;
(ii) design and implementation of fine-grain SIMD computer architectures for digital color image processing.
This list was generated on Mon Sep 10 16:38:44 BST 2007. |
