George Chrysanthakopoulos

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eScience applications need to use distributed Grid environments where each component is an individual or cluster of multicore machines. These are expected to have 64-128 cores 5 years from now and need to support scalable parallelism. Users will want to compose heterogeneous components into single jobs and run seamlessly in both distributed fashion and on a(More)
—The ever increasing number of cores per chip will be accompanied by a pervasive data deluge whose size will probably increase even faster than CPU core count over the next few years. This suggests the importance of parallel data analysis and data mining applications with good multicore, cluster and grid performance. This paper considers data clustering,(More)
We present a performance analysis of a scalable parallel data clustering algorithm with deterministic annealing for multicore systems that compares MPI and a new C# messaging runtime library CCR (Concurrency and Coordination Runtime) with Windows and Linux and using both threads and processes. We investigate effects of memory bandwidth and fluctuations of(More)
—An unsupervised learning system, implemented as an autonomous agent is presented. A simulation of a challenging path-planning problem is used to illustrate the agent design and demonstrate its problem solving ability. The agent, dubbed the ORG, employs fuzzy logic and clustering techniques to efficiently represent and retrieve knowledge and uses innovative(More)
Appearance-based localization compares the current image taken from a robot's camera to a set of pre-recorded images in order to estimate the current location of the robot. Such techniques often maintain a graph of images, modeling the dynamics of the image sequence. This graph is used to navigate in the space of images. In this paper we bring a set of(More)
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