Abstract
This chapter presents EPSO (Evolutionary Particle Swarm Optimization), as an evolutionary meta-heuristic that implements a scheme of self-adaptive recombination, borrowing the movement rule from PSO (Particle Swarm Optimization). Besides the basic model, it discusses a Stochastic Star topology for the communication among particles and presents a variant called differential EPSO or dEPSO. The chapter presents results in a didactic Unit Commitment/Generator Scheduling Power System problem and results of a competition among algorithms in an intelligent agent platform for Energy Retail Market simulation where EPSO comes out as the winner algorithm. © 2007 Springer-Verlag Berlin Heidelberg.

Abstract

Abstract
Functional magnetic resonance imaging (fMRI) is a medical imaging technique used to characterize brain physiological activity, usually presented as 3D volumes in function of time. Our previous work in nonlinear association studies in electroencephalogram (EEG) time series enabled us to identify EEG features relevant for clinical diagnosis. The use of a similar approach in fMRI (adapted for 3D time series) is both appealing and new. Such time series analysis however imposes challenging requirements regarding computational power and medical image management. In this paper we propose the use of Grid computing to cope with the demanding fMRI multi-voxel association analysis workflow and present a working prototype. The system, implemented using the gLite middleware, provides the necessary support to manage brain images and run different non-linear fMRI analysis methods.

Abstract
Underwater acoustic networks can be quite effective to establish communication links between autonomous underwater vehicles (AUVs) and other vehicles or control units, enabling complex vehicle applications and control scenarios. A communications and control framework to support the use of underwater acoustic networks and sample application scenarios are described for single and multi-AUV operation.

Abstract

Abstract