Abstract

Abstract

Abstract
The present paper introduces the GERES-nned project and proposes a Grid architecture to cope with its demanding requirements. GERES-med aims at developing a Grid-enabled repository of multimedia information relevant for medical applications, education and research, focussing the cardiology and gastroenterology areas. The overall goal of the project is to make available large volumes of already existing clinical information locked in Clinical Centers through a semantic aware layer providing high-quality classified cases. GERES-med will integrate and develop tools for automated generation of semantic descriptors to support content-based retrieval on the Grid-enabled repository. As a higher level goal, we will try to define a next generation standards-based platform for these medical and academic communities to access, share and discover clinical data, with emphasis on medical images and clinical reports. This paper is a technical contribution describing the GERES-med requirements and proposes a computing architecture to address the identified scenarios.

Abstract
We derive the time-variant second-order statistics of the depth-scan photocurrent in time-domain optical coherence tomography (TD-OCT) systems using polarized thermal light sources and superluminescent diodes (SLDs). Since the asymptotic-joint-probability-distribution function (JPDF) of the photocurrent due to polarized thermal light is Gaussian and the signal-noise-ratio in TD-OCT is typically high (> 80 dB), the JPDF of the depth-scan photocurrent could be approximated as a Gaussian random process that is completely determined by its second-order statistics. We analyze both direct and differential light detection schemes and include the effect of electronic thermal fluctuations. Our results are a necessary prerequisite for future development of statistical image processing techniques for TD-OCT. (c) 2007 Optical Society of America.

Abstract
This paper reports new results in adopting entropy concepts to the training of mappers such as neural networks to perform wind power prediction as a function of wind characteristics (mainly speed and direction) in wind parks connected to a power grid. It also addresses the differences relevant to power system operation between off-line and on-line training of neural networks. Real case examples are presented.

Abstract
An Adaptive Object-Model (AOM) is an architectural pattern based upon a dynamic meta-modeling technique where the object model of the system is explicitly defined as data to be interpreted at run-time. The object model encompasses the full specification of domain objects, states, events, conditions, constraints and business rules. Several design patterns, that have before been documented, describe a set of good-practices within this domain. This paper approaches data and metadata evolution issues in the context of AOMs, by describing three additional patterns - History of Operations, System Memento and Migration. They establish ways to track, version, and evolve information, at the several abstraction levels that may exist in an AOM. © 2008 is held by the author(s).