Abstract
In this paper, we propose and evaluate methodologies for the classification of images from thin-layer chromatography. Each individual sample is characterized by an intensity profile that is further represented into a feature space. The first steps of this process aim at obtaining a robust estimate of the intensity profile by filtering noise, reducing the influence of background changes, and by fitting a mixture of Gaussians. The resulting profiles are represented by a set of appropriate features trying to characterize the state of nature, here spread out over four classes, one for normal subjects and the other three corresponding to lysosomal diseases, which are disorders responsible for severe nerve degeneration. For classification purposes, a novel solution based on a hierarchical structure is proposed. The main conclusion of this paper is that an automatically generated decision tree presents better results than more conventional solutions, being able to deal with the natural imbalance of the data that, as consequence of the rarity of lysosomal disorders, has very few representative cases in the disease classes when compared with the normal population.

Abstract

Abstract
This paper describes a humanoid robot simulator supporting joint trajectory optimization, following accurately the real robot characteristics. The simulator, based on a rigid body simulator (Open Dynamics Engine) and an OpenGL based graphics librmy (GLScene), provides instant visual feedback and realistic dynamics. It allows to design and test behaviours and control methods without access to the real hardware, preventing damages in the real robot in the earlier stages of development. Having in mind the energy consumption minimization, the low level trajectory planning is discussed and experimental results are presented. The proposed methods arc shown to minimize the total energy assuming two intervals of constant acceleration. Copyright © 2008 by World Scientific Publishing Co. Pte. Ltd.

Abstract
In vivo observation and tracking of cell division in the Arabidopsis thaliana root meristem, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of cells. The possibility of automating the process of cell detection/marking is an important step to provide research tools to the biologists in order to ease the search for a special event as cell division. This paper discusses an automatic cell segmentation method, which selects the best cell candidates from a starting watershed based image segmentation. The selection of individual cells is obtained using a Support Vector Machine (SVM) classifier, based on the shape and edge strength of the cells' contour. The resulting segmentation is largely pruned of badly segmented cells, which can reduce the false positive detection of cell division. This is a good result on its own and a starting point for improvement of cell segmentation methodology.

Abstract
Arguably. the most successful data, structure for tabling is tries. However, while tries are very efficient for variant based tabled evaluation, they are limited in their ability to recognize and represent, repeated terms in different tabled calls or/and answers. lit this paper, we propose a new design for the table space where tabled terms are stored in a common global trie instead of being spread over several different tries.

Abstract
Service-oriented systems constitute a suitable approach for the development of modular, flexible and reconfigurable production systems, addressing the current requirements imposed by global markets. This paper focuses on the process control of Service-oriented production systems, whose behavior is regulated by the coordination of services that are available from distributed modular and collaborative control components. The proposed control is based on a kind of High-level Petri Nets tailored for the description, connection and synchronization of different concurrent processes. Several features can be distinguished by this approach, since its modularity, control reusability, flexibility and other valuable inherited characteristics from the proposed High-Level Petri Nets.