Abstract
Capsule endoscopy is a recent technology with a clear need for automatic tools that reduce the long exam annotation times of exams. We have previously developed a topographic segmentation method, which is now improved by using spatial and temporal position information. Two approaches are studied: using this information as a confidence measure for our previous segmentation method, and direct integrating of this data into the image classification process. These allow us not only to automatically know when we have obtained results with error magnitudes close to human errors, but also to reduce these automatic errors to much lower values. All the developed methods have been integrated in the CapView annotation software, currently used for clinical practice in hospitals responsible for over 250 capsule exams per year, and where we estimate that the two hour annotation times are reduced by around 15 minutes.

Abstract
In all R&D projects there's at least one phase of model verification and accuracy, and when we are working with visual information (such as pictures and video) this phase should be emphasised. When working with medical information and clinical trials the truth of automatic results must be accurate. This work is based on the need of a huge and well annotated dataset of pictures retrieved from endoscopic capsule. This datasets should be used to learn the computer vision algorithms focused on endoscopic capsule video processing, and event detection.

Abstract
Living Usability Lab for Next Generation Networks (www.livinglab.pt) is a Portuguese industry-academia collaborative R&D project, active in the field of live usability testing, focusing on the development of technologies and services to support healthy, productive and active citizens. The project adopts the principles of universal design and natural user interfaces (speech, gesture) making use of the benefits of next generation networks and distributed computing. Therefore, it will have impact on the general population, including the elderly and citizens with permanent or situational special needs. This paper presents project motivations, conceptual model, architecture and work in progress.

Abstract
In this paper, we present some original theoretical aspects of a fast nonlinear association measure based on the work of Cramer. The features of this new measure-the V measure-when applied to biosignals are also shown using simulated time series. A comparative study with other well-known association measures available in the literature of biosignals is presented, V was found to be twice as fast and more robust to nonlinearities than the classical cross-correlation ratio (r(2)) and more than 100 times faster than the nonlinear regression coefficient (h(2)), presenting similar behavior in the presence of nonlinear simulated situations. This new measure is very fast and versatile, It is appropriate to deal with nonlinear relations presenting usually a sharp peak in the association function enabling a high degree of selectivity for maxima detection. It seems to constitute an improvement over linear methods of association which is faster and more robust to the existing nonlinearities. It can be used as an alternative to more complex nonlinear association measures when computational speed is an important feature.

Abstract
In this paper the requirements of Neurophysiology and Neurotraumatology monitoring are analyzed. As a result a set of designated systems were developed by the authors a short description of which is given in the paper. Finally the future perspectives and problems to be faced are briefly described.

Abstract
An interactive software tool for epileptiform spike detection and focus localization is presented. The method for focus localization uses a multistrategy approach based on different and independent computerized methods. Spike detection is performed using an algorithm based on a set of parameters that characterize the waveforms in the time domain. A number of independent techniques is integrated in a Microsoft Windows based environment to select artifact-free electroencephalogram segments. With this software, the clinician may adjust and validate the automatic spike detector as well as combine and integrate a set of independent techniques to reinforce each other in order to achieve a reliable focal indication.