Abstract
This paper studies the suitability of brain activity, namely electroencephalogram signals, as raw material for conducting biometric authentication of individuals. Brain responses were extracted with visual stimulation, leading to biological brain responses known as Visual Evoked Potentials. We evaluated a novel method, using only 8 occipital electrodes and the energy of differential EEG signals, to extract information about the subjects for further use as their biometric features. To classify the features obtained from each individual, we used a one-class classifier per subject and we tested four types of classifiers: K-Nearest Neighbor, Support Vector Data Description and two other classifiers resulting from the combination of the two ones previously mentioned. After testing these four classifiers with features of 70 subjects, the results showed that visual evoked potentials are suitable for an accurate biometric authentication.

Abstract
eHealth is expected to integrate a comprehensive set of patient data sources into a coherent continuum, but implementations vary and Portugal is still lacking on electronic patient data sharing. In this work, we present a clinical information hub to aggregate multi-institution patient data and bridge the information silos. This integration platform enables a coherent object model, services-oriented applications development and a trust framework. It has been instantiated in the Rede Telematica de Sa de (www.RTSaude.org) to support a regional Electronic Health Record approach, fed dynamically from production systems at eight partner institutions, providing access to more than 11,000,000 care episodes, relating to over 350,000 citizens. The network has obtained the necessary clearance from the Portuguese data protection agency.

Abstract
Understanding driver behavior is critical towards ensuring superior levels of safety and environmental sustainability in intelligent transportation systems. Existing solutions for vital sign extraction are generally intrusive in that they affect the comfort of the driver and may consequently lead to biased observations. Moreover, low-complexity devices such as GPS receivers and the multitude of sensors present in the vehicle are yet to be exploited to the full extent of their capabilities. We present a real-life system that combines wearable non-intrusive heart wave monitors with a wireless enabled computing platform capable of gathering and processing the data streams of multiple in-vehicle sources. Observed variables include electrocardiogram, vehicle location, speed, acceleration, fuel consumption, and pedal position, among others. Preliminary results show that the proposed system is well suited not only for characterizing driver behavior but also for identifying and mapping potentially dangerous road segments and intersections. ©2010 IEEE.

Abstract
The influence of light intensity upon biomass and fatty acid productivity by the microalga Pavlova lutheri was experimentally studied using a novel device. This device was designed to automatically adjust light intensity in a photobioreactor: it takes on-line measurements of biomass concentration, and was successfully tested to implement a feedback control of light based on the growth rate variation. Using said device, batch and semicontinuous cultures of P lutheri were maintained at maximum growth rates and biomass productivities - hence avoiding photoinhibition, and consequent waste of radiant energy. Several cultures were run with said device, and their performances were compared with those of control cultures submitted to constant light intensity; the biomass levels attained, as well as the yields of eicosapentaenoic and docosahexaenoic acids were calculated - and were consistently higher than those of their uncontrolled counterpart.

Abstract
A flow injection analysis (FIA) device has been developed, which is able to assay successfully for biomass in a microalga bioreactor. The device is fully computerized and is operated via diluting small aliquots of the culture followed by measuring optical density (OD); this figure is then accurately correlated with biomass, in terms of both cell number and ash-free dry weight, during the entire culture time. Furthermore, the device is not expensive, is highly versatile, and is easy to operate owing to specifically developed, user-friendly software. The growth rate-and biomass productivity of Pavlova lutheri, cultivated under batch and semicontinuous modes, were monitored. as experiniental testing model.

Abstract
The paper proposes an integrated system to automatically assess motor function after neurological injury. A portable motion capture system was developed in order to obtain all the relevant three dimensional kinematics of the upper limb movement. These kinematics were analyzed by means of a decision tree classifier which features where inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test (WMFT). In addition, the system is able to correctly quantify the performance time of each selected task of the WMFT. In terms of the FAS the system and the clinician show coherent results for 3 out of 5 patients in the first task tested and 4 out of 5 for the second task tested. Regarding performance time, the mean error between the system and the clinician was of 0.216 s for the 25 trials performed (5 patients, 5 tasks each). These results represent an important proof of concept towards a system capable of precisely evaluate upper limb motor function after neurological injury.