Abstract
A system using laser speckle effect is proposed to segment images reflecting vibration movements of diffuse targets. Longitudinal movements are difficult to identify when simple imaging systems are used. The proposed system produces a two dimensional segmentation of the target and it is sensitive to longitudinal movements. The speckle effect, produced when coherent light is reflected and interferes when hitting rough surfaces, can be used in order to accomplish this purpose. A pattern with high and low intensity spots is observed depending on the illuminated scene. In our optical system, two silicone membranes are illuminated using a beam expanded laser source and their patterns are recorded using a video camera. One of the membranes experiences a longitudinal controlled movement while the remaining scene is still. Speckle data is processed using a temporal gradient and a regional entropy computation. This method produces a binary individual pixel classification. Four sets of parameters have been tested for the entropy computation and the area under the receiver operating characteristic (ROC) curve was used to select the best one. The selected set-up achieved a ROC value of 0.9879. A data set with 12 different membrane velocities was used to define the threshold that maximizes the classifier accuracy. This threshold was applied to a validation data-set composed by 4 sinusoidal movements with distinct velocities. The accuracy of this technique has achieved values between 92% and 97%. The results show that the target was accurately identified with the optical non-contact apparatus and the developed algorithm.

Abstract
Patients with medically refractory epilepsy may benefit from surgical resection of the epileptic focus. Subdural electrodes are implanted to accurately locate the seizure onset and locate the eloquent areas to be spared. However, the visualization of the subdural electrodes may be limited by the current methods. The aim of this work was to assist physicians in the localization of subdural electrodes in relation to anatomical landmarks using co-registration methods and by removing the cerebellum from MRI images. Three patients with refractory epilepsy were studied, in whom subdural electrodes were implanted. All electrodes were correctly localized in a 3D view over the cortex and their visualization was improved by the removal of cerebellum. This method promises to be useful in the optimization of the surgical plan.

Abstract
Plant diseases, such as the pinewood disease, PWD, have become a problem of economical and forestall huge proportions. These diseases, that are asymptomatic and characterized by a fast spread, have no cure developed to date. Besides, there are no technical means to diagnose the disease in situ, without causing tree damage, and help to assist the forest management. Herein is proposed a portable and non-damage system, based on electrical impedance spectroscopy, EIS, for biological applications. In fact, EIS has been proving efficacy and utility in wide range of areas. However, although commercial equipment is available, it is expensive and unfeasible for in vivo and in field applications. The developed EIS system is able to perform AC current or voltage scans, within a selectable frequency range, and its effectiveness in assessing pine decay was proven. The procedure and the results obtained for a population of 24 young pine trees (Pinus pinaster Aiton) are presented. Pine trees were kept in a controlled environment and were inoculated with the nematode (Bursaphelenchus xylophilus Nickle), that causes the PWD, and also with bark beetles (Tomicus destruens Wollaston). The obtained results may constitute a first innovative approach to the diagnosis of such types of diseases.

Abstract
Presently the interest in non-invasive devices for monitoring the cardiovascular system has increased in importance, especially in the diagnosis of some pathologies. The proposed optical device reveals an attractive instrumental solution for local pulse wave velocity (PWV) assessment and other hemodynamic parameters analysis, such as Augmentation Index (AIx), Subendocardial Viability Ratio (SEVR), Maximum Rate of Pressure Change (dP/dtmax) and Ejection Time Index (ETI). These parameters allow a better knowledge on the cardiovascular condition and management of many disease states. Two studies were performed in order to validate this technology. Firstly, a comparative test between the optical system and a gold-standard in PWV assessment was carried out. Afterwards, a large study was performed in 131 young subjects to establish carotid PWV reference values as well as other hemodynamic parameters and to find correlations between these and the population characteristics. The results allowed the use of this new technique as a reliable method to determine these parameters. For the total of subjects values for carotid PWV vary between 3-7.69 m s-1 a clear correlation with age and smoking status was found out. The Aix varies between -6.15% and 11.46% and exhibit a negative correlation with heart, and dP/dtmax parameter shows a significant decrease with age.

Abstract
In this article, an algorithm to identify LPV State Space models is proposed. The LPV State Space system is in the companion reachable canonical form. Both the state matrix and the output vector coefficients are linear combinations of a set of nonlinear basis functions dependent on the scheduling signal. This model structure, although simple, can describe accurately the behaviour of many nonlinear systems by an adequate choice of the scheduling signal. The identification algorithm minimises a quadratic criterion of the output error. Since this error is a linear function of the output vector parameters, a separable nonlinear least squares approach is used to minimise the criterion function by a gradient method. The derivatives required by the algorithm are the states of LPV systems that need to be simulated at every iteration. The effectiveness of the algorithm is assessed by two simulated examples.

Abstract
Cardiovascular diseases (CVDs) are the leading cause of death in the world. The pulse wave analysis provides a new insight in the analysis of these pathologies, while data mining techniques can contribute for an efficient diagnostic method. Amongst the various available techniques, artificial neural networks (ANNs) are well established in biomedical applications and have numerous successful classification applications. Also, clustering procedures have proven to be very useful in assessing different risk groups in terms of cardiovascular function in healthy populations. In this paper, a robust data mining approach was performed for cardiac risk patterns identification. Eight classifiers were tested: C4.5, Random Forest, RIPPER, Naive Bayes, Bayesian Network, Multy-layer perceptron (MLP) (1 and 2-hidden layers) and radial basis function (RBF). As for clustering procedures, k-means clustering (using Euclidean distance) and expectation-maximization (EM) were the chosen algorithms. Two datasets were used as case studies to perform classification and clustering analysis. The accuracy values are good with intervals between 88.05% and 97.15%. The clustering techniques were essential in the analysis of a dataset where little information was available, allowing the identification of different clusters that represent different risk group in terms cardiovascular function. The three cluster analysis has allowed the characterization of distinctive features for each of the clusters. Reflected wave time (T_RP) and systolic wave time (T_SP) were the selected features for clusters visualization. Data mining methodologies have proven their usefulness in screening studies due to its descriptive and predictive power.