Abstract
High levels of dependability are required to promote the adherence by public and medical communities to wearable medical devices. The study presented herein addresses fault detection and diagnosis in these systems. The main objective resides on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause of a detected anomaly is a wearer health condition or a system functional flaw. Data fusion techniques, namely fuzzy logic, artificial neural networks, decision trees and naive Bayes classifiers are employed to process the captured data to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g., arrhythmia, tachycardia and bradycardia). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values. Selected features from the vital signals and from quantities that characterize the system performance serve as inputs to the data fusion algorithms for Patient and System Status diagnosis purposes. The algorithms performance was evaluated based on their sensitivity, specificity and accuracy. Based on these criteria the naive Bayes classifier presented the best performance.

Abstract
The health domain is rich in specific vocabulary and information structures. Previous work on this area includes the collection of this information in information systems. However, the language of these can limit their use. To overcome this, we present Health Translations, a web application that uses crowd-sourcing to translate a large vocabulary set that, currently, is only available in English. To increase usage, gamification methods are applied that reward both the quantity of collaboration and the quality of it. When completed, these translations can be made available without costs to the research community. This paper presents the platform as a responsive web application.

Abstract
The promotion and deployment of storage technologies in autonomous and grid-connected systems plays a relevant part in the massive integration of renewable power sources required for the worldwide development of a sustainable society. In this regard, analyzing the behavior of electrochemical storage devices such as lead-acid batteries installed on hybrid energy systems and microgrids in terms of their lifetime and economic profitability is an important research topic. Since renewable generation is characterized by its random nature, lead-acid batteries typically work under stress conditions, which directly influence their lifetime in a negative way by increasing the net present cost. Due to the fast growing of renewable sources as a consequence of governmental policies and incentives, the number of manufacturers to be considered worldwide is becoming really high, so that optimization techniques such as genetic algorithms (GM) are frequently used in order to consider the performance of a high number of manufacturers of wind turbines, photovoltaic panels and lead-acid batteries subject to the environmental conditions of the location under analysis to determine a cost-effective design. In this paper, GA method combined with weighted Ah ageing model is improved by including expert experiences by means of stress factors and the categorization of operating conditions, as a new contribution to earlier studies. The effectiveness of the proposed method is illustrated by analyzing a hybrid energy system to be installed in Zaragoza, Spain, resulting in a near-optimal design in a reduced computational time compared to the enumerative optimization method.

Abstract
Recommender systems represent user preferences for items that the user might be interested to view or purchase. These systems have become extremely common in electronic commerce, providing relevant suggestions and directing users towards those items that best meet their needs and preferences. Different techniques have been analysed including content-based, collaborative and hybrid approaches. The last one is used to improve performance prediction combining different recommender systems using the best features of each method, smoothing problems as cold-start. We evaluate our ensemble method using MovieLens dataset with promising results.

Abstract
In wireless visual sensor networks comprised of multiple camera-enabled sensors, source prioritization can be exploited to soften the impact of congestion, packet loss and energy depletion when higher relevant packets are processed. However, for such optimizations, source nodes have to be properly prioritized according to some effective metric. When performing visual sensing over moving targets, sensors may view different parts of the targets, which may have particular relevance for monitoring applications. In this context, this paper proposes a low-cost mathematical approach that associates a priority level to each visual source node according to the viewed segments of the targets' perimeter, and such priority may then be exploited for a large set of optimizations. A complete mathematical formulation and numerical results are presented to base the proposed approach.

Abstract
This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.