Abstract
Optical fibre Bragg grating (FBG) sensors are now quite established and widely used in strain measurements in composites. However, insufficient understanding of the limitations of the embedment and measuring techniques often leads to inaccurate results. This work is a continuation of a novel method to improve the reliability and accuracy of the strain measurements on unidirectional composites using embedded FBG sensors [1]. A new combination of the pair host material/sensor was studied and characterized. Test specimens were manufactured with longitudinally embedded FBG sensors, using a glass/epoxy prepreg system, in order to compare with a carbon/epoxy prepreg system. The combined behaviour of the sensors and the host material was characterized and a procedure to obtain a more accurate strain was defined for this new chosen material.

Abstract
Emerging large-scale, data intensive applications that use the graph abstraction to represent problems in a broad spectrum of scientific and analytics applications have radically different features from floating point intensive scientific applications. These complex graph applications, besides having large working datasets, exhibit very low spatial and temporal locality which makes designing algorithmic fault tolerance for these quite challenging. They will run on future exascale-class High Performance Computing (HPC) systems that will contain massive number of components, and will be built from devices far less reliable than those used today. In this paper we propose software based approaches that increase robustness for these data intensive, graph-based applications by managing the resiliency in terms of the data flow progress and validation of pointer computations. Our experimental results show that such a simple approach incurs fairly low execution time overheads while allowing these computations to survive a large number of faults that would otherwise always result in the termination of the computation. © 2013 IEEE.

Abstract

Abstract
In this paper, we present a WiSARD-based system facing the problem of Indoor Positioning (IP) by taking advantage of pervasively available infrastructures (WiFi Access Points -AP). The goal is to develop a system to be used to position users in indoor environments, such as: museums, malls, factories, offshore platforms etc. Based on the fingerprint approach, we show how the proposed weightless neural system provides very good results in terms of performance and positioning resolution. Both the approach to the problem and the system will be presented through two correlated experiments.

Abstract
The present phenomenon of technology convergence is blurring away the frontiers between the Internet and the TV, operating a shift on the way TV is consumed. TV viewers have now access to a huge selection of TV programming as well as online contents, either previously broadcasted or natively produced for the Internet. This reality creates new necessities whilst opening new opportunities for the creation of services capable of filtering this information and presenting the user with the most relevant content. This article describes an innovative hybrid strategy for delivering recommendations of TV content to individual users. It was developed specifically for the TV entertainment services of hotels, but it can be applied to any multimedia consumption service. Without requiring users to explicitly rate the programs they have watched, it is still able to recommend similar programs to similar users. It adopts an improved Pearson correlation method to establish similarities between different users, comparing profiles that have been automatically generated based on the user viewing history. It builds a predicted user profile, which is then used within a content-based approach to generate recommendations.

Abstract