Abstract
Temperature-independent strain and angle measurements are achieved resorting to a taper fabricated on a Bragg fiber using a CO2 laser. The characteristic bimodal interference of an untapered Bragg fiber is rendered multimode after taper fabrication and the resulting transmission spectra are analyzed as a function of strain, applied angle, and temperature variations. The intrinsic strain sensitivity exhibited by the Bragg fiber is increased 15 fold after tapering and reaches 22.68 pm/mu epsilon. The angle and temperature measurements are also performed with maximum sensitivities of 185.10 pm/deg and -12.20 pm/K, respectively. The difference in wavelength shift promoted by variations in strain, angle, and temperature for the two fringes studied is examined. Strain and angle sensing with little temperature sensitivity is achieved, presenting a response of 2.87 pm/mu epsilon and -57.31 pm/deg, respectively, for strain values up to 400 mu epsilon and angles up to 10 degrees. Simultaneous angle and strain measurements are demonstrated.

Abstract
Meta-heuristics have been applied for a long time to the Travelling Salesman Problem (TSP) but information is still lacking in the determination of the parameters with the best performance. This paper examines the impact of the Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) parameters in the TSP. One special consideration of this paper is how the Neighborhood Structure (NS) interact with the other parameters and impacts the performance of the meta-heuristics. NS performance has been the topic of much research, with NS proposed for the best-known problems, which seem to imply that the NS influences the performance of meta-heuristics, more that other parameters. Moreover, a comparative analysis of distinct meta-heuristics is carried out to demonstrate a non-proportional increase in the performance of the NS.

Abstract
Wireless Sensor Networks (WSNs) are susceptible to faults both in sensors and in communication. Information fusion techniques allow to extract precise information from a large amount of data. Detection, identification and treatment of outlier, in these techniques, is a key point. Outlier detection in WSNs is a challenge due to the low capacity of the nodes and low bandwidth of the network. This paper proposes a methodology that applies the clustering and lightweight statistics techniques for detection of outliers in WSNs. The assessment of the methodology involves a case study with temperature sensors in WSN nodes. The results show that this methodology is able to provide precise information, even in the presence of outliers.

Abstract
In this paper, we study the problem of projection kernel design for the reconstruction of high-dimensional signals from low-dimensional measurements in the presence of side information, assuming that the signal of interest and the side information signal are described by a joint Gaussian mixture model (GMM). In particular, we consider the case where the projection kernel for the signal of interest is random, whereas the projection kernel associated to the side information is designed. We then derive sufficient conditions on the number of measurements needed to guarantee that the minimum mean-squared error (MMSE) tends to zero in the low-noise regime. Our results demonstrate that the use of a designed kernel to capture side information can lead to substantial gains in relation to a random one, in terms of the number of linear projections required for reliable reconstruction. © 2016 IEEE.

Abstract
Scalability and topological stability are two of the most challenging issues in current wireless mesh networks (WMNs) deployments. In the literature, both the scalability and the topological stability of WMNs are described as likely to suffer from poor performance due to the ad hoc nature of the underlying IEEE 802.11 mechanisms. The main contribution of this article is a comprehensive review of the main topological stability and scalability-related issues in IEEE 802.11s-based networks. Moreover, the most relevant proposed solutions are surveyed, where both the drawbacks and the merits of each proposal are highlighted. At the end of the article, some open research challenges are presented and discussed. It is expected that this work may serve as motivation for more and deeper research on these issues to allow the design of future more stable and scalable IEEE 802.11s mesh networks deployments. Copyright (c) 2015 John Wiley & Sons, Ltd.

Abstract
Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items - the subset of items co-rated by both users typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process - a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.