Abstract
A curvature sensor based on a Fabry-Perot interferometer is proposed. A capillary tube of silica is fusion spliced between two single mode fibers, producing a Fabry-Perot cavity. The light propagates in air, when passing through the capillary tube. Two different cavities are subjected to curvature and temperature. The cavity with shorter length shows insensitivity to both measurands. The larger cavity shows two operating regions for curvature measurement, where a linear response is shown, with a maximum sensitivity of 18.77pm/m(-1) for the high curvature radius range. When subjected to temperature, the sensing head produces a similar response for different curvature radius, with a sensitivity of 0.87pm/degrees C.

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
This work describes the state of the art of electrochemical devices for the detection of an important class of neurotransmitters: the catecholamines. This class of biogenic amines includes dopamine, noradrenaline (also called norepinephrine) and adrenaline (also called epinephrine). Researchers have focused on the role of catecholamine molecules within the human body because they are involved in many important biological functions and are commonly associated with several diseases, such as Alzheimer's and Parkinson. Furthermore, the release of catecholamines as a consequence of induced stimulus is an important indicator of reward-related behaviors, such as food, drink, sex and drug addiction. Thus, the development of simple, fast and sensitive electroanalytical methodologies for the determination of catecholamines is currently needed in clinical and biomedical fields, as they have the potential to serve as clinically relevant biomarkers for specific disease states or to monitor treatment efficacy. Currently, three main strategies have used by researchers to detect catecholamine molecules, namely: the use electrochemical materials in combination with, for example, HPLC or FIA, the incorporation of new materials/layers on the sensor surfaces (Tables 1-7) and in vivo detection, manly by using FSCV at CFMEs (Section 10). The developed methodologies were able not only to accurately detect catecholamines at relevant concentration levels, but to do so in the presence of co-existing interferences in samples detected (ascorbate, for example). This review examines the progress made in electrochemical sensors for the selective detection of catecholamines in the last 15 years, with special focus on highly innovative features introduced by nanotechnology. As the literature in rather extensive, we try to simplify this work by summarizing and grouping electrochemical sensors according to the manner their substrates were chemically modified. We also discuss the current and future of electrochemical sensors for catecholamines in terms of the analytical performance of the devices and emerging applications.