Abstract
In this paper, we propose and experimentally investigate an optical sensor based on a novel combination of a long-period fiber grating (LPFG) with a permanent magnet to measure electrical current in unmanned aerial vehicles (UAVs). The proposed device uses a neodymium magnet attached to the grating structure, which suffers from an electromagnetic force produced when the current flows in the wire of the UAV engine. Therefore, it causes deformation on the sensor and thus, different shifts occur in the resonant bands of the transmission spectrum of the LPFG. Finally, the results show that it is possible to monitor electrical current throughout the entire operating range of the UAV engine from 0 A to 10 A in an effective and practical way with good linearity, reliability and response time, which are desirable characteristics in electrical current sensing. © 2016 by the authors; licensee MDPI, Basel, Switzerland.

Abstract

Abstract

Abstract
Focused ion beam technology is combined with dynamic chemical etching to create microcavities in tapered optical fiber tips, resulting in fiber probes for temperature and refractive index sensing. Dynamic chemical etching uses hydrofluoric acid and a syringe pump to etch standard optical fibers into cone structures called tapered fiber tips where the length, shape, and cone angle can be precisely controlled. On these tips, focused ion beam is used to mill several different types of Fabry-Perot microcavities. Two main cavity types are initially compared and then combined to form a third, complex cavity structure. In the first case, a gap is milled on the tapered fiber tip which allows the external medium to penetrate the light guiding region and thus presents sensitivity to external refractive index changes. In the second, two slots that function as mirrors are milled on the tip creating a silica cavity that is only sensitive to temperature changes. Finally, both cavities are combined on a single tapered fiber tip, resulting in a multi-cavity structure capable of discriminating between temperature and refractive index variations. This dual characterization is performed with the aid of a fast Fourier transform method to separate the contributions of each cavity and thus of temperature and refractive index. Ultimately, a tapered optical fiber tip probe with sub-standard dimensions containing a multi-cavity structure is projected, fabricated, characterized and applied as a sensing element for simultaneous temperature and refractive index discrimination.

Abstract
This paper deals with the distribution network reconfiguration problem in a multi-objective scope, aiming to determine the optimal radial configuration by means of minimizing the active power losses and a set of commonly used reliability indices formulated with reference to the number of customers. The indices are developed in a way consistent with a mixed-integer linear programming (MILP) approach. A key contribution of the paper is the efficient implementation of the epsilon-constraint method using lexicographic optimization in order to solve the multi-objective optimization problem. After the Pareto efficient solution set is generated, the resulting configurations are evaluated using a backward/forward sweep load-flow algorithm to verify that the solutions obtained are both non-dominated and feasible. Since the epsilon-constraint method generates the Pareto front but does not incorporate decision maker (DM) preferences, a multi-attribute decision making procedure, namely, the technique for order preference by similarity to ideal solution (TOPSIS) method, is used in order to rank the obtained solutions according to the DM preferences, facilitating the final selection. The applicability of the proposed method is assessed on a classical test system and on a practical distribution system.

Abstract
With the advent of social networking, a lot of user-specific, voluntarily provided data has been generated. Researchers and companies noticed the value that lied within those enormous amounts of data and developed algorithms and tools to extract patterns in order to act on them. TweeProfiles is an offline clustering tool that analyses tweets over multiple dimensions: spatial, temporal, content and social. This project was extended in TweeProfiles2 by enabling the processing of real-time data. In this work, we developed a visualization tool suitable for data streaming, using multiple widgets to better represent all the information. The usefulness of the developed tool for journalism was evaluated based on a usability test, which despite its reduced number of participants yielded good results.