Abstract
In this paper, two techniques for interrogation and multiplexing of fiber loop mirror (FLM) intensity sensors based on optical time domain reflectometer (OTDR) are proposed. These configurations enable series and parallel FLM sensor interrogation. A fiber taper characterized as a displacement sensor was used as the intensity sensor. The OTDR parameters were optimized in order to obtain the best results. The optimized parameters were 100-ns pulse width and 10-dB input signal attenuation which permitted to attain similar to 18 dB dynamic range in the operating wavelength of 1550 nm. The results show a linear behavior for both configurations with similar slope, -15.3 dB/mm, in the normalized displacement range of 0.2 to 0.7 mm. It was also achieved a displacement resolution of 0.027 and 0.093 mm, for the series and parallel configurations, respectively. Sensors multiplexing are demonstrated for both configurations and the systems do not present crosstalk. Based on the experimental results, the best configuration is the parallel one. The proposed approach is a viable alternative for multiplexing and interrogation of remote fiber sensors.

Abstract
This work presents the use of a genetic algorithm to design a Mandani Fuzzy Controller with two inputs and one output, written in Matlab (R) environment, applied to a two axis positioning system using a robot. The robot has 6 degrees of freedom and is controlled with the objective of capturing an object on a workspace using a fuzzy controller. A genetic algorithm is used in order to determine the main characteristics of the membership functions of the fuzzy controller. The complete system employed to simulate the two axes positioning system uses the Transfer Function of two axes of the robot and the Fuzzy controller. In this work was implemented and simulated an operating scenario, being the results and the performance of the controller presented regarding the controller energy effort and the evolution of the (x,y) trajectories over time.

Abstract

Abstract
The aggregation of network traffic has been shown to enhance the performance of wireless sensor networks. By reducing the number of packets that are transmitted, energy consumption, collisions and congestion are minimised. However, current data aggregation schemes restrict developers to a specific network structure or cannot handle multi-hop data aggregation. In this paper, we propose Hitch Hiker, a remote component binding model that provides support for multi-hop data aggregation. Hitch Hiker uses component meta-data to discover remote component bindings and to construct a multi-hop overlay network within the free payload space of existing traffic ows. This overlay network provides end-To-end routing of low-priority traffic while using only a small fraction of the energy of standard communication. We have developed a prototype implementation of Hitch Hiker for the LooCI component model. Our evaluation shows that Hitch Hiker consumes minimal resources and that using Hitch Hiker to deliver low-priority traffic reduces energy consumption by up to 15%.

Abstract
Trip duration is an important metric for the management of taxi companies, as it affects operational efficiency, driver satisfaction and, above all, customer satisfaction. In particular, the ability to predict trip duration in advance can be very useful for allocating taxis to stands and finding the best route for trips. A data mining approach can be used to generate models for trip time prediction. In fact, given the amount of data available, different models can be generated for different taxis. Given the difference between the data collected by different taxis, the best model for each one can be obtained with different algorithms and/or parameter settings. However, finding the configuration that generates the best model for each taxi is computationally very expensive. In this paper, we propose the use of metalearning to address the problem of selecting the algorithm that generates the model with the most accurate predictions for each taxi. The approach is tested on data collected in the Drive-In project. Our results show that metalearning can help to select the algorithm with the best accuracy.

Abstract
The present invention is directed to manufacturing, wherein each component of the outsourced process and system can be remotely controlled in decentralized manufacturing process, integrating resources and stakeholders in a global chain, that utilizes ubiquitous computing systems virtual and networked enterprises concepts, for anywhere-anytime control. Experiment involved 68 subjects, Serbian students that have used the interface for remote collaborative control to control CNC machine located in Portugal. Usability evaluation measures of the distributed remote user controlled manufacturing system, such as percentage of task completed, its accuracy and time to complete task, are measured on two types of "client" user interface ("Wall" and "Window"), in two modes of presentation (desktop and video beam), working individually or in small collaborative group consisted of two persons, results were statistically tested. Percentage of task completed is equal for both types of interfaces, while accuracy of task is significantly better for "Wall" interface. Time of task execution is longer for individuals when desktop is used comparing to video beam. There exist significant differences when work is conducted in groups via video beam and time is longer when desktop is used. Also, work time is longer with "Wall" interface. When working in group, work time is shorter than the time when working individually, whereby the working time on desktop is longer than on the video beam. Also, the time is longer when working individually using video beam, using "Wall" interface on desktop, and "Window" interface on video beam. These results show that group work consumes less time in most working options, giving the best results when working in collaborative small group on "Wall" interface via video beam. (C) 2015 The Authors. Published by Elsevier B.V.