Abstract
This work consists of using an optical fiber microsphere as a sensor for a wide range of curvature radii. The microsphere was manufactured in a standard fiber with an electric arc. In order to maximize system efficiency, the microsphere was spliced in the center of a taper. This work revealed that the variations of the wavelength where the maxima and minima of the spectrum are located varies linearly with the curvature of the system with a maximum sensitive of 580 +/- 20 (pm km). This is because the direction of the input beam in the microsphere depends on the system curvature, giving rise to interferometric variations within the microsphere.

Abstract
As mobile devices are supporting more and more of our daily activities, it is vital to widen their battery up-time as much as possible. In fact, according to the Wall Street Journal, 9/10 users suffer from low battery anxiety. The goal of our work is to understand how Android usage, apps, operating systems, hardware and user habits influence battery lifespan. Our strategy is to collect anonymous raw data from devices all over the world, through a mobile app, build and analyze a large-scale dataset containing real-world, day-to-day data, representative of user practices. So far, the dataset we collected includes 12 million+ (anonymous) data samples, across 900+ device brands and 5.000+ models. And, it keeps growing. The data we collect, which is publicly available and by different channels, is sufficiently heterogeneous for supporting studies with a wide range of focuses and research goals, thus opening the opportunity to inform and reshape user habits, and even influence the development of both hardware and software for mobile devices.

Abstract

Abstract
In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution when considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to reduce the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic with improvements dominates the base PA* heuristic built on the euclidean distance, and then present the results of the performance increase in terms of node expansion and computation time.

Abstract
The growing dependence of modern-day societies on electricity increases the importance of effective monitoring and maintenance of power lines. Endowing UAVs with the appropriate sensors for inspecting power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual methods are usually applied to locate the power lines and their components, but poor light conditions or a background rich in edges may compromise their results. To overcome those limitations, we propose to address the problem of power line detection and modeling based on LiDAR. A novel approach to the power line detection was developed, the PL2DM -Power Line LiDAR-based Detection and Modeling. It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. The algorithm was validated with a real dataset, showing promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Abstract
The adoption of energy transition technologies for residential use is accelerated through incentive designs. The structure of such incentives affects technology adoption patterns, that is, the locations where new technologies are installed and used. These spatial adoption patterns influence network expansion costs and provide indication on potential cross-subsidization between population groups. While until today, most programs have been involuntarily favoring households with high-income and above-average educated population groups, incentive designs are currently under review. This paper presents a spatiotemporal technology adoption model that can predict adoption behavior of residential electric vehicle (EV) chargers and photovoltaic (PV) modules up to a predefined time horizon. A set of EV and PV adoption patterns for nine incentive design combinations are compared in order to assess potential synergies that may arise under orchestrated EV and PV adoption. Effects on adoption asymmetries are evaluated using an Information-Theoretic inequality metric. Results for Continental Portugal show that global network expansion costs can be reduced while minimizing technology adoption asymmetries, if specific incentive designs are combined.