Abstract
This paper describes the development of a mobile indoor navigation system, supported by a GIS and built using only open source tools. For the sake of simplicity a single building was chosen for the tests converting the floors to digital information from paper plans. The rooms geometry was saved on a proper database with all the adjacent information associated, which can in turn be provided to the clients application by APIs and Web Services. The system is able to calculate the most adequate path between any of the rooms taking into account the user profile which is defined by it's degree of mobility (eg. wheelchair). By reading a QR code placed in key places inside the building the user can obtain, on a mobile phone, his current position and receive orientations to any room that he might want to go. The directions hints are complemented with the presentation of real pictures associated to key locations in the path to validate that the correct path is taken by the user. (C) 2015 The Authors. Published by Elsevier B.V.

Abstract
Animal experiments have gained importance in human diseases studies, namely neurological diseases and its biomechanical and physiological aspects. As a model of human disease, the rat offers many advantages over other organisms. For the biomechanical aspects of locomotion these studies are based on the analysis of animals' kinetic parameters, accessed through a locomotion measurement system. However, these systems are not yet thoroughly developed, are still scarce and are also very expensive when developed for studies using small rodents. In this paper, a system capable of measuring contact forces of small rodents is presented. The platform hardware is based on a 5 x 3 matrix of ultra-sensitive force sensors that produce a set of signals acquired in a LabVIEW (TM) environment, used for data acquisition and processing. The post processing steps include the removal of null data, curve normalization related to the rat's weight and expressed as percentage of passage, resulting in a gait pattern. The proposed cost-effective system has achieved excellent results regarding the locomotion profile of healthy animals.

Abstract
The usage of small-sized unmanned aerial systems (UAS) has increased in the last years, in many different areas, being agriculture and forestry those who benefit the most from this relatively new remote sensing platform. Leaf area index, canopy and plant volume are among the parameters that can be determined using the very high resolution aerial data obtained by sensors coupled in unmanned aerial vehicles (UAV). This remote sensing technology affords the possibility of monitoring the vegetative development, identifying different types of issues, enabling the application of the most appropriated treatments in the affected areas. In this paper, a methodology allowing to perform multi-temporal UAS-based data analysis obtained by different sensors is proposed. A case study in vineyards and chestnuts is used to prove the benefits of continuous crop monitoring in its management and productivity of agroforestry parcels/activities. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
In the last decade, SAR interferometry techniques, especially those that use time series analysis experienced a strong development in both, methodologies and applications, becoming an operational tool for deformation monitoring. The emergence of a growing number of SAR dedicated missions combined with the increasing interest from academics, but also private research groups, reflected in the number of available software packages developed with interferometric analysis purposes, were the major responsible for the InSAR/MTI achievements occurred over the past few years. Many free-of-charge (freeware or open-source) and commercial software packages exist. Due to its proven reliability and freeware distribution among the scientific community, Stanford Method for Persistent Scatterers/Multi-Temporal Interferometry (StaMPS/MTI) implementation, is widely used for ground deformation monitoring. This paper presents viStaMPS v1.2, a collaborative scientific project that appeared with three major purposes: (1) facilitate the usage by users nonfamiliar with the specificities of the programming language that supports StaMPS; (2) implement several visualization tasks not available in the StaMPS standard approach requiring that each user to develop its own code for visualization and interpretation purposes and (3) create a collaborative research project, continually under development counting on the dynamism of its users to improve and/or add new features. (C) 2014 Published by Elsevier Ltd.

Abstract
Due to strong improvements and developments achieved in the last decade, it is clear that applied research using remote sensing technology such as unmanned aerial vehicles (UAVs) can provide a flexible, efficient, non-destructive, and non-invasive means of acquiring geoscientific data, especially aerial imagery. Simultaneously, there has been an exponential increase in the development of sensors and instruments that can be installed in UAV platforms. By combining the aforementioned factors, unmanned aerial system (UAS) setups composed of UAVs, sensors, and ground control stations, have been increasingly used for remote sensing applications, with growing potential and abilities. This paper's overall goal is to identify advantages and challenges related to the use of UAVs for aerial imagery acquisition in forestry and coastal environments for preservation/prevention contexts. Moreover, the importance of monitoring these environments over time will be demonstrated. To achieve these goals, two case studies using UASs were conducted. The first focuses on phytosanitary problem detection and monitoring of chestnut tree health (Padrela region, Valpacos, Portugal). The acquired high-resolution imagery allowed for the identification of tree canopy cover decline by means of multi-temporal analysis. The second case study enabled the rigorous and non-evasive registry process of topographic changes that occurred in the sandspit of Cabedelo (Douro estuary, Porto, Portugal) in different time periods. The obtained results allow us to conclude that the UAS constitutes a low-cost, rigorous, and fairly autonomous form of remote sensing technology, capable of covering large geographical areas and acquiring high precision data to aid decision support systems in forestry preservation and coastal monitoring applications. Its swift evolution makes it a potential big player in remote sensing technologies today and in the near future.

Abstract
This paper introduces a fast Traffic Sign Recognition system developed for a robot, participant in the Autonomous Driving Competition in the Portuguese Festival of Robotics. The Autonomous Driving Robot performs detection and classification of traffic signs and traffic lights based on the analysis of images acquired by a camera mounted on its chassis. The proposed algorithm is composed of three processing stages: detection, pictogram extraction and classification. After the two firsts processing stages, a binary pattern matrix is obtained by color segmentation. In the classification stage two different neural networks were trained to recognize the traffic signs or the traffic light sign. Experimental results show that the system precision is very close to 100% whereas recall presents values above 90% in most of the signs. The proposed system also proves to be reliable and suitable for real-time processing.