Financing green development: 'Climate Bonds’ as a solution for institutional investors engagement

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Long-term behaviour of railway transitions under dynamic loading application to soft soil sites

Dissertação para obtenção do Grau de Doutor em Engenharia Civil

Validation of alternative additives for paste production

Dissertation presented to confer Master Degree in Chemical and Biochemical Engineering

Materials handling in hospitals: An analysis of how the storage and materials handling system can be improved, using the example of Hospital Santa Maria

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Cancer theranostics: multifunctional gold nanoparticles for diagnostics and therapy

Doctorate in Biology, Specialty in Biotechnology

Evaluation of pathogenic potential of Aeromonas spp. strains using in vitro methodologies

Dissertation to obtain a Master Degree in Molecular Genetics and Biomedicine

Design of future distribution grids

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Geospatial technologies for public participation: better decisions for smarter cities?

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Integrated functional genomics for improved manufacture of recombinant enveloped virus

Dissertation presented to obtain the Ph.D degree in Engineering and Technology Sciences-Biotechnology

Study on the development of an autonomous mobile robot

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

A clustering approach for vehicle routing problems with hard time windows

Dissertação para obtenção do Grau de Mestre em Logica Computicional

2D position system for a mobile robot in unstructured environments

Nowadays, several sensors and mechanisms are available to estimate a mobile robot trajectory and location with respect to its surroundings. Usually absolute positioning mechanisms are the most accurate, but they also are the most expensive ones, and require pre installed equipment in the environment. Therefore, a system capable of measuring its motion and location within the environment (relative positioning) has been a research goal since the beginning of autonomous vehicles. With the increasing of the computational performance, computer vision has become faster and, therefore, became possible to incorporate it in a mobile robot. In visual odometry feature based approaches, the model estimation requires absence of feature association outliers for an accurate motion. Outliers rejection is a delicate process considering there is always a trade-off between speed and reliability of the system. This dissertation proposes an indoor 2D position system using Visual Odometry. The mobile robot has a camera pointed to the ceiling, for image analysis. As requirements, the ceiling and the oor (where the robot moves) must be planes. In the literature, RANSAC is a widely used method for outlier rejection. However, it might be slow in critical circumstances. Therefore, it is proposed a new algorithm that accelerates RANSAC, maintaining its reliability. The algorithm, called FMBF, consists on comparing image texture patterns between pictures, preserving the most similar ones. There are several types of comparisons, with different computational cost and reliability. FMBF manages those comparisons in order to optimize the trade-off between speed and reliability.

Technology transition towards electric mobility - technology assessment as a tool for policy design

Paper presented at the Colloquium Gerpisa 2013, Paris (http://gerpisa.org/node/2085), Session n°: 19 New kinds of mobility: old and new business models

UAV - based imagery processing using structure from motion and remote sensing technology

With the recent advances in technology and miniaturization of devices such as GPS or IMU, Unmanned Aerial Vehicles became a feasible platform for a Remote Sensing applications. The use of UAVs compared to the conventional aerial platforms provides a set of advantages such as higher spatial resolution of the derived products. UAV - based imagery obtained by a user grade cameras introduces a set of problems which have to be solved, e. g. rotational or angular differences or unknown or insufficiently precise IO and EO camera parameters. In this work, UAV - based imagery of RGB and CIR type was processed using two different workflows based on PhotoScan and VisualSfM software solutions resulting in the DSM and orthophoto products. Feature detection and matching parameters influence on the result quality as well as a processing time was examined and the optimal parameter setup was presented. Products of the both workflows were compared in terms of a quality and a spatial accuracy. Both workflows were compared by presenting the processing times and quality of the results. Finally, the obtained products were used in order to demonstrate vegetation classification. Contribution of the IHS transformations was examined with respect to the classification accuracy.

Context classification for service robots

This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs. As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles.