Inserção profissional dos licenciados em medicina nuclear da ESTESL entre 2010 e 2013

A inserção profissional é baseada na integração de diferentes perspetivas, entre elas, o contexto académico, os empregadores e os graduados. O objetivo deste estudo é analisar o percurso profissional dos técnicos de medicina nuclear (TMN), graduados pela Escola Superior de Tecnologia da Saúde de Lisboa (ESTeSL) entre 2010 e 2013.

Estudos estruturais em ferredoxinas por ressonância magnética nuclear

Dissertação apresentada para obtenção do Grau de Doutor em Química, especialidade Química Inorgânica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Enhanced PCA-based localization using depth maps with missing data

In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.

Lithostratigraphy, sequence stratigraphy and depositional setting of the Pliensbachian and Toarcian series in the Lusitanian Basin, Portugal

In the Lusitanian Basin (Central Portugal), the Middle-Upper Liassic series are characterized by an expressive marly limestone accumulation, sediments that were deposited on a homoclinal carbonate ramp. These series belong to the Vale das Fontes, Lemede, S. Gião (and the lateral equivalents Prado and Cabo Carvoeiro Formations) and, partially, to the Póvoa da Lomba Formations. These units, in great part controlled by an accurate ammonite biostratigraphic scale, are organized into two secondorder transgressive-regressive sequences. The first one (SP) is dated of early Pliensbachian/lowermost early Toarcian age; the second (ST) is dated of early Toarcian to early Aalenian.

A physical packing sequence algorithm for the container loading problem with static mechanical equilibrium conditions

The container loading problem (CLP) is a combinatorial optimization problem for the spatial arrangement of cargo inside containers so as to maximize the usage of space. The algorithms for this problem are of limited practical applicability if real-world constraints are not considered, one of the most important of which is deemed to be stability. This paper addresses static stability, as opposed to dynamic stability, looking at the stability of the cargo during container loading. This paper proposes two algorithms. The first is a static stability algorithm based on static mechanical equilibrium conditions that can be used as a stability evaluation function embedded in CLP algorithms (e.g. constructive heuristics, metaheuristics). The second proposed algorithm is a physical packing sequence algorithm that, given a container loading arrangement, generates the actual sequence by which each box is placed inside the container, considering static stability and loading operation efficiency constraints.

O debate estratégico nuclear nos EUA (1945-2010): tendências e evolução

Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Ciência Política e Relações Internacionais,

Reactores a tório: o futuro da energia nuclear de fissão

Dissertação para a obtenção do grau de mestre em Engenharia Electrotécnica Ramo de Energia

Reversible current power supply for fast-field cycling nuclear magnetic resonance equipment

The Fast Field-Cycling Nuclear Magnetic Resonance (FFC-NMR) is a technique used to study the molecular dynamics of different types of materials. The main elements of this equipment are a magnet and its power supply. The magnet used as reference in this work is basically a ferromagnetic core with two sets of coils and an air-gap where the materials' sample is placed. The power supply should supply the magnet being the magnet current controlled in order to perform cycles. One of the technical issues of this type of solution is the compensation of the non-linearities associated to the magnetic characteristic of the magnet and to parasitic magnetic fields. To overcome this problem, this paper describes and discusses a solution for the FFC-NMR power supply based on a four quadrant DC/DC converter.

Comparative study on the localization of adult Schistosoma mansoni worms in albino mice anesthetized with pentobarbital sodium, ether or chlorophorm

The effect of anesthetic drugs on the localization of adult worms in albino mice was compared. The animals with 56 days of infection were anesthetized with pentobarbital sodium, ether or chlorophorm. Perfusion was carried out immediately after, recovering the worms and classifying them in relation to their localization on the liver or portal vein and the mesenteric veins. Our results showed that pentobarbital sodium produced a greater displacement of the worms to the liver (89%) than ether (76%) and chlorophorm (34%) did, when compared to the control group (22%). The difference between pentobarbital sodium and ether was significant (p < 0.05). We suggest that anesthetic drugs may not be used in studies on the distribution of adult worms in several hosts.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

Geo-localization System for People with Cognitive Disabilities

Technology is present in almost every simple aspect of the people’s daily life. As an instance, let us refer to the smartphone. This device is usually equipped with a GPS modulewhich may be used as an orientation system, if it carries the right functionalities. The problem is that these applications may be complex to operate and may not be within the bounds of everybody. Therefore, the main goal here is to develop an orientation system that may help people with cognitive disabilities in their day-to-day journeys, when the caregivers are absent. On the other hand, to keep paid helpers aware of the current location of the disable people, it will be also considered a localization system. Knowing their current locations, caregiversmay engage in others activities without neglecting their prime work, and, at the same time, turning people with cognitive disabilities more independent.

Step count algorithm adapted to indoor localization

This paper presents a step count algorithm designed to work in real-time using low computational power. This proposal is our first step for the development of an indoor navigation system, based on Pedestrian Dead Reckoning (PDR). We present two approaches to solve this problem and compare them based in their error on step counting, as well as, the capability of their use in a real time system.

Traveled Distance Estimation Algorithm for Indoor Localization

This paper presents an ankle mounted Inertial Navigation System (INS) used to estimate the distance traveled by a pedestrian. This distance is estimated by the number of steps given by the user. The proposed method is based on force sensors to enhance the results obtained from an INS. Experimental results have shown that, depending on the step frequency, the traveled distance error varies between 2.7% and 5.6%.

Person Localization Using Sensor Information Fusion

Nowadays the incredible grow of mobile devices market led to the need for location-aware applications. However, sometimes person location is difficult to obtain, since most of these devices only have a GPS (Global Positioning System) chip to retrieve location. In order to suppress this limitation and to provide location everywhere (even where a structured environment doesn’t exist) a wearable inertial navigation system is proposed, which is a convenient way to track people in situations where other localization systems fail. The system combines pedestrian dead reckoning with GPS, using widely available, low-cost and low-power hardware components. The system innovation is the information fusion and the use of probabilistic methods to learn persons gait behavior to correct, in real-time, the drift errors given by the sensors.

Localization system for pedestrians based on sensor and information fusion

Nowadays there is an increase of location-aware mobile applications. However, these applications only retrieve location with a mobile device's GPS chip. This means that in indoor or in more dense environments these applications don't work properly. To provide location information everywhere a pedestrian Inertial Navigation System (INS) is typically used, but these systems can have a large estimation error since, in order to turn the system wearable, they use low-cost and low-power sensors. In this work a pedestrian INS is proposed, where force sensors were included to combine with the accelerometer data in order to have a better detection of the stance phase of the human gait cycle, which leads to improvements in location estimation. Besides sensor fusion an information fusion architecture is proposed, based on the information from GPS and several inertial units placed on the pedestrian body, that will be used to learn the pedestrian gait behavior to correct, in real-time, the inertial sensors errors, thus improving location estimation.