Percursos de vida profissional: da formação inicial à entrada na carreira

Dissertação apresentada à Escola Superior de Educação de Lisboa para a obtenção do Grau de Mestre em Ciências da Educação - especialidade Supervisão em Educação

Avaliação de desempenho docente, supervisão e desenvolvimento profissional

Este texto incide sobre o papel da supervisão pedagógica no contexto da avaliação de desempenho docente (ADD), procurando aprofundar a forma como a dimensão formativa da avaliação foi equacionada e desenvolvida nas escolas. Para tal, foram realizados dois estudos num agrupamento de escolas da periferia de Lisboa, abrangendo professores avaliadores e avaliados do 1º e do 2º/3º ciclos. Os estudos tinham como objetivos gerais: i) conhecer asconceções de avaliadores e avaliados sobre os fundamentos e as práticas de avaliação de desempenho desenvolvidas nos seus contextos profissionais; ii) e definir o papel que avaliadores e avaliados atribuem à supervisão neste processo. Para a recolha de dados usou-se a entrevista semi-diretiva, recorrendo-se à análise de conteúdo para tratamento dos dados. O confronto dos resultados das entrevistas permite concluir que as conceções sobre a avaliação de desempenho dos docentes dos diferentes ciclos são semelhantes, mas o processo de avaliação e de supervisão foi vivido de forma distinta. O papel da supervisão na ADD depende, em larga escala, da competência dos avaliadores como supervisores e como professores e é facilitado pela existência prévia de uma cultura de colaboração entre docentes.

Estudo de um método de avaliação de desempenho aplicado aos técnicos de radiologia do Hospital Curry Cabral

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

A Spatial Econometrics Analysis for Road Accidents in Lisbon

This paper presents a spatial econometrics analysis for the number of road accidents with victims in the smallest administrative divisions of Lisbon, considering as a baseline a log-Poisson model for environmental factors. Spatial correlation on data is investigated for data alone and for the residuals of the baseline model without and with spatial-autocorrelated and spatial-lagged terms. In all the cases no spatial autocorrelation was detected.

Optimising protocol of acquisition and post-processing procedures in the assessment of coronary artery disease by computed tomography

Coronary artery disease (CAD) is currently one of the most prevalent diseases in the world population and calcium deposits in coronary arteries are one direct risk factor. These can be assessed by the calcium score (CS) application, available via a computed tomography (CT) scan, which gives an accurate indication of the development of the disease. However, the ionising radiation applied to patients is high. This study aimed to optimise the protocol acquisition in order to reduce the radiation dose and explain the flow of procedures to quantify CAD. The main differences in the clinical results, when automated or semiautomated post-processing is used, will be shown, and the epidemiology, imaging, risk factors and prognosis of the disease described. The software steps and the values that allow the risk of developingCADto be predicted will be presented. A64-row multidetector CT scan with dual source and two phantoms (pig hearts) were used to demonstrate the advantages and disadvantages of the Agatston method. The tube energy was balanced. Two measurements were obtained in each of the three experimental protocols (64, 128, 256 mAs). Considerable changes appeared between the values of CS relating to the protocol variation. The predefined standard protocol provided the lowest dose of radiation (0.43 mGy). This study found that the variation in the radiation dose between protocols, taking into consideration the dose control systems attached to the CT equipment and image quality, was not sufficient to justify changing the default protocol provided by the manufacturer.

Estimation of signal subspace on hyperspectral data

Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.

Analisador de vibrações de dois canais baseado em computador

O presente trabalho teve como principal objectivo o desenvolvimento de um analisador de vibrações de dois canais baseado em computador, para a realização de diagnóstico no âmbito do controlo de condição de máquinas. Foi desenvolvida uma aplicação num computador comum, no software LabVIEW, que através de transdutores de aceleração do tipo MEMS conectados via USB, faz a recolha de dados de vibração e procede ao seu processamento e apresentação ao utilizador. As ferramentas utilizadas para o processamento de dados são ferramentas comuns encontradas em vários analisadores de vibrações disponíveis no mercado. Estas podem ser: gráficos de espectro de frequência, sinal no tempo, cascata ou valores de nível global de vibração, entre outras. Apesar do analisador desenvolvido não apresentar inovação nas ferramentas de análise adoptadas, este pretende ser distinguido pelo baixo custo, simplicidade e carácter didáctico. Este trabalho vem evidenciar as vantagens, desvantagens e potencialidades de um analisador desta natureza. São tiradas algumas conclusões quanto à sua capacidade de diagnóstico de avarias, capacidades como ferramenta didáctica, sensores utilizados e linguagem de programação escolhida. Como conclusões principais, o trabalho revela que os sensores escolhidos não são os indicados para efectuar o diagnóstico de avarias em ambiente industrial, contudo são ideais para tornar este analisador numa boa ferramenta didáctica e de treino.

Análise de fiabilidade de sistemas e componentes de helicópteros

Este trabalho ocorre face à necessidade da empresa Helisuporte ter uma perspectiva a nível de fiabilidade das suas aeronaves. Para isso, foram traçados como objectivos de estudo a criação de uma base de dados de anomalias; identificação de sistemas e componentes problemáticos; caracterização dos mesmos, avaliar a condição de falha e, com isto, apresentar soluções de controlo de anomalias. Assim, foi desenvolvida uma metodologia que proporciona tratamento de dados com recurso a uma análise não-paramétrica, tendo sido escolhida a estatística de amostra. Esta irá permitir a identificação dos sistemas problemáticos e seus componentes anómalos. Efectuado o tratamento de dados, passamos para a caracterização fiabilística desses componentes, assumindo o tempo de operação e a vida útil específica de cada um. Esta foi possível recorrendo ao cálculo do nível de fiabilidade, MTBF, MTBUR e taxa de avarias. De modo a identificar as diferentes anomalias e caracterizar o “know-how” da equipa de manutenção, implementou-se a análise de condição de falha, mais propriamente a análise dos modos e efeitos de falha. Tendo isso em atenção, foi construído um encadeamento lógico simples, claro e eficaz, face a uma frota complexa. Implementada essa metodologia e analisados os resultados podemos afirmar que os objectivos foram alcançados, concluindo-se que os valores de fiabilidade que caracterizam alguns dos componentes das aeronaves pertencentes à frota em estudo não correspondem ao esperado e idealizado como referência de desempenho dos mesmos. Assim, foram sugeridas alterações no manual de manutenção de forma a melhorar estes índices. Com isto conseguiu-se desenvolver, o que se poderá chamar de, “fiabilidade na óptica do utilizador”.

Avaliação de dados funcionais em repouso do cérebro normal: causalidade de Granger

Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Ressonância Magnética

A aprendizagem cooperativa como ferramenta para a inclusão

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção do grau de Mestre em Ciências da Educação - Especialização em Educação Especial, Domínio Cognição e Multideficiência

Desenvolvimento de uma plataforma SIG para apoio ao controlo da segurança das grandes barragens portuguesas

Trabalho Final de Mestrado elaborado no Laboratório Nacional de Engenharia Civil (LNEC) para a obtenção do grau de Mestre em Engenharia Civil pelo Instituto Superior de Engenharia de Lisboa no âmbito do protocolo de cooperação entre o ISEL e o LNEC

Ser mulher, aqui: teatro, género e comunidade

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Artística, na especialização de Teatro na Educação

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Implementação em hardware reconfigurável de método de separação de dados hiperespetrais

Relatório do Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações