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.

Mapping atmospheric pollutants emissions in European countries

In this paper we present a methodology which enables the graphical representation, in a bi-dimensional Euclidean space, of atmospheric pollutants emissions in European countries. This approach relies on the use of Multidimensional Unfolding (MDU), an exploratory multivariate data analysis technique. This technique illustrates both the relationships between the emitted gases and the gases and their geographical origins. The main contribution of this work concerns the evaluation of MDU solutions. We use simulated data to define thresholds for the model fitting measures, allowing the MDU output quality evaluation. The quality assessment of the model adjustment is thus carried out as a step before interpretation of the gas types and geographical origins results. The MDU maps analysis generates useful insights, with an immediate substantive result and enables the formulation of hypotheses for further analysis and modeling.

O trabalho de projeto no desenvolvimento da cidadania

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Matemática na Educação Pré-Escolar e nos 1º e 2º Ciclos do Ensino Básico

Ética e responsabilidade social das organizações perante os Técnicos Oficiais de Contas

Mestrado em Contabilidade

O impacto do ruído na qualidade de vida dos enfermeiros de cuidados intensivos

Mestrado em Segurança e Higiene do Trabalho.

A aprendizagem da Matemática no 1º ciclo através de atividades de investigação numa comunidade de aprendizagem

Dissertação apresentada para obtenção do grau de Mestre em Educação Matemática na Educação Pré-Escolar e nos 1º e 2º Ciclos do Ensino Básico na especialidade de Didática da Matemática

Direitos de participação das crianças: estudo de caso num jardim de Infância em contexto do movimento da escola moderna

Dissertação apresentada à Escola Superior de Educação de Lisboa para a obtenção de grau de mestre em Ciências da Educação, Especialização em Intervenção Precoce

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.

Estudo exploratório : as estratégias motivacionais no ensino da técnica da dança clássica, para alunos do 3º ano, no Ginasiano Escola de Dança

Relatório Final de Estágio apresentado à Escola Superior de Dança, com vista à obtenção do grau de Mestre em Ensino de Dança.

Perspetivas de professores, pais e alunos sobre a prática dos trabalhos para casa

Relatório Final apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e do 2.º Ciclo do Ensino Básico

Caracterização do desempenho das redes rodoviárias com base em modelos de afetação de tráfego: aplicação à Rede Rodoviária Nacional

Relatório de estágio para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização em Vias de Comunicação e Transportes

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.