Unsupervised classification of remote sensing images combining Self Organizing Maps and segmentation techniques

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

Contract-program evaluation: Impact of the DRG classification system on hospitals' outputs

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

EEG (eletroencefalografia) e as próteses auditivas: um instrumento de avaliação de Tecnologia?

Como parte integrante da Escola de Inverno do Curso Doutoral em Avaliação de Tecnologia, que decorreu no dia 13 de Dezembro de 2011, na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, apresentou-se o Plano de Tese “Estudo Eletroencefalográfico em Crianças com Deficiência Auditiva Com e Sem Prótese”. O presente documento é o resultado da mesma apresentação onde salientamos as suas partes fundamentais. O sistema auditivo é um dos sistemas sensoriais mais interveniente no desenvolvimento cognitivo, psicológico, social e consequentemente relacional do indivíduo pelo que qualquer perturbação que o afete tem um enorme impacto a todos os níveis. Avaliar, diagnosticar e (re)abilitar auditivamente o indivíduo é a área profissional da doutoranda. Este estudo surge no contexto profissional mas também pessoal onde se pensa contribuir de forma efetiva na melhoria da qualidade de vida dos portadores de défice auditivo, desde a mais tenra idade. Este estudo irá utilizar o EEG como instrumento na avaliação da perceção auditiva. Para tal, serão avaliadas crianças com défice auditivo adaptadas proteticamente, seguidas na consulta de otorrinolaringologia pediátrica do Hospital Garcia de Orta. O EEG será realizado em dois passos – com e sem prótese auditiva – enquanto a criança é submetida a um estímulo sonoro.

Exploratory analysis of OpenStreetMap for land use classification

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas’ values are remarkable and promising results that encourages us for future research on this topic.

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.

Estudo do controlo motor via Sincronias entre sinais de EEG e de EMG / Motor control study through sincrony between EEG and EMG signals

Um dos maiores desafios da neurofisiologia é o de compreender a forma como a informação é transmitida através do sistema nervoso. O estudo do sistema nervoso tem várias aplicações, tanto na neurologia, permitindo avanços ao nível clínico, como noutras áreas, e.g., nos sistemas de processamento de informação baseados em redes neuronais. A transmissão de informação entre neurónios é feita por via de sinais elétricos. A compreensão deste fenómeno é ainda incompleta e há projectos a nível europeu e mundial com o objetivo de modular o sistema nervoso no seu todo de forma a melhor o compreender. Uma das teses que se desenvolve hoje em dia é a de que a transmissão de sinais elétricos no sistema nervoso é influenciada por fenómenos de sincronia. O objetivo desta dissertação é o de otimizar um protocolo de aquisição e análise de dados reais de eletroencefalograma e eletromiograma com o propósito de observar fenómenos de sincronia, baseando-se num algoritmo (análise por referência de fase, ou RPA, do inglês reference phase analysis) que deteta sincronias de fase entre os sinais de eletroencefalograma (EEG) e um sinal de referência, que é, no caso presente, o eletromiograma (EMG). A otimização deste protocolo e sua validação indicaram a existência de fenómenos significativos de sincronia no sinal elétrico, transmitido entre os músculos da mão e o córtex motor, no decorrer da ação motora.

Analysis of classification algorithms for crop detection using LANDSAT 8 images

Remote sensing - the acquisition of information about an object or phenomenon without making physical contact with the object - is applied in a multitude of different areas, ranging from agriculture, forestry, cartography, hydrology, geology, meteorology, aerial traffic control, among many others. Regarding agriculture, an example of application of this information is regarding crop detection, to monitor existing crops easily and help in the region’s strategic planning. In any of these areas, there is always an ongoing search for better methods that allow us to obtain better results. For over forty years, the Landsat program has utilized satellites to collect spectral information from Earth’s surface, creating a historical archive unmatched in quality, detail, coverage, and length. The most recent one was launched on February 11, 2013, having a number of improvements regarding its predecessors. This project aims to compare classification methods in Portugal’s Ribatejo region, specifically regarding crop detection. The state of the art algorithms will be used in this region and their performance will be analyzed.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.