Urban land cover change detection analysis and modeling spatio-temporal Growth dynamics using Remote Sensing and GIS Techniques: A case study of Dhaka, Bangladesh

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

Criação de clusters de clientes de um banco através do self-organizing maps hierárquico: comparação entre HSOM e SOM

Trabalho de Projeto apresentado como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Analysis of urban land use and land cover changes: a case of study in Bahir Dar, Ethiopia

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

Trans-Synaptic Signaling by Activity-Dependent Cleavage of Neuroligin-1

Throughout the brain, patterns of activity in postsynaptic neurons influence the properties of synaptic inputs. Such feedback regulation is central to neural network stability that underlies proper information processing and feature representation in the central nervous system. At the cellular level, tight coupling of presynaptic and postsynaptic function is fundamental to neural computation and synaptic plasticity. The cohort of protein complexes at the pre and postsynaptic membrane allows for tight synapse-specific segregation and integration of diverse molecular and electrical signals.(...)

State detection in a financial portfolio: a self-organizing maps approach for financial time series

This study analyses financial data using the result characterization of a self-organized neural network model. The goal was prototyping a tool that may help an economist or a market analyst to analyse stock market series. To reach this goal, the tool shows economic dependencies and statistics measures over stock market series. The neural network SOM (self-organizing maps) model was used to ex-tract behavioural patterns of the data analysed. Based on this model, it was de-veloped an application to analyse financial data. This application uses a portfo-lio of correlated markets or inverse-correlated markets as input. After the anal-ysis with SOM, the result is represented by micro clusters that are organized by its behaviour tendency. During the study appeared the need of a better analysis for SOM algo-rithm results. This problem was solved with a cluster solution technique, which groups the micro clusters from SOM U-Matrix analyses. The study showed that the correlation and inverse-correlation markets projects multiple clusters of data. These clusters represent multiple trend states that may be useful for technical professionals.

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

Contributo para a modelação do estudo do comportamento de barragens de aterro com o auxílio de redes neuronais artificiais

O conhecimento do comportamento das barragens de aterro é essencial para garantir o seu funcionamento adequado e para que os objetivos de utilização delineados inicialmente para o respetivo aproveitamento hidráulico possam ser cumpridos. Os fatores de maior relevância num estudo deste tipo de barragens, considerando que apenas estão sob solicitações estáticas, são as pressões de água, registadas em piezómetros, os caudais percolados e os deslocamentos superficiais, geralmente medidos em marcas de nivelamento ou em alvos colocados em peças de centragem forçada. Na presente dissertação pretende-se, com base no conhecimento dos registos dessas medições feitas anteriormente e recorrendo a modelos de inteligência artificial, predizer o valor que se obteria em próximas medições, ajudando assim a decidir qual o melhor procedimento para remediar ou tratar um problema de comportamento relacionado com as variáveis em estudo. Esta dissertação divide-se essencialmente em três partes. Primeiramente, introduzem-se os conceitos relativos à segurança de barragens de aterro, de acordo com o regulamento de segurança adotado em Portugal, dando relevo ao tipo de observação a que são submetidas. Seguidamente, introduz-se o conceito de redes neuronais artificiais e apresenta-se a base de dados, criada com o intuito de uniformizar e melhorar a organização dos valores em estudo das barragens de aterro, que têm sido acompanhadas pelo Laboratório Nacional de Engenharia Civil. Com esta pretende-se facilitar a utilização destes elementos por programas de inteligência artificial. Por último, é feito o enquadramento de um caso de estudo, uma barragem de aterro no Norte de Portugal – barragem de Valtorno-Mourão. Utilizando o Neuroph Studio, os dados relativos à observação desta barragem são aplicados numa rede neuronal artificial, Multi Layer Perceptron Backpropagation Neural Network, permitindo antever comportamentos futuros. Os resultados obtidos são discutidos e perspetivam-se trabalhos para continuar a desenvolver a investigação efetuada.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Search for the Standard Model Higgs boson produced in association with top quarks and decaying into bb in pp collisions at √s = 8 TeV with the ATLAS detector

A search for the Standard Model Higgs boson produced in association with a pair of top quarks, tt¯H, is presented. The analysis uses 20.3 fb−1 of pp collision data at s√ = 8 TeV, collected with the ATLAS detector at the Large Hadron Collider during 2012. The search is designed for the H to bb¯ decay mode and uses events containing one or two electrons or muons. In order to improve the sensitivity of the search, events are categorised according to their jet and b-tagged jet multiplicities. A neural network is used to discriminate between signal and background events, the latter being dominated by tt¯+jets production. In the single-lepton channel, variables calculated using a matrix element method are included as inputs to the neural network to improve discrimination of the irreducible tt¯+bb¯ background. No significant excess of events above the background expectation is found and an observed (expected) limit of 3.4 (2.2) times the Standard Model cross section is obtained at 95% confidence level. The ratio of the measured tt¯H signal cross section to the Standard Model expectation is found to be μ=1.5±1.1 assuming a Higgs boson mass of 125 GeV.

Desenvolvimento de rede neuronal artificial para o diagnóstico de doenças neurodegenerativas

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

Estimativa dos parâmetros de compactação de solos usando técnicas de data mining

Dissertação de mestrado integrado em Engenharia Civil

Objective analysis of gait coordination for total knee arthroplasty outcome

Introduction: Coordination is a strategy chosen by the central nervous system to control the movements and maintain stability during gait. Coordinated multi-joint movements require a complex interaction between nervous outputs, biomechanical constraints, and pro-prioception. Quantitatively understanding and modeling gait coordination still remain a challenge. Surgeons lack a way to model and appreciate the coordination of patients before and after surgery of the lower limbs. Patients alter their gait patterns and their kinematic synergies when they walk faster or slower than normal speed to maintain their stability and minimize the energy cost of locomotion. The goal of this study was to provide a dynamical system approach to quantitatively describe human gait coordination and apply it to patients before and after total knee arthroplasty. Methods: A new method of quantitative analysis of interjoint coordination during gait was designed, providing a general model to capture the whole dynamics and showing the kinematic synergies at various walking speeds. The proposed model imposed a relationship among lower limb joint angles (hips and knees) to parameterize the dynamics of locomotion of each individual. An integration of different analysis tools such as Harmonic analysis, Principal Component Analysis, and Artificial Neural Network helped overcome high-dimensionality, temporal dependence, and non-linear relationships of the gait patterns. Ten patients were studied using an ambulatory gait device (Physilog®). Each participant was asked to perform two walking trials of 30m long at 3 different speeds and to complete an EQ-5D questionnaire, a WOMAC and Knee Society Score. Lower limbs rotations were measured by four miniature angular rate sensors mounted respectively, on each shank and thigh. The outcomes of the eight patients undergoing total knee arthroplasty, recorded pre-operatively and post-operatively at 6 weeks, 3 months, 6 months and 1 year were compared to 2 age-matched healthy subjects. Results: The new method provided coordination scores at various walking speeds, ranged between 0 and 10. It determined the overall coordination of the lower limbs as well as the contribution of each joint to the total coordination. The difference between the pre-operative and post-operative coordination values were correlated with the improvements of the subjective outcome scores. Although the study group was small, the results showed a new way to objectively quantify gait coordination of patients undergoing total knee arthroplasty, using only portable body-fixed sensors. Conclusion: A new method for objective gait coordination analysis has been developed with very encouraging results regarding the objective outcome of lower limb surgery.

Multilayer perceptron with local constraint as an emerging method in spatial data analysis

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

Myotonic dystrophy transgenic mice exhibit pathologic abnormalities in diaphragm neuromuscular junctions and phrenic nerves

Myotonic dystrophy Type 1 (DM-1) is caused by abnormal expansion of a (CTG) repeat located in the DM protein kinase gene. Respiratory problems have long been recognized to be a major feature of this disorder. Because respiratory failure can be associated with dysfunction of phrenic nerves and diaphragm muscle, we examined the diaphragm and respiratory neural network in transgenic mice carrying the human genomic DM-1 region with expanded repeats of more than 300 CTG, a valid model of the human disease. Morphologic and morphometric analyses revealed distal denervation of diaphragm neuromuscular junctions in DM-1 transgenic mice indicated by a decrease in the size and shape complexity of end-plates and a reduction in the concentration of acetyl choline receptors on the postsynaptic membrane. More importantly, there was a significant reduction in numbers of unmyelinated, but not of myelinated, fibers in DM-1 phrenic nerves; no morphologic alternations of the nerves or loss of neuronal cells were detected in medullary respiratory centers or cervical phrenic motor neurons. Because neuromuscular junctions are involved in action potential transmission and the afferent phrenic unmyelinated fibers control the inspiratory activity, our results suggest that the respiratory impairment associated with DM-1 may be partially due to pathologic alterations in neuromuscular junctions and phrenic nerves.