Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

A lightweight distributed super peer election algorithm for unstructured dynamic P2P systems

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization Algorithm

The recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Genetic diversity of arginine catabolic mobile element in Staphylococcus epidermidis

PLos One, 4(11): ARTe7722

Technology market transfer plan launch of DELIVES to E.U. Market©

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Biotecnologia

The role of halouracils in radiotherapy studied by electron transfer in atom-molecule collisions experiments

Dissertação para obtenção do Grau de Doutor em Engenharia Física

Geodupa - a geography-driven partial membership algorithm

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Efficient Legendre spectral tau algorithm for solving two-sided space-time Caputo fractional advection-dispersion equation

In this paper we present the operational matrices of the left Caputo fractional derivative, right Caputo fractional derivative and Riemann–Liouville fractional integral for shifted Legendre polynomials. We develop an accurate numerical algorithm to solve the two-sided space–time fractional advection–dispersion equation (FADE) based on a spectral shifted Legendre tau (SLT) method in combination with the derived shifted Legendre operational matrices. The fractional derivatives are described in the Caputo sense. We propose a spectral SLT method, both in temporal and spatial discretizations for the two-sided space–time FADE. This technique reduces the two-sided space–time FADE to a system of algebraic equations that simplifies the problem. Numerical results carried out to confirm the spectral accuracy and efficiency of the proposed algorithm. By selecting relatively few Legendre polynomial degrees, we are able to get very accurate approximations, demonstrating the utility of the new approach over other numerical methods.

Alterations in lipid transfer to High-Density Lipoprotein (HDL) and activity of paraoxonase-1 in HIV+ patients

HIV+ patients often develop alterations of the plasma lipids that may implicate in development of premature coronary artery disease. High-density lipoprotein (HDL) has an important role in preventing atherogenesis and the aim of this study was to investigate aspects of HDL function in HIV+ patients. HIV+ patients (n = 48) and healthy control subjects (n = 45) of both sexes with similar age were studied. Twenty-five were not being treated with antiretroviral agents, 13 were under reverse transcriptase inhibitor nucleosidic and non-nucleosidic (NRTI+NNRTI) and 10 were under NRTI + protease inhibitors (NRTI+PI) treatment. Paraoxonase 1 (PON1) activity and the transfer of free and esterified cholesterol, tryglicerides and phospholipids from a lipidic nanoemulsion to HDL were analyzed. In comparison with healthy controls, HIV+ patients presented low PON-1 activity and diminished transfer of free cholesterol and tryglicerides. In contrast, phospholipid transfer was increased in those patients, whereas the transfer of cholesteryl esters was unchanged. NRTI+NNRTI increases the transfer of cholesteryl esters and triglycerides but in NRTI+PI there was no difference in respect to non-treated HIV+ patients. HDL from HIV+ patients has smaller antioxidant properties, as shown by lower PON-1 activity, and the transfer of lipids to this lipoprotein fraction is also altered, suggesting that HDL function is defective in those patients.

Measuring data transfer in heterogeneous IoT environments

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

A filter algorithm: comparison with NLP solvers

The purpose of this work is to present an algorithm to solve nonlinear constrained optimization problems, using the filter method with the inexact restoration (IR) approach. In the IR approach two independent phases are performed in each iteration—the feasibility and the optimality phases. The first one directs the iterative process into the feasible region, i.e. finds one point with less constraints violation. The optimality phase starts from this point and its goal is to optimize the objective function into the satisfied constraints space. To evaluate the solution approximations in each iteration a scheme based on the filter method is used in both phases of the algorithm. This method replaces the merit functions that are based on penalty schemes, avoiding the related difficulties such as the penalty parameter estimation and the non-differentiability of some of them. The filter method is implemented in the context of the line search globalization technique. A set of more than two hundred AMPL test problems is solved. The algorithm developed is compared with LOQO and NPSOL software packages.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Localização Ótima de Aparelhos de Corte Normalmente Abertos e Normalmente Fechados em Redes de Distribuição

Tipicamente as redes elétricas de distribuição apresentam uma topologia parcialmente malhada e são exploradas radialmente. A topologia radial é obtida através da abertura das malhas nos locais que otimizam o ponto de operação da rede, através da instalação de aparelhos de corte que operam normalmente abertos. Para além de manterem a topologia radial, estes equipamentos possibilitam também a transferência de cargas entre saídas, aquando da ocorrência de defeitos. As saídas radiais são ainda dotadas de aparelhos de corte que operam normalmente fechados, estes têm como objetivo maximizar a fiabilidade e isolar defeitos, minimizando a área afetada pelos mesmos. Assim, na presente dissertação são desenvolvidos dois algoritmos determinísticos para a localização ótima de aparelhos de corte normalmente abertos e fechados, minimizando a potência ativa de perdas e o custo da energia não distribuída. O algoritmo de localização de aparelhos de corte normalmente abertos visa encontrar a topologia radial ótima que minimiza a potência ativa de perdas. O método é desenvolvido em ambiente Matlab – Tomlab, e é formulado como um problema de programação quadrática inteira mista. A topologia radial ótima é garantida através do cálculo de um trânsito de potências ótimo baseado no modelo DC. A função objetivo é dada pelas perdas por efeito de Joule. Por outro lado o problema é restringido pela primeira lei de Kirchhoff, limites de geração das subestações, limites térmicos dos condutores, trânsito de potência unidirecional e pela condição de radialidade. Os aparelhos de corte normalmente fechados são localizados ao longo das saídas radiais obtidas pelo anterior algoritmo, e permite minimizar o custo da energia não distribuída. No limite é possível localizar um aparelho de corte normalmente fechado em todas as linhas de uma rede de distribuição, sendo esta a solução que minimiza a energia não distribuída. No entanto, tendo em conta que a cada aparelho de corte está associado um investimento, é fundamental encontrar um equilíbrio entre a melhoria de fiabilidade e o investimento. Desta forma, o algoritmo desenvolvido avalia os benefícios obtidos com a instalação de aparelhos de corte normalmente fechados, e retorna o número e a localização dos mesmo que minimiza o custo da energia não distribuída. Os métodos apresentados são testados em duas redes de distribuição reais, exploradas com um nível de tensão de 15 kV e 30 kV, respetivamente. A primeira rede é localizada no distrito do Porto e é caraterizada por uma topologia mista e urbana. A segunda rede é localizada no distrito de Bragança e é caracterizada por uma topologia maioritariamente aérea e rural.