Multiple sequence alignment correction using constraints

Trabalho apresentado no âmbito do European Master in Computational Logics, como requisito parcial para obtenção do grau de Mestre em Computational Logics

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.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Every normal logic program has a 2-valued semantics: theory, extensions, applications, implementations

Trabalho apresentado no âmbito do Doutoramento em Informática, como requisito parcial para obtenção do grau de Doutor em Informática

Health care marketing: the theory of planned behavior applied to patients' choise between private and public providers in the portuguese health care system.

Dissertation submitted in partial fulfillment of the requirements for degree of Master in Statistics and Information Management.

Depositions of rulers in the later middle ages: on the theory of th "useless ruler" and its practical utilization

pp. 157-168

“The past is a foreign country”? Acculturation theory and the anthropology of globalization

Etnográfica, 15 (2): 313-336

Coalition of distributed generation units to Virtual Power Players - a game theory approach

Smart Grids (SGs) have emerged as the new paradigm for power system operation and management, being designed to include large amounts of distributed energy resources. This new paradigm requires new Energy Resource Management (ERM) methodologies considering different operation strategies and the existence of new management players such as several types of aggregators. This paper proposes a methodology to facilitate the coalition between distributed generation units originating Virtual Power Players (VPP) considering a game theory approach. The proposed approach consists in the analysis of the classifications that were attributed by each VPP to the distributed generation units, as well as in the analysis of the previous established contracts by each player. The proposed classification model is based in fourteen parameters including technical, economical and behavioural ones. Depending of the VPP strategies, size and goals, each parameter has different importance. VPP can also manage other type of energy resources, like storage units, electric vehicles, demand response programs or even parts of the MV and LV distribution network. A case study with twelve VPPs with different characteristics and one hundred and fifty real distributed generation units is included in the paper.

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

Estudio de la gestión de la variable ambiental en la generación termoeléctrica

En la actualidad, el cambio climático es uno de los temas de mayor preocupación para la población mundial y los científicos de todo el mundo. Debido al crecimiento de la población de forma exponencial, la demanda de energía aumenta acorde con ello, por lo que las actividades de producción energética aumentan consecuentemente, siendo éstas las principales causantes de la aceleración del cambio climático. Pese a que muchos países previamente habían apostado por la producción energética mediante tecnologías limpias a partir de energías renovables, hoy en día es imposible prescindir de los combustibles fósiles pues, junto a la energía nuclear, suponen el mayor porcentaje dentro del mix energético de los países más grandes del mundo, por lo que el cambio debe ser global y con todos los países implicados al unísono. Por ello, los países desarrollados decidieron acordar una serie de leyes y normas para la regulación y el control de la expansión energética en el mundo, mediante programas de incentivo a las empresas para la producción de energía limpia, libre de emisiones, sustituyendo y mejorando los procesos tecnológicos para que garanticen un desarrollo sostenible. De esta forma, se conseguiría también reducir la dependencia energética de los países productores de los recursos fósiles más importantes y a su vez, ayudar a otros sectores a diversificar su negocio y mejorar así la economía de las áreas colindantes a las centrales de producción térmica. Gracias a estos programas de incentivo o, también llamados mecanismos de flexibilidad, las empresas productoras de energía, al acometer inversiones en tecnologia limpia, dejan de emitir gases de efecto invernadero a la atmósfera. Por tanto, gracias al comercio de emisiones y al mercado voluntario, las empresas pueden vender dichas emisiones aumentando la rentabilidad de sus proyectos, haciendo más atractivo de por sí el hecho de invertir en tecnología limpia. En el proyecto desarrollado, se podrá comprobar de una forma más extensa todo lo anteriormente citado. Para ello, se desarrollará una herramienta de cálculo que nos permitirá analizar los beneficios obtenidos por la sustitución de un combustible fósil, no renovable, por otro renovable y sostenible, como es la biomasa. En esta herramienta se calcularán, de forma estimada, las reducciones de las emisiones de CO2 que supone dicha sustitución y se hallará, en función del valor de las cotizaciones de los bonos de carbono en los diferentes mercados, cuál será el beneficio económico obtenido por la venta de las emisiones no emitidas que supone esta sustitución. Por último, dicho beneficio será insertado en un balance económico de la central donde se tendrán en cuenta otras variables como el precio del combustible o las fluctuaciones del precio de la electricidad, para hallar finalmente la rentabilidad que supondría la inversión de esta adaptación en la central. Con el fin de complementar y aplicar la herramienta de cálculo, se analizarán dos casos prácticos de una central de carbón, en los cuales se decide su suscripción dentro del contexto de los mecanismos de flexibilidad creados en los acuerdos internacionales.

Tendências de recompensas em empresas portuguesas

Esta dissertação tem como objetivo central conhecer os sistemas de recompensas em empresas portuguesas. Primeiramente é realizada uma abordagem aos conceitos subjacentes ao sistema de recompensas e, simultaneamente é exposta teoria sobre o tipo de recompensas mais utilizadas e como estas diferem consoante o cargo/função. Foi aplicado um inquérito por questionário a uma amostra de 144 empresas localizadas por todo o país. Foram testadas hipóteses de estudo capazes de permitir caraterizar o sistema de recompensas desenvolvido nas empresas em estudo. Os resultados do estudo permitem concluir que: 1) os sistemas de recompensas baseados na antiguidade estão atualmente em desuso, ao contrário dos sistemas de recompensas baseados na função/cargo, no desempenho e nas competências; 2) o principal objetivo do sistema de recompensas das empresas do estudo é a motivação dos colaboradores; 3) a conjugação das recompensas monetárias e não monetárias são as mais valorizadas pelas empresas; 4) predomina a compensação variável nos sistemas de recompensas das empresas bem como os incentivos mistos (individuais e de grupo); 5) predominam os incentivos de curto-prazo no sistema de recompensas; 6) o bónus anual é o incentivo mais utilizado; 7) os benefícios e alguns dos incentivos tendem a ser maiores à medida que se sobe na hierarquia funcional da empresa; 8) o benefício predominante é a atribuição de telemóvel e o seguro de saúde; 9) as formas de reconhecimento mais comuns são os prémios de desempenho, o feedback contínuo, as promoções e as placas comemorativas; 10) a maioria dos inquiridos acredita que as oportunidades de desenvolvimento de carreira é uma importante medida para a retenção dos colaboradores; 11) as oportunidades de desenvolvimento são mais utilizadas pelas empresas de maior dimensão e; 12) os benefícios sociais são considerados a componente mais importante para garantir a retenção dos colaboradores. Com base nestes resultados, na parte final da dissertação são apresentadas algumas implicações teóricas e práticas, algumas limitações do estudo, bem como pistas para investigações futuras.

Fiscal decentralisation and economic theory

There is no complete overview or discussion of the literature of the economics of federalism and fiscal decentralization, even though scholarly interest in the topic has been increasing significantly over recent years. This paper provides a general, brief but comprehensive overview of the main insights from the literature on fiscal federalism and decentralization. In doing so, literature on fiscal federalism and decentralization is grouped into two main approaches: “first generation of theories” and “second generation of theories”.

Understanding the internet banking adoption by Portuguese customers - a unified theory of acceptance and use of technology and perceived risk application.

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

The hidden value behind capital structure decisions

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Men’s purchase intention of shampoo: a model based on the theory of planned behavior and self-concept

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