Nanocrystalline p-layer for a-Si:H p-i-n solar cells and photodiodes

We report on structural, electronic, and optical properties of boron-doped, hydrogenated nanocrystalline silicon (nc-Si:H) thin films deposited by plasma-enhanced chemical vapor deposition (PECVD) at a substrate temperature of 150 degrees C. Film properties were studied as a function of trimethylboron-to-silane ratio and film thickness. The absorption loss of 25% at a wavelength of 400 nm was measured for the 20 nm thick films on glass and glass/ZnO:Al substrates. By employing the p(+) nc-Si:H as a window layer, complete p-i-n structures were fabricated and characterized. Low leakage current and enhanced sensitivity in the UV/blue range were achieved by incorporating an a-SiC:H buffer between the p- and i-layers.

Torre solar: estudo do rendimento e viabilidade de centrais térmicas solares para produção de energia eléctrica de pequena e média dimensão

O aproveitamento da radiação solar representa um recurso energético extremamente benéfico, quer no âmbito energético, quer no ambiental, contribuindo para a redução das emissões de gases nocivos para a atmosfera. Portugal apresenta uma radiação solar total média anual bastante elevada, colocando-se entre um dos países que apresentam melhores potencialidades para o aproveitamento da energia solar. A torre solar consiste numa estufa com uma determinada extensão com uma chaminé no seu centro e o seu funcionamento baseia-se no aquecimento do ar que circula por baixo da estufa, sendo expelido pela chaminé. Nesta tese é abordado o tema da torre solar e os princípios físicos inerentes ao seu funcionamento. Foi estudado e descrito o método de cálculo de diversos parâmetros e resultados associados ao funcionamento da torre solar. Elaborou-se uma folha de cálculo para obtenção dos valores de simulações de torres com diversas dimensões, tecendo-se conclusões quanto aos resultados e às variações dos mesmos, consoante as alterações de dimensão dos elementos que a constituem. Foram descritos os vários elementos que constituem uma torre solar, bem como as suas características e tipologias. Efectuou-se um estudo com aplicação a um caso real, para se tecer algumas conclusões e comentários relativamente à viabilidade de uma torre solar para a situação em causa. Por fim, com base em todo o trabalho desenvolvido e abordado, foi possível tecer-se algumas conclusões quanto à viabilidade das torres solares.

Execução de um projecto solar térmico para uma instalação de A.Q.S. da Escola Básica Guilherme Stephens

O presente documento tem como principal objectivo efectuar o projecto de dimensionamento de um sistema de águas quentes sanitárias para uma escola. Numa primeira fase foi elaborado uma pesquisa sobre o contexto energético, a nível mundial, europeu e nacional, bem como o seu contexto jurídico a nível europeu e nacional, e uma explicação superficial sobre os fundamentos da energia solar, onde se foca a importância da radiação solar e os vários tipos de sistemas solares térmicos, bem como os seus constituintes. Segue-se a abordagem ao caso de estudo onde foram efectuados inicialmente inquéritos como forma de determinar os consumos de água quente utilizada nessa escola. Continuou-se o estudo efectuando-se a variação de duas características do sistema solar: o tamanho dos depósitos e o tipo de colectores solares a aplicar. Após as simulações efectuadas para a determinação das soluções a aplicar ao sistemasolar e apresentadas ao longo do presente documento, foram efectuadas análises económicas como forma de se verificar a viabilidade do sistema a aplicar. Por último foram elaboradas conclusões sobre o sistema a aplicar e apresentados alguns cenários financeiros do mesmo.

Planeamento de curto prazo de sistemas de energia solar usando técnicas de otimização robusta

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica

Otimização estocástica para a exploração de centrais de concentração solar considerando incerteza e risco

Dissertação para obtenção do grau de Mestre em Engenharia Eletrotécnica

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.