Sensor de direcção luminosa para seguimento solar

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

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. 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. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Preparation and characterization of dye-sensitized TiO2 nanorod solar cells

TiO2 nanorodswere prepared by DC reactive magnetron sputtering technique and applied to dye-sensitized solar cells (DSSCs). The length of the TiO2 nanorods was varied from 1 μm to 6 μm. The scanning electronmicroscopy images showthat the nanorods are perpendicular to the substrate. Both the X-ray diffraction patterns and Raman scattering results show that the nanorods have an anatase phase; no other phase has been observed. (101) and the (220) diffraction peaks have been observed for the TiO2 nanorods. The (101) diffraction peak intensity remained constant despite the increase of nanorod length, while the intensity of the (220) diffraction peak increased almost linearly with the nanorod length. These nanorods were used as the working electrodes in DSSCs and the effect of the nanorod length on the conversion efficiency has been studied. An optimumphotoelectric conversion efficiency of 4.8% has been achieved for 4 μm length nanorods.

Effect of wide spectrum anti-helminthic drugs upon Schistosoma mansoni experimentally infected mice

Mebendazole, albendazole, levamisole and thiabendazole are well known as active drugs against several nematode species, and against cestodes as well, when the first two drugs are considered. None of the drugs have proven activity, however, against trematodes. We tested the effect of these drugs on the fecal shedding of schistosome eggs and the recovering of adult schistosomes, after portal perfusion in Schistosoma mansoni experimentally infected mice. Balb/c mice infected with 80 S. mansoni cercariae were divided into three groups, each in turn subdivided into four other groups, for each tested drug. The first group was treated with each one of the studied drugs 25 days after S. mansoni infection; the second group was submitted to treatment with each one of the drugs 60 days after infection. Finally, the third group, considered as control, received no treatment. No effect upon fecal shedding of S. mansoni eggs and recovering of schistosomes after portal perfusion was observed when mice were treated with either mebendazole or albendazole. Mice treated with either levamisole or thiabendazole, on the other hand, showed a significant reduction in the recovering of adult schistosomes after portal perfusion, mainly when both drugs were given during the schistosomula evolution period, i.e., 25 days after cercariae penetration, probably due to unspecific immunomodulation

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

A contribuição de um sistema solar térmico no desempenho energético do edifício solar XXI

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa Para a obtenção do Grau de Mestre em Energia e Bioenergia

Fotocatálise solar para tratamento de águas de captação

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

Desinfestação de diferentes tipos de papel utilizando o calor solar: estudo dos seus efeitos em estufa e aplicação sob diferentes condições climatéricas

Dissertação de Mestrado em Conservação e Restauro área de especialização: Documentos Gráficos

Optimização térmica de um sistema de concentração solar fotovoltaico

Dissertação para obtenção do Grau de Mestre em Engenharia de Materiais

The spectrum of computerized tomography (CT) findings in central nervous system (CNS) infection due to Cryptococcus neoformans var. gattii in immunocompetent children

Cranial CT scans of eleven immunocompetent children with central nervous system (CNS) infection due to Cryptococcus neoformans var. gattii were retrospectively reviewed. These children had an average age of 8.8 years and positive culture for C. n. var. gattii in cerebrospinal fluid. The most common signs and symptoms were headache, fever, nuchal rigidity, nausea and vomiting. No normal cranial CT was detected in any patient. Hypodense nodules were observed in all patients . The remaining scan abnormalities were as follows: nine had diffuse atrophy, six had hydrocephalus, and five had hydrocephalus coexistent with diffuse atrophy.

Design and Development of a Solar Dryer for Microalgae Retrieval - An EPS@ISEP 2013 Spring Project

Currently excessive fossil fuel consumption has become a serious problem. People are searching for new solutions of energy production and there are several options to obtain alternative sources of energy without further devastating the already destroyed environment. One of these solutions is growing microalgae, from which biodiesel can be obtained. The microalgae production is a growing business because of its many useful compounds. In order to collect these compounds microalgae must first be harvested and then dried. Nowadays the solutions used for drying use too much energy and therefore are too expensive and not sustainable. The goal of this project, one of the possible choices during the EPS@ISEP 2013 Spring, was to develop a solar microalgae dryer. The multinational team involved in its development was composed of five students, from distinct countries and fields of study, and was the responsible for designing a solar microalgae dryer prototype for the microalgae laboratory of the chemical engineering department at ISEP, suitable for future tests and incorporating control process (in order not to destroy the microalgae during the drying process). The solar microalgae dryer was built to work as a distiller that gets rid of the excess water from the microalgae suspension. This paper presents a possible solution for this problem, the steps to create the device to harvest the microalgae by drying them with the use of solar energy (also used as an energy source for the solar dryer control system), the technologies used to build the solar microalgae dryer, and the benefits it presents compared to current solutions. It also presents the device from the ethical and sustainable viewpoint. Such alternative to already existing methods is competitive as far as energy usage is concerned.

Viabilidade da aplicação da energia solar no reaquecimento de uma central termoeléctrica

Dissertação para a obtenção do Grau de Mestre em Engenharia Mecânica

Desenvolvimento de um concentrador solar fixo para foto-estimulação

Dissertação para obtenção do grau de Mestre em Engenharia Biomédica

Thermographic and visible spectrum camera calibration for marine robotic target detection

Oceans - San Diego, 2013

Integração de dados LIDAR com imagens de muito alta resolução espacial para determinação de áreas urbanas com potencial solar

Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Gestão do Território, área de especialização em Detecção Remota e SIG