Optical Filter Design Using Background Wavelength Processing Techniques

Amorphous SiC tandem heterostructures are used to filter a specific band, in the visible range. Experimental and simulated results are compared to validate the use of SiC multilayered structures in applications where gain compensation is needed or to attenuate unwanted wavelengths. Spectral response data acquired under different frequencies, optical wavelength control and side irradiations are analyzed. Transfer function characteristics are discussed. Color pulsed communication channels are transmitted together and the output signal analyzed under different background conditions. Results show that under controlled wavelength backgrounds, the device sensitivity is enhanced in a precise wavelength range and quenched in the others, tuning or suppressing a specific band. Depending on the background wavelength and irradiation side, the device acts either as a long-, a short-, or a band-rejection pass filter. An optoelectronic model supports the experimental results and gives insight on the physics of the device.

Driving Scheme Using MIS Photosensor for Luminance Control of AMOLED Pixel

This paper presents a new driving scheme utilizing an in-pixel metal-insulator-semiconductor (MIS) photosensor for luminance control of active-matrix organic light-emitting diode (AMOLED) pixel. The proposed 3-TFT circuit is controlled by an external driver performing the signal readout, processing, and programming operations according to a luminance adjusting algorithm. To maintain the fabrication simplicity, the embedded MIS photosensor shares the same layer stack with pixel TFTs. Performance characteristics of the MIS structure with a nc-Si : H/a-Si : H bilayer absorber were measured and analyzed to prove the concept. The observed transient dark current is associated with charge trapping at the insulator-semiconductor interface that can be largely eliminated by adjusting the bias voltage during the refresh cycle. Other factors limiting the dynamic range and external quantum efficiency are also determined and verified using a small-signal model of the device. Experimental results demonstrate the feasibility of the MIS photosensor for the discussed driving scheme.

Natureza, princípios e fundamentos das práticas e relações profissionais-doentes no contexto do Gabinete de Sub-visão do Instituto Dr. Gama Pinto

A institucionalização do direito à saúde, na constituição de 1976, como direito social e humano não parece ter conseguido, na prática dos profissionais de saúde, abalar a relação paternalista que coloca o doente numa situação de submissão face à dominância do poder/saber médico central ou periférico que o doente, nos termos de Parsons, deve acatar humildemente como “um bom doente”. É este papel de passividade e submissão do doente que nos propomos problematizar nos meandros dos direitos humanos/direitos sociais de cidadania como campo de construção social assente em práticas norteadas por direitos e deveres que, nos termos de Foucault, submetem os cidadãos a constrangimentos inerentes às relações de poder.

Sub-micron structuring of silicon using femtosecond laser interferometry

We report the fabrication of planar sub-micron gratings in silicon with a period of 720 nm using a modified Michelson interferometer and femtosecond laser radiation. The gratings consist of alternated stripes of laser ablated and unmodified material. Ablated stripes are bordered by parallel ridges which protrude above the unmodified material. In the regions where ridges are formed, the laser radiation intensity is not sufficient to cause ablation. Nevertheless, melting and a significant temperature increase are expected, and ridges may be formed due to expansion of silicon during resolidification or silicon oxidation. These conclusions are consistent with the evolution of the stripes morphology as a function of the distance from the center of the grating. (C) 2013 Elsevier Ltd. All rights reserved.

Integration of biomonitoring and instrumental techniques to assess the air quality in an industrial area located in the coastal of Central Asturias, Spain

Throughout the world, epidemiological studies were established to examine the relationship between air pollution and mortality rates and adverse respiratory health effects. However, despite the years of discussion the correlation between adverse health effects and atmospheric pollution remains controversial, partly because these studies are frequently restricted to small and well-monitored areas. Monitoring air pollution is complex due to the large spatial and temporal variations of pollution phenomena, the high costs of recording instruments, and the low sampling density of a purely instrumental approach. Therefore, together with the traditional instrumental monitoring, bioindication techniques allow for the mapping of pollution effects over wide areas with a high sampling density. In this study, instrumental and biomonitoring techniques were integrated to support an epidemiological study that will be developed in an industrial area located in Gijon in the coastal of central Asturias, Spain. Three main objectives were proposed to (i) analyze temporal patterns of PM10 concentrations in order to apportion emissions sources, (ii) investigate spatial patterns of lichen conductivity to identify the impact of the studied industrial area in air quality, and (iii) establish relationships amongst lichen conductivity with some site-specific characteristics. Samples of the epiphytic lichen Parmelia sulcata were transplanted in a grid of 18 by 20 km with an industrial area in the center. Lichens were exposed for a 5-mo period starting in April 2010. After exposure, lichen samples were soaked in 18-MΩ water aimed at determination of water electrical conductivity and, consequently, lichen vitality and cell damage. A marked decreasing gradient of lichens conductivity relative to distance from the emitting sources was observed. Transplants from a sampling site proximal to the industrial area reached values 10-fold higher than levels far from it. This finding showed that lichens reacted physiologically in the polluted industrial area as evidenced by increased conductivity correlated to contamination level. The integration of temporal PM10 measurements and analysis of wind direction corroborated the importance of this industrialized region for air quality measurements and identified the relevance of traffic for the urban area.

Comparison of different breast planning techniques and algorithms for radiation therapy treatment

This work aims at investigating the impact of treating breast cancer using different radiation therapy (RT) techniques – forwardly-planned intensity-modulated, f-IMRT, inversely-planned IMRT and dynamic conformal arc (DCART) RT – and their effects on the whole-breast irradiation and in the undesirable irradiation of the surrounding healthy tissues. Two algorithms of iPlan BrainLAB treatment planning system were compared: Pencil Beam Convolution (PBC) and commercial Monte Carlo (iMC). Seven left-sided breast patients submitted to breast-conserving surgery were enrolled in the study. For each patient, four RT techniques – f-IMRT, IMRT using 2-fields and 5-fields (IMRT2 and IMRT5, respectively) and DCART – were applied. The dose distributions in the planned target volume (PTV) and the dose to the organs at risk (OAR) were compared analyzing dose–volume histograms; further statistical analysis was performed using IBM SPSS v20 software. For PBC, all techniques provided adequate coverage of the PTV. However, statistically significant dose differences were observed between the techniques, in the PTV, OAR and also in the pattern of dose distribution spreading into normal tissues. IMRT5 and DCART spread low doses into greater volumes of normal tissue, right breast, right lung and heart than tangential techniques. However, IMRT5 plans improved distributions for the PTV, exhibiting better conformity and homogeneity in target and reduced high dose percentages in ipsilateral OAR. DCART did not present advantages over any of the techniques investigated. Differences were also found comparing the calculation algorithms: PBC estimated higher doses for the PTV, ipsilateral lung and heart than the iMC algorithm predicted.

Estudo sobre o efeito de contágio nos mercados financeiros de acções: aplicação aos índices de acções europeus, americanos, asiáticos e brasileiro no período de Janeiro de 1996 a Junho de 2009 e no sub-período da crise subprime

Mestrado em Contabilidade

STIR, SPIR and SPAIR techniques in magnetic resonance of the breast: a comparative study

The amount of fat is a component that complicates the clinical evaluation and the differential diagnostic between benign and malign lesions in the breast MRI examinations. To overcome this problem, an effective erasing of the fat signal over the images acquisition process, is essentials. This study aims to compare three fat suppression techniques (STIR, SPIR, SPAIR) in the MR images of the breast and to evaluate the best image quality regarding its clinical usefulness. To mimic breast women, a breast phantom was constructed. First the exterior contour and, in second time, its content which was selected based on 7 samples with different components. Finally it was undergone to a MRI breast protocol with the three different fat saturation techniques. The examinations were performed on a 1.5 T MRI system (Philips®). A group of 5 experts evaluated 9 sequences, 3 of each with fat suppression techniques, in which the frequency offset and TI (Inversion Time) were the variables changed. This qualitative image analysis was performed according 4 parameters (saturation uniformity, saturation efficacy, detail of the anatomical structures and differentiation between the fibroglandular and adipose tissue), using a five-point Likert scale. The statistics analysis showed that anyone of the fat suppression techniques demonstrated significant differences compared to the others with (p > 0.05) and regarding each parameter independently. By Fleiss’ kappa coefficient there was a good agreement among observers P(e) = 0.68. When comparing STIR, SPIR and SPAIR techniques it was confirmed that all of them have advantages in the study of the breast MRI. For the studied parameters, the results through the Friedman Test showed that there are similar advantages applying anyone of these techniques.

Projecto de saneamento do sub-sistema da Ericeira: interceptor, emissários e sistemas elevatórios: fase III: emissário da Quinta das Pedras, Fonte Boa dos Nabos, interceptor do Outeirinho e desactivação da estação elevatória EE10

Relatório de Estágio para obtenção do grau de Mestre em Engenharia na Área de Especialização em Edificações

Synthesis of Sub-5 NM Co-Doped SnO2 nanoparticles and their structural microstructural, optical and photocatalytic properties

A swift chemical route to synthesize Co-doped SnO2 nanopowders is described. Pure and highly stable Sn1-xCoxO2-delta (0 <= x <= 0.15) crystalline nanoparticles were synthesized, with mean grain sizes <5 nm and the dopant element homogeneously distributed in the SnO2 matrix. The UV-visible diffuse reflectance spectra of the Sn1-xCoxO2-delta samples reveal red shifts, the optical bandgap energies decreasing with increasing Co concentration. The samples' Urbach energies were calculated and correlated with their bandgap energies. The photocatalytic activity of the Sn1-xCoxO2-delta samples was investigated for the 4-hydroxylbenzoic acid (4-HBA) degradation process. A complete photodegradation of a 10 ppm 4-HBA solution was achieved using 0.02% (w/w) of Sn0.95Co0.05O2-delta nanoparticles in 60 min of irradiation. (C) 2014 Elsevier B.V. All rights reserved.

Sub-micron structuring of silicon using femtosecond laser interferometry

We report the fabrication of planar sub-micron gratings in silicon with a period of 720 nm using a modified Michelson interferometer and femtosecond laser radiation. The gratings consist of alternated stripes of laser ablated and unmodified material. Ablated stripes are bordered by parallel ridges which protrude above the unmodified material. In the regions where ridges are formed, the laser radiation intensity is not sufficient to cause ablation. Nevertheless, melting and a significant temperature increase are expected, and ridges may be formed due to expansion of silicon during resolidification or silicon oxidation. These conclusions are consistent with the evolution of the stripes morphology as a function of the distance from the center of the grating.

Complementary filter design with three frequency bands: robot attitude estimation

This paper extents the by now classic sensor fusion complementary filter (CF) design, involving two sensors, to the case where three sensors that provide measurements in different bands are available. This paper shows that the use of classical CF techniques to tackle a generic three sensors fusion problem, based solely on their frequency domain characteristics, leads to a minimal realization, stable, sub-optimal solution, denoted as Complementary Filters3 (CF3). Then, a new approach for the estimation problem at hand is used, based on optimal linear Kalman filtering techniques. Moreover, the solution is shown to preserve the complementary property, i.e. the sum of the three transfer functions of the respective sensors add up to one, both in continuous and discrete time domains. This new class of filters are denoted as Complementary Kalman Filters3 (CKF3). The attitude estimation of a mobile robot is addressed, based on data from a rate gyroscope, a digital compass, and odometry. The experimental results obtained are reported.

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.

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.