Enhanced short wavelength response in laser-scanned-photodiode image sensor using an a-SiC : H/a-Si : H tandem structure

We report in this paper the recent advances we obtained in optimizing a color image sensor based on the laser-scanned-photodiode (LSP) technique. A novel device structure based on a a-SiC:H/a-Si:H pin/pin tandem structure has been tested for a proper color separation process that takes advantage on the different filtering properties due to the different light penetration depth at different wavelengths a-SM and a-SiC:H. While the green and the red images give, in comparison with previous tested structures, a weak response, this structure shows a very good recognition of blue color under reverse bias, leaving a good margin for future device optimization in order to achieve a complete and satisfactory RGB image mapping. Experimental results about the spectral collection efficiency are presented and discussed from the point of view of the color sensor applications. The physics behind the device functioning is explained by recurring to a numerical simulation of the internal electrical configuration of the device.

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.

Optical confinement and colour separation in a double colour laser scanned photodiode (D/CLSP)

Large area n-i-p-n-i-p a-SiC:H heterostructures are used as sensing element in a double colour laser scanned photodiode image sensor (D/CLSP). This work aims to clarify possible improvements, physical limits and performance of CLSP image sensor when used as non-pixel image reader. Here, the image capture device and the scanning reader are optimized and the effects of the sensor structure on the output characteristics discussed. The role of the design of the sensing element, the doped layer composition and thickness, the read-out parameters (applied voltage and scanner frequency) on the image acquisition and the colour detection process are analysed. A physical model is presented and supported by a numerical simulation of the output characteristics of the sensor.

Optical confinement and colour separation in a double colour laser scanned photodiode (D/CLSP)

Large area n-i-p-n-i-p a-SiC:H heterostructures are used as sensing element in a Double Color Laser Scanned Photodiode image sensor (D/CLSP). This work aims to clarify possible improvements, physical limits and performance of CLSP image sensor when used as non-pixel image reader. Here, the image capture device and the scanning reader are optimized and the effects of the sensor structure on the output characteristics discussed. The role of the design of the sensing element, the doped layer composition and thickness, the read-out parameters (applied voltage and scanner frequency) on the image acquisition and the color detection process are analyzed. A physical model is presented and supported by a numerical simulation of the output characteristics of the sensor.

The portuguese insurance industry: basis for strategic process decision

Nowadays, the Portuguese insurance industry operates in a market with a much more aggressive structure than a few decades ago. Markets and the economy have become globalised since the last decade of the 20th century. Market forces have gradually shifted – power is now mainly on the demand side. In order to meet the new requirements, the insurance industry must develop a strong strategic ability to respond to constant changes of the new international economic order.One of the basic aspects of this strategic development will focus on the ability to predict the future. We introduce the subject by briefly describing the sector, its organisational structure in the Portuguese market, and challenges arising from the development of the European Union. We then analyse the economic and financial structure of the sector. From this point of view, we aim at the possibility of designing models that could explain the demand for insurance, claims and technical reserves evolution. Such models, (even if based on the past), would resolve, at least partly, one of the greatest difficulties experienced by insurance companies when estimating the budget. Thus, we examine the existence of variables that explain the previous points, which are capable of forming a basis for designing models that are simple but efficient, and can be used for strategic planning.

Advances on the development of novel heterogeneous catalysts for transesterification of triglycerides in biodiesel

This paper describes experimental work done towards the search for more profitable and sustainable alternatives regarding biodiesel production, using heterogeneous catalysts instead of the conventional homogenous alkaline catalysts, such as NaOH, KOH or sodium methoxide, for the methanolysis reaction. This experimental work is a first stage on the development and optimization of new solid catalysts, able to produce biodiesel from vegetable oils. The heterogeneous catalytic process has many differences from the currently used in industry homogeneous process. The main advantage is that, it requires lower investment costs, since no need for separation steps of methanol/catalyst, biodiesel/catalyst and glycerine/catalyst. This work resulted in the selection of CaO and CaO modified with Li catalysts, which showed very good catalytic performances with high activity and stability. In fact FAME yields higher than 92% were observed in two consecutive reaction batches without expensive intermediate reactivation procedures. Therefore, those catalysts appear to be suitable for biodiesel production.

RF Receiver Front End for 28.5 GHz Applications on a 70 GHz F-T SiGe BiCMOS Process

This article presents the design and test of a receiver front end aimed at LMDS applications at 28.5 GHz. It presents a system-level design after which the receiver was designed. The receiver comprises an LNA, quadrature mixer and quadrature local oscillator. Experimental results at 24 GHz center frequency show a conversion voltage gain of 15 dB and conversion noise figure of 14 5 dB. The receiver operates from a 2 5 V power supply with a total current consumption of 31 mA.

CVD of CrO(2): Towards a lower temperature deposition process

The deposition of highly oriented a-axis CrO(2) films onto Al(2)O(3)(0001) by atmospheric pressure (AP)CVD at temperatures as low as 330 C is reported. Deposition rates strongly depend on the substrate temperature, whereas for film surface microstructures the dependence is mainly on film thickness. For the experimental conditions used in this work, CrO(2) growth kinetics are dominated by a surface reaction mechanism with an apparent activation energy of (121.0 +/- 4.3) kJ mol(-1). The magnitude and temperature dependence of the saturation magnetization, up to room temperature, is consistent with bulk measurements.

The (well) informed piano : internalization and growth process

The subject matter of this book is about piano methodology, including technical, musical, artistic, ethical and philosophical issues and reflections. The purpose of this work is to share a personal professional experience insight in the field of piano performance. This text assumes a certain continuity to the major contributions of artists like Ludwig Deppe, Tobias Matthay, Grigory Kogan, Heinrich Neuhaus and George Kochevitsky. At the same time, it tries to integrate and complement this selected literature, bringing new ideas and hints to specific professional issues.

Biodegradation rate constants in different NF/UF fractions of cork processing wastewaters

Cork processing wastewater is an aqueous complex mixture of organic compounds that have been extracted from cork planks during the boiling process. These compounds, such as polysaccharides and polyphenols, have different biodegradability rates, which depend not only on the natureof the compound but also on the size of the compound. The aim of this study is to determine the biochemical oxygen demands (BOD) and biodegradationrate constants (k) for different cork wastewater fractions with different organic matter characteristics. These wastewater fractions were obtained using membrane separation processes, namely nanofiltration (NF) and ultrafiltration (UF). The nanofiltration and ultrafiltration membranes molecular weight cut-offs (MWCO) ranged from 0.125 to 91 kDa. The results obtained showed that the biodegradation rate constant for the cork processing wastewater was around 0.3 d(-1) and the k values for the permeates varied between 0.27-0.72 d(-1), being the lower values observed for permeates generated by the membranes with higher MWCO and the higher values observed for the permeates generated by the membranes with lower MWCO. These higher k values indicate that the biodegradable organic matter that is permeated by the membranes with tighter MWCO is more readily biodegradated.

Towards an Academic Social Network for Bologna Process

The Bologna Process aimed to build a European Higher Education Area promoting student's mobility. The adoption of Bologna Declaration directives requires a self management distributed approach to deal with student's mobility, allowing frequent updates in institutions rules or legislation. This paper suggests a computational system architecture, which follows a social network design. A set of structured annotations is proposed in order to organize the user's information. For instance, when the user is a student its annotations are organized into an academic record. The academic record data is used to discover interests, namely mobility interests, among students that belongs the academic network. These ideas have been applied into a demonstrator that includes a mobility simulator to compare and show the student's academic evolution.

A student personal system for Bologna process mobility

The Bologna Process aimed to build a European Higher Education Area with the objective of promoting students mobility. The adoption of Bologna Declaration directives requires a decentralized approach that accelerates student's mobility, based on frequently updated legislation. This paper proposes a student personal system to manage student's academic information. This system is supported by a flexible model that integrates, for instance, knowledge about the student attended courses or about a course that the student wishes to apply. Essentially, this model holds a (i) Student's Academic Record with skills acquired in academic course units, professional experience or training and an (ii) Individual Studies Plan, which places the student in a particular (iii) Course Plan setting the curricular structure that the student wishes to apply.

Distributed process execution in collaborative networks

Processes are a central entity in enterprise collaboration. Collaborative processes need to be executed and coordinated in a distributed Computational platform where computers are connected through heterogeneous networks and systems. Life cycle management of such collaborative processes requires a framework able to handle their diversity based on different computational and communication requirements. This paper proposes a rational for such framework, points out key requirements and proposes it strategy for a supporting technological infrastructure. Beyond the portability of collaborative process definitions among different technological bindings, a framework to handle different life cycle phases of those definitions is presented and discussed. (c) 2007 Elsevier Ltd. All rights reserved.

Lubrificação de compressores que aspiram fluido frigorigéneo a temperaturas inferiores a -30° C

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Characterization of airborne particles generated from metal active gas welding process

This study is focused on the characterization of particles emitted in the metal active gas welding of carbon steel using mixture of Ar + CO2, and intends to analyze which are the main process parameters that influence the emission itself. It was found that the amount of emitted particles (measured by particle number and alveolar deposited surface area) are clearly dependent on the distance to the welding front and also on the main welding parameters, namely the current intensity and heat input in the welding process. The emission of airborne fine particles seems to increase with the current intensity as fume-formation rate does. When comparing the tested gas mixtures, higher emissions are observed for more oxidant mixtures, that is, mixtures with higher CO2 content, which result in higher arc stability. These mixtures originate higher concentrations of fine particles (as measured by number of particles by cm 3 of air) and higher values of alveolar deposited surface area of particles, thus resulting in a more severe worker's exposure.