Adaption of the Creative Commons approach and the roaming concept to spatial data infrastructures (SDI)

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

An high-speed parametric ADC and a co-designed mixer for CMOS RF receivers

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

ECG wave detector and delineation with wavelets

Proceedings of the International Conference on Computational Intelligence in Medicine Healthcare, CIMED 2005, Costa da Caparica, June 29 - July 1, 2005

KM and politics at the highest level: an exploratory analysis

This paper analyzes Knowledge Management (KM) as a political activity, made by the great political leaders of the world. We try to inspect if at the macro political level KM is made, and how. The research is interesting because given that we live in a Knowledge society, in the Information Era, it is more or less obvious that the political leaders should also do KM. However we don’t know of any previous study on KM and world leaders and this paper wants to be a first step to fill that gap. As a methodology we use literature review: given this one is a first preliminary study we use data we found in the Internet and other databases like EBSCO. We divide the analysis in two main parts: theoretical ideas first, and an application second. The second part is it self divided in two segments: the past and the present times. We find that rather not surprisingly, KM always was and is pervasive in the activity of the world leaders, and has become more and more diverse has power itself became to be more and more disseminated in the world. The study has the limitation of relying on insights and texts and not on interviews. But we believe it is very interesting to make this kind of analysis and such studies may help improving the democracies in the world.

Estudo de caso do programa Flash! Vidas : informação ou entretenimento?

O presente estudo de caso resulta de três meses de estágio no Grupo Cofina Media, onde foram desempenhadas funções jornalísticas na secção Vidas, do jornal Correio da Manhã (CM) e no programa televisivo Flash!Vidas, do canal Correio da Manhã TV (CMTV), desde o dia 2 de fevereiro até ao dia 2 de maio de 2015. Foi elaborado no âmbito do Mestrado em Jornalismo, no Instituto Politécnico de Lisboa, Escola Superior de Comunicação Social, e por objetivo a obtenção do grau de mestre no curso anteriormente referido. A análise tem como propósito perceber se as alegadas peças noticiosas transmitidas pelo programa televisivo Flash!Vidas, no canal Correio da Manhã TV, podem ser consideradas informação, entretenimento ou os dois géneros. É importante referir que o presente trabalho terá como base fundamental os valores-notícias estudados pelo teórico Nelson Traquina e o conceito de infotainment.

A quest for the right word enhancing reflexivity and technology in terminology training

INTED2010, the 4th International Technology, Education and Development Conference was held in Valencia (Spain), on March 8, 9 and 10, 2010.

Thermodynamic and kinetic modelling of the redox properties of tetrahaem cytochromes C3

Dissertação apresentada para obtenção do grau de Doutor em Bioquímica,especialidade Bioquímica-Física, pela Universidade Nova de Lisboa, Faculdade de Cincias e Tecnologia

Nanoparticles based Permeable Reactive Barriers as an Eco-efficient Technology for nitrate remediation in soil and groundwater

The need to increase agricultural yield led, among others, to an increase in the consumption of nitrogen based fertilizers. As a consequence, there are excessive concentrations of nitrates, the most abundant of the reactive nitrogen (Nr) species, in several areas of the world. The demographic changes and projected population growth for the next decades, and the economic shifts which are already shaping the near future are powerful drivers for a further intensification in the use of fertilizers, with a predicted increase of the nitrogen loads in soils. Nitrate easily diffuses in the subsurface environments, portraying high mobility in soils. Moreover, the presence of high nitrate loads in water has the potential to cause an array of health dysfunctions, such as methemoglobinemia and several cancers. Permeable Reactive Barriers (PRB) placed strategically relatively to the nitrate source constitute an effective technology to tackle nitrate pollution. Ergo, PRB avoid various adverse impacts resulting from the displacement of reactive nitrogen downstream along water bodies. A four stages literature review was carried out in 34 databases. Initially, a set of pertinent key words were identified to perform the initial databases searches. Then, the synonyms of those initial key words were used to carry out a second set of databases searches. The third stage comprised the identification of other additional relevant terms from the research papers identified in the previous two stages. Again, databases searches were performed with this third set of key words. The final step consisted of the identification of relevant papers from the bibliography of the relevant papers identified in the previous three stages of the literature review process. The set of papers identified as relevant for in-depth analysis were assessed considering a set of relevant characterization variables.

Microencapsulation of herbicide MCPA with native beta-cyclodextrin and its methyl and hydroxypropyl derivatives: An experimental and theoretical investigation

When a pesticide is released into the environment, most of it is lost before it reaches its target. An effective way to reduce environmental losses of pesticides is by using controlled release technology. Microencapsulation becomes a promising technique for the production of controlled release agricultural formulations. In this work, the microencapsulation of chlorophenoxy herbicide MCPA with native b-cyclodextrin and its methyl and hydroxypropyl derivatives was investigated. The phase solubility study showed that both native and b-CD derivatives increased the water solubility of the herbicide and inclusion complexes are formed in a stoichiometric ratio of 1:1. The stability constants describing the extent of formation of the complexes have been determined by phase solubility studies. 1H NMR experiments were also accomplished for the prepared solid systems and the data gathered confirm the formation of the inclusion complexes. 1H NMR data obtained for the MCPA/CDs complexes disclosed noticeable proton shift displacements for OCH2 group and H6 aromatic proton of MCPA provided clear evidence of inclusion complexation process, suggesting that the phenyl moiety of the herbicide was included in the hydrophobic cavity of CDs. Free energy molecular mechanics calculations confirm all these findings. The gathered results can be regarded as an essential step to the development of controlled release agricultural formulations containing herbicide MCPA.

GLODS: Global and Local Optimization using Direct Search

Locating and identifying points as global minimizers is, in general, a hard and time-consuming task. Difficulties increase in the impossibility of using the derivatives of the functions defining the problem. In this work, we propose a new class of methods suited for global derivative-free constrained optimization. Using direct search of directional type, the algorithm alternates between a search step, where potentially good regions are located, and a poll step where the previously located promising regions are explored. This exploitation is made through the launching of several instances of directional direct searches, one in each of the regions of interest. Differently from a simple multistart strategy, direct searches will merge when sufficiently close. The goal is to end with as many direct searches as the number of local minimizers, which would easily allow locating the global extreme value. We describe the algorithmic structure considered, present the corresponding convergence analysis and report numerical results, showing that the proposed method is competitive with currently commonly used global derivative-free optimization solvers.

Spent coffee grounds for biodiesel production and other applications

This work evaluates the possibility of using spent coffee grounds (SCG) for biodiesel production and other applications. An experimental study was conducted with different solvents showing that lipid content up to 6 wt% can be obtained from SCG. Results also show that besides biodiesel production, SCG can be used as fertilizer as it is rich in nitrogen, and as solid fuel with higher heating value (HHV) equivalent to some agriculture and wood residues. The extracted lipids were characterized for their properties of acid value, density at 15 °C, viscosity at 40 °C, iodine number, and HHV, which are negatively influenced by water content and solvents used in lipid extraction. Results suggest that for lipids with high free fatty acids (FFA), the best procedure for conversion to biodiesel would be a two-step process of acid esterification followed by alkaline transesterification, instead of a sole step of direct transesterification with acid catalyst. Biodiesel was characterized for its properties of iodine number, acid value, and ester content. Although these quality parameters were not within the limits of NP EN 14214:2009 standard, SCG lipids can be used for biodiesel, blended with higher-quality vegetable oils before transesterification, or the biodiesel produced from SCG can be blended with higher-quality biodiesel or even with fossil diesel, in order to meet the standard requirements.

Welcome to the golden land: sexual and gender-based violence against women refugees from Burma

European Master’s Degree in Human Rights and Democratisation Academic Year 2005/2006

Step count algorithm adapted to indoor localization

This paper presents a step count algorithm designed to work in real-time using low computational power. This proposal is our first step for the development of an indoor navigation system, based on Pedestrian Dead Reckoning (PDR). We present two approaches to solve this problem and compare them based in their error on step counting, as well as, the capability of their use in a real time system.

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and Technology

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.