Exploratory geospatial data analysis using self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

SENSITIVITY ANALYSIS OF TRACK VIBRATIONS DUE TO VERTICAL STIFFNESS VARIATION IN HIGH-SPEED RAILWAYS

High speed trains, when crossing regions with abrupt changes in vertical stiffness of the track and/or subsoil, may generate excessive ground and track vibrations. There is an urgent need for specific analyses of this problem so as to allow reliable esimates of vibration amplitude. Full understanding of these phenomena will lead to new construction solutions and mitigation of undesirable features. In this paper analytical transient solutions of dynamic response of one-dimensional systems with sudden change of foundation stiffness are derived. Results are expressed in terms of vertical displacement. Sensitivity analysis of the response amplitude is also performed. The analytical expressions presented herein, to the authors’ knowledge, have not been published yet. Although related to one-dimensional cases, they can give useful insight into the problem. Nevertheless, in order to obtain realistic response, vehicle- rail interaction cannot be omitted. Results and conclusions are confirmed using general purpose commercial software ANSYS. In conclusion, this work contributes to a better understanding of the additional vibration phenomenon due to vertical stiffness variation, permitting better control of the train velocity and optimization of the track design.

Layerwise mixed models for analysis of multilayered piezoelectric composite plates using least-squares formulation

This work provides an assessment of layerwise mixed models using least-squares formulation for the coupled electromechanical static analysis of multilayered plates. In agreement with three-dimensional (3D) exact solutions, due to compatibility and equilibrium conditions at the layers interfaces, certain mechanical and electrical variables must fulfill interlaminar C-0 continuity, namely: displacements, in-plane strains, transverse stresses, electric potential, in-plane electric field components and transverse electric displacement (if no potential is imposed between layers). Hence, two layerwise mixed least-squares models are here investigated, with two different sets of chosen independent variables: Model A, developed earlier, fulfills a priori the interiaminar C-0 continuity of all those aforementioned variables, taken as independent variables; Model B, here newly developed, rather reduces the number of independent variables, but also fulfills a priori the interlaminar C-0 continuity of displacements, transverse stresses, electric potential and transverse electric displacement, taken as independent variables. The predictive capabilities of both models are assessed by comparison with 3D exact solutions, considering multilayered piezoelectric composite plates of different aspect ratios, under an applied transverse load or surface potential. It is shown that both models are able to predict an accurate quasi-3D description of the static electromechanical analysis of multilayered plates for all aspect ratios.

Analysis of Forest Fires by means of Pseudo Phase Plane and Multidimensional Scaling Methods

Forest fires dynamics is often characterized by the absence of a characteristic length-scale, long range correlations in space and time, and long memory, which are features also associated with fractional order systems. In this paper a public domain forest fires catalogue, containing information of events for Portugal, covering the period from 1980 up to 2012, is tackled. The events are modelled as time series of Dirac impulses with amplitude proportional to the burnt area. The time series are viewed as the system output and are interpreted as a manifestation of the system dynamics. In the first phase we use the pseudo phase plane (PPP) technique to describe forest fires dynamics. In the second phase we use multidimensional scaling (MDS) visualization tools. The PPP allows the representation of forest fires dynamics in two-dimensional space, by taking time series representative of the phenomena. The MDS approach generates maps where objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to better understand forest fires behaviour.

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.

Integer and Fractional Order Entropy Analysis of Earthquake Data-series

This paper studies the statistical distributions of worldwide earthquakes from year 1963 up to year 2012. A Cartesian grid, dividing Earth into geographic regions, is considered. Entropy and the Jensen–Shannon divergence are used to analyze and compare real-world data. Hierarchical clustering and multi-dimensional scaling techniques are adopted for data visualization. Entropy-based indices have the advantage of leading to a single parameter expressing the relationships between the seismic data. Classical and generalized (fractional) entropy and Jensen–Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand earthquake distributions.

Multidimensional Scalling Analysis of the World Economy During the Period 1972-2012

The last 40 years of the world economy are analyzed by means of computer visualization methods. Multidimensional scaling and the hierarchical clustering tree techniques are used. The current Western downturn in favor of Asian partners may still be reversed in the coming decades.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Integrated study by NMR and X-ray Crystallography on the analysis of the molecular interactions in heme-binding proteins

Dissertação para obtenção do Grau de Doutor em Bioquímica, Especialidade Bioquímica Estrutural

Learning networks and moodle use in online courses: a social network analysis study

Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e Formação

Analysis of the chemical components of hydatid fluid from Echinococcus granulosus

Introduction The aim of this study was to explore the environment of Echinococcus granulosus (E. granulosus) protoscolices and their relationship with their host. Methods Proteins from the hydatid-cyst fluid (HCF) from E. granulosus were identified by proteomics. An inductively coupled plasma atomic emission spectrometer (ICP-AES) was used to determine the elements, an automatic biochemical analyzer was used to detect the types and levels of biochemical indices, and an automatic amino acid analyzer was used to detect the types and levels of amino acids in the E. granulosus HCF. Results I) Approximately 30 protein spots and 21 peptide mass fingerprints (PMF) were acquired in the two-dimensional gel electrophoresis (2-DE) pattern of hydatid fluid; II) We detected 10 chemical elements in the cyst fluid, including sodium, potassium, calcium, magnesium, copper, and zinc; III) We measured 19 biochemical metabolites in the cyst fluid, and the amount of most of these metabolites was lower than that in normal human serum; IV) We detected 17 free amino acids and measured some of these, including alanine, glycine, and valine. Conclusions We identified and measured many chemical components of the cyst fluid, providing a theoretical basis for developing new drugs to prevent and treat hydatid disease by inhibiting or blocking nutrition, metabolism, and other functions of the pathogen.

Proteomic analysis of a neurodegeneration cell model, treated with plant extracts with potential neuroprotective activity

Nowadays, a significant increase in chronic diseases is observed. Epidemiological studies showed a consistent relationship between the consumption of fruits and vegetables and a reduced risk of certain chronic diseases, namely neurodegenerative disorders. One factor common to these diseases is oxidative stress, which is highly related with proteins, lipids, carbohydrates and nucleic acids damage, leading to cellular dysfunction. Polyphenols, highly abundant in berries and associated products, were described as having antioxidant properties, with beneficial effect in these pathologies. The aims of this study were to evaluate by proteomic analyses the effect of oxidative insult in a neuroblastoma cell line (SK-N-MC) and understand the mechanisms involved in the neuroprotective effects of digested extracts from commercial and wild blackberry (R. vagabundus Samp.). The analysis of the total proteome by two-dimensional electrophoresis revealed that oxidative stress in SK-N-MC cells resulted in altered expression of 12 protein spots from a total of 318. Regarding some redox proteomics alterations, particularly proteins carbonylation and glutathionylation, protein carbonyl alterations during stress suggest that cells produce an early and late response; on the other hand, no glutathionylated polypeptides were detected. Relatively to the incubation of SK-N-MC cells with digested berry extracts, commercial blackberry promotes more changes in protein pattern of these cells than R. vagabundus. From 9 statistically different protein spots of cells incubated with commercial blackberry, only β-tubulin and GRP 78 were until now identified by mass spectrometry. Further studies involving the selection of sub proteomes will be necessary to have a better understanding of the mechanisms underlying the neuroprotective effects of berries.

Analysis and Treatment of a male portrait in oil and a study of the size layer with reconstructions from Vibert’s recipe (1892)

This thesis project concentrated on both the study and treatment of an early 20th century male portrait in oil from Colecção Caixa Geral de Depósitos, Lisbon, Portugal. The portrait of Januário Correia de Almeida, exhibits a tear (approximately 4.0 cm by 2.3 cm) associated with paint loss on the right upper side, where it is possible to observe an unusually thick size layer (approximately 50 microns) and an open weave mesh canvas. Size layers made from animal glue remain subject to severe dimensional changes due to changes in relative humidity (RH), thereby affecting the stability of the painting. In this case, the response to moisture of the size layer is minimal and the painting is largely uncracked with very little active flaking. This suggests that the size layer has undergone pre-treatment to render it unresponsive to moisture or water. Reconstructions based on late nineteenth century recipes using historically appropriate materials are used to explore various options for modifying the characteristics of gelatine, some of which may relate to the Portrait’s size layer. The thesis is separated into two parts: Part 1: Describes the history, condition, materials and techniques of the painting. It also details the treatment of Januário Correia de Almeida as well as the choices made and problems encountered during the treatment. Part 2: Discusses the history of commercial gelatine production, the choice of the appropriate animal source to extract the collagen to produce reconstructions of the portrait’s size layer as well as the characterization of selected reconstructions. The execution of a shallow textured infill led to one publication and one presentation: Abstract accepted for presentation and publication, International Meeting on Retouching of Cultural Heritage (RECH3), Francisco Brites, Leslie Carlyle and Raquel Marques, ‘’Hand building a Low Profile Textured Fill for a Large Loss’’.

Visual data analysis in air traffic management

Visual data mining, multi-dimensional scaling, POLARMAP, Sammon's mapping, clustering, outlier detection

Does abdominoplasty with liposuction of the love handles yield a shorter scar? An analysis with abdominal 3D laser scanning.

The aim of this study was to evaluate the combination of abdominoplasty with liposuction of both flanks with regards to length of scar, complications, and patient's satisfaction. A retrospective analysis of 35 patients who underwent esthetic abdominoplasty at our institution between 2002 and 2004 was performed. Thirteen patients underwent abdominoplasty with liposuction of both flanks, 22 patients underwent conventional abdominoplasty. Liposuction of the flanks did not increase the rate of complications of the abdominoplasty procedures. We found a tendency toward shorter scars in patients who underwent abdominoplasty combined with liposuction of the flanks. Implementation of 3-dimensional laser surface scanning to objectify the postoperative outcomes, documented a comparable degree of flatness of the achieved body contouring in both procedures. 3-dimensional laser surface scanning can be a valuable tool to objectify assessment of postoperative results.