Projeto topológico de uma rede rodoviária

Mestrado em Engenharia Informática - Área de Especialização em Sistemas Gráficos e Multimédia

Surveillance of arbovirus infections in the atlantic forest region, State of São Paulo, Brazil: I. detection of hemagglutination-inhibition antibodies in wild birds between 1978 and 1990

We report data related to arbovirus antibodies detected in wild birds periodically captured from January 1978 to December 1990 in the counties of Salesópolis (Casa Grande Station), Itapetininga and Ribeira Valley, considering the different capture environments. Plasmas were examined using hemagglutination-inhibition (HI) tests. Only monotypic reactions were considered, except for two heterotypic reactions in which a significant difference in titer was observed for a determined virus of the same antigenic group. Among a total of 39,911 birds, 269 birds (0.7%) belonging to 66 species and 22 families were found to have a monotypic reaction for Eastern equine encephalitis (EEE), Venezuelan equine encephalitis (VEE), Western equine encephalitis (WEE), Ilheus (ILH), Rocio (ROC), St. Louis encephalitis (SLE), SP An 71686, or Caraparu (CAR) viruses. Analysis of the data provided information of epidemiologic interest with respect to these agents. Birds with positive serology were distributed among different habitats, with a predominance of unforested habitats. The greatest diversity of positive reactions was observed among species which concentrate in culture fields.

Educating for earthquake science and risk in a tectonically slowly deforming region

Over the past decade, scientists have been called to participate more actively in public education and outreach (E&O). This is particularly true in fields of significant societal impact, such as earthquake science. Local earthquake risk culture plays a role in the way that the public engages in educational efforts. In this article, we describe an adapted E&O program for earthquake science and risk. The program is tailored for a region of slow tectonic deformation, where large earthquakes are extreme events that occur with long return periods. The adapted program has two main goals: (1) to increase the awareness and preparedness of the population to earthquake and related risks (tsunami, liquefaction, fires, etc.), and (2) to increase the quality of earthquake science education, so as to attract talented students to geosciences. Our integrated program relies on activities tuned for different population groups who have different interests and abilities, namely young children, teenagers, young adults, and professionals.

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.

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.

Susceptibility of Biomphalaria tenagophila and Biomphalaria glabrata from the same region to two Brazilian strains of Schistosoma mansoni

B. tenagophila snails from Ouro Branco, MG, showed positivity for S. mansoni, with infection rates of 5%, 10%, (SJ strain), and 1% (LE strain) using a pool of miracidia. The mollusks were found to be susceptive from the 3rd generation reared in laboratory onwards. The B. tenagophila (OB, MG) when individually exposed to 10 miracidia, showed infection rate of 2% for LE strain. B. glabrata snails from Gagé, MG, showed a positivity rate of 58% for S. mansoni (LE strain), under experimental conditions. The B. tenagophila from Cabo Frio, RJ and B. glabrata from Belo Horizonte, MG used as a control for SJ strain showed infection rates of 47% - 85% and 36% respectivily. For the LE strain, B. glabrata (BH, MG) used as control showed infection rate of 40% - 75%.

A lightweight distributed super peer election algorithm for unstructured dynamic P2P systems

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

Bioenergy competency centre: study of the bioenergy potencial in the centre region of Portugal.

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

Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization Algorithm

The recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.

POSSIBLE ROLE OF LUTZOMYIA MARANONENSIS AND LUTZOMYIA ROBUSTA (DIPTERA: PSYCHODIDAE) AS VECTORS OF HUMAN BARTONELLOSIS IN THREE PROVINCES OF REGION NOR ORIENTAL DEL MARAÑON, PERU

Human bartonellosis is found predominantly in Perú2, 6, 8, 12, 15, as well as in Ecuador3, 7, 10 and Colombia13, 15. In Peru, the disease is restricted to the valleys of the western-side and a few inter-andean and eastern-slopes of the andean valleys6, 15, 18 at altitudes between 1000 and 3200 masl. Most human cases are reported from the regions of Chavin, Nor Oriental del Marañon and Lima16. Lutzomyia verrucarum is presumed to be the only vector of human bartonellosis in the valleys of Peru1, 2, 8, 11, 17, 19/ Our research objetive was to detect the presence of Lu. verrucarum in various localities known to be endemic for human bartonellosis in three provinces of Region Nor Oriental del Marañon. Sandfly collections were made between 1987 and 1992 during four visits to bartonellosis-endemic provinces: San Ignacio (districts of San José de Lourdes: 1020-1260 m and La Coipa: 1200-1560 m), Jaén (districts of Santa Rosa: 1300-1680 m and Jaén: 1220-1680 m) and Utcubamba (districts of Lonya Grande: 1200 m and El Milagro: 1200-1540 m)

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Streptococcus pneumoniae and Haemophilus influenzae as etiological agents of conjunctivitis outbreaks in the region of Ribeirão Preto, SP, Brazil

In the study of conjunctivitis outbreaks occurring from September 1994 to September 1996 in the region of Ribeirão Preto, conjunctival exudates of 92 patients were cultivated in Instituto Adolfo Lutz Laboratory I, Ribeirão Preto. Most cases occurred in the age range 2-7 years. The etiological agents which were most frequently isolated from the analyzed cases were: Streptococcus pneumoniae and Haemophilus influenzae, in 40.22% and 21.74%, respectively. 51.35% of the S. pneumoniae isolated strains were not typable. The oxacillin-resistant S. pneumoniae strains were submitted to the minimum inhibitory concentration test (MIC) and three of them presented intermediate resistance, whereas only one was highly resistant to penicillin.

RESEARCH OF ANTIGEN AND ANTIBODIES FROM RETROVIRUSES, CMV AND HBV AMONG PRISONERS OF THE PENITENTIARY COMPLEX OF THE REGION OF CAMPINAS, SP, BRAZIL

Some viruses of the families Retroviridae, such as Human T Lymphotropic Virus (HTLV); Herpesviridae as the Cytomegalovirus (CMV) and Hepadnaviridae such as the Hepatitis B Virus (HBV) are liable to be co-transmitted with the Human Immunodeficiency Virus (HIV). Since prisoners are exposed to several and important risk factors involved in the transmission of HIV and the above mentioned viruses, male inmates from the penitentiary complex of Campinas, SP, Brazil, including HIV + and HIV - ones, were examined for the presence of HTLV-I and/or II antibodies; IgG and IgM anti-CMV antibodies, and the research of the superficial hepatitis B antigen (HBsAg). The presence of anti-HTLV-I and/or II was determined by the Western Blot (WB) technique, whereas IgG and IgM anti-CMV and the search of HBsAg were carried out by the Microparticle Enzyme Immunoassay (MEIA-Abbott Lab).With regard to anti-HTLV-I and/or II, 58.3% (14/24-Number of positive reactions/number of sera examined) were reactive among the anti-HIV positive sera. Conversely, only 12.5% (3/24) among the HIV- negative sera showed positive reactions to HTLV-I and/or II antibodies. When looking for IgG anti-CMV percentages of 97.7% (43/44) and 95% (38/40) were obtained for anti-HIV positive and negative sera, respectively. As to IgM anti-CMV antibodies 11.36% (5/44) and 2.5% (1/40) of reactive sera were found for anti-HIV positive and negative, respectively. The HBsAg was found in 12.8% (5/39) of the sera which were anti-HIV positive.

Imagiologia Espetral - Análise Forense de Notas e Escrita Manual

A análise forense de documentos é uma das áreas das Ciências Forenses, responsável pela verificação da autenticidade dos documentos. Os documentos podem ser de diferentes tipos, sendo a moeda ou escrita manual as evidências forenses que mais frequentemente motivam a análise. A associação de novas tecnologias a este processo de análise permite uma melhor avaliação dessas evidências, tornando o processo mais célere. Esta tese baseia-se na análise forense de dois tipos de documentos - notas de euro e formulários preenchidos por escrita manual. Neste trabalho pretendeu-se desenvolver técnicas de processamento e análise de imagens de evidências dos tipos referidos com vista a extração de medidas que permitam aferir da autenticidade dos mesmos. A aquisição das imagens das notas foi realizada por imagiologia espetral, tendo-se definidas quatro modalidades de aquisição: luz visível transmitida, luz visível refletida, ultravioleta A e ultravioleta C. Para cada uma destas modalidades de aquisição, foram também definidos 2 protocolos: frente e verso. A aquisição das imagens dos documentos escritos manualmente efetuou-se através da digitalização dos mesmos com recurso a um digitalizador automático de um aparelho multifunções. Para as imagens das notas desenvolveram-se vários algoritmos de processamento e análise de imagem, específicos para este tipo de evidências. Esses algoritmos permitem a segmentação da região de interesse da imagem, a segmentação das sub-regiões que contém as marcas de segurança a avaliar bem como da extração de algumas características. Relativamente as imagens dos documentos escritos manualmente, foram também desenvolvidos algoritmos de segmentação que permitem obter todas as sub-regiões de interesse dos formulários, de forma a serem analisados os vários elementos. Neste tipo de evidências, desenvolveu-se ainda um algoritmo de análise para os elementos correspondentes à escrita de uma sequência numérica o qual permite a obtenção das imagens correspondentes aos caracteres individuais. O trabalho desenvolvido e os resultados obtidos permitiram a definição de protocolos de aquisição de imagens destes tipos de evidências. Os algoritmos automáticos de segmentação e análise desenvolvidos ao longo deste trabalho podem ser auxiliares preciosos no processo de análise da autenticidade dos documentos, o qual, ate então, é feito manualmente. Apresentam-se ainda os resultados dos estudos feitos às diversas evidências, nomeadamente as performances dos diversos algoritmos analisados, bem como algumas das adversidades encontradas durante o processo. Apresenta-se também uma discussão da metodologia adotada e dos resultados, bem como de propostas de continuação deste trabalho, nomeadamente, a extração de características e a implementação de classificadores capazes aferir da autenticidade dos documentos.