Satellite image classification and segmentation using non-additive entropy

Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis entropy on the pattern recognition and segmentation of coloured images obtained by satellites, via "Google Earth". By segmentation we mean split an image to locate regions of interest. Here, we discriminate and define an image partition classes according to a training basis. This training basis consists of three pattern classes: aquatic, urban and vegetation regions. Our numerical experiments demonstrate that the Tsallis entropy, used as a feature vector composed of distinct entropic indexes $q$ outperforms the standard entropy. There are several applications of our proposed methodology, once satellite images can be used to monitor migration form rural to urban regions, agricultural activities, oil spreading on the ocean etc.Comment: 4 pages, 5 figures, ICMSquare 201

MODELING SPATIAL EFFECT ON TRAVEL MODE CHOICE USING A SYNTHETIC SPATIALLY CORRELATED DATA SET

Urban dynamics can be characterized more effectively by considering spatial aspects in studies. This paper, using a synthetic spatially correlated data set, aims to model the spatial effect on travel mode choice based on geostatistics precepts. A method was proposed based on three main steps. The first step consists of building synthetic spatially correlated data, using the intrinsic spatial dependence on travel demand data and mathematical principles of bilinear interpolation. The following two steps correspond to the modeling approach. The Exploratory Spatial Data Analysis stage aimed to attest the existence of spatial autocorrelation of the data set using two indicators: Moran and G-SIVAR (Global Spatial Indicator Based on Variogram). The Confirmatory Spatial Data Analysis stage proposed the calibration of two Binomial Logit models. The first model includes only the original database variables (nonspatial model). The second one is analogous to the original but added to spatial covariates obtained by geostatistical concepts (spatial model). A 15% increase in cross-validation hit rates is achieved when spatial variables are included. This paper presents three significant research contributions: (1) The methodological procedure to model spatial effect on travel mode choice; (2) The proposal of spatial covariates based on geostatistical assumptions; and (3) The suggestion of a simple procedure to propose a simulation of a spatially correlated database

Performing edge detection by difference of Gaussians using q-Gaussian kernels

In image processing, edge detection is a valuable tool to perform the extraction of features from an image. This detection reduces the amount of information to be processed, since the redundant information (considered less relevant) can be unconsidered. The technique of edge detection consists of determining the points of a digital image whose intensity changes sharply. This changes are due to the discontinuities of the orientation on a surface for example. A well known method of edge detection is the Difference of Gaussians (DoG). The method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image. This paper introduces a method of extracting edges using DoG with kernels based on the q-Gaussian probability distribution, derived from the q-statistic proposed by Constantino Tsallis. To demonstrate the method's potential, we compare the introduced method with the traditional DoG using Gaussians kernels. The results showed that the proposed method can extract edges with more accurate details.Comment: 5 pages, 5 figures, IC-MSQUARE 201

USE OF THE LINEAR LIGHT SENSOR ILX554 IN OPTICAL SPECTROSCOPY

USE OF THE LINEAR LIGHT SENSOR ILX554 IN OPTICAL SPECTROSCOPY. This technical note describes the construction of a low-cost optical detector. This device is composed by a high-sensitive linear light sensor (model ILX554) and a microcontroller. The performance or the detector was demonstrated by the detection of emission and Raman spectra of the several atomic systems and the results reproduce those found in the literature

Polymerization shrinkage stress of composites photoactivated by different light sources

The purpose of this study was to compare the polymerization shrinkage stress of composite resins (microfilled, microhybrid and hybrid) photoactivated by quartz-tungsten halogen light (QTH) and light-emitting diode (LED). Glass rods (5.0 mm x 5.0 cm) were fabricated and had one of the surfaces air-abraded with aluminum oxide and coated with a layer of an adhesive system, which was photoactivated with the QTH unit. The glass rods were vertically assembled, in pairs, to a universal testing machine and the composites were applied to the lower rod. The upper rod was placed closer, at 2 mm, and an extensometer was attached to the rods. The 20 composites were polymerized by either QTH (n=10) or LED (n=10) curing units. Polymerization was carried out using 2 devices positioned in opposite sides, which were simultaneously activated for 40 s. Shrinkage stress was analyzed twice: shortly after polymerization (t40s) and 10 min later (t10min). Data were analyzed statistically by 2-way ANOVA and Tukey's test (a=5%). The shrinkage stress for all composites was higher at t10min than at t40s, regardless of the activation source. Microfilled composite resins showed lower shrinkage stress values compared to the other composite resins. For the hybrid and microhybrid composite resins, the light source had no influence on the shrinkage stress, except for microfilled composite at t10min. It may be concluded that the composition of composite resins is the factor with the strongest influence on shrinkage stress.Este estudo comparou a contração de polimerização de resinas compostas fotoativadas por luz halógena (QTH) e diodo emissor de luz (LED). Foram confeccionados bastões de vidro (5,0 mm x 5,0 cm), e uma de suas extremidades sofreu jateamento com óxido de alumínio, sobre a qual foi aplicado um adesivo e fotoativado com luz halógena. Os bastões de vidro foram acoplados verticalmente, em pares, em uma máquina universal de ensaios (EMIC DL-2000) e as resinas compostas aplicadas no bastão inferior. A distância entre os bastões foi padronizada em 2 mm e um extensômetro foi acoplado a eles. As resinas foram fotoativadas (n=20), sendo 10 por QTH e 10 por LED utilizando dois aparelhos posicionados em lados opostos, acionados simultaneamente por 40 s. A tensão de contração foi analisada em dois momentos: logo após a polimerização (t40s) e 10 min após (t10min). A tensão de contração apresentada por todas as resinas foi maior em t10min do que em t40s, independente da fonte ativadora. A resina de micropartículas apresentou menores valores de tensão de contração com valores estatisticamente significantes em relação às demais resinas. Para as resinas híbrida e microhíbrida não houve influência da unidade ativadora sobre a tensão de contração, com exceção para a resina de micropartículas em t10min. Concluiu-se que a composição da resina composta foi o fator que mais interferiu na tensão de contração da resina composta

Use of the linear light sensor ILX554 in optical spectroscopy

This technical note describes the construction of a low-cost optical detector. This device is composed by a high-sensitive linear light sensor (model ILX554) and a microcontroller. The performance of the detector was demonstrated by the detection of emission and Raman spectra of the several atomic systems and the results reproduce those found in the literature

Analysis of neighborhood influence on travel behaviour through panel data

O comportamento individual relativo a viagens sofre a influência de fatores individuais e do meio urbano. Assim, a vizinhança seria uma das variáveis a serem consideradas na análise comportamental relacionada aos deslocamentos. O objetivo principal deste trabalho é analisar a influência da vizinhança no comportamento individual relativo a viagens, através de dados em painel. Dados em painel constituem importante ferramenta em análises comportamentais subjacentes a viagens urbanas, uma vez que propiciam maior quantidade de informações quando comparados aos dados seccionais. Padrões de viagens são mais bem evidenciados, através de dados em painel, caracterizando as habituais rotinas de atividades e viagens, além de melhor identificar comportamentos atípicos. Todavia, a obtenção desses dados comumente não é atividade trivial, demandando recursos monetários e de tempo. Um dos objetivos secundários deste trabalho é apresentar uma maneira prática e pouco onerosa de obtenção de dados em painel através de Smartphones. Tais dados, posteriormente, são aplicados à classificação de indivíduos segundo comportamento relacionado às viagens. A potencialidade da proposta sugerida é validada por meio de um estudo de caso relativo aos estudantes universitários do município de São Carlos - SP, Brasil. Através dos dados em painel, fornecidos pelos estudantes, utilizou-se o algoritmo k-médias considerando quatro variáveis relativas aos deslocamentos. As três categorias obtidas apresentam estrutura espacial e, portanto, possibilitam análises espaciais exploratórias e confirmatórias, almejando a compreensão de influências da vizinhança nas dinâmicas cotidianas. Este trabalho atesta a existência de autocorrelação espacial do conjunto de dados por meio de dois indicadores: Moran e SivarG (Global Spatial Indicator Based on Variogram). A corroboração da dependência espacial, apontada pelos indicadores globais, é confirmada por meio de dois modelos de escolha discreta. Um contendo apenas variáveis originais da base de dados. Outro, análogo ao primeiro, porém adicionado de covariáveis regionais, obtidas por preceitos da geoestatística. A incorporação das covariáveis regionais aumenta a precisão do modelo e promove um incremento das taxas de acertos em validações cruzadas.Individual travel behaviour is influenced by individual factors and the urban environment. Thus, the neighborhood influence would be one of the variables to be considered in travel behavior analysis related to urban displacements. The main objective of this work is to analyze the influence of neighborhood on travel behavior by panel data. Panel data is an important tool in urban travel behavioral analyzes, since they provide a greater amount of information when compared to sectional data. Travel patterns are more evident through panel data, characterizing the usual routines of activities, as well the atypical behaviors. However, obtaining these data is not a simple task, requiring monetary and time resources. Secondary goals of this work aim to present a practical and inexpensive way to obtain panel data through Smartphones. These data are then applied to the classification of individuals according to travel behavior. The potential of the proposal is validated by a case of study concerning undergraduate and PhD students from São Carlos - SP, Brazil. Using the data provided by the students, a k-means algorithm was used considering four variables regarding displacements. These three categories have spatial structure and allow exploratory and confirmatory spatial data analyzes aiming the comprehension of the nearby influence of data at daily dynamics. This work attests to the existence of spatial autocorrelation of the data set by two indicators: Moran and SivarG (Global Spatial Indicator Based on Variogram). Corroboration of spatial dependence, pointed by the global indicators, is confirmed by two discrete choice models. The first one includes just the original database variables. The second one, analogous to the first, but added of regional covariates obtained by geostatistical concepts. The addition of regional variables leads to a more accurate model, increasing cross-validations hit rates

Entropy applied to pattern recognition in images

Este trabalho faz um estudo do uso da entropia como ferramenta para o reconhecimento de padrões em imagens. A entropia é um conceito utilizado em termodinâmica para medir o grau de organização de um meio. Entretanto, este conceito pode ser ampliado para outras áreas do conhecimento. A adoção do conceito em Teoria da Informação e, por consequência, em reconhecimento de padrões foi introduzida por Shannon no trabalho intitulado \"A Mathematical Theory of Communication\", publicado no ano de 1948. Neste mestrado, além da entropia clássica de Boltzman-Gibbs-Shannon, são investigadas a entropia generalizada de Tsallis e suas variantes (análise multi-escala, múltiplo índice q e seleção de atributos), aplicadas ao reconhecimento de padrões em imagens. Utilizando bases de dados bem conhecidas na literatura, realizou-se estudos comparativos entre as técnicas. Os resultados mostram que a entropia de Tsallis, através de análise multi-escala e múltiplo índice q, tem grande vantagem sobre a entropia de Boltzman-Gibbs-Shannon. Aplicações práticas deste estudo são propostas com o intuito de demonstrar o potencial do método.This work studies the use of entropy as a tool for pattern recognition in images. Entropy is a concept used in thermodynamics to measure the degree of organization of a system. However, this concept can be extended to other areas of knowledge. The adoption of the concept in information theory and, consequently, in pattern recognition was introduced by Shannon in the paper entitled \"A Mathematical Theory of Communication\", published in 1948. In this master thesis, the classical Boltzmann-Gibbs-Shannon entropy, the generalized Tsallis entropy and its variants (multi-scale analysis, multiple q index, and feature selection) are studied, applied to pattern recognition in images. Using well known databases, we performed comparative studies between the techniques. The results show that the Tsallis entropy, through multiscale analysis and multiple q index has a great advantage over the classical Boltzmann-Gibbs- Shannon entropy. Practical applications of this study are proposed in order to demonstrate the potential of the method