Analysis and visualization of seismic data using mutual information

Seismic data is difficult to analyze and classical mathematical tools reveal strong limitations in exposing hidden relationships between earthquakes. In this paper, we study earthquake phenomena in the perspective of complex systems. Global seismic data, covering the period from 1962 up to 2011 is analyzed. The events, characterized by their magnitude, geographic location and time of occurrence, are divided into groups, either according to the Flinn-Engdahl (F-E) seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Two methods of analysis are considered and compared in this study. In a first method, the distributions of magnitudes are approximated by Gutenberg-Richter (G-R) distributions and the parameters used to reveal the relationships among regions. In the second method, the mutual information is calculated and adopted as a measure of similarity between regions. In both cases, using clustering analysis, visualization maps are generated, providing an intuitive and useful representation of the complex relationships that are present among seismic data. Such relationships might not be perceived on classical geographic maps. Therefore, the generated charts are a valid alternative to other visualization tools, for understanding the global behavior of earthquakes.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Estágio na empresa SPECMAN para a prática de técnicas de controlo da condição

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

Shannon information and power law analysis of the chromosome code

This paper studies the information content of the chromosomes of twenty-three species. Several statistics considering different number of bases for alphabet character encoding are derived. Based on the resulting histograms, word delimiters and character relative frequencies are identified. The knowledge of this data allows moving along each chromosome while evaluating the flow of characters and words. The resulting flux of information is captured by means of Shannon entropy. The results are explored in the perspective of power law relationships allowing a quantitative evaluation of the DNA of the species.

Complex evolution of a multi-particle system

This paper studies a discrete dynamical system of interacting particles that evolve by interacting among them. The computational model is an abstraction of the natural world, and real systems can range from the huge cosmological scale down to the scale of biological cell, or even molecules. Different conditions for the system evolution are tested. The emerging patterns are analysed by means of fractal dimension and entropy measures. It is observed that the population of particles evolves towards geometrical objects with a fractal nature. Moreover, the time signature of the entropy can be interpreted at the light of complex dynamical systems.

Cluster de volatilidade no mercado de taxas de câmbio USD/GBP

Mestrado em Controlo de Gestão e dos Negócios

Estudo sobre a memória longa na volatilidade dos preços do ouro : uma abordagem com base nos modelos de tipo ARCH

Mestrado em Contabilidade e Análise Financeira

Brownian motion with drift threshold model

In this thesis we implement estimating procedures in order to estimate threshold parameters for the continuous time threshold models driven by stochastic di®erential equations. The ¯rst procedure is based on the EM (expectation-maximization) algorithm applied to the threshold model built from the Brownian motion with drift process. The second procedure mimics one of the fundamental ideas in the estimation of the thresholds in time series context, that is, conditional least squares estimation. We implement this procedure not only for the threshold model built from the Brownian motion with drift process but also for more generic models as the ones built from the geometric Brownian motion or the Ornstein-Uhlenbeck process. Both procedures are implemented for simu- lated data and the least squares estimation procedure is also implemented for real data of daily prices from a set of international funds. The ¯rst fund is the PF-European Sus- tainable Equities-R fund from the Pictet Funds company and the second is the Parvest Europe Dynamic Growth fund from the BNP Paribas company. The data for both funds are daily prices from the year 2004. The last fund to be considered is the Converging Europe Bond fund from the Schroder company and the data are daily prices from the year 2005.

Factors associated with lack of prenatal care in a large municipality

OBJECTIVE To analyze the factors associated with a lack of prenatal care in a large municipality in southern Brazil. METHODS In this case-control age-matched study, 716 women were evaluated; of these, 179 did not receive prenatal care and 537 received prenatal care (controls). These women were identified using the Sistema Nacional de Informação sobre Nascidos Vivos (Live Birth Information System) of Pelotas, RS, Southern Brazil, between 2009 and 2010. Multivariate analysis was performed using conditional logistic regression to estimate the odds ratios (OR). RESULTS In the final model, the variables associated with a lack of prenatal care were the level of education, particularly when it was lesser than four years [OR 4.46; 95% confidence interval (CI) 1.92;10.36], being single (OR 3.61; 95%CI 1.85;7.04), and multiparity (OR 2.89; 95%CI 1.72;4.85). The prevalence of a lack of prenatal care among administrative regions varied between 0.7% and 3.9%. CONCLUSIONS The risk factors identified must be considered when planning actions for the inclusion of women in prenatal care by both the central management and healthcare teams. These indicated the municipal areas with greater deficits in prenatal care. The reorganization of the actions to identify women with risk factors in the community can be considered to be a starting point of this process. In addition, the integration of the activities of local programs that target the mother and child is essential to constantly identify pregnant women without prenatal care.

Fractional dynamics of a system with particles subjected to impacts

This paper studies the dynamics of a system composed of a collection of particles that exhibit collisions between them. Several entropy measures and different impact conditions of the particles are tested. The results reveal a Power Law evolution both of the system energy and the entropy measures, typical in systems having fractional dynamics.

A multidimensional scaling classification of robotic sensors

This chapter analyzes the signals captured during impacts and vibrations of a mechanical manipulator. Eighteen signals are captured and several metrics are calculated between them, such as the correlation, the mutual information and the entropy. A sensor classification scheme based on the multidimensional scaling technique is presented.

Trends in healthy life expectancy among older Brazilian women between 1998 and 2008

OBJECTIVE To analyze conditional and unconditional healthy life expectancy among older Brazilian women.METHODS This cross-sectional study used the intercensal technique to estimate, in the absence of longitudinal data, healthy life expectancy that is conditional and unconditional on the individual’s current health status. The data used were obtained from the Pesquisa Nacional por Amostra de Domicílios (National Household Sample Survey) of 1998, 2003, and 2008. This sample comprised 11,171; 13,694; and 16,259 women aged 65 years or more, respectively. Complete mortality tables from the Brazilian Institute of Geography and Statistics for the years 2001 and 2006 were also used. The definition of health status was based on the difficulty in performing activities of daily living.RESULTS The remaining lifetime was strongly dependent on the current health status of the older women. Between 1998 and 2003, the amount of time lived with disability for healthy women at age 65 was 9.8%. This percentage increased to 66.2% when the women already presented some disability at age 65. Temporal analysis showed that the active life expectancy of the women at age 65 increased between 1998-2003 (19.3 years) and 2003-2008 (19.4 years). However, life years gained have been mainly focused on the unhealthy state.CONCLUSIONS Analysis of conditional and unconditional life expectancy indicated that live years gained are a result of the decline of mortality in unhealthy states. This pattern suggests that there has been no reduction in morbidity among older women in Brazil between 1998 and 2008.

Global localization with non-quantized local image features

In the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.

Urban land use change analysis and modelling: a case study of Setubal-Sesimbra, Portugal

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

Generalized models from Beta(p,2) densities with strong allee effect: dynamical approach

A dynamical approach to study the behaviour of generalized populational growth models from Bets(p, 2) densities, with strong Allee effect, is presented. The dynamical analysis of the respective unimodal maps is performed using symbolic dynamics techniques. The complexity of the correspondent discrete dynamical systems is measured in terms of topological entropy. Different populational dynamics regimes are obtained when the intrinsic growth rates are modified: extinction, bistability, chaotic semistability and essential extinction.