Analysis and visualization of chromosome information

This paper analyzes the DNA code of several species in the perspective of information content. For that purpose several concepts and mathematical tools are selected towards establishing a quantitative method without a priori distorting the alphabet represented by the sequence of DNA bases. The synergies of associating Gray code, histogram characterization and multidimensional scaling visualization lead to a collection of plots with a categorical representation of species and chromosomes.

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.

Dynamical Analysis and Visualization of Tornadoes Time Series

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

Fractional dynamics and MDS visualization of earthquake phenomena

This paper analyses earthquake data in the perspective of dynamical systems and fractional calculus (FC). This new standpoint uses Multidimensional Scaling (MDS) as a powerful clustering and visualization tool. FC extends the concepts of integrals and derivatives to non-integer and complex orders. MDS is a technique that produces spatial or geometric representations of complex objects, such that those objects that are perceived to be similar in some sense are placed on the MDS maps forming clusters. In this study, over three million seismic occurrences, covering the period from January 1, 1904 up to March 14, 2012 are analysed. The events are characterized by their magnitude and spatiotemporal distributions and are divided into fifty groups, according to the Flinn–Engdahl (F–E) seismic regions of Earth. Several correlation indices are proposed to quantify the similarities among regions. MDS maps are proven as an intuitive and useful visual representation of the complex relationships that are present among seismic events, which may not be perceived on traditional geographic maps. Therefore, MDS constitutes a valid alternative to classic visualization tools for understanding the global behaviour of earthquakes.

Effects of emotional valence and three-dimensionality of visual stimuli on brain activation: an fMRI study

BACKGROUND: Examining changes in brain activation linked with emotion-inducing stimuli is essential to the study of emotions. Due to the ecological potential of techniques such as virtual reality (VR), inspection of whether brain activation in response to emotional stimuli can be modulated by the three-dimensional (3D) properties of the images is important. OBJECTIVE: The current study sought to test whether the activation of brain areas involved in the emotional processing of scenarios of different valences can be modulated by 3D. Therefore, the focus was made on the interaction effect between emotion-inducing stimuli of different emotional valences (pleasant, unpleasant and neutral valences) and visualization types (2D, 3D). However, main effects were also analyzed.METHODS: The effect of emotional valence and visualization types and their interaction were analyzed through a 3x2 repeated measures ANOVA. Post-hoc t-tests were performed under a ROI-analysis approach. RESULTS: The results show increased brain activation for the 3D affective-inducing stimuli in comparison with the same stimuli in 2D scenarios, mostly in cortical and subcortical regions that are related to emotional processing, in addition to visual processing regions. CONCLUSIONS: This study has the potential of clarify brain mechanisms involved in the processing of emotional stimuli (scenarios’ valence) and their interaction with three-dimensionality.

Wavelet analysis of human DNA

This paper studies the human DNA in the perspective of signal processing. Six wavelets are tested for analyzing the information content of the human DNA. By adopting real Shannon wavelet several fundamental properties of the code are revealed. A quantitative comparison of the chromosomes and visualization through multidimensional and dendograms is developed.

Multi-dimensional scaling applied to histogram-based DNA analysis

This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendall τ rank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of stream data, like DNA sequences, with almost no assumptions other than the predefined DNA “word length.” This methodology is able to produce comprehensible three-dimensional visualizations of CR clustering and related spatial and structural patterns. The results of the four test correlation scenarios show that the high-level information clusterings produced by the MDS tool are qualitatively similar, with small variations due to each correlation method characteristics, and that the clusterings are a consequence of the input data and not method’s artifacts.

Aplicação de algoritmos genéticos para sintonia de controladores

Mestrado em Engenharia Electrotécnica e de Computadores

Balanceamento de linhas de produção usando algoritmos genéticos

Aquando da definição de um layout por fluxo de produto, ou linha de produção, é necessário proceder-se à melhor selecção de combinações de tarefas a serem executadas em cada estação / posto de trabalho para que o trabalho seja executado numa sequência exequível e sejam necessárias quantidades de tempo aproximadamente iguais em cada estação / posto de trabalho. Este processo é chamado de balanceamento da linha de produção. Verifica-se que as estações de trabalho e equipamentos podem ser combinados de muitas maneiras diferentes; daí que a necessidade de efectuar o balanceamento das linhas de produção implique a distribuição de actividades sequenciais por postos de trabalho de modo a permitir uma elevada utilização de trabalho e de equipamentos e a minimizar o tempo de vazio. Os problemas de balanceamento de linhas são tipicamente problemas complexos de tratar, devido ao elevado número de combinações possíveis. Entre os métodos utilizados para resolver estes problemas encontram-se métodos de tentativa e erro, métodos heurísticos, métodos computacionais de avaliação de diferentes opções até se encontrar uma boa solução e métodos de optimização. O objectivo deste trabalho passou pelo desenvolvimento de uma ferramenta computacional para efectuar o balanceamento de linhas de produção recorrendo a algoritmos genéticos. Foi desenvolvida uma aplicação que implementa dois algoritmos genéticos, um primeiro que obtém soluções para o problema e um segundo que optimiza essas soluções, associada a uma interface gráfica em C# que permite a inserção do problema e a visualização de resultados. Obtiveram-se resultados exequíveis demonstrando vantagens em relação aos métodos heurísticos, pois é possível obter-se mais do que uma solução. Além disso, para problemas complexos torna-se mais prático o uso da aplicação desenvolvida. No entanto, esta aplicação permite no máximo seis precedências por cada operação e resultados com o máximo de nove estações de trabalho.

Modeling of the lung impedance using a fractional order ladder network with constant phase elements

The self similar branching arrangement of the airways makes the respiratory system an ideal candidate for the application of fractional calculus theory. The fractal geometry is typically characterized by a recurrent structure. This study investigates the identification of a model for the respiratory tree by means of its electrical equivalent based on intrinsic morphology. Measurements were obtained from seven volunteers, in terms of their respiratory impedance by means of its complex representation for frequencies below 5 Hz. A parametric modeling is then applied to the complex valued data points. Since at low-frequency range the inertance is negligible, each airway branch is modeled by using gamma cell resistance and capacitance, the latter having a fractional-order constant phase element (CPE), which is identified from measurements. In addition, the complex impedance is also approximated by means of a model consisting of a lumped series resistance and a lumped fractional-order capacitance. The results reveal that both models characterize the data well, whereas the averaged CPE values are supraunitary and subunitary for the ladder network and the lumped model, respectively.

PHYSQuiz - Aplicação computacional para formação contínua em Física Radiológica

Mestrado em Engenharia de Computação e Instrumentação Médica

Identificação forense de ossadas por tomografia computorizada

A antropologia forense é uma disciplina das ciências forenses que trata da análise de restos cadavéricos humanos para fins legais. Uma das suas aplicações mais populares é a identificação forense que consiste em determinar o perfil biológico (idade, sexo, ancestralidade e estatura) de um indivíduo. No entanto, este processo muitas vezes é dificultado quando o corpo se encontra em avançado estado de decomposição apenas existindo restos esqueléticos. Neste caso, áreas médicas comummente utilizadas na identificação de cadáveres, como a patologia, tem de ser descartadas e surge a necessidade de aplicar outras técnicas. Neste contexto, muitos métodos antropométricos são propostos de forma a caracterizar uma pessoa através do seu esqueleto. Contudo, constata-se que a maioria dos procedimentos sugeridos é baseada em equipamentos básicos de medição, não usufruindo da tecnologia contemporânea. Assim, em parceria com a Delegação Norte do NMLCF, I. P., esta Tese teve na sua génese a criação de um sistema computacional baseado em imagens de Tomografia Computorizada (TC) de ossadas que, através de ferramentas open source, permita a realização de identificação forense. O trabalho apresentado baseia-se no processo de gestão de informação, aquisição, processamento e visualização de imagens TC. No decorrer da realização da presente Tese foi desenvolvida uma base de dados que permite organizar a informação de cada ossada e foram implementados algoritmos que levam a uma extracção de características muito mais vasta que a efetuada manualmente com os equipamentos de medição clássicos. O resultado final deste estudo consistiu num conjunto de técnicas que poderão ser englobadas num sistema computacional de identificação forense e deste modo criar uma aplicação com vantagens tecnológicas evidentes.

State Space Analysis of Forest Fires

This paper studies forest fires from the perspective of dynamical systems. Burnt area, precipitation and atmospheric temperatures are interpreted as state variables of a complex system and the correlations between them are investigated by means of different mathematical tools. First, we use mutual information to reveal potential relationships in the data. Second, we adopt the state space portrait to characterize the system’s behavior. Third, we compare the annual state space curves and we apply clustering and visualization tools to unveil long-range patterns. We use forest fire data for Portugal, covering the years 1980–2003. The territory is divided into two regions (North and South), characterized by different climates and vegetation. The adopted methodology represents a new viewpoint in the context of forest fires, shedding light on a complex phenomenon that needs to be better understood in order to mitigate its devastating consequences, at both economical and environmental levels.

Entropy Analysis of Industrial Accident Data Series

Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback–Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback–Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.