Le but de ce travail est de déterminer par méthodes optiques la distribution de taille (pdf) des nanoparticules de suie, agrégats de morphologie fractale. Après des études préliminaires qui utilisent DDSCAT pour valider la théorie RDG-FA et permettent de convertir un rayon de giration en rayon de mobilité, deux diagnostics optiques sont étudiés. Le premier consiste à exploiter une mesure d'extinction spectrale de la lumière. Nous montrons que pour exploiter ce signal, il faut connaître les propriétés optiques des suies, leur préfacteur et dimension fractale, la loi de distribution et le diamètre des sphérules primaires. Le second diagnostic tire parti de la mesure angulaire de la diffusion de la lumière. Nous montrons qu'il est possible de déterminer la pdf à l'aide de la mesure de diffusion à trois angles. Il faut supposer la loi de distribution et la dimension fractale. Cette deuxième approche, in-situ, est plus appropriée que la première pour déterminer optiquement la pdf des suies. / The objective of this thesis is to determine by optical methods the soot size distribution. Soot are fractal-like morphology nanoparticles aggregates. After preliminary studies which used DDSCAT to validate the RDG-FA theory and allow converting gyration radius to mobility radius, two optical approaches are considered. The first one is based on a measure of spectral light extinction by soot. To exploit this signal, the knowledge of soot optical properties, fractal prefactor, type of law distribution, fractal dimension and primary spheres diameters are needed. The second one exploits the measure of angular scattering by particles. It is possible to determine the size distribution by using scattering measurement at only three angles. However, it's necessary to assume the type of law distribution and the fractal dimension. This second approach is more appropriate than the first one to determine optically the size distribution of soot and hold the interest to be in-situ.

In der Statistik und der Mathematik ist die Normalverteilung der am meisten verbreitete, stochastische Term für die Mehrheit der statistischen Modelle. Wir zeigen, dass der entsprechende stochastische Prozess, die Brownsche Bewegung, drei entscheidende empirische Beobachtungen nicht abbildet: schwere Ränder, Langzeitabhängigkeiten und Skalierungsgesetze. Ein selbstähnlicher Prozess, der in der Lage ist Langzeitabhängigkeiten zu modellieren, ist die Gebrochene Brownsche Bewegung, welche durch die Faltung der Inkremente im Limit nicht normalverteilt sein muss. Die Inkremente der Gebrochenen Brownschen Bewegung können durch einen Parameter H, dem Hurst Exponenten, Langzeitabhängigkeiten darstellt werden. Für die Gebrochene Brownsche Bewegung müssten die Skalierungs-(Hurst-) Exponenten über die Momente verschiedener Ordnung konstant sein. Empirisch beobachten wir variierende Hölder-Exponenten, die multifraktales Verhalten implizieren. Wir erklären dieses multifraktale Verhalten durch die Änderung des alpha-stabilen Indizes der alpha-stabilen Verteilung, indem wir Filter für Saisonalitäten und Langzeitabhängigkeiten über verschiedene Zeitfrequenzen anwenden, startend bei 1-minütigen Hochfrequenzdaten. Durch die Anwendung eines Filters für die Langzeitabhängigkeit zeigen wir, dass die Residuen des stochastischen Prozesses geringer Zeitfrequenz (wöchentlich) durch die alpha-stabile Bewegung beschrieben werden können. Dies erlaubt es uns, den empirischen, hochfrequenten Datensatz auf die niederfrequente Zeitfrequenz zu skalieren. Die generierten wöchentlichen Daten aus der Frequenz-Reskalierungs-Methode (FRM) haben schwerere Ränder als der ursprüngliche, wöchentliche Prozess. Wir zeigen, dass eine Teilmenge des Datensatzes genügt, um aus Risikosicht bessere Vorhersagen für den gesamten Datensatz zu erzielen. Im Besonderen wäre die Frequenz-Reskalierungs-Methode (FRM) in der Lage gewesen, die seltenen Events der Finanzkrise 2008 zu modellieren. / Coming from a sphere in statistics and mathematics in which the Normal distribution is the dominating underlying stochastic term for the majority of the models, we indicate that the relevant diffusion, the Brownian Motion, is not accounting for three crucial empirical observations for financial data: Heavy tails, long memory and scaling laws. A self-similar process, which is able to account for long-memory behavior is the Fractional Brownian Motion, which has a possible non-Gaussian limit under convolution of the increments. The increments of the Fractional Brownian Motion can exhibit long memory through a parameter H, the Hurst exponent. For the Fractional Brownian Motion this scaling (Hurst) exponent would be constant over different orders of moments, being unifractal. But empirically, we observe varying Hölder exponents, the continuum of Hurst exponents, which implies multifractal behavior. We explain the multifractal behavior through the changing alpha-stable indices from the alpha-stable distributions over sampling frequencies by applying filters for seasonality and time dependence (long memory) over different sampling frequencies, starting at high-frequencies up to one minute. By utilizing a filter for long memory we show, that the low-sampling frequency process, not containing the time dependence component, can be governed by the alpha-stable motion. Under the alpha-stable motion we propose a semiparametric method coined Frequency Rescaling Methodology (FRM), which allows to rescale the filtered high-frequency data set to the lower sampling frequency. The data sets for e.g. weekly data which we obtain by rescaling high-frequency data with the Frequency Rescaling Method (FRM) are more heavy tailed than we observe empirically. We show that using a subset of the whole data set suffices for the FRM to obtain a better forecast in terms of risk for the whole data set. Specifically, the FRM would have been able to account for tail events of the financial crisis 2008.

Dynamic light scattering (DLS) is a method to size submicron particles by measuring their thermal motion (diffusion) in suspensions and emulsions. However, the validity of the Stokes-Einstein equation that relates the diffusion coefficient and the particle size is limited to spherical particles and very low concentrations. Within this thesis, DLS is used for the characterization of suspensions of pyrogenic silica which consists of fractal-like aggregates composed of sintered spherical primary particles. These structural features clearly complicate the understanding of DLS experiments and have been a severe obstacle to employing DLS as routine standard tool for the characterization of pyrogenic silica. The main objective of this thesis is therefore to evaluate the application of DLS in product development and quality assurance of pyrogenic silica industry, what essentially means to identify those structural properties of fractal aggregates which are measurable with DLS and to quantify the method’s sensitivity to changes in these properties. The investigations presented here are split up into four parts, simulations that establish a relation between structural and hydrodynamic properties, experiments validating the simulation results, the characterization of concentrated suspensions and the application-oriented analysis of DLS data for specific industrially relevant measurement tasks. / Die Dynamische Lichtstreuung (DLS) ist eine Messmethode zur Größenbestimmung submikroner Partikel. Dabei wird primär die stochastische Bewegung der Teilchen (Diffusion) in Suspensionen und Emulsionen bewertet. Die Stokes-Einstein Gleichung, die das Verhältnis zwischen gemessenem Diffusionskoeffizienten und Partikelgröße wiedergibt, ist jedoch nur für kugelförmige Teilchen, die in sehr niedriger Konzentration vorliegen, gültig. In der vorliegenden Arbeit wird die dynamische Lichtstreuung zur Charakterisierung von Suspensionen pyrogener Kieselsäure eingesetzt. Diese besteht aus fraktalen Aggregaten, die wiederum aus versinterten aber meist kugelförmigen Primärpartikeln zusammengesetzt sind. Diese strukturellen Eigenschaften erschweren die Anwendbarkeit der DLS bzw. die Interpretation der Messergebnisse und verhinderten bisher den Einsatz der DLS als Routinemethode zur Charakterisierung pyrogener Kieselsäuren. Das Hauptziel dieser Arbeit ist daher eine Bewertung der Möglichkeiten der DLS für die Produktentwicklung und Qualitätssicherung in der Herstellung pyrogener Kieselsäuren. Das bedeutet im Besonderen, dass sowohl die messbaren granulometrischen Eigenschaften als auch die Sensitivität der Methode bei Eigenschaftsänderungen ermittelt werden müssen. Die hier durchgeführten Arbeiten sind in vier Teile gegliedert: Simulationen, die eine Beziehung zwischen strukturellen und hydrodynamischen Eigenschaften herstellen, Experimente zur Validierung der Simulationsergebnisse, die Charakterisierung konzentrierter Suspensionen und die anwendungsorientierte Auswertung von DLS-Daten für spezifische industrierelevante Messaufgaben.

Indiana University-Purdue University Indianapolis (IUPUI) / This paper is a study of the dynamics of a new family of maps from the complex plane to itself, which we call twisted tent maps. A twisted tent map is a complex generalization of a real tent map. The action of this map can be visualized as the complex scaling of the plane followed by folding the plane once. Most of the time, scaling by a complex number will \twist" the plane, hence the name. The "folding" both breaks analyticity (and even smoothness) and leads to interesting dynamics ranging from easily understood and highly geometric behavior to chaotic behavior and fractals.

This thesis covers pretty much everything about the 4’th movement of “I Ching” by Per Nørgård (“IV. Towards Completion. Fire over Water”). I have delved deep into an analysis, that helped me develop my own interpretation and understanding of the piece.  While I was doing my research, I discovered fascinating links between music and mathematics, that showed me how much we don’t know and that there are interesting subjects left for us to find. My exploration did not stop there just yet, I also found out about the “I Ching”, an ancient Chinese book, that covers a broad topic, which can be summed up in two words: Yin & Yang. This, combined with a bit of mathematics contributed to the creation of a unique vocabulary that Per Nørgård pioneered, called: “infinity series”. My thesis aim is to promote Per Nørgård’s music, that has not yet been discovered by enough percussionists.

O presente texto descreve métodos e apresenta resultados do projeto de pesquisa de mestrado intitulado \"Dois Problemas em Análise de Formas de Estruturas de Ramificação\". Ambos os problemas abordados estão relacionados às sub-áreas da Análise de Formas denominadas Caracterização e Descrição de Formas. O primeiro problema consiste na investigação de um conjunto de características propostas para distingüir, primeiramente, entre estruturas de ramificação de vasos sangüíneos em imagens de retina segmentadas manualmente e automaticamente. A seguir, as mesmas características são aplicadas para discernir entre estruturas de ramificação de vasos sangüíneos em imagens de retina com e sem retinopatia diabética proliferativa (Proliferative Diabetic Retinopathy - PDR). A PDR é uma das patologias associadas à diabetes, que pode culminar na cegueira do indivíduo. Diagnósticos são possíveis por meio de imagens de fundo de olho e, quando efetuados precocemente, viabilizam intervenções oportunas evitando a perda da visão. Neste trabalho, 27 imagens digitais de fundo de olho foram segmentadas por dois processos distintos, isto é, segmentação manual por um especialista e a segmentação automática, mediante a transformada contínua Wavelet - CWT e classificadores estatísticos. Visando à caracterização destas formas, um conjunto de 08 características foi proposto. Este conjunto foi formado por três grupos, a saber: descritores tradicionais geométricos (Área, Perímetro e Circularidade), descritores associados à transformada wavelet ( 2o momento estatístico da distribuição de módulos da CWT, Entropia de Orientação da distribuição de fases da CWT e Curvatura) e um descritor fractal (Dimensão de Correlação - Global e Mediana). Uma Análise Discriminante Linear LDA revelou que as características geométricas tradicionais não detectam o início da retinopatia diabética proliferativa. A maior capacidade discriminante individual foi exibida pela Curvatura, com Área sob a curva ROC de 0.76. Um subconjunto com 6 características apresentou grande capacidade discriminante com Área sob a curva ROC de 0.90. O segundo problema diz respeito à extração de contorno de estruturas de ramificação bidimensionais de neurônios tridimensionais. Este trabalho contribui originalmente com uma solução para este problema, propondo dois algoritmos desenvolvidos para Rastreamento de Ramos e Extração do Contorno Paramétrico de estruturas de ramificação, capazes de transpor regiões críticas formadas por cruzamentos ocasionados pela projeção de estruturas 3D no plano das imagens 2D. Grande parte dos métodos baseados em contorno para análise de formas de estruturas de ramificação de células neuronais não produz representações corretas destas formas, devido à presença de sobreposições entre processos neuronais, levando os algoritmos tradicionais de extração de contorno a ignorar as regiões mais internas destas estruturas, gerando representações incompletas. O sistema proposto neste trabalho foi desenvolvido objetivando a solução do problema de extração de contorno, mesmo na presença de múltiplas sobreposições. Inicialmente, a imagem de entrada é pré-processada, gerando um esqueleto 8-conexo com ramos de um pixel de largura, um conjunto de sementes de sub-árvores dendríticas e um conjunto de regiões críticas (bifurcações e cruzamentos). Para cada sub-árvore, o algoritmo de rastreamento rotula todos os pixels válidos de um ramo, até chegar em uma região crítica, onde o algoritmo decide a direção em que deve continuar o rastreamento. Nosso algoritmo mostrou-se robusto, mesmo quando aplicado a imagens com segmentos paralelos muito próximos. Resultados obtidos com imagens reais (neurônios) são apresentados. / This document describes methods and presents results from the Master of Science\'s research project in computer science entitled \"Two Problems in Shape Analysis of Branching Structures\". Both tackled problems herein are related to Shape Analysis sub-fields, namely Characterization and Description of shapes. The former problem consists of an investigation on a proposed set of features aimed at discriminating, firstly, between blood vessels branching structures manually and automatically segmented. In the sequel, the same features are used to assess their discriminative capability in distinguishing between blood vessels branching structures with and withoud proliferative diabetic retinopathy (PDR). The PDR is a pathology related to diabetes, which may lead to the blindness. Diagnosis is possible through optic fundus image analysis, which may allow timely interventions preventing vision loss. In this work, 27 digital optic fundus images were segmented by two distinct segmentation processes, i.e. manual segmentation carried out by an especialist and automated segmentation, through the CWT (Continuous Wavelet Transform) and statistical classifiers. In order to characterize such a shapes, a set of 8 features has been proposed. The aforementioned set was comprised of three features groups, that is: traditional geometric descriptors (Area, Perimeter and Circularity), wavelet-based descriptors (2nd statistical moment from the CWT Modulus distribution, Orientation Entropy from the CWT Phase distribution and Curvature) and a fractal descriptor (Correlation Dimension - global and median). Linear Discriminant Analysis LDA revelead that the traditional geometric features are not able to detect early proliferative diabetic retinopathy. The largest singular discriminant capability was shown by the Curvature, with area under the ROC curve of 0.76. A subset of 6 features presented a good discriminating power with area under the curve of 0.90. The second problem concerns contour extraction from 2D branching structures of 3D neurons. This work contributes with an original solution for such a problem, proposing two algorithms devised for Branches Tracking and Branching Structures Contour Extraction. The proposed algorithms are able to traverse critical regions implied by the projection of 3D structures onto a 2D image plane. Most of contour-based methods intended to shape analysis of neuronal branching structures fall short of yielding proper shape representations, owing to the presence of overlapings among neuronal processes, causing the traditional algorithms for contour following to ignore the innermost regions, thus generating incomplete representations. The proposed framework system was developed aiming at the solution of the contour extraction problem, even in the presence of multiple overlapings. The input image is pre-processed, so as to obtain an 8-connected skeleton with one-pixel wide branches, a set of seeds of dendritic sub-trees and a set of critical regions (bifurcations, crossings and superpositions). For each sub-tree, the Branches Tracking Algorithm labels all valid pixels of a branch, until reaching a critical region, where the algorithm decides about the direction to go on with the tracking. Our algorithm has shown robustness, even in images plenty of very close parallel segments. Results with real images (neurons) are presented.

Ce travail de thèse de doctorat propose et évalue différentes solutions pour caractériser, avec des outils optiques et électromagnétiques non intrusifs, les nanoparticules et agrégats observés dans différents systèmes physiques : suspensions colloïdales, aérosols et plasma poussiéreux. Deux types de modèles sont utilisés pour décrire la morphologie: d'agrégats fractals (p. ex. : suies issues de la combustion, de procédés plasma) et agrégats compacts (qualifiés de " Buckyballs " et observés dans des aérosols produits par séchage de nano suspensions). Nous utilisons différentes théories et modèles électromagnétiques (T-Matrice et approximations du type dipôles discrets) pour calculer les diagrammes de diffusion (ou facteur de structure optique) de ces agrégats, de même que leurs spectres d'extinction sur une large gamme spectrale. Ceci, dans le but d'inverser les données expérimentales obtenues en temps réel. Différents outils numériques originaux ont également été mis au point pour parvenir à une analyse morphologique quantitative de clichés de microscopie électronique. La validation expérimentale des outils théoriques et numériques développés au cours de ce travail est focalisée sur la spectrométrie d'extinction appliquée à des nano agrégats de silice, tungstène et silicium.

O presente texto descreve métodos e apresenta resultados do projeto de pesquisa de mestrado intitulado \"Dois Problemas em Análise de Formas de Estruturas de Ramificação\". Ambos os problemas abordados estão relacionados às sub-áreas da Análise de Formas denominadas Caracterização e Descrição de Formas. O primeiro problema consiste na investigação de um conjunto de características propostas para distingüir, primeiramente, entre estruturas de ramificação de vasos sangüíneos em imagens de retina segmentadas manualmente e automaticamente. A seguir, as mesmas características são aplicadas para discernir entre estruturas de ramificação de vasos sangüíneos em imagens de retina com e sem retinopatia diabética proliferativa (Proliferative Diabetic Retinopathy - PDR). A PDR é uma das patologias associadas à diabetes, que pode culminar na cegueira do indivíduo. Diagnósticos são possíveis por meio de imagens de fundo de olho e, quando efetuados precocemente, viabilizam intervenções oportunas evitando a perda da visão. Neste trabalho, 27 imagens digitais de fundo de olho foram segmentadas por dois processos distintos, isto é, segmentação manual por um especialista e a segmentação automática, mediante a transformada contínua Wavelet - CWT e classificadores estatísticos. Visando à caracterização destas formas, um conjunto de 08 características foi proposto. Este conjunto foi formado por três grupos, a saber: descritores tradicionais geométricos (Área, Perímetro e Circularidade), descritores associados à transformada wavelet ( 2o momento estatístico da distribuição de módulos da CWT, Entropia de Orientação da distribuição de fases da CWT e Curvatura) e um descritor fractal (Dimensão de Correlação - Global e Mediana). Uma Análise Discriminante Linear LDA revelou que as características geométricas tradicionais não detectam o início da retinopatia diabética proliferativa. A maior capacidade discriminante individual foi exibida pela Curvatura, com Área sob a curva ROC de 0.76. Um subconjunto com 6 características apresentou grande capacidade discriminante com Área sob a curva ROC de 0.90. O segundo problema diz respeito à extração de contorno de estruturas de ramificação bidimensionais de neurônios tridimensionais. Este trabalho contribui originalmente com uma solução para este problema, propondo dois algoritmos desenvolvidos para Rastreamento de Ramos e Extração do Contorno Paramétrico de estruturas de ramificação, capazes de transpor regiões críticas formadas por cruzamentos ocasionados pela projeção de estruturas 3D no plano das imagens 2D. Grande parte dos métodos baseados em contorno para análise de formas de estruturas de ramificação de células neuronais não produz representações corretas destas formas, devido à presença de sobreposições entre processos neuronais, levando os algoritmos tradicionais de extração de contorno a ignorar as regiões mais internas destas estruturas, gerando representações incompletas. O sistema proposto neste trabalho foi desenvolvido objetivando a solução do problema de extração de contorno, mesmo na presença de múltiplas sobreposições. Inicialmente, a imagem de entrada é pré-processada, gerando um esqueleto 8-conexo com ramos de um pixel de largura, um conjunto de sementes de sub-árvores dendríticas e um conjunto de regiões críticas (bifurcações e cruzamentos). Para cada sub-árvore, o algoritmo de rastreamento rotula todos os pixels válidos de um ramo, até chegar em uma região crítica, onde o algoritmo decide a direção em que deve continuar o rastreamento. Nosso algoritmo mostrou-se robusto, mesmo quando aplicado a imagens com segmentos paralelos muito próximos. Resultados obtidos com imagens reais (neurônios) são apresentados. / This document describes methods and presents results from the Master of Science\'s research project in computer science entitled \"Two Problems in Shape Analysis of Branching Structures\". Both tackled problems herein are related to Shape Analysis sub-fields, namely Characterization and Description of shapes. The former problem consists of an investigation on a proposed set of features aimed at discriminating, firstly, between blood vessels branching structures manually and automatically segmented. In the sequel, the same features are used to assess their discriminative capability in distinguishing between blood vessels branching structures with and withoud proliferative diabetic retinopathy (PDR). The PDR is a pathology related to diabetes, which may lead to the blindness. Diagnosis is possible through optic fundus image analysis, which may allow timely interventions preventing vision loss. In this work, 27 digital optic fundus images were segmented by two distinct segmentation processes, i.e. manual segmentation carried out by an especialist and automated segmentation, through the CWT (Continuous Wavelet Transform) and statistical classifiers. In order to characterize such a shapes, a set of 8 features has been proposed. The aforementioned set was comprised of three features groups, that is: traditional geometric descriptors (Area, Perimeter and Circularity), wavelet-based descriptors (2nd statistical moment from the CWT Modulus distribution, Orientation Entropy from the CWT Phase distribution and Curvature) and a fractal descriptor (Correlation Dimension - global and median). Linear Discriminant Analysis LDA revelead that the traditional geometric features are not able to detect early proliferative diabetic retinopathy. The largest singular discriminant capability was shown by the Curvature, with area under the ROC curve of 0.76. A subset of 6 features presented a good discriminating power with area under the curve of 0.90. The second problem concerns contour extraction from 2D branching structures of 3D neurons. This work contributes with an original solution for such a problem, proposing two algorithms devised for Branches Tracking and Branching Structures Contour Extraction. The proposed algorithms are able to traverse critical regions implied by the projection of 3D structures onto a 2D image plane. Most of contour-based methods intended to shape analysis of neuronal branching structures fall short of yielding proper shape representations, owing to the presence of overlapings among neuronal processes, causing the traditional algorithms for contour following to ignore the innermost regions, thus generating incomplete representations. The proposed framework system was developed aiming at the solution of the contour extraction problem, even in the presence of multiple overlapings. The input image is pre-processed, so as to obtain an 8-connected skeleton with one-pixel wide branches, a set of seeds of dendritic sub-trees and a set of critical regions (bifurcations, crossings and superpositions). For each sub-tree, the Branches Tracking Algorithm labels all valid pixels of a branch, until reaching a critical region, where the algorithm decides about the direction to go on with the tracking. Our algorithm has shown robustness, even in images plenty of very close parallel segments. Results with real images (neurons) are presented.

Existuje mnoho různých způsobů jak analyzovat disperzní systémy a fyzikálně chemické processy ke kterým v takových systémech dochází. Tato práce byla zaměřena na charakterizaci těchto procesů pomocí metod harmonické fraktální analýzy. Obrazová data sledovaných systémů byly analyzovány pomocí waveletové analýzy. V průběhu práce byly navrženy různé optimalizace samotné analýzy, převážně zaměřené na odstranění manuálních operací během analýzy a tyto optimalizace byly také inkorporovány do softérového vybavení pro Harmonickou Fraktální Analýzu HarFA, který je vyvíjen na Fakultě chemické, VUT Brno.

This Master's thesis deals with history of Fractal geometry and describes the fractal science development. In the begining there are essential Fractal science terms explained. Then description of fractal types and typical or most known examples of them are mentioned. Fractal knowledge application besides computer graphics area is discussed. Thesis informs about fractal geometry practical usage. Few present software packages or more programs which can be used for making fractal pictures are described in this work. Some of theirs capabilities are described. Thesis' practical part consists of slides, demonstrational program and poster. Electronical slides represents brief scheme usable for fractal geometry realm lectures. Program generates selected fractal types. Thesis results are projected on poster.