A caracterização estática dos sistemas caóticos clássicos dissipativos tem sido realizada através do cálculo das dimensões generalizadas \'D IND. q\' e do espectro de singularidades f(alfa). Os métodos mais comuns de cálculo numérico dessas funções utilizam algoritmos de contagem de caixa. Porém, esses algoritmos produzem um erro sistemático através de \'caixas espúrias\', levando a resultados distorcidos. Por essa razão, estudamos métodos numéricos que não utilizam o algoritmo de contagem de caixa, verificando em que casos eles podem ser aplicados eficazmente e propusemos um novo algoritmo de contagem de caixa que reduz o número de \'caixas espúrias\', obtendo melhores resultados. / The static caracterization of classical dissipative chaotical systems has been achieved by the calculation of the generalized dimensions \'D IND. q\' and the spectrum of singularities f(alfa). The most used numerical methods of evaluating these functions are based on box counting algorithms. The results obtained by those methods are distorced by the presence of \'spurious boxes\' generated intrinsecally by these algorithms. For this reason, we have studied numerical methods that don\'t use box counting algorithms, and we have tried to verify in which kind of sets they give best results. We also have proposed a new box counting algorithm that reduces the number of \'spurious boxes\', and led to better results.

A caracterização estática dos sistemas caóticos clássicos dissipativos tem sido realizada através do cálculo das dimensões generalizadas \'D IND. q\' e do espectro de singularidades f(alfa). Os métodos mais comuns de cálculo numérico dessas funções utilizam algoritmos de contagem de caixa. Porém, esses algoritmos produzem um erro sistemático através de \'caixas espúrias\', levando a resultados distorcidos. Por essa razão, estudamos métodos numéricos que não utilizam o algoritmo de contagem de caixa, verificando em que casos eles podem ser aplicados eficazmente e propusemos um novo algoritmo de contagem de caixa que reduz o número de \'caixas espúrias\', obtendo melhores resultados. / The static caracterization of classical dissipative chaotical systems has been achieved by the calculation of the generalized dimensions \'D IND. q\' and the spectrum of singularities f(alfa). The most used numerical methods of evaluating these functions are based on box counting algorithms. The results obtained by those methods are distorced by the presence of \'spurious boxes\' generated intrinsecally by these algorithms. For this reason, we have studied numerical methods that don\'t use box counting algorithms, and we have tried to verify in which kind of sets they give best results. We also have proposed a new box counting algorithm that reduces the number of \'spurious boxes\', and led to better results.

We consider random fractals generated by random recursive constructions, prove zero-one laws concerning their dimensions and find their packing and Minkowski dimensions. Also we investigate the packing measure in corresponding dimension. For a class of random distribution functions we prove that their packing and Hausdorff dimensions coincide.

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

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The subject of fractal geometry has exploded over the past 40 years with the availability of computer generated images. It was seen early on that there are many interesting questions at the intersection of probability and fractal geometry. In this dissertation we will introduce two random models for constructing fractals and prove various facts about them.

This diploma project is showen possibilities in classification of genomic sequences with CGR and FCGR methods in pictures. From this picture is computed classificator with BCM. Next here is written about the programme and its opportunities for classification. In the end is compared many of sequences computed in different options of programme.

Orientador: Roberto Venegeroles Nascimento / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Matemática

A city consists of many elements such as humans, buildings, and roads. The complexity of cities is difficult to measure using Euclidean geometry. In this study, we use fractal geometry (scaling analysis) to measure the complexity of urban areas. We observe urban development from different perspectives using the bottom-up approach. In a bottom-up approach, we observe an urban region from a basic to higher level from our daily life perspective to an overall view. Furthermore, an urban environment is not constant, but it is complex; cities with greater complexity are more prosperous. There are many disciplines that analyze changes in the Earth’s surface, such as urban planning, detection of melting ice, and deforestation management. Moreover, these disciplines can take advantage of remote sensing for research. This study not only uses satellite imaging to analyze urban areas but also uses check-in and points of interest (POI) data. It uses straightforward means to observe an urban environment using the bottom-up approach and measure its complexity using fractal geometry.   Web 2.0, which has many volunteers who share their information on different platforms, was one of the most important tools in this study. We can easily obtain rough data from various platforms such as the Stanford Large Network Dataset Collection (SLNDC), the Earth Observation Group (EOG), and CloudMade. The check-in data in this thesis were downloaded from SLNDC, the POI data were obtained from CloudMade, and the nighttime lights imaging data were collected from EOG. In this study, we used these three types of data to derive natural cities representing city regions using a bottom-up approach. Natural cities were derived from open geographic data without human manipulation. After refining data, we used rough data to derive natural cities. This study used a triangulated irregular network to derive natural cities from check-in and POI data.   In this study, we focus on the four largest US natural cities regions: Chicago, New York, San Francisco, and Los Angeles. The result is that the New York City region is the most complex area in the United States. Box-counting fractal dimension, lacunarity, and ht-index (head/tail breaks index) can be used to explain this. Box-counting fractal dimension is used to represent the New York City region as the most prosperous of the four city regions. Lacunarity indicates the New York City region as the most compact area in the United States. Ht-index shows the New York City region having the highest hierarchy of the four city regions. This conforms to central place theory: higher-level cities have better service than lower-level cities. In addition, ht-index cannot represent hierarchy clearly when data distribution does not fit a long-tail distribution exactly. However, the ht-index is the only method that can analyze the complexity of natural cities without using images.

We study the geometric and topological properties of strange non-chaotic attractors created in non-smooth saddle-node bifurcations of quasiperiodically forced interval maps. By interpreting the attractors as limit objects of the iterates of a continuous curve and controlling the geometry of the latter, we determine their Hausdorff and box-counting dimension and show that these take distinct values. Moreover, the same approach allows us to describe the topological structure of the attractors and to prove their minimality.