Automorphism Groups of Generalized (Binary) Icosahedral, Tetrahedral and Octahedral Groups

Let G be any of the (binary) icosahedral, generalized octahedral (tetrahedral) groups or their quotients by the center. We calculate the automorphism group Aut(G).

O uso da astronomia como instrumento para a introdução ao estudo das radiações eletromagnéticas no ensino médio

Este trabalho relata a utilização de tópicos ligados à Astronomia, como motivação educacional, a fim de inserir o estudo das radiações eletromagnéticas com abordagem na Física Moderna e Contemporânea. Em geral, a bibliografia atual disponível trata deste assunto (sob o título “ondas eletromagnéticas”) no capítulo relacionado a “ondas”, dentro da Física Clássica, não abordando assim, conceitos da Física Moderna: dualidade onda partícula, quantização da energia, efeito fotoelétrico, Lei de Planck, etc. O tema foi abordado utilizando atividades práticas: observação do Sol, observação do céu com telescópio, simulação computacional através de applets disponíveis na Internet, atividades de laboratório, sempre partindo de aspectos relacionados à Astronomia. A proposta foi aplicada em uma turma de terceira série de Ensino Médio, do Instituto Estadual de Educação de Sapiranga, cidade do Vale do Rio dos Sinos. Este trabalho foi embasado pela Teoria de Aprendizagem Significativa de Ausubel e de Novak, e pela Teoria de mediação de Vygotsky. A partir dos dados colhidos através de testes comparativos, entrevistas e depoimentos dos alunos, foi possível detectar que eles estiveram motivados para a aprendizagem, sendo que alguns inclusive passaram a interessar-se mais pela disciplina de Física. Ao finalizar a aplicação desta proposta de trabalho, foi criado um CD com textos, simulações e sugestões para aplicações em sala de aula.

Compressão de imagem e vídeo com controlador ARM

Este trabalho foi desenvolvido num estágio na empresa ABS GmbH sucursal em Portugal, e teve como foco a compressão de imagem e vídeo com os padrões JPEG e H.264, respetivamente. Foi utilizada a plataforma LeopardBoard DM368, com um controlador ARM9. A análise do desempenho de compressão de ambos os padrões foi realizada através de programas em linguagem C, para execução no processador DM368. O programa para compressão de imagem recebe como parâmetros de entrada o nome e a resolução da imagem a comprimir, e comprime-a com 10 níveis de quantização diferentes. Os resultados mostram que é possível obter uma velocidade de compressão até 73 fps (frames per second) para a resolução 1280x720, e que imagens de boa qualidade podem ser obtidas com rácios de compressão até cerca de 22:1. No programa para compressão de vídeo, o codificador está configurado de acordo com as recomendações para as seguintes aplicações: videoconferência, videovigilância, armazenamento e broadcasting/streaming. As configurações em cada processo de codificação, o nome do ficheiro, o número de frames e a resolução do mesmo representam os parâmetros de entrada. Para a resolução 1280x720, foram obtidas velocidades de compressão até cerca de 68 fps, enquanto para a resolução 1920x1088 esse valor foi cerca de 30 fps. Foi ainda desenvolvida uma aplicação com capacidades para capturar imagens ou vídeos, aplicar processamento de imagem, compressão, armazenamento e transmissão para uma saída DVI (Digital Visual Interface). O processamento de imagem em software permite melhorar dinamicamente as imagens, e a taxa média de captura, compressão e armazenamento é cerca de 5 fps para a resolução 1280x720, adequando-se à captura de imagens individuais. Sem processamento em software, a taxa sobe para cerca de 23 fps para a resolução 1280x720, sendo cerca de 28 fps para a resolução 1280x1088, o que é favorável à captura de vídeo.

Uma fundamentação para sinais e sistemas intervalares

In this work we use Interval Mathematics to establish interval counterparts for the main tools used in digital signal processing. More specifically, the approach developed here is oriented to signals, systems, sampling, quantization, coding and Fourier transforms. A detailed study for some interval arithmetics which handle with complex numbers is provided; they are: complex interval arithmetic (or rectangular), circular complex arithmetic, and interval arithmetic for polar sectors. This lead us to investigate some properties that are relevant for the development of a theory of interval digital signal processing. It is shown that the sets IR and R(C) endowed with any correct arithmetic is not an algebraic field, meaning that those sets do not behave like real and complex numbers. An alternative to the notion of interval complex width is also provided and the Kulisch- Miranker order is used in order to write complex numbers in the interval form enabling operations on endpoints. The use of interval signals and systems is possible thanks to the representation of complex values into floating point systems. That is, if a number x 2 R is not representable in a floating point system F then it is mapped to an interval [x;x], such that x is the largest number in F which is smaller than x and x is the smallest one in F which is greater than x. This interval representation is the starting point for definitions like interval signals and systems which take real or complex values. It provides the extension for notions like: causality, stability, time invariance, homogeneity, additivity and linearity to interval systems. The process of quantization is extended to its interval counterpart. Thereafter the interval versions for: quantization levels, quantization error and encoded signal are provided. It is shown that the interval levels of quantization represent complex quantization levels and the classical quantization error ranges over the interval quantization error. An estimation for the interval quantization error and an interval version for Z-transform (and hence Fourier transform) is provided. Finally, the results of an Matlab implementation is given

Contribuições aos Processos de Clustering com Base em Métricas não-Euclidianas

In this work we present a new clustering method that groups up points of a data set in classes. The method is based in a algorithm to link auxiliary clusters that are obtained using traditional vector quantization techniques. It is described some approaches during the development of the work that are based in measures of distances or dissimilarities (divergence) between the auxiliary clusters. This new method uses only two a priori information, the number of auxiliary clusters Na and a threshold distance dt that will be used to decide about the linkage or not of the auxiliary clusters. The number os classes could be automatically found by the method, that do it based in the chosen threshold distance dt, or it is given as additional information to help in the choice of the correct threshold. Some analysis are made and the results are compared with traditional clustering methods. In this work different dissimilarities metrics are analyzed and a new one is proposed based on the concept of negentropy. Besides grouping points of a set in classes, it is proposed a method to statistical modeling the classes aiming to obtain a expression to the probability of a point to belong to one of the classes. Experiments with several values of Na e dt are made in tests sets and the results are analyzed aiming to study the robustness of the method and to consider heuristics to the choice of the correct threshold. During this work it is explored the aspects of information theory applied to the calculation of the divergences. It will be explored specifically the different measures of information and divergence using the Rényi entropy. The results using the different metrics are compared and commented. The work also has appendix where are exposed real applications using the proposed method

Técnicas neuronais de agrupamentos aplicadas à detecção de anomalias em mamogramas digitais

This work proposes the development of an intelligent system for analysis of digital mammograms, capable to detect and to classify masses and microcalcifications. The digital mammograms will be pre-processed through techniques of digital processing of images with the purpose of adapting the image to the detection system and automatic classification of the existent calcifications in the suckles. The model adopted for the detection and classification of the mammograms uses the neural network of Kohonen by the algorithm Self Organization Map - SOM. The algorithm of Vector quantization, Kmeans it is also used with the same purpose of the SOM. An analysis of the performance of the two algorithms in the automatic classification of digital mammograms is developed. The developed system will aid the radiologist in the diagnosis and accompaniment of the development of abnormalities

Algoritmo SOM com estrutura hierárquica e dinâmica aplicado a compressão de imagens

ln this work the implementation of the SOM (Self Organizing Maps) algorithm or Kohonen neural network is presented in the form of hierarchical structures, applied to the compression of images. The main objective of this approach is to develop an Hierarchical SOM algorithm with static structure and another one with dynamic structure to generate codebooks (books of codes) in the process of the image Vector Quantization (VQ), reducing the time of processing and obtaining a good rate of compression of images with a minimum degradation of the quality in relation to the original image. Both self-organizing neural networks developed here, were denominated HSOM, for static case, and DHSOM, for the dynamic case. ln the first form, the hierarchical structure is previously defined and in the later this structure grows in an automatic way in agreement with heuristic rules that explore the data of the training group without use of external parameters. For the network, the heuristic mIes determine the dynamics of growth, the pruning of ramifications criteria, the flexibility and the size of children maps. The LBO (Linde-Buzo-Oray) algorithm or K-means, one ofthe more used algorithms to develop codebook for Vector Quantization, was used together with the algorithm of Kohonen in its basic form, that is, not hierarchical, as a reference to compare the performance of the algorithms here proposed. A performance analysis between the two hierarchical structures is also accomplished in this work. The efficiency of the proposed processing is verified by the reduction in the complexity computational compared to the traditional algorithms, as well as, through the quantitative analysis of the images reconstructed in function of the parameters: (PSNR) peak signal-to-noise ratio and (MSE) medium squared error

Modulação quantizada para sistemas com codificação wavelet sujeitos ao desvanecimento rayleigh

Wavelet coding is an efficient technique to overcome the multipath fading effects, which are characterized by fluctuations in the intensity of the transmitted signals over wireless channels. Since the wavelet symbols are non-equiprobable, modulation schemes play a significant role in the overall performance of wavelet systems. Thus the development of an efficient design method is crucial to obtain modulation schemes suitable for wavelet systems, principally when these systems employ wavelet encoding matrixes of great dimensions. In this work, it is proposed a design methodology to obtain sub-optimum modulation schemes for wavelet systems over Rayleigh fading channels. In this context, novels signal constellations and quantization schemes are obtained via genetic algorithm and mathematical tools. Numerical results obtained from simulations show that the wavelet-coded systems derived here have very good performance characteristics over fading channels

Contribuições a técnicas de agrupamento e visualização de dados multivariados utilizando mapas auto-organizáveis

Self-organizing maps (SOM) are artificial neural networks widely used in the data mining field, mainly because they constitute a dimensionality reduction technique given the fixed grid of neurons associated with the network. In order to properly the partition and visualize the SOM network, the various methods available in the literature must be applied in a post-processing stage, that consists of inferring, through its neurons, relevant characteristics of the data set. In general, such processing applied to the network neurons, instead of the entire database, reduces the computational costs due to vector quantization. This work proposes a post-processing of the SOM neurons in the input and output spaces, combining visualization techniques with algorithms based on gravitational forces and the search for the shortest path with the greatest reward. Such methods take into account the connection strength between neighbouring neurons and characteristics of pattern density and distances among neurons, both associated with the position that the neurons occupy in the data space after training the network. Thus, the goal consists of defining more clearly the arrangement of the clusters present in the data. Experiments were carried out so as to evaluate the proposed methods using various artificially generated data sets, as well as real world data sets. The results obtained were compared with those from a number of well-known methods existent in the literature

Compressão Seletiva de Imagens Coloridas com Detecção Automática de Regiões de Interesse

There has been an increasing tendency on the use of selective image compression, since several applications make use of digital images and the loss of information in certain regions is not allowed in some cases. However, there are applications in which these images are captured and stored automatically making it impossible to the user to select the regions of interest to be compressed in a lossless manner. A possible solution for this matter would be the automatic selection of these regions, a very difficult problem to solve in general cases. Nevertheless, it is possible to use intelligent techniques to detect these regions in specific cases. This work proposes a selective color image compression method in which regions of interest, previously chosen, are compressed in a lossless manner. This method uses the wavelet transform to decorrelate the pixels of the image, competitive neural network to make a vectorial quantization, mathematical morphology, and Huffman adaptive coding. There are two options for automatic detection in addition to the manual one: a method of texture segmentation, in which the highest frequency texture is selected to be the region of interest, and a new face detection method where the region of the face will be lossless compressed. The results show that both can be successfully used with the compression method, giving the map of the region of interest as an input

Espectro das ondas de spin lineares e não-lineares em multicamadas magnéticas anisotrópicas

Conselho Nacional de Desenvolvimento Científico e Tecnológico

A Obra Lógica racional, geométrica e analítica (1744) de Manoel de Azevedo Fortes (1660-1749): um estudo das possíveis contribuições para o desenvolvimento educacional luso-brasileiro

Pós-graduação em Educação Matemática - IGCE

Princípio variacional de Schwinger e teoria quântica - aplicações à mecânica quântica quaterniônica e ao estudo de sistemas singulares

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Numerical study of the Ginzburg-Landau-Langevin equation: coherent structures and noise perturbation theory

Pós-graduação em Física - IFT

Proposta de implementação de redes de base radial em tecnologias CMOS e BiCMOS

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)