Estudo e aplicação da transformada de Fourier na regularização de dados sísmicos na exploração de petróleo

In the oil prospection research seismic data are usually irregular and sparsely sampled along the spatial coordinates due to obstacles in placement of geophones. Fourier methods provide a way to make the regularization of seismic data which are efficient if the input data is sampled on a regular grid. However, when these methods are applied to a set of irregularly sampled data, the orthogonality among the Fourier components is broken and the energy of a Fourier component may "leak" to other components, a phenomenon called "spectral leakage". The objective of this research is to study the spectral representation of irregularly sampled data method. In particular, it will be presented the basic structure of representation of the NDFT (nonuniform discrete Fourier transform), study their properties and demonstrate its potential in the processing of the seismic signal. In this way we study the FFT (fast Fourier transform) and the NFFT (nonuniform fast Fourier transform) which rapidly calculate the DFT (discrete Fourier transform) and NDFT. We compare the recovery of the signal using the FFT, DFT and NFFT. We approach the interpolation of seismic trace using the ALFT (antileakage Fourier transform) to overcome the problem of spectral leakage caused by uneven sampling. Applications to synthetic and real data showed that ALFT method works well on complex geology seismic data and suffers little with irregular spatial sampling of the data and edge effects, in addition it is robust and stable with noisy data. However, it is not as efficient as the FFT and its reconstruction is not as good in the case of irregular filling with large holes in the acquisition.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

Estudo e desenvolvimento de algoritmos criptográficos para redes de sensores sem fio, utilizando técnicas de programação genética

Cryptography is the main form to obtain security in any network. Even in networks with great energy consumption restrictions, processing and memory limitations, as the Wireless Sensors Networks (WSN), this is no different. Aiming to improve the cryptography performance, security and the lifetime of these networks, we propose a new cryptographic algorithm developed through the Genetic Programming (GP) techniques. For the development of the cryptographic algorithm’s fitness criteria, established by the genetic GP, nine new cryptographic algorithms were tested: AES, Blowfish, DES, RC6, Skipjack, Twofish, T-DES, XTEA and XXTEA. Starting from these tests, fitness functions was build taking into account the execution time, occupied memory space, maximum deviation, irregular deviation and correlation coefficient. After obtaining the genetic GP, the CRYSEED and CRYSEED2 was created, algorithms for the 8-bits devices, optimized for WSNs, i.e., with low complexity, few memory consumption and good security for sensing and instrumentation applications.

Estudo e desenvolvimento de algoritmos criptográficos para redes de sensores sem fio, utilizando técnicas de programação genética

Cryptography is the main form to obtain security in any network. Even in networks with great energy consumption restrictions, processing and memory limitations, as the Wireless Sensors Networks (WSN), this is no different. Aiming to improve the cryptography performance, security and the lifetime of these networks, we propose a new cryptographic algorithm developed through the Genetic Programming (GP) techniques. For the development of the cryptographic algorithm’s fitness criteria, established by the genetic GP, nine new cryptographic algorithms were tested: AES, Blowfish, DES, RC6, Skipjack, Twofish, T-DES, XTEA and XXTEA. Starting from these tests, fitness functions was build taking into account the execution time, occupied memory space, maximum deviation, irregular deviation and correlation coefficient. After obtaining the genetic GP, the CRYSEED and CRYSEED2 was created, algorithms for the 8-bits devices, optimized for WSNs, i.e., with low complexity, few memory consumption and good security for sensing and instrumentation applications.

Avaliação demográfica, clinico-laboratorial e genética de indivíduos com lupus eritematoso sistêmico e artrite reumatóide residentes em região tropical

The aetiology of autoimmunes disease is multifactorial and involves interactions among environmental, hormonal and genetic factors. Many different genes may contribute to autoimmunes disease susceptibility. The major histocompatibility complex (MHC) genes have been extensively studied, however many non-polymorphic MHC genes have also been reported to contribute to autoimmune diseases susceptibility. The aim of the present study was to evaluate the influence of SLC11A1 gene in systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Ninety-six patients with SLE, 37 with RA and 202 controls enrolled in this case-control study, were evaluated with regard to demographic, genetic, laboratorial and clinical data. SLE mainly affects females in the ratio of 18 women for each man, 88,3% of the patients aged from 15 to 45 years old and it occurs with similar frequency in whites and mulattos. The rate of RA between women and men was 11:1, with 77,1% of the cases occurring from 31 to 60 years. The genetic analysis of the point mutation -236 of the SLC11A1 gene by SSCP did not show significant differences between alleles/genotypes in patients with SLE or RA when compared to controls. The most frequent clinical manifestations in patients with SLE were cutaneous (87%) and joint (84.9%). In patients with RA, the most frequent out-joint clinical manifestation were rheumatoid nodules (13,5%). Antinuclear antibodies were present in 100% of the patients with SLE. There was no significant relation between activity of disease and presence of rheumatoid factor in patients with RA, however 55,6% of patients with active disease presented positive rheumatoid factor. Significant association between alleles/genotypes of point mutation -236 and clinical manifestations was not found

Estrutura genética populacional de Lutjanus analis cioba e Lutjanus jocu dentão (Lutjanidae) ao longo do litoral brasileiro

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization