Metodologia númerico-experimental para avaliação da espessura de filmes de nitreto de titânio obtidos por processamento a plasma

In recent decades, changes in the surface properties of materials have been used to improve their tribological characteristics. However, this improvement depends on the process, treatment time and, primarily, the thickness of this surface film layer. Physical vapor deposition (PVD) of titanium nitrate (TiN) has been used to increase the surface hardness of metallic materials. Thus, the aim of the present study was to propose a numerical-experimental method to assess the film thickness (l) of TiN deposited by PVD. To reach this objective, experimental results of hardness (H) assays were combined with a numerical simulation to study the behavior of this property as a function of maximum penetration depth of the indenter (hmax) into the film/substrate conjugate. Two methodologies were adopted to determine film thickness. The first consists of the numerical results of the H x hmax curve with the experimental curve obtained by the instrumental indentation test. This methodology was used successfully in a TiN-coated titanium (Ti) conjugate. A second strategy combined the numerical results of the Hv x hmax curve with Vickers experimental hardness data (Hv). This methodology was applied to a TiN-coated M2 tool steel conjugate. The mechanical properties of the materials studied were also determined in the present study. The thicknesses results obtained for the two conjugates were compatible with their experimental data.

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.

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.

Pirometamorfismo provocado por intrusões básicas cenozoicas em rochas da Bacia Potiguar, RN: integração de dados geológicos e petrofísicos

The Potiguar Basin is located in the Brazilian Equatorial Margin and presents sedimentary rocks affected by Cenozoic basic igneous intrusions, known as Macau Magmatism. The most prominent effect related to these intrusions is the formation of buchites, pyrometamorphic rocks that occur at very high temperatures and very low pressures in the sanidinite metamorphic facies. Through literature review, field observations, petrographic and petrophysical data, accessing the database of previous studies and results from this research, it was possible to characterize and estimate the effects produced in the thermal aureole of some hypabyssal bodies in the basin. The most relevant features associated with the intrusions are: compactation, hydraulic fracturing, partial melting and recrystallization of country rocks. According to the observed mineral occurrences, temperature of 800 to 1200 °C and pressure below 0,5 kbar were estimated at the contacts of the igneous bodies. The thermal modeling of the São João plug indicates thermal effects extending up to 150 m away from the contact and cooling time of approximately 265,000 years. After the peak of temperature, followed a cooling phase registered by remobilization and precipitation of minerals at low-temperature in faults, fractures and geodes, interpreted as derived from reactions with sedimentary rocks and metasomatic / hydrothermal fluids with abundant carbonatization and silicification.

Pirometamorfismo provocado por intrusões básicas cenozoicas em rochas da Bacia Potiguar, RN: integração de dados geológicos e petrofísicos

The Potiguar Basin is located in the Brazilian Equatorial Margin and presents sedimentary rocks affected by Cenozoic basic igneous intrusions, known as Macau Magmatism. The most prominent effect related to these intrusions is the formation of buchites, pyrometamorphic rocks that occur at very high temperatures and very low pressures in the sanidinite metamorphic facies. Through literature review, field observations, petrographic and petrophysical data, accessing the database of previous studies and results from this research, it was possible to characterize and estimate the effects produced in the thermal aureole of some hypabyssal bodies in the basin. The most relevant features associated with the intrusions are: compactation, hydraulic fracturing, partial melting and recrystallization of country rocks. According to the observed mineral occurrences, temperature of 800 to 1200 °C and pressure below 0,5 kbar were estimated at the contacts of the igneous bodies. The thermal modeling of the São João plug indicates thermal effects extending up to 150 m away from the contact and cooling time of approximately 265,000 years. After the peak of temperature, followed a cooling phase registered by remobilization and precipitation of minerals at low-temperature in faults, fractures and geodes, interpreted as derived from reactions with sedimentary rocks and metasomatic / hydrothermal fluids with abundant carbonatization and silicification.

O uso da Divergência de Kullback-Leibler e da Divergência Generalizada como medida de similaridade em sistemas CBIR

The content-based image retrieval is important for various purposes like disease diagnoses from computerized tomography, for example. The relevance, social and economic of image retrieval systems has created the necessity of its improvement. Within this context, the content-based image retrieval systems are composed of two stages, the feature extraction and similarity measurement. The stage of similarity is still a challenge due to the wide variety of similarity measurement functions, which can be combined with the different techniques present in the recovery process and return results that aren’t always the most satisfactory. The most common functions used to measure the similarity are the Euclidean and Cosine, but some researchers have noted some limitations in these functions conventional proximity, in the step of search by similarity. For that reason, the Bregman divergences (Kullback Leibler and I-Generalized) have attracted the attention of researchers, due to its flexibility in the similarity analysis. Thus, the aim of this research was to conduct a comparative study over the use of Bregman divergences in relation the Euclidean and Cosine functions, in the step similarity of content-based image retrieval, checking the advantages and disadvantages of each function. For this, it was created a content-based image retrieval system in two stages: offline and online, using approaches BSM, FISM, BoVW and BoVW-SPM. With this system was created three groups of experiments using databases: Caltech101, Oxford and UK-bench. The performance of content-based image retrieval system using the different functions of similarity was tested through of evaluation measures: Mean Average Precision, normalized Discounted Cumulative Gain, precision at k, precision x recall. Finally, this study shows that the use of Bregman divergences (Kullback Leibler and Generalized) obtains better results than the Euclidean and Cosine measures with significant gains for content-based image retrieval.

Estimação do subespaço de sinal em dados hiperespectrais

A redução de dimensionalidade é uma tarefa crucial no processamento e análise de dados hiperespectrais. Esta comunicação propõe um método de estimação do subespaço de sinal baseado no erro quadrático médio. O método consiste em primeiro estimar as matrizes de correlação do sinal e do ruído e em segundo seleccionar o conjunto de vectores próprios que melhor representa o subespaço de sinal. O eficiência deste método é ilustrada em imagens hiperespectrais sintéticas e reais.

Influência da experiência profissional e das características visuais do operador no processamento da cintigrafia de perfusão do miocárdio

A cintigrafia de perfusão do miocárdio (CPM) é uma técnica usada no diagnóstico e estratificação de risco em pacientes com suspeita ou conhecida doença arterial coronária. O processamento da CPM é realizado maioritariamente de forma semi-automática. Neste permanecem passos manuais, que envolvem: delimitação da área de reconstrução; reorientação e ajustamento dos limites do miocárdio (longo eixo vertical - LEV; longo eixo horizontal - LEH; curto eixo. O desempenho dos técnicos de Medicina Nuclear (TMN) pode ser afetado por: fatores ambientais; fatores individuais (experiência profissional e características visuais). Acredita-se que a perceção visual ao nível do processamento da CPM se encontra relacionada com o estado de visão binocular. Assim, diferentes TMN que processem os mesmos dados poderão obter diferentes estimativas dos parâmetros quantitativos. Questão de investigação: Será que a experiência profissional e as características visuais do operador interferem na determinação dos PQ no processamento da CPM? Objetivos do estudo: Avaliar a influência da experiência profissional e das características visuais dos TMN na determinação dos PQ obtidos na CPM; e Analisar a variabilidade intra e inter-operador na determinação dos PQ obtidos na CPM.

Adoecimento osteomuscular de trabalhadores portuários avulsos e o processamento do raciocínio clínico da enfermagem

Introdução: O trabalho portuário é composto por condicionantes socioambientais necessários à manutenção das funções operativas, mas que influenciam na produção de doenças osteomusculares. O conhecimento desses condicionantes instrumentaliza o raciocínio clínico da Enfermagem para o planejamento de ações em saúde. Desta forma, defende-se a tese de que “O conhecimento dos condicionantes socioambientais e pessoais do adoecimento osteomuscular do trabalhador portuário avulso fornece elementos ao processamento do raciocínio clínico da Enfermagem, para assistência em saúde do trabalhador”. Objetivos: identificar evidências científicas de adoecimento ocupacional do trabalhador portuário publicadas na literatura cientifica; caracterizar o tipo, a localização e a intensidade de sintomas osteomusculares relacionados com os condicionantes socioambientais do trabalho portuário; Relacionar as doenças osteomusculares autorreferidas por trabalhadores portuários e os condicionantes socioambientais deste trabalho. Percurso Metodológico: o estudo apresentou revisão sistemática, fundamentada no método Cochrane; e estudos descritivos e exploratórios de abordagem quantitativa, realizado por meio de entrevista semi-estruturada com 232 trabalhadores portuários avulsos. Os dados foram analisados no software Statistical Package for the Social Sciences (SPSS) 21.0, por frequência simples, proporções e testes inferenciais não-paramétricos. A tese integra o macro projeto de pesquisa “Saúde do Trabalhador, Riscos, Acidentes e Doenças Relacionadas ao Trabalho: Estudo com Trabalhadores em um Porto no Extremo Sul do Brasil”, aprovado pelo Comitê de Ética em Pesquisa da Universidade Federal do Rio Grande (CEPAS-FURG) sob parecer número 118/2013. Resultados: Na revisão sistemática, selecionaram-se 16 publicações; todas as publicações pertenceram ao nível de evidência quatro, destacando o câncer pulmonar, doenças osteomusculares e isquêmicas, com nexo causal em riscos químicos oriundos da exaustão veicular e das cargas transportadas. Nos estudos descritivos, os sintomas prevalentes foram a dor leve em membros superiores (51,7%) e intensa a insuportável na coluna vertebral (19%). Os dois adoecimentos mais autorreferidos foram lombocitalgia (36,8%; n=50 – em terra e 28,1%; n=27 – a bordo) e tendinite (27,9% - em terra e 31,3% - a bordo). Discussão: O câncer pulmonar ocupacional foi causado por componentes químicos da exaustão veicular e do amianto transportado nas operações portuárias. Com relação à saúde muscular, a idade, o tempo e a jornada de trabalho mostraram-se condicionantes importantes na identificação de sintomas e adoecimentos, e o quanto estes fatores interveem na percepção da intensidade, contribuindo no autocuidado para prevenção e tratamento. Conclusão: O conhecimento dos condicionantes socioambientais relacionados ao trabalhador e caracterizados nos ambientes de trabalho deve ser atual e pregresso, o que somado à apreensão dos sintomas e adoecimentos autorreferidos pelos trabalhadores instrumentalizou o RC, identificando uma atuação profissional em longo prazo para dirimir os adoecimentos identificados. As características clínicas obtidas, em conjunto com a literatura, conduziram ao processamento do RC da enfermagem nesta realidade, sendo a informação em saúde um ponto chave para a promoção da saúde muscular dos trabalhadores.

Ferramenta computacional para medição de deslocamentos em ensaios de carga através de técnicas de processamento de imagem

Dissertação de Mestrado para a obtenção de grau de Mestre em Engenharia Eletrotécnica Ramo de Automação e Eletrónica Industrial

Potencialidades de cultivo de macroalgas no litoral do Rio Grande do Norte (NE, Brasil): uma perspectiva de integração de dados ambientais em sistemas de informação geográfica (SIG)

Since centuries ago, the Asians use seaweed as an important source of feeding and are their greatest world-wide consumers. The migration of these peoples for other countries, made the demand for seaweed to increase. This increasing demand prompted an industry with annual values of around US$ 6 billion. The algal biomass used for the industry is collected in natural reservoirs or cultivated. The market necessity for products of the seaweed base promotes an unsustainable exploration of the natural banks, compromising its associated biological balance. In this context, seaweed culture appears as a viable alternative to prevent the depletion of these natural supplies. Geographic Information Systems (GIS) provide space and produce information that can facilitate the evaluation of important physical and socio-economic characteristics for the planning of seaweed culture. This objective of this study is to identify potential coastal areas for seaweed culture in the state of Rio Grande do Norte, from the integration of social-environmental data in the SIG. In order to achieve this objective, a geo-referred database composed of geographical maps, nautical maps and orbital digital images was assembled; and a bank of attributes including physical and oceanographical variables (winds, chains, bathymetry, operational distance from the culture) and social and environmental factors (main income, experience with seaweed harvesting, demographic density, proximity of the sheltered coast and distance of the banks) was produced. In the modeling of the data, the integration of the space database with the bank of attributes for the attainment of the map of potentiality of seaweed culture was carried out. Of a total of 2,011 ha analyzed by the GIS for the culture of seaweed, around 34% or 682 ha were indicated as high potential, 55% or 1,101 ha as medium potential, and 11% or 228 ha as low potential. The good indices of potentiality obtained in the localities studied demonstrate that there are adequate conditions for the installation of seaweed culture in the state of Rio Grande do Norte

O risco dos bancos, regulação e a crise financeira internacional

Dissertação de Mestrado, Ciências Económicas e Empresariais, 13 de Julho de 2016, Universidade dos Açores.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.