992 resultados para Árvores Fuzzy de padrões


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Pós-graduação em Engenharia Elétrica - FEIS

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A utilização de ecossistemas naturais como meta a ser atingida e a seleção de indicadores para monitoramento dos projetos são temas controversos na ciência e na prática da restauração. Analisamos a vegetação ripária em quatro remanescentes de Floresta Estacional Semidecidual, para verificar se alguns atributos dessas comunidades se repetem em diferentes locais, podendo ser referência para esta região fitogeográfica. Instalamos dez parcelas de 100 m² em cada local, amostramos plantas lenhosas com altura ≥ 0,5 m, divididas em estrato regenerante (DAP < 5 cm) e estrato arbóreo (DAP ≥ 5 cm) e classificamos as espécies com base em atributos funcionais, raridade e status de ameaça. Contabilizamos lianas, pteridófitas e árvores com epífitas. As variáveis estruturais de densidade (estrato arbóreo e regenerante e árvores com epífitas), área basal e cobertura de copas não diferiram entre locais. Foram pouco variáveis entre as áreas a riqueza rarefeita para 100 indivíduos no estrato arbóreo, a riqueza total estimada por Jackknife e as proporções de espécies raras, tolerantes à sombra, de crescimento lento e zoocóricas. Porém, analisando-se a proporção de indivíduos na comunidade, somente a tolerância à sombra foi pouco variável. Para as outras variáveis analisadas não existem padrões que possam ser considerados referência para esta região fitogeográfica. No entanto, ainda que para algumas variáveis existam padrões, sua utilização como meta da restauração depende de: 1) prazos longos para monitoramento de projetos e, sobretudo, 2) estudos que demonstrem que os ecossistemas restaurados podem, um dia, igualar aos ecossistemas pré-existentes.

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Pós-graduação em Engenharia Elétrica - FEIS

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

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Computational Intelligence Methods have been expanding to industrial applications motivated by their ability to solve problems in engineering. Therefore, the embedded systems follow the same idea of using computational intelligence tools embedded on machines. There are several works in the area of embedded systems and intelligent systems. However, there are a few papers that have joined both areas. The aim of this study was to implement an adaptive fuzzy neural hardware with online training embedded on Field Programmable Gate Array – FPGA. The system adaptation can occur during the execution of a given application, aiming online performance improvement. The proposed system architecture is modular, allowing different configurations of fuzzy neural network topologies with online training. The proposed system was applied to: mathematical function interpolation, pattern classification and selfcompensation of industrial sensors. The proposed system achieves satisfactory performance in both tasks. The experiments results shows the advantages and disadvantages of online training in hardware when performed in parallel and sequentially ways. The sequentially training method provides economy in FPGA area, however, increases the complexity of architecture actions. The parallel training method achieves high performance and reduced processing time, the pipeline technique is used to increase the proposed architecture performance. The study development was based on available tools for FPGA circuits.

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Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been used successfully in the segmentation of images from several modalities. However, the traditional fuzzy segmentation algorithm fails to segment objects that are characterized by textures whose patterns cannot be successfully described by simple statistics computed over a very restricted area. In this paper we present an extension of the fuzzy segmentation algorithm that achieves the segmentation of textures by employing adaptive affinity functions as long as we extend the algorithm to tridimensional images. The adaptive affinity functions change the size of the area where they compute the texture descriptors, according to the characteristics of the texture being processed, while three dimensional images can be described as a finite set of two-dimensional images. The algorithm then segments the volume image with an appropriate calculation area for each texture, making it possible to produce good estimates of actual volumes of the target structures of the segmentation process. We will perform experiments with synthetic and real data in applications such as segmentation of medical imaging obtained from magnetic rosonance