PROCESSAMENTO DE IMAGENS APLICADO À ANÁLISE DAS CONDIÇÕES DO PAVIMENTO ASFÁLTICO DE AEROPORTOS

Este trabalho apresenta os resultados obtidos no desenvolvimento de um sistema para a indicação das condições do pavimento asfáltico de pistas de pousos e decolagens de aeroportos. O desenvolvimento baseou-se na aquisição da imagem do local a ser analisado, através de câmera digital Full HD 720P interligada a um computador portátil e um software de análise e apresentação dos dados, elaborado na plataforma Matlab®. A apresentação dos resultados foi realizada através da detecção de bordas das ranhuras, do histograma de concentração de pontos de reta e da indicação de falhas existentes obtidas através da Transformada de Hough. A apresentação de resultados em formas distintas propiciou a facilidade de detecção de falhas para o operador aeroportuário. O dispositivo apresentou um percentual de acertos na indicação de falhas verticais de 81% e de 83% para falhas existentes na horizontal da imagem capturada do pavimento

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Risco : um microprocessador risc CMOS de 32 bits

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Risco : um microprocessador risc CMOS de 32 bits

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