Fish show rapid movements in various behavioral activities or associated with the presence of food. However, in periods of rapid movement, the rate at which occlusion occurs among the fish is quite high, causing inconsistency in the detection and tracking of fish, hindering the fish's identity and behavioral trajectory over a long period of time. Although some algorithms have been proposed to solve these problems, most of their applications were made in groups of fish that swim in shallow water and calm behavior, with few sudden movements. To solve these problems, a convolutional network of object recognition, YOLOv2, was used to delimit the region of the fish heads to optimize individual fish detection. In the tracking phase, the Kalman filter was used to estimate the best state of the fish's head position in each frame and, subsequently, the trajectories of each fish were connected among the frames. The results of the algorithm show adequate performances in the trajectories of groups of zebrafish that exhibited rapid movements.

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Breast cancer is the second type of cancer that affects women in the world, losing only for non melanoma skin cancer. The mammography is the most accurate exam for the diagnostic of this disease, allowing to discovery in its initial stage and increasing the chance of prompt treatment. This paper presents the modeling and implementation of Web based telediagnostic system for automated detection and register of lesions in mammographic images, based on Independent Component Analysis (ICA) and Support Vector Machine (SVM). The system analyses digital mammography images submited for Internet providing an image diagnosis and indicating the presence of suspicious regions, which can be evaluated and treated by an specialist. Also presents the methodology for development of proposed system. A system's prototype was developed to run tests that could measure its efficiency. Mini-MIAS was the training database used to test the algorithms. We also used SMV to classify image interest regions as normal or suspicious. Test results showed that 89.13% of normal samples were correctly classified and 92.17% of suspicious samples were also classified apropiately. We note that the proposed method provides support to detect suspicious regions in mammography images, incorporated in a automated CAD system.

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Um nanossatélite é um um tipo de satélite com massa variando entre 1 e 10 kg, desenvolvido para missões específicas para o seu tamanho. Ele é formado por vários subsistemas, cada um responsável por uma função. A redução no tamanho dos satélites ocorreu devido a miniaturização dos circuitos integrados e a padronização das estruturas de integração dos pequenos satélites. Neste trabalho é apresentado um projeto de um subsistema de suprimento de energia para um nanossatélite educacional, usando uma abordagem de monitoramento do ponto de máxima eficiência dos painéis solares. Foram feitos testes de ciclagem térmica das baterias de modo a verificar o comportamento das mesmas quando submetidas a variações de temperatura. Efetuaram-se também testes de carregamento solar das baterias através dos painéis fotovoltaicos, a fim de verificar o comportamento das correntes de carga e descarga das baterias. Através de simulação computacional, observou-se o comportamento do sistema EPS comparando o mesmo com e sem o auxílio do MPPT.