Browsing by Author "Braga, Mateus T."

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    Combined weightless neural network FPGA architecture for deforestation surveillance and visual navigation of UAVs.
    (2020) Torres, Vitor Angelo Maria Ferreira; Jaimes, Brayan Rene Acevedo; Ribeiro, Eduardo S.; Braga, Mateus T.; Shiguemori, Elcio Hideiti; Velho, Haroldo Fraga de Campos; Torres, Luiz Carlos Bambirra; Braga, Antônio de Pádua
    This work presents a combined weightless neural network architecture for deforestation surveillance and visual navigation of Unmanned Aerial Vehicles (UAVs). Binary images, which are required for position estimation and UAV navigation, are provided by the deforestation surveillance circuit. Learned models are evaluated in a real UAV flight over a green countryside area, while deforestation surveillance is assessed with an Amazon forest benchmarking image data. Small utilization percentage of Field Programmable Gate Arrays (FPGAs) allows for a higher degree of parallelization and block processing of larger regions of input images.
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    Embedded real-time feature extraction for electrode inversion detection in telemedicine electrocardiograms.
    (2020) Torres, Vitor Angelo Maria Ferreira; Silva, D. A.C.; Torres, Luiz Carlos Bambirra; Braga, Mateus T.; Cardoso, M. B. R.; Lino, Vinicius Terra; Torres, Frank Sill; Braga, Antônio de Pádua
    Early detection of technical errors in medical examinations, especially in remote locations, is of utmost importance in order to avoid invalid measurements that would require costly and time consuming repeti- tions. This paper proposes a highly efficient method for the identification of an erroneous inversion of the measuring electrodes during a multichannel electrocardiogram. Therefore, a widely applied approach for heart beat detection is modified and approximated feature extraction techniques are employed. In con- trast to existing works, the improved heart beat identification requires no removal of baseline wandering and no amplitude related thresholds. Furthermore, a piecewise linear approximation of the baseline and basic calculations are sufficient for extracting the cardiac axis, which allows the construction of a clas- sifier capable of quickly detecting electrode reversals. Our implementation indicates that the proposed method has minimal hardware costs and is able to operate in real-time on a simple micro-controller.