DECSI - Departamento de Computação e Sistemas de Informação

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Browsing DECSI - Departamento de Computação e Sistemas de Informação by Subject "Artificial neural networks"

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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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    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 da Silva; Braga, Mateus Taulois; Shiguemori, Elcio Hideit; Velho, Haroldo Fraga de Campos; Torres, Luiz Carlos Bambirra; Braga, Antônio 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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    Multi-objective neural network model selection with a graph-based large margin approach.
    (2022) Torres, Luiz Carlos Bambirra; Castro, Cristiano Leite de; Rocha, Honovan Paz; Almeida, Gustavo Matheus de; Braga, Antônio de Pádua
    This work presents a new decision-making strategy for multi-objective learning problem of artificial neural networks (ANN). The proposed decision-maker searches for the solution that minimizes a margin-based validation error amongst Pareto set solutions. The proposal is based on a geometric approximation to find the large margin (distance) of separation among the classes. Several benchmarks commonly available in the literature were used for testing. The obtained results showed that the proposal is more efficient in controlling the generalization capacity of neural models than other learning machines. It yields smooth (noise robustness) and well-fitted models straightforwardly, i.e., without the necessity of parameter set definition in advance or validation data use, as often required by learning machines.