Benevenuti, Fabio 
; Oliveira, Ádria ; Lopes, Israel da Costa ; Kastensmidt, Fernanda ; Medina, Nilberto Heder ; Added, Nemitala ; Aguiar, Vitor Ângelo Paulino de ; Guazzelli, Marcilei Aparecida
Assuntos: FÍSICA NUCLEAR, RADIAÇÃO IONIZANTE, ELETRÔNICA QUÂNTICA, NANOTECNOLOGIA
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BENEVENUTI, Fabio et al. Heavy Ions Testing of an All-Convolutional Neural Network for Image Classification Evolved by Genetic Algorithms and Implemented on SRAM-Based FPGA. 2022, Anais.. New York: IEEE, 2022. Disponível em: https://doi.org/10.1109/RADECS47380.2019.9745650. Acesso em: 04 dez. 2025.APA
Benevenuti, F., Oliveira, Á., Lopes, I. da C., Kastensmidt, F., Medina, N. H., Added, N., et al. (2022). Heavy Ions Testing of an All-Convolutional Neural Network for Image Classification Evolved by Genetic Algorithms and Implemented on SRAM-Based FPGA. In . New York: IEEE. doi:10.1109/RADECS47380.2019.9745650NLM
Benevenuti F, Oliveira Á, Lopes I da C, Kastensmidt F, Medina NH, Added N, Aguiar VÂP de, Guazzelli MA. Heavy Ions Testing of an All-Convolutional Neural Network for Image Classification Evolved by Genetic Algorithms and Implemented on SRAM-Based FPGA [Internet]. 2022 ;[citado 2025 dez. 04 ] Available from: https://doi.org/10.1109/RADECS47380.2019.9745650Vancouver
Benevenuti F, Oliveira Á, Lopes I da C, Kastensmidt F, Medina NH, Added N, Aguiar VÂP de, Guazzelli MA. Heavy Ions Testing of an All-Convolutional Neural Network for Image Classification Evolved by Genetic Algorithms and Implemented on SRAM-Based FPGA [Internet]. 2022 ;[citado 2025 dez. 04 ] Available from: https://doi.org/10.1109/RADECS47380.2019.9745650
