Chang, Y.-C.; Lin, C.-H.; Dmitriev, A.V.; Hsieh, M.-C.; Hsu, H.-W.; Lin, Y.-C.; Mendoza, M.M.; Huang, G.-H.; Tsai, L.-C.; Li, Y.-H.;
et al. State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere. Sensors 2022, 22, 2758.
https://doi.org/10.3390/s22072758
AMA Style
Chang Y-C, Lin C-H, Dmitriev AV, Hsieh M-C, Hsu H-W, Lin Y-C, Mendoza MM, Huang G-H, Tsai L-C, Li Y-H,
et al. State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere. Sensors. 2022; 22(7):2758.
https://doi.org/10.3390/s22072758
Chicago/Turabian Style
Chang, Yu-Chi, Chia-Hsien Lin, Alexei V. Dmitriev, Mon-Chai Hsieh, Hao-Wei Hsu, Yu-Ciang Lin, Merlin M. Mendoza, Guan-Han Huang, Lung-Chih Tsai, Yung-Hui Li,
and et al. 2022. "State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere" Sensors 22, no. 7: 2758.
https://doi.org/10.3390/s22072758
APA Style
Chang, Y. -C., Lin, C. -H., Dmitriev, A. V., Hsieh, M. -C., Hsu, H. -W., Lin, Y. -C., Mendoza, M. M., Huang, G. -H., Tsai, L. -C., Li, Y. -H., & Tsogtbaatar, E.
(2022). State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere. Sensors, 22(7), 2758.
https://doi.org/10.3390/s22072758
