APA (7th ed.) Citation

Li, X., Zhou, Y., Yan, H., Goos, G., Hartmanis, J., Bertino, E., . . . Zou, Y. (2025). Enhancing Adversarial Robustness in Automatic Modulation Recognition with Dynamical Systems-Inspired Deep Learning Frameworks. Wireless Artificial Intelligent Computing Systems and Applications : 18th International Conference, WASA 2024, Qindao, China, June 21–23, 2024, Proceedings, Part I, 14997, 387. https://doi.org/10.1007/978-3-031-71464-1_32

Chicago Style (17th ed.) Citation

Li, Xiaohu, et al. "Enhancing Adversarial Robustness in Automatic Modulation Recognition with Dynamical Systems-Inspired Deep Learning Frameworks." Wireless Artificial Intelligent Computing Systems and Applications : 18th International Conference, WASA 2024, Qindao, China, June 21–23, 2024, Proceedings, Part I 14997 (2025): 387. https://doi.org/10.1007/978-3-031-71464-1_32.

MLA (9th ed.) Citation

Li, Xiaohu, et al. "Enhancing Adversarial Robustness in Automatic Modulation Recognition with Dynamical Systems-Inspired Deep Learning Frameworks." Wireless Artificial Intelligent Computing Systems and Applications : 18th International Conference, WASA 2024, Qindao, China, June 21–23, 2024, Proceedings, Part I, vol. 14997, 2025, p. 387, https://doi.org/10.1007/978-3-031-71464-1_32.