Hongzhe Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Hongzhe Wang has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Guohui Ren
Southwest Branch of China National Petroleum Corporation Logging Company Ltd., Chongqing, China
1 shared publication - Jun-Jie Wang
State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
1 shared publication - Kai Tang
China National Logging Corporation, China
1 shared publication - Kunsong Liu
State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, China
1 shared publication - Mingyuan Sun
Beijing China Changfeng Electromechanical Technology Research and Design, China
1 shared publication - Qiang Deng
Southwest Institute of Electronic Technology, Chengdu, Sichuan, China
1 shared publication
Latest Publications
- Open Access
- 25 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 32-36
A pipelined hardware implementation of AES with S-box based on right-skewed ECA
- 25 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 32-36
Open AccessA pipelined hardware implementation of AES with S-box based on right-skewed ECA
- Open Access
- 17 November 2023
- Published by Association for Computing Machinery (ACM)
- p. 241-244
FPGA-implemented Memristor-based Transient Chaotic Neural Network for AES Edge Encryption
In response to the escalating demand for edge encryption in the big data era, we introduce the implementation of the Memristor-based Transient Chaotic Neural Network (MTCNN) on FPGA. This innovative approach aims to elevate the security of key generation for the Advanced Encryption Standard (AES) algorithm. Through meticulous mapping and optimization of MTCNN-AES encryption algorithm on FPGA, we achieve efficient edge encryption. Notably, the FPGA-implemented MTCNN-AES features a key space 22 orders of magnitude larger than traditional AES, underscoring its substantial resilience against brute-force attacks. The proposed approach aligns with federal information processing standards, emphasizing its reliability, robustness and randomness.
- 17 November 2023
- Published by Association for Computing Machinery (ACM)
- p. 241-244
Open AccessFPGA-implemented Memristor-based Transient Chaotic Neural Network for AES Edge Encryption
In response to the escalating demand for edge encryption in the big data era, we introduce the implementation of the Memristor-based Transient Chaotic Neural Network (MTCNN) on FPGA. This innovative approach aims to elevate the security of key generation for the Advanced Encryption Standard (AES) algorithm. Through meticulous mapping and optimization of MTCNN-AES encryption algorithm on FPGA, we achieve efficient edge encryption. Notably, the FPGA-implemented MTCNN-AES features a key space 22 orders of magnitude larger than traditional AES, underscoring its substantial resilience against brute-force attacks. The proposed approach aligns with federal information processing standards, emphasizing its reliability, robustness and randomness.