A5046045055- Advanced Memory and Neural Computing
- Chaos-based Image/Signal Encryption
- Neural Networks and Reservoir Computing
- Physical Unclonable Functions (PUFs) and Hardware Security
- CCD and CMOS Imaging Sensors
National Engineering Research Center of Electromagnetic Radiation Control Materials
2023-2025
University of Electronic Science and Technology of China
2022-2025
Institute of Microelectronics
2025
This paper proposes an innovative approach to designing image encryption hardware by leveraging the Negative-resistance Memristor-based Hopfield Neural Network (NMHNN) model. In this method, conventional (HNN) is modified substituting one of its synaptic weights with a negative-resistance memristor modification demonstrates enhanced complex dynamics while maintaining simplified structure. The NMHNN generates chaotic sequence, which serves as key for and decryption through confusion–diffusion...
In response to the escalating demand for edge encryption in big data era, we introduce implementation of Memristor-based Transient Chaotic Neural Network (MTCNN) on FPGA. This innovative approach aims elevate security key generation Advanced Encryption Standard (AES) algorithm. Through meticulous mapping and optimization MTCNN-AES algorithm FPGA, achieve efficient encryption. Notably, FPGA-implemented features a space 22 orders magnitude larger than traditional AES, underscoring its...
In this work, a dynamic S-box is designed based on the chaotic neural network using memristor model of LiNbO3 (LNO) single crystalline thin film. The LNO memristor, which shows good repeatability and characteristic gradual resistance change, used as self-feedback weight in Transient Chaotic Neural Network (TCNN) to form network. Double pseudo-random digits are generated for dynamically adjusting elements according function. experimental results show that proposed design can achieve physical...