Minghui Zhang

In recent years, with the development of novel hardware such as memristors, integrating chaotic systems with hardware implementations has enabled efficient and controllable generation of chaotic signals, providing new avenues for both theoretical research and engineering applications. In this work, we propose a novel memristor-based chaotic system, named the three-dimensional discrete echo-memristor map (3D-DEMM). The 3D-DEMM is capable of generating complex dynamic behaviors with self-similar attractor structures; specifically, under different parameters and initial conditions, the system produces similar attractor shapes at different amplitudes, which we refer to as an echo chaotic map. By incorporating the discrete nonlinear characteristics of memristors, the 3D-DEMM is systematically designed. We first conduct a thorough dynamic analysis of the 3D-DEMM, including attractor visualization, Lyapunov exponents, and NIST tests, to verify its chaoticity and self-similarity. Subsequently, the attractors of the 3D-DEMM are captured on a DSP platform, demonstrating discrete-time hardware simulation and real-time operation. Experimental results show that the proposed system not only exhibits highly controllable chaotic behavior but also demonstrates strong robustness in maintaining amplitude-invariant attractor shapes, providing a new theoretical and practical approach for memristor-based chaotic signal generation and applications in information security.

Multi-behavior sequential recommendation (MBSRec) is a form of sequential recommendation. It leverages users’ historical interaction behavior types to better predict their next actions. This approach fits real-world scenarios better than traditional models do. With the rise of the transformer model, attention mechanisms are widely used in recommendation algorithms. However, they suffer from low-pass filtering, and the simple learnable positional encodings in existing models offer limited performance gains. To address these problems, we introduce the context-aware multi-behavior sequential recommendation model (CAMBSRec). It separately encodes items and behavior types, replaces traditional positional encoding with context-similarity positional encoding, and applies the discrete Fourier transform to separate the high and low frequency components and enhance the high frequency components, countering the low-pass filtering effect. Experiments on three public datasets show that CAMBSRec performs better than five baseline models, demonstrating its advantages in terms of recommendation performance.