Human Activity Recognition (HAR) has emerged as a critical component in intent-aware, AI-driven Internet of Things (IoT) Communication systems, enabling context-aware responses in smart environments. Recently, WiFi-based HAR has gained significant attention due to its non-intrusive nature, low deployment cost, and ability to preserve privacy. However, they face a major challenge across domains. To address this limitation, we propose a novel cross-domain HAR framework (called TransHAR) by introducing a lightweight and efficient transformer model. On one hand, we design a feature representation block that processes the Wi-Fi channel frequency response (CFR) phase data to estimate Doppler shifts, capturing motion-related dynamics while remaining invariant to static, environment-specific structures, enhancing generalization across domains. On the other hand, we propose a lightweight Transformer architecture, termed ResDyTFormer, which minimizes reliance on normalization layers by incorporating a novel Residual Dynamic Tanh function. This function dynamically learns to balance between traditional normalization and the Dynamic Tanh operation, thereby maintaining training stability and avoiding gradient vanishing issues often encountered when using Dynamic Tanh alone. Extensive experiments on two benchmark datasets demonstrate that the proposed TransHAR framework achieves state-of-the-art performance in both in-domain and cross-domain HAR tasks with only 0.17M parameters. On the SHARP dataset, it attains an impressive 99.04% F1 score and 98.94% accuracy. On the 3DO dataset, it achieves 86.10% accuracy and 84.95% F1 score. These results highlight the potential of TransHAR as an efficient and scalable framework for real-world WiFi-based human activity sensing.

Current variable-convergence-parameter zeroing neural networks (ZNNs), including the VCP-ZNN and the FCP-ZNN, are either inefficient or unintelligent. Although researchers have discussed the application of ZNN in chaos synchronization, these ZNN-based chaos synchronization controllers are rarely used in real-world applications. To the best of the authors’ knowledge, no researchers have applied the ZNN-based chaos synchronization controllers in secure voice communication. In this study, we established a novel chaos synchronization controller based on the proportional–integral-convergence-parameter ZNN (PICP-ZNN) model, which is both computationally efficient and intelligent. It was then used in secure voice communication. To demonstrate the superior features of the proposed PICP-ZNN model, we presented both theoretical analysis and numerical experiments to show its fixed-time convergence, robustness, and adaptiveness. In addition, a detailed comparison with other state-of-the-art variable-convergence-parameter ZNNs was presented to highlight our contribution further. The upper bound of the settling time is also estimated in both noisy and noise-free environments. Overall, this study offers a novel ZNN-based secure communication scheme. The PICP-ZNN models may serve as a novel source of inspiration for enhancing the variable-convergence-parameter ZNN even further.

The real-world continuous double auction (CDA) market is a dynamic environment. However, most of the existing agent bidding strategies are simply designed for static markets. A new detecting method for bidding strategy is necessary for more practical