Continual learning for action recognition is a critical capability for next-generation Extended Reality (XR) systems. Yet it faces a severe real-world challenge: strict user privacy that prohibits data rehearsal. While recent prompt-based continual learning methods show promise, we argue their core 'flat,' single-granularity design fundamentally misaligns with the complexity of human actions. This monolithic architecture fails to model the inherent hierarchical structure and overlooks standard action primitives shared across tasks, resulting in suboptimal performance and hindered knowledge transfer. To overcome this limitation, we propose DPCA, a novel spatio-temporal continual learning framework with multi-granularity adaptive prompting. DPCA learns three synergistic components to resolve this mismatch. First, the task-specific prompter employs a multi-granularity query system to capture the unique, compositional semantics of each action. Second, the task-agnostic prompter learns a globally shared vocabulary of ``action primitives," providing a stable and generalizable knowledge base to mitigate catastrophic forgetting. Finally, we introduce a Dissimilarity Attention Rectification at each granularity level, leveraging a reverse attention mechanism to model class-agnostic background information and effectively alleviating overfitting. The synergy between these components enables robust model adaptation without requiring access to past data. Rigorous experiments on multiple large-scale benchmarks (including NTU RGB+D), under a strict rehearsal-free, few-shot protocol, confirm that DPCA establishes a new state-of-the-art. This advance paves the way for the realization of truly adaptive and privacy-respecting XR systems.

Accurate traffic flow prediction is crucial to alleviating traffic congestion, optimizing road resource allocation, and enhancing public travel experience. However, the complex and dynamic nature of spatial-temporal heterogeneity in traffic flow poses challenges to this task. Existing methods often neglect the interactions between spatial-temporal correlations and struggle to capture dynamic traffic patterns across various perspectives. To address these issues, we propose a Spatial-Temporal Multi-Scale Interactive Graph Convolutional Network (STMIGCN) for traffic flow prediction. In this work, we employ a downsampling-based interactive learning framework to model traffic spatial-temporal patterns from a global-to-local perspective. This framework partitions and extracts spatial-temporal features at different scales, employing an interactive information passing and feedback mechanism to deeply mine latent spatial-temporal correlations. To address the static limitations of adaptive graphs, STMIGCN introduces a multi-graph fusion convolution method that incorporates dynamic features in spatial modeling, thereby enhancing the capture of dynamic spatial relationships between nodes. We perform comprehensive experiments on two real-world datasets and evaluate STMIGCN’s performance against ten baseline models. The results indicate that STMIGCN demonstrates more advanced performance.

The study of using the Global Navigation Satellite System (GNSS) to locate Geostationary Earth Orbit (GEO) satellites is very valuable. In this thesis, a new RF strategy is proposed. The back‐lobe signal and the main‐lobe signal of GNSS satellites are used to provide positioning services for GEO satellites and the receiver antenna beam‐forming method is used to improve receiver sensitivity. Additionally, this thesis proposes a satellite selection algorithm that matches the proposed RF strategy. The simulation found that the receiver needs higher sensitivity to detect the back‐lobe signal of the GNSS satellite but that the number of visible satellites had doubled and the geometric distribution of the satellite had also been greatly improved. The simulation based on the measured data proves that using a 16*16 antenna array and beam‐forming by maximum signal‐to‐interference and noise ratio algorithm can make five beams of GNSS receivers in GEO orbit and that the side‐lobe suppression ratio is up to dB, which greatly improves the signal receiving gain. After adopting the above technology, the GPS positioning accuracy of GEO spacecraft can be optimised from the order of kilometres to the metres.