In recent years, point cloud analysis methods based on the Transformer architecture have made significant progress, particularly in the context of multimedia applications such as 3D modeling, virtual reality, and autonomous systems. However, the Transformer architecture's high computational demands limit its scalability and deployment on resource-constrained platforms, hindering its practical applications in on-device multimedia processing. To address this challenge, we propose an efficient point cloud analysis architecture, Point MLP-Transformer (PointMT). This study tackles the quadratic complexity of the self-attention mechanism by introducing a linear complexity local attention mechanism for effective feature aggregation. Additionally, to counter the Transformer's focus on token differences while neglecting channel differences, we introduce a parameter-free channel temperature adaptation mechanism that adaptively adjusts the attention weight distribution in each channel, enhancing the precision of feature aggregation. To improve the Transformer's slow convergence speed due to the limited scale of point cloud datasets, we propose an MLP-Transformer hybrid module, which significantly enhances the model's convergence speed. Furthermore, to boost the feature representation capability of point tokens, we refine the classification head, enabling point tokens to directly participate in prediction. Experimental results on multiple evaluation benchmarks demonstrate that PointMT achieves performance comparable to state-of-the-art methods while maintaining an optimal balance between performance and accuracy. This research provides an innovative solution for efficient point cloud analysis, offering significant potential for multimedia applications and other domains.

Pulsars autonomous navigation is characterized by its credible performance and high navigation accuracy, which will have broad prospects for use in deep-space autonomous navigation. In our study, we find the observation time required for different pulsars to obtain the same time of arrival (TOA) accuracy is different, which means the rates of different sensors are different. However, the previous studies assume the pulsars used for navigation have the same observation time. And it will greatly influence navigational accuracy. Therefore, a new multi-rate observation model is presented and multi sensor data fusion is applied to the X-ray pulsar navigation. Combined with unscented Kalman filter (UKF) through simulation, this multi-rate observation model can improve the filtering accuracy effectively.