Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance both achievable rate and energy efficiency in next-generation wireless networks. This article investigates a novel energy-efficient UAV-RIS-assisted architecture for simultaneous wireless information and power transfer (SWIPT) in integrated ground–aerial–space networks, where a satellite cooperates with multiple UAV-RISs to provide downlink wireless energy transfer (WET) and support uplink wireless information transmission (WIT) for energy-constrained IoT devices in remote environments. We formulate an alternating iterative joint optimization problem (AIJOP) that aims to maximize the system sum achievable rate while ensuring causality of device energy via jointly optimizing UAV-RIS trajectories, RIS phase shift matrices, and satellite power allocation and time allocation between WET and WIT. The problem is highly non-convex due to the strong coupling among variables. To address this challenge, we propose a trajectory–phase–power–time alternating optimization algorithm (TPPTAOA), which decomposes the original problem into four tractable subproblems and solves them iteratively. Specifically, the UAV-RIS trajectories is first optimized via using a device scheduling and TSP-based path planning approach to solve the first subproblem, followed by the proposition of a two-stage heuristic phase optimization algorithm under fixed parameters to solve the second subproblem. Subsequently, the satellite power allocation is solved using a Lagrangian dual method, while the time allocation is optimized through a two-stage strategy combining grid-based coarse search with gradient-based refinement. Simulation results under various system settings verify the fast convergence, robustness, and superior performance of the proposed TPPTAOA, showing significant improvements in both energy efficiency and uplink achievable rate compared with benchmark schemes.

MOTIVATION: Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge. RESULTS: We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3 h. We also demonstrated that our pipeline could be applied to the vascular analysis. AVAILABILITY AND IMPLEMENTATION: The dataset is available at http://atlas.brainsmatics.org/a/li2301. The source code is freely available at https://github.com/visionlyx/HP-VSP.