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Airborne lidar has a wide range of applications in mapping, exploration and so on. Its data processing uses the ranging data of LiDAR and the position and orientation information to obtain high precision surface models. In order to meet the real-time requirements of airborne LiDAR points position cloud calculation, a software-hardware co-design based method is used to design and implement the SoC of points position cloud calculation. A high-performance hardware accelerator is realized by using the AXI-4-based DMA high-speed transmission and using pipeline optimization and storage optimization methods. The experimental results show that the proposed SoC of points position cloud calculation can meet the real-time processing requirements of the airborne platform.

Introduction: Blood glucose monitoring was vitally important in diabetic kidney disease (DKD) patients for preventing complications and improving survival rates. The associations between glycemic variability and blood biochemical indicators were underestimated in patients with DKD undergoing hemodialysis. Therefore, we primarily aimed to investigate the glycemic variability and 1-year risk of cardiovascular disease events in diabetic hemodialysis patients. And we secondarily aimed to explore the association between glycemic variability and blood biochemical indicators. Methods: In total, 27 patients were included in the final analysis. Continuous glucose monitoring (CGM) was used to evaluate glucose variability for 14 days. Patients were divided into two groups by the cutoff level of time in range (TIR; >70% or ≤70%). The three-point major adverse cardiovascular event (3P MACE) was recorded within 1 year. Results: After 1 year of follow-up, 4 patients in the high-TIR group and 3 patients in the low-TIR group had 3p MACE. Higher low blood glucose index (LBGI) level in diabetic hemodialysis patients increased the risk of 3p MACE outcomes (HR = 2.37, p = 0.018). And the level of albumin was positively associated with LBGI (β = 0.51, p = 0.036). The plasma levels of albumin, glycosylated hemoglobin, and hemoglobin were positively associated with other CGM parameters. Conclusion: LBGI during 14 days was positively associated with the risk of cardiovascular events in diabetic hemodialysis patients. [ABSTRACT FROM AUTHOR]