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An adaptive confidence-driven framework for real-time lidar and visual data fusion in autonomous aerial vehicle landing site assessment
Abstract Drones and Aerial Vehicles are now increasingly relying on autonomous landing site assessment to find a safe landing spot. The modern day Technique for finding safe landing site involves use of Both LiDAR and Image data, LiDAR data is used for detecting slope, uneven surfaces, and obstacles like rocks, trees, poles, or buildings based on their 3D structure and image data is used for identifying texture and material understanding. For example – A flat water surface may look safe as per LiDAR data due to its flatness but only through image data we can identify that it is a water surface. Hence it is important to assess safe landing site based on both LiDAR data and image data. The current approach seems to be perfect for static environments but how about in the case of dynamic environments where a safe landing spot identified at a particular time might no longer be safe due to some new obstacle appearance at that spot. This paper addresses this issue by proposing an Adaptive confidence driven algorithm that helps in finding safe landing spots in dynamic or changing environments.
Deep learning for apple leaf disease diagnosis: a comparative study with convolutional neural networks and transformers
Stereoselective Access to Tetrasubstituted Alkenylboronates via Cobalt-Catalyzed, Regiodivergent <i>Syn</i> -Borylfunctionalization of Internal Alkynes
Expected-depth selection for interpretable decision trees
Quantitative Relationships between Lewis Acidity and Catalytic Activity in Atomically Precise Copper Nanoclusters
Engineering turbulence resilience in Bessel-Vortex beams through partial coherence and topological charge pairing
Fostering psychological safety in classroom assessments: moderated-mediation effects of bullying, gender and counselling
A multi-fractal vascular tree model for characterizing blood flow in cardiovascular system
Abstract The cardiovascular system’s hemodynamics is pivotal for tissue oxygen and nutrient delivery. However, accurately characterizing its blood flow characteristics remains a major challenge due to the complex distribution and geometry of the cardiovascular system. This study introduces a novel multi - fractal vascular tree (MFVT) model. By integrating heterogeneous fractal dimensions and scaling laws, considering vessel branching and tortuosity, the model depicts blood flow across various vessels. Formulas for blood flow rates in single vessels, fractal vessel trees, and bundles of fractal vessel trees were derived, along with the analytical expression for cardiovascular system permeability. The model’s reliability was verified via four case studies, with a maximum relative error of less than 5% between theoretical and experimental values. Sensitivity analyses showed that parameters significantly influenced blood flow. For instance, as the maximum diameter of zero-level vessels increased from 0.2 to 4 mm, the flow rate rose notably. When the diameter ratio increased from 0.5 to 0.8, the flow rate also increased, especially at higher pressure differences. An increase in the initial length of zero-level vessels from 5 to 50 mm led to a decrease in the flow rate. These phenomena indicate that blood flow resistance is negatively correlated with vessel diameter and positively correlated with vessel length. This work advances the mechanistic understanding of blood flow distribution and provides a computational tool for studying vascular pathologies.