ABSTRACT
Resource optimization, timely data capture, and efficient unmanned aerial vehicle (UAV) operations are of utmost importance for mission success. Latency, bandwidth constraints, and scalability problems are the problems that conventional centralized processing architectures encounter. In addition, optimizing for robust communication between ground stations and UAVs while protecting data privacy and security is a daunting task in and of itself. Employing edge computing infrastructure, artificial intelligence-driven decision-making, and dynamic task offloading mechanisms, this research proposes the dynamic task offloading edge-aware optimization framework (DTOE-AOF) for UAV operations optimization. Edge computing and artificial intelligence (AI) algorithms integrate to decrease latency, increase mission efficiency, and conserve onboard resources. This system dynamically assigns computing duties to edge nodes and UAVs according to proximity, available resources, and the urgency of the tasks. Reduced latency, increased mission efficiency, and onboard resource conservation result from dynamic task offloading edge-aware implementation framework (DTOE-AIF)'s integration of AI algorithms with edge computing. DTOE-AOF is useful in many fields, such as precision agriculture, emergency management, infrastructure inspection, and monitoring. UAVs powered by AI and outfitted with DTOE-AOF can swiftly survey the damage, find survivors, and launch rescue missions. By comparing DTOE-AOF to conventional centralized methods, thorough simulation research confirms that it improves mission efficiency, response time, and resource utilization.
ABSTRACT
PURPOSE: To compare AngioTool (AT) vascular parameters (VP) between MacTel2 eyes and normal eyes. Secondary outcome measures were to correlate VP with BCVA and to analyze VP between various grades of Simple MacTel Classification. METHODS: This is a retrospective study. SD OCTA images of the superficial vascular complex (SVC) and deep capillary complex (DVC) were exported into Image J and AT. The explant area (EA), vessel area (VA), vessel percentage area (VPA), total number of junctions (TNJ), junction density (JD), total vessel length (TVL), average vessel length (AVL), total number of endpoints (TNE) and mean E lacunarity (MEL) were studied. RESULTS: Group 1 had 120 MacTel2 eyes. Group 2 had 60 age-matched normal eyes. All VP were significantly different between the two groups except EA and TNE in both complexes. None of the VP had a correlation with BCVA. Interquadrant analysis (IQA) in SVC and DVC showed statistical significance in VPA, AVL and JD and in AVL, TNE, JD, VPA respectively. Post hoc analysis in SVC and DVC showed statistical significance in TNJ, JD, TVL and AVL between grade 1 and grade 3, and in VA, VPA, TNJ, JD, TVL and MEL between grade 0 and grade 3 respectively. CONCLUSION: VP were affected in MacTel2 eyes. VP did not correlate with BCVA. Occurrence of pigmentation is an important event in the progression of disease. AT may provide quantitative markers to measure disease progression.
