![]() The recent outbreak of coronavirus SARS-CoV-2 infection early detected in December 2019 in Wuhan, China. ![]() ![]() The framework further equips government agency, system providers to design and construct technology-oriented models in community setup to increase the quality of life using emerging technologies into smart urban environments. The paper enriches the technological advancement in artificial intelligence and edge computing applied to problems in society and healthcare systems. ![]() The paper proposes new Edge-AI algorithm for building technology-oriented solutions for detecting mask in human movement and social distance. Overall YOLO model outperforms in object detection task and is faster enough for mask detection and HRNetV2 outperform semantic segmentation problem applied to solve social distancing task in AI-Edge inferencing environmental setup. The study includes selective AI models for benchmark analysis and were assessed on performance and accuracy in edge computing environment for large-scale societal setup. The conceptual framework presented is validated through quantitative data analysis via secondary data collection from researcher’s public websites, GitHub repositories and renowned journals and further benchmarking were conducted for experimental results in Microsoft Azure cloud environment. The adoption will thus lead in smart city planning and development focusing on citizen health systems contributing to improved quality of life. The framework further provides implementation guideline in industrial setup as well for governance and contact tracing tasks. The paper proposes a unique outbreak response system framework based on artificial intelligence and edge computing for citizen centric services to help track and trace people eluding safety policies like mask detection and social distancing measure in public or workplace setup. The purpose of the paper is to provide innovative emerging technology framework for community to combat epidemic situations. Keep an eye out for more news about DLSS in Unity 2021.2 by subscribing to NVIDIA game development news and following the Unity Technologies Blog. You get the performance headroom you need to maximize ray tracing settings and increase output resolution.Īt GTC 2021, Light Brick Studio demonstrated how stunning Unity games can look when real-time ray tracing and DLSS are combined. From those inputs, DLSS composes a beautifully sharp high-resolution image, to which post-processing and UI/HUD is then applied. ![]() Once trained, the model can be integrated into the core DLSS library, the game itself or even downloaded by NVIDIA’s Game Ready driver.Īt runtime, DLSS takes three inputs: 1) a low-resolution, aliased image 2) motion vectors for the current frame, and 3) the high resolution previous frame. #Nvidia surround custom resolution calculator OfflineWe built a supercomputer to train the DLSS deep neural net with extremely high quality 16K offline rendered images of many kinds of content. NVIDIA’s solution is to ray trace fewer pixels and use AI on our dedicated Tensor Core units to intelligently scale up to a higher resolution, and while doing so, significantly boost framerates. While ray tracing produces far more realistic images than rasterization, it also requires a lot more computation, which then leads to lower framerates. ![]()
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