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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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  1. 首页
  2. 在物联网边缘使用机器视觉和网格传感算法进行云iaas优化.
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  2. 在物联网边缘使用机器视觉和网格传感算法进行云iaas优化.

相关实验视频

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
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在物联网边缘使用机器视觉和网格传感算法进行云IaaS优化.

Nuruzzaman Faruqui1, Sandesh Achar2, Sandeepkumar Racherla3

  • 1Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka 1216, Bangladesh.

Sensors (Basel, Switzerland)
|November 9, 2024

在PubMed 上查看摘要

概括
此摘要是机器生成的。

本研究介绍了物联网边缘 (Mez) 的机器视觉和电网传感 (GRS) 算法,以降低安全摄像头网络的云成本. 综合方法显著减少了组织的带宽和存储需求.

关键词:
边缘 边缘 边缘 边缘这就是IaaS.物联网摄像头物联网摄像头物联网摄像头这是一个很好的方法 Mez Mez Mez Mez带宽 带宽 带宽 带宽这是一个云云云.机器视觉 机器视觉 机器视觉优化的优化优化优化.安全网格安全网格安全网格储存 储存 储存 储存 是一个

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科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 云计算 云计算 云计算

背景情况:

  • 高清晰度 (HD) 物联网 (IoT) 摄像头越来越多地用于安全,但传输高清视频数据会给云基础设施带来压力.
  • 大规模的安全网格在最小化云网络带宽和存储成本方面面临着挑战.
  • 优化云资源分配对于具有成本效益的监控系统至关重要.

研究的目的:

  • 介绍机器视觉在物联网边缘 (Mez) 技术的应用,结合一个新的网格传感 (GRS) 算法.
  • 为大规模安全网格优化云基础设施即服务 (IaaS) 资源配置.
  • 为了实现云带宽和存储的显著成本最小化.

主要方法:

  • 机器视觉在物联网边缘 (Mez) 技术中的应用.
  • 集成一个新的网络传感 (GRS) 算法用于物联网摄像头网络.
  • 在 Mez 中使用网络延迟反模块进行视频转换.
  • 通过GRS算法,通过不同的物联网节点对带宽要求进行自动排名.

主要成果:

  • 实现了对带宽需求的31.29%的减少.
  • 显示储存需求减少了22.43%.
  • 通过优化资源配置,最大限度地降低整个电网的吞吐量.
  • 结论:

    • 拟议的系统有效地优化了物联网摄像头安全网的云基础设施即服务 (IaaS) 资源配置.
    • Mez技术和GRS算法为减少带宽和存储成本提供了可行的解决方案.
    • 集成带来了显著的云资源优化和企业的成本节省.