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相关概念视频

Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

27
Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short...
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Errors in Global Positioning System01:26

Errors in Global Positioning System

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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相关实验视频

Updated: Jun 5, 2025

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces
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基于机器学习的目标表面用于到达方向估计.

Min Huang1, Bin Zheng1,2,3, Tong Cai1,2,3

  • 1Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.

Nanophotonics (Berlin, Germany)
|December 5, 2024
PubMed
概括

这项研究引入了人工智能驱动的超表面,用于准确的到达方向 (DOA) 估计. 这种创新方法简化了设备需求,并增强了各种应用的检测能力.

关键词:
抵达方向估计的方向.metasurface 地表的表面是什么随机的森林随机的森林

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Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms
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相关实验视频

Last Updated: Jun 5, 2025

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科学领域:

  • 地表表面技术的技术.
  • 在信号处理中的人工智能.
  • 电磁学和波浪现象的现象

背景情况:

  • 传统的到达方向 (DOA) 估计方法通常需要庞大的设备或复杂的算法.
  • 由于尺寸和复杂性,现有技术对于现场检测往往是不切实际的.

研究的目的:

  • 提出和演示一个支持机器学习的超表面,以高效地估计DOA.
  • 克服传统的DOA估计技术对实时和简化应用的局限性.

主要方法:

  • 一个可调节的超表面被顺序控制,以生成事件信号的场强度数据.
  • 一个预训练的随机森林模型处理收集的数据以确定事件角度.
  • 拟议的智能DOA估计方法的实验验证.

主要成果:

  • 在多种事件角度中,在DOA估计中实现了高精度 (超过95%).
  • 在实验结果中显示出小于的误差.
  • 该方法在全空间和宽带检测方面被证明是有效的.

结论:

  • 开发的支持机器学习的超表面为智能DOA检测提供了可行和准确的解决方案.
  • 这一战略通过简化设备和节省时间,为传统应用提供了突破性进展.
  • 这项研究为使用智能超表面的先进信号处理开辟了新的途径.