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相关概念视频

Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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相关实验视频

Updated: May 10, 2025

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
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A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

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多模式光纤光谱传感器用于多位置负载识别,使用横向阻塞.

Bohao Shen1, Jianzhi Li2, Zhe Ji1

  • 1School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用多模光纤 (MMF) 光谱传感器进行多位置负载识别的新方法. 该方法在识别负载位置和大小方面达到近100%的准确性,提供高效的分布式传感.

关键词:
分布式传感器传感器分布式传感器多位置负载多位置负载.多模光纤光斑图 (speklegram) 是一个多模光纤光斑图.多重扰动多重扰动过度适应 过度适应浅浅的CNN和CNN是没有深度的.穿越性阻塞 穿越性阻塞

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Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals
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相关实验视频

Last Updated: May 10, 2025

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A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

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Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
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Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals
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Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

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科学领域:

  • 光子学和光学传感器
  • 机器学习用于传感器系统
  • 分布式光纤光学传感器传感器

背景情况:

  • 在多模光纤 (MMF) 分布传感器中同时测量多个扰动位置和强度是具有挑战性的,因为需要大量的光谱数据.
  • 现有的方法难以有效地识别多重扰动,阻碍了高分辨率分布式传感应用的发展.

研究的目的:

  • 提出和验证一种新的方法,用于识别使用MMF光谱传感器的多位置负载.
  • 提高货币市场基金分布式传感器识别模型的样本多样性和稳定性.
  • 为高分辨率分布式测量提供具有成本效益和效率的解决方案.

主要方法:

  • 开发一种多变量,多类,一次性光谱数据集构建方法.
  • 总局部强度及其对干扰的灵敏度的数学模型的理论推导.
  • 使用浅卷积神经网络 (CNN) 实现斑点图穿越闭塞数据增强.

主要成果:

  • 拟议的方法可以实现近100%的准确性,同时识别高达1545种不同的负载形式的负载位置和大小.
  • 与现有的MMF传感模型相比,浅CNN模型显示出更高的训练效率和稳定性.
  • 实验验证证证实了这种方法在分布式传感应用中的有效性.

结论:

  • 该研究提出了一种分布式MMF光谱传感器的概念证明,该传感器能够在各种干扰下进行高分辨率测量.
  • 开发的方法通过提供更高的准确性和效率,显著提升了基于货币货币基金的分布式传感.
  • 这种方法为各种分布式传感应用提供了一个有希望的,具有成本效益的解决方案.