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相关概念视频

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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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: Jul 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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基于影子特征的可见光传感使用多尺度区域卷积神经网络.

Xiaoxiao Du, Yanyu Zhang, Chao Wang

    Optics express
    |January 5, 2024
    PubMed
    概括

    本研究介绍了用于工业物联网 (IIoT) 的可见光传感框架. 它通过使用共享的影子功能来增强对象识别和定位准确性,改善智能生产.

    科学领域:

    • 计算机科学 计算机科学
    • 电气工程 电气工程
    • 机器人技术 机器人技术 机器人技术

    背景情况:

    • 工业物联网 (IIoT) 环境需要同时进行通信和传感,以实现智能生产.
    • 传统的传感方法往往缺乏用于识别和定位等任务的综合通信能力.
    • 可见光传感在速度,准确性,安全性和低能耗方面具有优势.

    研究的目的:

    • 提出一种新的多尺度可见光传感框架,用于在IIoT中强大的多重目标传感.
    • 为了利用可见光的独特特性,实现综合通信和传感.
    • 通过统一的方法提高识别精度和定位精度.

    主要方法:

    • 开发一个多尺度可见光感应区域卷积神经网络 (VLS-RCNN) 框架.
    • 利用共享可见光影功能,在识别和定位之间相互协助.
    • 实施影子地区的多层次补偿战略,以增强强性.

    主要成果:

    • 模拟结果表明,改进定位精度可以提高识别精度.
    • 识别结果有助于减少定位错误,而不会增加系统开销.
    • 在IIoT环境中,VLS-RCNN框架在感知任务方面表现得更好.

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    结论:

    • 可见光传感为未来的IIoT传感应用提供了一个有前途的技术.
    • 拟议的VLS-RCNN框架有效地利用可见光特征来增强传感.
    • 这种方法通过利用固有的光特性,为感知物体提供了新的视角.