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Related Concept Videos

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 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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Visible light sensing based on shadow features using multi-scale region convolutional neural network.

Xiaoxiao Du, Yanyu Zhang, Chao Wang

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    |January 5, 2024
    PubMed
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    This study introduces a visible light sensing framework for the Industrial Internet of Things (IIoT). It enhances object recognition and positioning accuracy by using shared shadow features, improving intelligent production.

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    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Robotics

    Background:

    • Industrial Internet of Things (IIoT) environments require simultaneous communication and sensing for intelligent production.
    • Traditional sensing methods often lack integrated communication capabilities for tasks like recognition and positioning.
    • Visible light sensing offers advantages in speed, accuracy, security, and low energy consumption.

    Purpose of the Study:

    • To propose a novel multi-scale visible light sensing framework for robust multiple target sensing in IIoT.
    • To leverage visible light's unique properties for integrated communication and sensing.
    • To improve both recognition accuracy and positioning precision through a unified approach.

    Main Methods:

    • Development of a multi-scale visible light sensing-region convolutional neural network (VLS-RCNN) framework.
    • Utilizing shared visible light shadow features for mutual assistance between recognition and positioning.
    • Implementing a multi-scale compensation strategy for shadow regions to enhance robustness.

    Main Results:

    • Simulation results demonstrate that improved positioning accuracy enhances recognition accuracy.
    • Recognition results contribute to reducing positioning errors without increasing system overhead.
    • The VLS-RCNN framework shows improved performance in sensing tasks within the IIoT environment.

    Conclusions:

    • Visible light sensing presents a promising technology for future IIoT sensing applications.
    • The proposed VLS-RCNN framework effectively utilizes visible light characteristics for enhanced sensing.
    • This approach offers a new perspective for sensing objects by exploiting inherent light properties.