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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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Mobile recognition and positioning for multiple visible light communication cells using a convolutional neural

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    This study introduces a new framework for the industrial Internet of Things (IIoT) using LED image features for accurate recognition and positioning of mobile robots and vehicles. The system achieves high accuracy, enhancing visible light communication services.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Industrial Internet of Things (IIoT)

    Background:

    • Industrial environments require robust communication and precise localization for mobile production items like robots and automated guided vehicles (AGVs).
    • Existing perception systems often struggle to integrate communication with simultaneous mobile recognition and positioning.
    • Accurate multi-object recognition and spatial awareness are critical for advanced IIoT applications.

    Purpose of the Study:

    • To propose a novel multi-optical cell recognition and positioning framework for IIoT environments.
    • To leverage LED image features for enhanced perception and localization capabilities.
    • To provide location-aware services for visible light communication in industrial settings.

    Main Methods:

    • Utilizing a CMOS image sensor to capture LED images from production items.
    • Employing Convolutional Neural Networks (CNNs) for training and recognizing multiple optical cells based on LED image features.
    • Implementing region recognition within optical cells for precise positioning.

    Main Results:

    • The CNN model achieved a mean accuracy above 99% for recognizing two LED cells.
    • The region recognition within optical cells demonstrated a mean accuracy as high as 100%.
    • The proposed framework significantly outperformed traditional recognition algorithms.

    Conclusions:

    • The developed framework effectively integrates communication with accurate recognition and positioning in IIoT.
    • High-accuracy LED image feature recognition and positioning are achievable using CNNs.
    • The system offers a promising solution for location-aware services in visible light communication for IIoT applications.