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Related Concept Videos

Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Turbulent Flow01:24

Turbulent Flow

Turbulent flow is characterized by unpredictable fluctuations in velocity and pressure, which result in a chaotic fluid movement distinct from the orderly patterns of laminar flow. While laminar flow is governed by smooth, parallel layers with minimal mixing, turbulent flow exhibits highly irregular, three-dimensional patterns. This behavior arises due to instabilities in the fluid's velocity profile, and amplifies as the flow velocity increases. Minor disturbances, known as turbulent spots,...
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures enhance...

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Related Experiment Videos

Deep learning-based robust OAM mode recognition in atmospheric turbulence.

Yuxin Bi, Lei Xin, Xingang Zhuang

    Applied Optics
    |June 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces ARNet, a deep learning network for robust orbital angular momentum (OAM) recognition in free-space optical communication. ARNet achieves high accuracy even in strong atmospheric turbulence, improving OAM identification for FSO systems.

    Related Experiment Videos

    Area of Science:

    • Optical communications
    • Signal processing
    • Machine learning

    Background:

    • Vortex beams with orbital angular momentum (OAM) can increase free-space optical (FSO) communication capacity via multiplexing.
    • Atmospheric turbulence severely distorts OAM signals, hindering accurate recognition and limiting FSO system performance.

    Purpose of the Study:

    • To develop a robust deep learning network for accurate OAM state identification in FSO communication under atmospheric turbulence.
    • To enhance the resilience of OAM recognition against turbulence-induced signal distortions.

    Main Methods:

    • Proposed ARNet, a deep learning network incorporating channel-attention-enhanced residual blocks and dynamic L2 regularization.
    • Conducted experiments to evaluate ARNet's performance in identifying OAM states under strong atmospheric turbulence (Cn2=5×10^-13 m^-2/3).

    Main Results:

    • ARNet achieved 95.52% recognition accuracy for OAM states from -5 to +5 under strong turbulence.
    • The proposed method significantly outperformed traditional interference-based techniques in OAM recognition accuracy and robustness.

    Conclusions:

    • ARNet demonstrates enhanced resilience and accuracy for OAM identification in turbulent FSO environments.
    • The findings support the potential for practical deployment of ARNet in FSO systems facing atmospheric challenges.