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Related Concept Videos

Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

83
Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
83
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

90
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
90

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Phase screen prediction using deep phase network for FSO links.

Ming Li, Zhigeng Wu, Tianyi Wang

    Applied Optics
    |April 3, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a Deep Phase Network (DPN) for predicting phase screens in free-space optical communication (FSOC) systems. The DPN method offers high accuracy and speed, significantly improving FSOC performance under air turbulence.

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    Area of Science:

    • Optical Engineering
    • Telecommunications
    • Signal Processing

    Background:

    • Air turbulence in free-space optical (FSO) links causes signal distortions, degrading free-space optical communication (FSOC) system performance.
    • Phase screens model these turbulence-induced distortions, making accurate prediction crucial for robust FSOC.

    Purpose of the Study:

    • To propose and evaluate a novel phase screen prediction method for FSOC systems.
    • To enhance the robustness, accuracy, and speed of phase screen prediction under atmospheric turbulence.

    Main Methods:

    • Development of a Deep Phase Network (DPN) for phase screen prediction.
    • Integration of mean square deviation loss and pixel penalty terms for improved prediction accuracy.
    • Comparative analysis against traditional methods like the Gerchberg-Saxton algorithm.

    Main Results:

    • The DPN achieved up to 95% accuracy in phase screen prediction.
    • Prediction times were in the milliseconds range across various turbulence conditions.
    • DPN demonstrated superior convergence speed compared to the Gerchberg-Saxton algorithm.

    Conclusions:

    • The proposed DPN method offers a robust, low-complexity solution for phase screen prediction in FSOC.
    • DPN provides significant improvements in inference accuracy and speed over existing models.
    • This advancement is vital for enhancing the reliability of FSOC systems operating in turbulent environments.