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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Related Experiment Video

Updated: Jan 1, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Loss weight adaptive multi-task learning based optical performance monitor for multiple parameters estimation.

Zhenming Yu, Zhiquan Wan, Liang Shu

    Optics Express
    |December 25, 2019
    PubMed
    Summary

    A novel artificial neural network (ANN) monitors optical signal-to-noise ratio (OSNR) and identifies modulation formats with 100% accuracy. This adaptive multi-task learning approach optimizes performance for future optical networks.

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    Area of Science:

    • Optical communications engineering
    • Artificial intelligence in telecommunications
    • Signal processing for optical networks

    Background:

    • Accurate monitoring of optical signal-to-noise ratio (OSNR) and identification of modulation formats are crucial for reliable optical network operation.
    • Existing methods often require fixed parameters or separate monitoring systems, limiting adaptability and efficiency.
    • Polarization division multiplexing (PDM) coherent optical systems present complex signal characteristics that necessitate advanced monitoring techniques.

    Purpose of the Study:

    • To develop and experimentally validate a loss weight adaptive multi-task learning based artificial neural network (MTL-ANN) for joint OSNR monitoring and modulation format identification (MFI).
    • To investigate the effectiveness of using amplitude histograms as input features for the proposed MTL-ANN.
    • To demonstrate the advantages of adaptive loss weighting over fixed weighting in diverse link configurations.

    Main Methods:

    • Implementation of a loss weight adaptive MTL-ANN model.
    • Experimental setup involving a PDM coherent optical system with 5 km standard single mode fiber (SSMF) transmission.
    • Utilizing amplitude histograms derived from constant modulus algorithm (CMA)-based polarization de-multiplexing as input features.
    • Testing with nine adaptive M-QAM modulation formats.

    Main Results:

    • Achieved 100% accuracy for modulation format identification (MFI) across the estimated OSNR range.
    • Obtained OSNR estimation accuracy of 98.7% (classification) and a root-mean-square error (RMSE) of 0.68 dB (regression).
    • Demonstrated that the adaptive MTL-ANN automatically optimizes loss weights for different link configurations, outperforming fixed-weight approaches.

    Conclusions:

    • The proposed loss weight adaptive MTL-ANN provides a highly accurate and efficient solution for joint OSNR monitoring and MFI in PDM coherent optical systems.
    • The method's adaptability and potential for parameter expansion make it suitable for future heterogeneous optical network monitoring.
    • Amplitude histograms offer a viable alternative to circular constellations as input features for ANN-based optical signal monitoring.