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Related Experiment Video

Updated: Jun 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Machine learning assisted authentication using chaotic diversity index modulation for data centers.

Xinshuai Liang, Chongfu Zhang, Wenjun Zeng

    Optics Express
    |February 20, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a secure data center communication method using chaotic diversity for authentication and a neural network (NN) for efficient decryption and detection, enhancing data security.

    Related Experiment Videos

    Last Updated: Jun 30, 2026

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    Area of Science:

    • Information Security
    • Optical Communications
    • Signal Processing

    Background:

    • Secure communication in data centers is critical.
    • Existing authentication methods face challenges with sophisticated eavesdropping.
    • Efficient decryption and detection at the receiver are essential for high-speed data transmission.

    Purpose of the Study:

    • To propose a novel secure communication scheme for data centers.
    • To enhance identity authentication using chaotic diversity index modulation.
    • To design a neural network (NN) for efficient decryption and detection.

    Main Methods:

    • Chaotic diversity is generated from a partitioned 16QAM constellation.
    • Watermarking is embedded into the symbol index using chaotic diversity.
    • A neural network (NN) is designed to process received signals and chaotic diversity for watermark extraction and identity authentication.

    Main Results:

    • The scheme achieves 56.37Gb/s transmission over 10km SSMF with high security (key space of 10^90).
    • Watermark detection accuracy reaches 100% under high optical power, effectively distinguishing legitimate and illegitimate parties.
    • The NN detector outperforms the log-likelihood ratio (LLR) detector in accuracy and reduces time complexity by 4 orders of magnitude.

    Conclusions:

    • The proposed chaotic diversity and NN-based scheme offers robust security and efficient data processing for data center communications.
    • The method effectively conceals watermarks within data, making eavesdropping difficult and preventing identity disguise attacks.
    • This approach significantly improves detection accuracy and reduces computational complexity compared to traditional methods.