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Updated: Jan 19, 2026

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Application of graphics processing unit parallel computing in pattern recognition for vibration events based on a

Tingkun Wen, Pengyang Zhu, Wei Ye

    Applied Optics
    |September 11, 2019
    PubMed
    Summary

    Graphics processing unit (GPU) parallel computing significantly speeds up pattern recognition for vibration events using phase-sensitive optical time domain reflectometer (Φ-OTDR). This approach offers an efficient, lower-cost solution for complex event analysis.

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    Area of Science:

    • Optoelectronics
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Phase-sensitive optical time domain reflectometer (Φ-OTDR) technology for vibration event pattern recognition has advanced significantly.
    • Developing efficient algorithms with accessible, low-cost computing resources remains a challenge.

    Purpose of the Study:

    • To analyze the benefits of graphics processing unit (GPU) parallel computing for improving the performance of Φ-OTDR-based vibration event pattern recognition.
    • To compare the time efficiency of CPU-based versus GPU-based implementation of pattern recognition algorithms.

    Main Methods:

    • Implemented a pattern recognition algorithm, incorporating spectral subtraction and artificial neural networks, on both Central Processing Unit (CPU) and GPU.
    • Recorded and compared the computational time consumption for both CPU and GPU implementations.

    Main Results:

    • GPU parallel computing demonstrated superior performance in reducing the time required for pattern recognition.
    • The GPU-based method achieved significant speed improvements compared to the CPU-based method.

    Conclusions:

    • GPU parallel computing provides a viable and cost-effective solution for enhancing the efficiency of Φ-OTDR vibration event pattern recognition.
    • This approach enables the development of more accessible and performant systems for vibration analysis.