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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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GPU-Accelerated Signal Processing for Distributed Vibration Sensing Based on OVNA Method.

Alessandro Meoli1, Raffaele Vallifuoco1, Agnese Coscetta1

  • 1Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

This study introduces a GPU-accelerated pipeline to speed up distributed vibration sensing using optical vector network analysis (OVNA). The optimized method significantly reduces processing time, enabling real-time applications.

Keywords:
OVNAdistributed optical fiber sensorsreal-time signal processing

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Area of Science:

  • Optical Engineering
  • Signal Processing
  • Computational Science

Background:

  • Distributed vibration sensing using optical vector network analysis (OVNA) offers potential for dynamic perturbation measurement.
  • Practical applications are hindered by the high computational demands of short-time Fourier transform (STFT) and cross-correlation algorithms.

Purpose of the Study:

  • To develop and evaluate a GPU-accelerated signal processing pipeline for OVNA.
  • To optimize the pipeline using dataflow reduction, mixed-precision arithmetic, and hardware-aware tuning.

Main Methods:

  • Implementation of a GPU-accelerated signal processing pipeline.
  • Application of optimization strategies including dataflow reduction and mixed-precision arithmetic.
  • Hardware-aware tuning for performance enhancement across multiple GPU generations.

Main Results:

  • Processing time for 200 sweeps reduced from 64.7 s (CPU) to 0.199 s (GPU).
  • Zero mismatches observed over 199,199 measurement points, preserving accuracy.
  • Benchmarking revealed STFT performance correlates with cache size, while cross-correlation scales with memory bandwidth.

Conclusions:

  • Modern GPUs can drastically reduce the computational load of OVNA and similar distributed sensing techniques.
  • The optimized pipeline enables kHz-rate aggregate throughput for batched processing.
  • This advancement supports real-time operational capabilities for distributed sensing systems.