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Related Concept Videos

Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
Discrete-time Fourier transform01:26

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Related Experiment Videos

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Jiayao Sun, Deyu Xu, Jingming Zhang

    Optics Express
    |July 2, 2026
    PubMed
    Summary

    A novel fractional wavelet transform (NFRWT) effectively suppresses background noise in phase-sensitive optical time-domain reflectometry (Φ-OTDR) signals. This method enhances signal-to-noise ratio (SNR) for long-range sensing and intrusion monitoring.

    Related Experiment Videos

    Area of Science:

    • Photonics and Sensing Technologies
    • Signal Processing
    • Wavelet Theory

    Background:

    • Phase-sensitive optical time-domain reflectometry (Φ-OTDR) is crucial for long-range sensing.
    • Background noise and low signal-to-noise ratio (SNR) challenge Φ-OTDR performance in complex environments.
    • Existing denoising methods struggle to preserve signal fidelity while suppressing broadband noise.

    Purpose of the Study:

    • To propose a novel adaptive fractional wavelet transform (NFRWT) for denoising Φ-OTDR signals.
    • To enhance SNR and suppress background noise in long-range sensing and engineering applications.
    • To improve the performance of distributed acoustic sensing (DAS) signal analysis.

    Main Methods:

    • Constructed a time-domain coupled wavelet basis with fractional-order phase modulation and translation phase compensation.
    • Developed an adaptive fractional-order selection strategy maximizing NFRWT-domain energy concentration.
    • Applied multiscale wavelet decomposition and soft-threshold shrinkage for noise suppression and waveform preservation.

    Main Results:

    • The NFRWT method effectively distinguishes useful signal components from broadband background noise.
    • Demonstrated superior performance compared to existing denoising algorithms in 20.2 km long-range sensing.
    • Achieved an average SNR of 63.38 dB and a noise floor of -84.32 dB rad²/Hz in intrusion monitoring.

    Conclusions:

    • The proposed NFRWT-based denoising method significantly improves Φ-OTDR signal quality.
    • NFRWT offers robust background noise suppression and waveform fidelity preservation.
    • The method is effective for long-range sensing and real-world engineering scenarios like intrusion detection.