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Related Concept Videos

Downsampling01:20

Downsampling

731
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
731
Upsampling01:22

Upsampling

673
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
673

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Related Experiment Video

Updated: Mar 3, 2026

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
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Compressed sampling and dictionary learning framework for wavelength-division-multiplexing-based distributed fiber

Christian Weiss, Abdelhak M Zoubir

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |May 3, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel compressed sensing and dictionary learning method for fiber-optic sensing. The technique accurately detects impairments along sensing fibers by analyzing reflection delays from reflected signals.

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    Area of Science:

    • Optics and Photonics
    • Signal Processing
    • Materials Science

    Background:

    • Fiber-optic sensing offers precise measurement capabilities.
    • Wavelength-tunable lasers are crucial for advanced sensing applications.
    • Compressed sensing and dictionary learning provide powerful tools for signal reconstruction.

    Purpose of the Study:

    • To develop a compressed sampling and dictionary learning framework for fiber-optic sensing.
    • To address challenges posed by imperfect prior knowledge in sensing parameters.
    • To enable accurate measurement of impairments along sensing fibers.

    Main Methods:

    • A redundant dictionary is generated based on a model of the reflected sensor signal.
    • An alternating minimization algorithm is employed for sparse representation and dictionary parameter estimation.
    • A preprocessing routine is incorporated to manage dictionary coherence.

    Main Results:

    • The framework successfully estimates sparse signals, revealing reflection delays.
    • The method demonstrates effectiveness in identifying impairments in fiber sensor systems.
    • Performance is validated through both simulations and experimental data.

    Conclusions:

    • The proposed framework offers an effective approach for fiber-optic sensing and impairment detection.
    • Dictionary learning and compressed sensing provide a robust solution for analyzing reflected sensor signals.
    • The technique shows promise for real-world applications in fiber sensor systems.