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Related Concept Videos

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Prediction Intervals

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Related Experiment Video

Updated: Apr 22, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

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Phase discontinuity predictions using a machine-learning trained kernel.

Firas Sawaf, Roger M Groves

    Applied Optics
    |October 17, 2014
    PubMed
    Summary
    This summary is machine-generated.

    A machine learning kernel accurately identifies phase discontinuities in noisy interferograms. This sliding-window approach with voting enhances reliability for shearography and speckle interferometry, outperforming traditional tiling methods.

    Related Experiment Videos

    Last Updated: Apr 22, 2026

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
    06:22

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

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    Area of Science:

    • Optical Metrology
    • Machine Learning Applications
    • Image Processing

    Background:

    • Phase unwrapping is crucial for interferogram analysis, but accuracy is challenged by noisy phase fields from techniques like shearography.
    • Identifying phase discontinuities is key to reliable phase unwrapping.

    Purpose of the Study:

    • To develop and evaluate a machine learning-based kernel for accurate phase discontinuity detection in noisy interferograms.
    • To improve the reliability and efficiency of phase unwrapping in speckle-based interferometry.

    Main Methods:

    • A 10x10 pixel kernel trained via machine learning predicts phase discontinuity locations.
    • The kernel is applied in a sliding-window fashion, with each pixel examined 100 times.
    • Predictions are aggregated using a voting system.

    Main Results:

    • The sliding-window kernel approach with voting significantly outperforms processing with non-overlapping tiles.
    • The 10x10 kernel is sufficient for effective full-field interferogram processing.
    • This method avoids the need for computationally intensive larger kernels.

    Conclusions:

    • The developed machine learning kernel and sliding-window voting system provide a reliable and computationally efficient method for phase unwrapping.
    • This technique enhances the analysis of noisy interferograms in shearography and related fields.