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Related Concept Videos

Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short distances...

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Related Experiment Video

Updated: Jun 11, 2026

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

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Published on: March 22, 2019

Laser self-mixing displacement sensing using entropy-based wavelets.

Awais A Khan, Olivier D Bernal, Usman Zabit

    Applied Optics
    |June 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study enhances laser feedback self-mixing interferometry (SMI) signal processing using discrete wavelet transform (DWT) and entropy. The new method improves displacement measurement accuracy and robustness for various motion conditions.

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    Area of Science:

    • Optics and Photonics
    • Signal Processing
    • Metrology

    Background:

    • Self-mixing interferometry (SMI) offers compact and cost-effective sensing but suffers from complex signal processing challenges.
    • Retrieving accurate displacement information from SMI signals is difficult, especially under non-stationary conditions.

    Purpose of the Study:

    • To improve the measurement performance of SMI by enhancing signal processing techniques.
    • To develop a more robust and self-adaptive method for displacement retrieval from SMI signals.

    Main Methods:

    • Utilized discrete wavelet transform (DWT) combined with entropy-based analysis.
    • Integrated the DWT-entropy method with the consecutive samples unwrapping (CSU) technique for SMI signal processing.
    • Validated the proposed method using experimental SMI signals from a 1550 nm laser diode under non-stationary target motion.

    Main Results:

    • Achieved improved measurement performance under both stationary and non-stationary motion conditions.
    • Demonstrated enhanced robustness and self-adaptability across a wider range of optical feedback strengths.
    • Reduced relative RMS error by approximately 27% and max error by 54% compared to the original CSU method, verified against a 2 nm resolution piezoelectric transducer.

    Conclusions:

    • The entropy-based DWT method significantly enhances the accuracy and reliability of displacement measurements in SMI systems.
    • This advanced signal processing approach makes SMI more practical for applications requiring precise motion tracking under dynamic conditions.