Advanced Signal Processing Techniques for Engineering Measurements

  • Authors

    • Dr. Matteo Rossi Professor, Sapienza University of Rome, Italy Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2023PI9Q7L

    Published 05-03-2023

  • Signal Processing, Engineering Measurements, Digital Filtering, Wavelet Transform, Spectral Analysis, Adaptive Filtering, Time-Frequency Analysis, Noise Reduction, Feature Extraction, Measurement Accuracy

    Issue

    Vol. 6 No. 1 (2023)

    Section

    Articles

    How to Cite

    Advanced Signal Processing Techniques for Engineering Measurements. (2023). International Journal of Modern Research in Science & Engineering, 6(1), 01-14. https://doi.org/10.67228/30716357/IJMRSE-2023PI9Q7L

  • Abstract

    Advanced signal processing plays a vital role in improving the accuracy, reliability, and efficiency of engineering measurement systems used in automation, biomedical instrumentation, aerospace, communications, and structural monitoring. Traditional measurement systems often face challenges such as noise, signal distortion, interference, sensor nonlinearity, and bandwidth limitations. This study reviews advanced techniques including digital filtering, spectral analysis, wavelet transforms, adaptive filtering, statistical signal analysis, and time-frequency processing. A systematic framework involving signal acquisition, preprocessing, feature extraction, adaptive filtering, and performance evaluation is proposed to enhance measurement quality and reduce uncertainty. The analysis shows that wavelet-based methods are highly effective for transient signal processing, while adaptive filtering significantly reduces noise and interference. Hybrid approaches combining multiple signal processing techniques achieve superior measurement accuracy compared to individual methods. The findings highlight the growing importance of intelligent signal processing in developing reliable, high-precision engineering measurement systems for applications such as smart sensing, predictive maintenance, industrial diagnostics, and automated monitoring.

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