Adaptation of Fourier transform for reproducing non-periodic signals
Abstract
The Fast Fourier Transform (FFT) is a widely used algorithm for spectral analysis and signal
processing. However, the FFT is limited to analyzing periodic signals and is not suitable for nonperiodic signals. In recent years, various techniques have been developed to address this
limitation and improve the accuracy of non-periodic function approximation. In this article, we
review the limitations of using the FFT for non-periodic function approximation and discuss potential future directions for improvement. We first provide a brief overview of the FFT and its
limitations before exploring alternative methods, such as the windowed Fourier transform, the
short-time Fourier transform, and machine learning-based methods for function approximation.
We also discuss potential future directions for improvement, including the use of hybrid methods
that combine the FFT with other techniques, such as wavelet transforms or machine learningbased approaches. Finally, we discuss the implications of these developments for future research
and applications. Our review provides insights into the limitations of the FFT for non-periodic
function approximation and highlights the potential for alternative methods, such as Genetic
Algorithms (GA), to overcome these limitations and improve the accuracy of function
approximation.
References
James W. Cooley, John W. Tukey (1965). An algorithm for the machine calculation of complex https://doi.org/10.1090/S0025-5718-1965-0178586-1
Vaseghi, S. V. (2005). Advanced digital signal processing and noise reduction. John Wiley & Sons, https://doi.org/10.1002/0470094966
Stoica, P., & Moses, R. L. (2005). Spectral analysis of signals. Pearson Prentice Hall. ISBN 0-13-113956-8.
Geoffrey C. Scott , et al. (1987). FFT Performance in the Presence of Noise, 34(6), 424 - 429, https://doi.org/10.1109/TBME.1987.326058
Ingram J. Brown (2009). A wavelet tour of signal processing: the sparse way. Investigación Operacional 30(1). Gale Document Number: GALEIA360358815.
K. M. M. Prabhu (2014). Window Functions and Their Applications in Signal Processing, CRC https://doi.org/10.1201/b15570
Srinivasan, S. S., & Divya, M. (2015). Adaptive windowing technique for reducing leakage in FFT spectrum analysis. 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), 1-5.
Denis Selimović, Jonatan Lerga, Péter Kovács, Jasna Prpić-Oršić (2022). Improved parametrized multiple window spectrogram with application in ship navigation systems, https://doi.org/10.1016/j.dsp.2022.103491
Xuerong Ye; Yifan Hu; Junxian Shen; Rui Feng; Guofu Zhai (2020). An Improved Empirical Mode Decomposition Based on Adaptive Weighted Rational Quartic Spline for Rolling Bearing Fault Diagnosis, IEEE Access 8, 123813 - 123827, https://doi.org/10.1109/ACCESS.2020.3006030
Hadhrami Ab. Ghani et al. (2020). A review on sparse Fast Fourier Transform applications in image processing. International Journal of Electrical and Computer Engineering, 10(2), 1346-1351, doi: 10.11591/ijece.v10i2.pp1346-1351.
https://doi.org/10.11591/ijece.v10i2.pp1346-1351
Xin Li, Zengqiang Ma, De Kang, Xiang Li. (2020). Fault diagnosis for rolling bearing based on https://doi.org/10.1016/j.measurement.2020.107554
Copyright (c) 2023 Денис Хомюк, Володимир Самотий (Автор)

This work is licensed under a Creative Commons Attribution 4.0 International License.