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FIF

version 2.12.1 (234 KB) by Antonio
Fast Iterative Filtering

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Updated 13 Feb 2021

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Fast Iterative Filtering for the decompostion of non-stationary signals [1,2,3].

Please refer to "Example_v8.m" and "Example_real_life_v6.m" for examples of how to use the code.

It is based on FFT, which makes FIF to be really fast [2,3]. This implies that it is required a periodical extension at the boundaries.

To overcome this limitation we can preextend the signal under investigation [4]. We do it thanks to the function "Extend_sig_v2.m". See "Example_real_life_v6.m" for an example of application.

Please cite our works:

[1] A. Cicone, J. Liu, H. Zhou. "Adaptive Local Iterative Filtering for Signal Decomposition and Instantaneous Frequency analysis". Applied and Computational Harmonic Analysis, Volume 41, Issue 2, September 2016, Pages 384-411. doi:10.1016/j.acha.2016.03.001 Arxiv http://arxiv.org/abs/1411.6051

[2] A. Cicone, H. Zhou. "Numerical Analysis for Iterative Filtering with New Efficient Implementations Based on FFT". Numerische Mathematik, 2020. doi: 10.1007/s00211-020-01165-5 ArXiv http://arxiv.org/abs/1802.01359

[3] A. Cicone. "Iterative Filtering as a direct method for the decomposition of nonstationary signals". Numerical Algorithms, Volume 373, 2020, 112248. doi: 10.1007/s11075-019-00838-z ArXiv http://arxiv.org/abs/1811.03536

[4] A. Stallone, A. Cicone, M. Materassi. "New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms". Scientific Reports, Volume 10, article number 15161, 2020. doi: 10.1038/s41598-020-72193-2

Cite As

Antonio (2021). FIF (https://github.com/Acicone/FIF/releases/tag/2.12.1), GitHub. Retrieved .

Cicone, Antonio, et al. “Adaptive Local Iterative Filtering for Signal Decomposition and Instantaneous Frequency Analysis.” Applied and Computational Harmonic Analysis, vol. 41, no. 2, Elsevier BV, Sept. 2016, pp. 384–411, doi:10.1016/j.acha.2016.03.001.

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Cicone, Antonio, and Haomin Zhou. “Numerical Analysis for Iterative Filtering with New Efficient Implementations Based on FFT.” Numerische Mathematik, vol. 147, no. 1, Springer Science and Business Media LLC, Jan. 2021, pp. 1–28, doi:10.1007/s00211-020-01165-5.

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Cicone, Antonio. “Iterative Filtering as a Direct Method for the Decomposition of Nonstationary Signals.” Numerical Algorithms, vol. 85, no. 3, Springer Science and Business Media LLC, Feb. 2020, pp. 811–27, doi:10.1007/s11075-019-00838-z.

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Stallone, Angela, et al. “New Insights and Best Practices for the Successful Use of Empirical Mode Decomposition, Iterative Filtering and Derived Algorithms.” Scientific Reports, vol. 10, no. 1, Springer Science and Business Media LLC, Sept. 2020, doi:10.1038/s41598-020-72193-2.

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.