FIF

Versión 2.12.1 (244 KB) por Antonio

Fast Iterative Filtering

http://people.disim.univaq.it/~antonio.cicone/Software.html

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Actualizado 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

Citar como

Antonio (2025). FIF (https://github.com/Acicone/FIF/releases/tag/2.12.1), GitHub. Recuperado 11 diciembre, 2025.

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.

Ver más estilos

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.

Ver más estilos

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.

Ver más estilos

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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Versión	Publicado	Notas de la versión
2.12.1	13 feb 2021	See release notes for this release on GitHub: https://github.com/Acicone/FIF/releases/tag/2.12.1	Descargar
2.12.0	24 mar 2020	-new get_mask_v1_1 function -we discontinue using Maxmins functions. We now rely on Matlab functions islocalmin and islocalmax
1.0.0	26 nov 2018

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.

MLA	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.
APA	Cicone, A., Liu, J., & Zhou, H. (2016). Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis. Applied and Computational Harmonic Analysis, 41(2), 384–411. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.acha.2016.03.001
BibTeX	@article{Cicone_2016, doi = {10.1016/j.acha.2016.03.001}, url = {https://doi.org/10.1016%2Fj.acha.2016.03.001}, year = 2016, month = {sep}, publisher = {Elsevier {BV}}, volume = {41}, number = {2}, pages = {384--411}, author = {Antonio Cicone and Jingfang Liu and Haomin Zhou}, title = {Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis}, journal = {Applied and Computational Harmonic Analysis} }

MLA	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.
APA	Cicone, A., & Zhou, H. (2021). Numerical analysis for iterative filtering with new efficient implementations based on FFT. Numerische Mathematik, 147(1), 1–28. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs00211-020-01165-5
BibTeX	@article{Cicone_2021, doi = {10.1007/s00211-020-01165-5}, url = {https://doi.org/10.1007%2Fs00211-020-01165-5}, year = 2021, month = {jan}, publisher = {Springer Science and Business Media {LLC}}, volume = {147}, number = {1}, pages = {1--28}, author = {Antonio Cicone and Haomin Zhou}, title = {Numerical analysis for iterative filtering with new efficient implementations based on {FFT}}, journal = {Numerische Mathematik} }

MLA	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.
APA	Cicone, A. (2020). Iterative filtering as a direct method for the decomposition of nonstationary signals. Numerical Algorithms, 85(3), 811–827. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs11075-019-00838-z
BibTeX	@article{Cicone_2020, doi = {10.1007/s11075-019-00838-z}, url = {https://doi.org/10.1007%2Fs11075-019-00838-z}, year = 2020, month = {feb}, publisher = {Springer Science and Business Media {LLC}}, volume = {85}, number = {3}, pages = {811--827}, author = {Antonio Cicone}, title = {Iterative filtering as a direct method for the decomposition of nonstationary signals}, journal = {Numerical Algorithms} }

MLA	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.
APA	Stallone, A., Cicone, A., & Materassi, M. (2020). New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms. Scientific Reports, 10(1). Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1038%2Fs41598-020-72193-2
BibTeX	@article{Stallone_2020, doi = {10.1038/s41598-020-72193-2}, url = {https://doi.org/10.1038%2Fs41598-020-72193-2}, year = 2020, month = {sep}, publisher = {Springer Science and Business Media {LLC}}, volume = {10}, number = {1}, author = {Angela Stallone and Antonio Cicone and Massimo Materassi}, title = {New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms}, journal = {Scientific Reports} }

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