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Multidimensional Fast Iterative Filtering for the decompostion of 2D non-stationary signals [1,2].
Please refer to "Example_v3.m" for an example of how to use the code.
It is based on FFT, which makes FIF2 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].
Please cite our works:
[1] A. Cicone, H. Zhou. "Multidimensional Iterative Filtering method for the decomposition of high-dimensional non-stationary signals". Cambridge Core in Numerical Mathematics: Theory, Methods and Applications, Volume 10, Issue 2, Pages 278-298, 2017. doi:10.4208/nmtma.2017.s05
[2] S. Sfarra, A. Cicone, B. Yousefi, S. Perilli, L. Robol, X. P.V. Maldague. "Maximizing the detection of thermal imprints in civil engineering composites after a thermal stimulus - The contribution of an innovative mathematical pre-processing tool: the 2D Fast Iterative Filtering algorithm. Philosophy, comparisons, numerical, qualitative and quantitative results". 2021. Submitted
[3] A. Cicone, H. Zhou. "Numerical Analysis for Iterative Filtering with New Efficient Implementations Based on FFT". Numerische Mathematik, 147 (1), pages 1-28, 2021. doi: 10.1007/s00211-020-01165-5
[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
Cita come
Antonio (2026). FIF2 (https://github.com/Acicone/FIF2/releases/tag/v3.0.1), GitHub. Recuperato .
Cicone, Antonio, and Haomin Zhou. “Multidimensional Iterative Filtering Method for the Decomposition of High–Dimensional Non–Stationary Signals.” Numerical Mathematics: Theory, Methods and Applications, vol. 10, no. 2, Global Science Press, May 2017, pp. 278–98, doi:10.4208/nmtma.2017.s05.
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.
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.
S. Sfarra, A. Cicone, B. Yousefi, S. Perilli, L. Robol, X. P.V. Maldague. "Maximizing the detection of thermal imprints in civil engineering composites after a thermal stimulus - The contribution of an innovative mathematical pre-processing tool: the 2D Fast Iterative Filtering algorithm. Philosophy, comparisons, numerical, qualitative and quantitative results". 2021. Submitted
Informazioni generali
Compatibilità della release di MATLAB
- Compatibile con qualsiasi release
Compatibilità della piattaforma
- Windows
- macOS
- Linux
| Versione | Pubblicato | Note della release | Action |
|---|---|---|---|
| 3.0.1 |
