EMD - Empirical Mode Decomposition and Hilbert Spectral Analysis

For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).

Last updated 3 years ago

4.17 score 1 stars 9 dependents 55 scripts 459 downloads

GSD - Graph Signal Decomposition

Graph signals residing on the vertices of a graph have recently gained prominence in research in various fields. Many methodologies have been proposed to analyze graph signals by adapting classical signal processing tools. Recently, several notable graph signal decomposition methods have been proposed, which include graph Fourier decomposition based on graph Fourier transform, graph empirical mode decomposition, and statistical graph empirical mode decomposition. This package efficiently implements multiscale analysis applicable to various fields, and offers an effective tool for visualizing and decomposing graph signals. For the detailed methodology, see Ortega et al. (2018) <doi:10.1109/JPROC.2018.2820126>, Shuman et al. (2013) <doi:10.1109/MSP.2012.2235192>, Tremblay et al. (2014) <https://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569922141.pdf>, and Cho et al. (2024) "Statistical graph empirical mode decomposition by graph denoising and boundary treatment".

Last updated 1 years ago

2.70 score 140 downloads

SynchWave - Synchrosqueezed Wavelet Transform

The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.

Last updated 3 years ago

fortran

2.08 score 2 stars 1 dependents 20 scripts 138 downloads

EPT - Ensemble Patch Transform, Visualization and Decomposition

For multiscale analysis, this package carries out ensemble patch transform, its visualization and multiscale decomposition. The detailed procedure is described in Kim et al. (2020), and Oh and Kim (2020). D. Kim, G. Choi, H.-S. Oh, Ensemble patch transformation: a flexible framework for decomposition and filtering of signal, EURASIP Journal on Advances in Signal Processing 30 (2020) 1-27 <doi:10.1186/s13634-020-00690-7>. H.-S. Oh, D. Kim, Image decomposition by bidimensional ensemble patch transform, Pattern Recognition Letters 135 (2020) 173-179 <doi:10.1016/j.patrec.2020.03.029>.

Last updated 3 years ago

2.00 score 3 scripts 212 downloads

CVThresh - Level-Dependent Cross-Validation Thresholding

The level-dependent cross-validation method is implemented for the selection of thresholding value in wavelet shrinkage. This procedure is implemented by coupling a conventional cross validation with an imputation method due to a limitation of data length, a power of 2. It can be easily applied to classical leave-one-out and k-fold cross validation. Since the procedure is computationally fast, a level-dependent cross validation can be performed for wavelet shrinkage of various data such as a data with correlated errors.

Last updated 3 years ago

1.04 score 11 scripts 282 downloads