@@ -101,11 +101,10 @@ plot_IMFs(signal, IMFs)             # per-channel panels
 | [`EMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd/emd.py) (Empirical Mode Decomposition) | [[paper]](https://royalsocietypublishing.org/doi/abs/10.1098/rspa.1998.0193) | [[code]](https://www.mathworks.com/help/signal/ref/emd.html) |   ✔️   |
 | [`MEMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd/memd.py) (Multivariate Empirical Mode Decomposition) | [[paper]](https://royalsocietypublishing.org/doi/full/10.1098/rspa.2009.0502) | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/71270-fast-and-adaptive-multivariate-and-multidimensional-emd) |   ✔️   |
 | [`BEMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd2d/bemd.py) (Bidimensional Empirical Mode Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0262885603000945) | [[code]](https://github.com/laszukdawid/PyEMD/blob/master/PyEMD/BEMD.py) |   ✔️   |
-|      [`CEMD`]() (Complex Empirical Mode Decomposition)       | [[paper]](https://ieeexplore.ieee.org/abstract/document/4063369) |                          [[code]]()                          |   ✖️   |
 | [`EEMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd/eemd.py) (Ensemble Empirical Mode Decomposition) | [[paper]](https://www.sciencedirect.com/topics/physics-and-astronomy/ensemble-empirical-mode-decomposition) | [[code]](https://github.com/laszukdawid/PyEMD/blob/master/PyEMD/EEMD.py) |   ✔️   |
 | [`REMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd/remd.py) (Robust Empirical Mode Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/pii/S0019057821003785) | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/70032-robust-empirical-mode-decomposition-remd) |   ✔️   |
 | [`EMD2D`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd2d/emd2d.py) (Empirical Mode Decomposition 2D for images) |          [[paper]](http://aquador.vovve.net/IEMD/)           |           [[code]](http://aquador.vovve.net/IEMD/)           |   ✔️   |
-| [`BMEMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd2d/bmemd.py) (Bidimensional Multivariate Empirical Mode Decomposition) |   [[paper]](https://ieeexplore.ieee.org/document/8805082)    | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/72343-bidimensional-multivariate-empirical-mode-decomposition?s_tid=FX_rc1_behav) |   ✖️   |
+| [`BMEMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd2d/bmemd.py) (Bidimensional Multivariate Empirical Mode Decomposition) |   [[paper]](https://ieeexplore.ieee.org/document/8805082)    | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/72343-bidimensional-multivariate-empirical-mode-decomposition?s_tid=FX_rc1_behav) |   ✔️   |
 | [`CEEMDAN`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd/ceemdan.py) (Complete Ensemble EMD with Adaptive Noise) |   [[paper]](https://ieeexplore.ieee.org/document/5947265)    | [[code]](https://github.com/laszukdawid/PyEMD/blob/master/PyEMD/EEMD.py) |   ✔️   |
 | [`TVF_EMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd/tvf_emd.py) (Time Varying Filter Based EMD) | [[paper]](https://www.sciencedirect.com/science/article/pii/S0165168417301135) |   [[code]](https://github.com/stfbnc/pytvfemd/tree/master)   |   ✔️   |
 | [`EFD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_emd/efd.py) (Empirical Fourier Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0888327021005355) | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/97747-empirical-fourier-decomposition-efd) |   ✔️   |
@@ -119,8 +118,7 @@ plot_IMFs(signal, IMFs)             # per-channel panels
 | [`APMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_imd/apmd.py) (Adaptive Polymorphic Mode Decomposition) |     [[paper]](https://doi.org/10.1016/j.dsp.2024.104913)     | [[code]](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_imd/apmd.py) |   ✔️   |
 | [`LMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_lmd/lmd.py) (Local Mean Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1051200418308133?via%3Dihub) | [[code]](https://github.com/shownlin/PyLMD/blob/master/PyLMD/LMD.py) |   ✔️   |
 | [`RLMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_lmd/rlmd.py) (Robust Local Mean Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0888327017301619) | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/66935-robust-local-mean-decomposition-rlmd) |   ✔️   |
-| [`FMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_fmd/fmd.py) (Feature Mode Decomposition) |   [[paper]](https://ieeexplore.ieee.org/document/9732251)    | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/108099-feature-mode-decomposition-fmd) |   ✔️   |
-| [`NFMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_fmd/nfmd.py) (Non-stationary Feature Mode Decomposition) |   [[paper]](https://ieeexplore.ieee.org/document/9732251)    | [[code]](https://github.com/sheadan/NFMD-ExtractionInstantaneous/blob/master/nfmd/NFMD.py) |   ✖️   |
+| [`FMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_tid/fmd.py) (Feature Mode Decomposition) |   [[paper]](https://ieeexplore.ieee.org/document/9732251)    | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/108099-feature-mode-decomposition-fmd) |   ✔️   |
 | [`SSA`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_tid/ssa.py) (Singular Spectral Analysis) | [[paper]](https://orca.cardiff.ac.uk/id/eprint/15208/1/Zhiglavsky_SSA_encyclopedia.pdf) | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/58967-singular-spectrum-analysis-beginners-guide) |   ✔️   |
 | [`EWT`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_ewt/ewt.py) (Empirical Wavelet Transform) |   [[paper]](https://ieeexplore.ieee.org/document/6522142)    | [[code]](https://www.mathworks.com/help/wavelet/ug/empirical-wavelet-transform.html) |   ✔️   |
 | [`EWT2D`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_ewt/ewt2d.py) (2D Empirical Wavelet Transform) |         [[paper]](https://arxiv.org/abs/2405.06188)          |        [[code]](https://github.com/bhurat/EWT-Python)        |   ✔️   |
@@ -136,8 +134,9 @@ plot_IMFs(signal, IMFs)             # per-channel panels
 | [`INCMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_vncmd/incmd.py) (Iterative Nonlinear Chirp Mode Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/pii/S0022460X2030403X?via%3Dihub) |      [[code]](https://github.com/sheadan/IterativeNCMD)      |   ✔️   |
 | [`MNCMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_vncmd/mncmd.py) (Multivariate Nonlinear Chirp Mode Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0165168420302103) |                          [[code]]()                          |   ✖️   |
 | [`AVNCMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_vncmd/avncmd.py) (Adaptive Variational Nonlinear Chirp Mode Decomposition) | [[paper]](https://ieeexplore.ieee.org/abstract/document/9746147) |         [[code]](https://github.com/HauLiang/AVNCMD)         |   ✔️   |
-| [`ACMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_vmd/acmd.py) (Adaptive Chirp Mode Decomposition) |                         [[paper]]()                          | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/121373-data-driven-adaptive-chirp-mode-decomposition?s_tid=srchtitle) |   ✔️   |
-| [`BA-ACMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_vmd/ba_acmd.py) (Bandwidth-aware adaptive chirp mode decomposition) | [[paper]](https://journals.sagepub.com/doi/abs/10.1177/14759217231174699) | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/132792-bandwidth-aware-adaptive-chirp-mode-decomposition-ba-acmd?s_tid=srchtitle) |   ✖️   |
+| [`ACMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_acmd/acmd.py) (Adaptive Chirp Mode Decomposition) |                         [[paper]]()                          | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/121373-data-driven-adaptive-chirp-mode-decomposition?s_tid=srchtitle) |   ✔️   |
+| [`BA-ACMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_acmd/ba_acmd.py) (Bandwidth-aware Adaptive Chirp Mode Decomposition) | [[paper]](https://journals.sagepub.com/doi/abs/10.1177/14759217231174699) | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/132792-bandwidth-aware-adaptive-chirp-mode-decomposition-ba-acmd?s_tid=srchtitle) |   ✔️   |
+| [`DD-ACMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_acmd/dd_acmd.py) (Data-driven Adaptive Chirp Mode Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0888327022010652) | [[code]](https://ww2.mathworks.cn/matlabcentral/fileexchange/121373-data-driven-adaptive-chirp-mode-decomposition) |   ✖️   |
 | [`GDMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_gdmd/gdmd.py) (Generalized Dispersion Mode Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/pii/S0022460X20306295) | [[code]](https://www.sciencedirect.com/science/article/pii/S0022460X20306295) |   ✔️   |
 | [`VGNMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_gdmd/vgnmd.py) (Variational Generalized Nonlinear Mode Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/pii/S088832702300821X) | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/154792-variational-generalized-nonlinear-mode-decomposition-vgmnd) |   ✔️   |
 | [`IVGNMD`](https://github.com/wwhenxuan/PySDKit/blob/main/pysdkit/_gdmd/ivgnmd.py) (Improved Variational Generalized Nonlinear Mode Decomposition) | [[paper]](https://www.sciencedirect.com/science/article/pii/S0888327025001086) | [[code]](https://www.mathworks.com/matlabcentral/fileexchange/180043-improved-vgnmd) |   ✔️   |
@@ -159,7 +158,7 @@ plot_IMFs(signal, IMFs)             # per-channel panels
 
 We would like to thank the researchers in signal processing for providing us with valuable algorithms and promoting the continuous progress in this field. However, since the main programming language used in `signal processing` is `Matlab`, and `Python` is the main battlefield of `machine learning` and `deep learning`, the usage of signal decomposition in machine learning and deep learning is far less extensive than `wavelet transformation`. In order to further promote the organic combination of signal decomposition and machine learning, we developed `PySDKit`. We would like to express our gratitude to [PyEMD](https://github.com/laszukdawid/PyEMD), [Sktime](https://www.sktime.net/en/latest/index.html), [Scikit-learn](https://scikit-learn.org/stable/), [Scikit-Image](https://scikit-image.org/docs/stable/), [statsmodels](https://www.statsmodels.org/stable/index.html), [vmdpy](https://github.com/vrcarva/vmdpy),  [MEMD-Python-](https://github.com/mariogrune/MEMD-Python-),  [ewtpy](https://github.com/vrcarva/ewtpy), [EWT-Python](https://github.com/bhurat/EWT-Python), [PyLMD](https://github.com/shownlin/PyLMD), [pywt](https://github.com/PyWavelets/pywt), [SP_Lib](https://github.com/hustcxl/SP_Lib), [dsatools](https://github.com/MVRonkin/dsatools) and [signal-decomposition](https://github.com/cvxgrp/signal-decomposition).
 
-## Contributing
+## Contributing 🤗
 
 This project exists thanks to all the people who contribute.