├── LICENSE ├── MED.md ├── README.md ├── VMD.md ├── demo └── readme.md ├── doc ├── ALIF.md ├── ApEn.md ├── EMD.md ├── EWT.md ├── HVD.md ├── ITD.md ├── LMD.md ├── MCKD.md ├── NMD.md ├── PE.md ├── SSD.md ├── STNBMD.md ├── SamEn.md ├── SwD.md ├── TFA_Tools.md ├── TFRD.md ├── TVF_EMD.md └── VMD.md └── img ├── list └── mathtex.gif /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Cuixiaolong 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /MED.md: -------------------------------------------------------------------------------- 1 | # 最小熵反褶积(MED) 2 | ## 1. 基本原理 3 | 最小熵反褶积的出发点即寻找最优的滤波器使的原信号的峭度值最大，最大程度的还原原信号的特性，使冲击成分突出，降噪后的信号最大程度与满足原信号的物理特性。因此最小熵反褶积可以提高原信号的信噪比[5]。 4 | 最小熵反褶积(Minimum entropy deconvolution MED)首先由Wiggins[6]提出，是一种自适应的信号处理方法。2007年Sawalhi[6]将MED方法应用于滚动轴承与齿轮故障诊断中，MED算法的目的就是以最大峭度为迭代的终止条件提高信号的信噪比[7]。滚动轴承故障时信号表达可以表达为: 5 | ![](https://github.com/hustcxl/Signal-processing-notes/blob/master/img/mathtex.gif) 6 | 7 | ## Algotithm 8 | * [Multipoint Optimal Minimum Entropy Deconvolution with Convolution Adjustment (MOMEDA)](https://ww2.mathworks.cn/matlabcentral/fileexchange/53483-multipoint-optimal-minimum-entropy-deconvolution-with-convolution-adjustment-momeda) 9 | * [Minimum Entropy Deconvolution Multipack (MED, MEDA, OMEDA, MOMEDA, MCKD)](https://ww2.mathworks.cn/matlabcentral/fileexchange/53484-minimum-entropy-deconvolution-multipack-med-meda-omeda-momeda-mckd?s_tid=FX_rc1_behav) 10 | 11 | ## Papers 12 | 13 | - [1] R.A. Wiggins, Minimum entropy deconvolution, Geoexploration 16 (1--2)(1978) 21--35. 14 | 15 | - [2] Cabrelli, Carlos A. "Minimum entropy deconvolution and simplicity: A noniterative algorithm." Geophysics 50.3 (1985): 394-413. 16 | 17 | - [3] McDonald, Geoff L., Qing Zhao, and Ming J. Zuo. "Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection." Mechanical Systems and Signal Processing 33 (2012). For PDF see [blog](http://split-code.com/rotation.html.) 18 | 19 | - [4] McDonald, Geoff L., and Qing Zhao. "Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection." Mechanical Systems and Signal Processing 82 (2017): 461-477. For PDF see [blog](http://split-code.com/rotation.html.) 20 | 21 | [:back:返回主目录](README.me) 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 信号处理算法库 2 | 3 | 本项目旨在追踪建立最先进的信号处理算法库。从算法理论原理，实现方法，到落地demo。 4 | 目前是个人业余收集和整理，知识和精力都有限，欢迎有识之士参与贡献。 5 | 欢迎大家任意`Fork`,`Star`,`watch`. 6 | demo一般来自网络共享开源的资源。或者自己根据论文原理实现。欢迎批评指正。 7 | 如涉及侵权，请随时联系删除787452269@qq.com。 8 | 9 | > 声明：本算法库仅作学习交流之用。如未经许可用作其他商业用途，保留追责权利。 10 | 11 | ## 1.经典的传统方法 12 | ### 时域 13 | 时域特征计算相对简单，MATLAB都有非常多的库函数。也有人已经进行了总结。直接引用。源自知乎专栏。 14 | * [时域特征值提取的MATLAB代码实现（均方根、峰值因子、脉冲因子、裕度因子、峭度因子、波形因子和偏度等）](https://zhuanlan.zhihu.com/p/36162561) 15 | * [信号时域分析方法的理解（峰值因子、脉冲因子、裕度因子、峭度因子、波形因子和偏度等）](https://zhuanlan.zhihu.com/p/35362151) 16 | * [时域分析——无量纲特征值含义一网打尽](https://zhuanlan.zhihu.com/p/57445453) 17 | * [时域分析——有量纲特征值含义一网打尽](https://zhuanlan.zhihu.com/p/57153601) 18 | * [python实现信号时域统计特征提取](https://blog.csdn.net/qq_34705900/article/details/88389319) 19 | * [Envelope Detction for Signal Analysis](https://ww2.mathworks.cn/matlabcentral/fileexchange/37545-envelope-detction-for-signal-analysis?s_tid=FX_rc1_behav) 20 | 21 | ### 频域 22 | * [信号频域分析方法的理解（频谱、能量谱、功率谱、倒频谱、小波分析）](https://zhuanlan.zhihu.com/p/34989414) 23 | * [频域特征值提取的MATLAB代码实现（频谱、功率谱、倒频谱）](https://zhuanlan.zhihu.com/p/36163931) 24 | * fft专题 25 | 26 | ### 滤波 27 | * 无限长单位脉冲响应（Infinite impulse response,IIR）滤波器 28 | [Infinite impulse response](https://en.wikipedia.org/wiki/Infinite_impulse_response) 29 | * [使用matlab设计IIR巴特沃斯低通滤波器](https://blog.csdn.net/Stynis/article/details/80531803) 30 | [matlab buttord 函数](https://ww2.mathworks.cn/help/signal/ref/buttord.html?searchHighlight=buttord&s_tid=doc_srchtitle) 31 | * [MATLAB|切比雪夫低通滤波器设计与滤波实现](https://www.jianshu.com/p/2a0d6d587bc3) 32 | [matlab cheb1ord 函数](https://ww2.mathworks.cn/help/signal/ref/cheb1ord.html?s_tid=doc_ta) 33 | 34 | * 有限长单位脉冲响应（Finite impulse response,FIR）滤波器 35 | * [Finite impulse response](https://en.wikipedia.org/wiki/Finite_impulse_response) 36 | 37 | * [维纳滤波 Wiener filter](https://zh.wikipedia.org/wiki/%E7%BB%B4%E7%BA%B3%E6%BB%A4%E6%B3%A2) 38 | 由于后续算法变分模态分解中使用到。 39 | * [卡尔曼滤波 Kalman filter](https://zh.wikipedia.org/wiki/%E5%8D%A1%E5%B0%94%E6%9B%BC%E6%BB%A4%E6%B3%A2) 40 | 扩展的关于Vold-Kalman Order Filtering 附matlab分享的相关代码。用于转速跟踪是当前研究的热点。 41 | [Vold-Kalman order tracking code](https://ww2.mathworks.cn/matlabcentral/fileexchange/32639-vold-kalman-order-tracking-code?s_tid=FX_rc1_behav) 42 | [Second generation Vold-Kalman Order Filtering](https://ww2.mathworks.cn/matlabcentral/fileexchange/36277-second-generation-vold-kalman-order-filtering) 43 | 44 | 45 | 46 | 关于数字滤波，相关详细原理，请参考 ：程佩青. 数字信号处理教程(第五版)[M]. 清华大学出版社, 2017. 47 | 48 | ### 其他 49 | 50 | 51 | ## 2.信号自适应分解 52 | 53 | * [经验模态分解EMD&EEMD及其扩展和改进](./doc/EMD.md) 54 | * [变分模态分解(Variational Mode Decomposition,VMD)](./doc/VMD.md) 55 | * [局部均值分解(local mean decomposition,LMD)](./doc/LMD.md) 56 | * [自适应局部迭代滤波(Adaptive Local Iterative Filtering,ALIF)](./doc/ALIF.md) 57 | * [非线性模态分解 Nonlinear Mode Decomposition.NMD](./doc/NMD.md) 58 | * [固有时间尺度分解(Intrinsic time-scale decomposition,ITD)](./doc/ITD.md) 59 | * [希尔伯特振动分解(Hilbert Vibration Decomposition,HVD)](./doc/HVD.md) 60 | * [时变滤波经验模态分解Time varying filter based empirical mode,TVF-EMD](./doc/TVF_EMD.md) 61 | * [奇异谱分解 (Singular Spectrum Decomposition,SSD)](./doc/SSD.md) 62 | * [经验小波 (Empirical Wavelet Transforms,EWT)](./doc/EWT.md) 63 | * [短时窄带模式分解 (short-time narrow-banded mode decomposition,STNBMD)](./doc/STNBMD.md) 64 | * 一些新的分解方法 65 | * 极值点加权模式分解EWMD 66 | * 极值点对称模式分解 67 | * 辛几何模式分解symplectic geometry mode decomposition 68 | * [群分解(Swarm-Decomposition,SwD)](./doc/SwD.md) 69 | * 原子分解 70 | 71 | ## 3.时频分析方法 72 | * [TFAT 时频工具](./doc/TFA_Tools.md) 73 | * STFT 74 | * SST 75 | * [Time-frequency ridge detection](./doc/TFRD.md) 76 | 77 | ## 3.冲击特征检测 78 | 79 | * [最小熵反褶积](./MED.md) 80 | 81 | ## 4.时间序列熵分析 82 | * [排列熵(Permutation Entropy,PE)](./doc/PE.md) 83 | * [近似熵（Approxiamte Entropy，ApEn）](./doc/ApEn.md) 84 | * [样本熵（Sample Entropy，SampEn)](./doc/SamEn.md) 85 | * 条件熵（Conditional Entropy） 86 | * 修正条件熵（Corrected Conditional Entropy） 87 | * 模糊熵（Fuzzy Entropy，FuzzyEn) 88 | * 香农熵（Shannon Entropy，SE） 89 | * 能量熵 90 | * 谱熵 91 | * 色散熵Dispersion entropy (DispEn) 92 | 93 | ## 5.一些开源的信号处理综合算法资源 94 | * [Python for signal processing](https://github.com/unpingco/Python-for-Signal-Processing) 95 | * [Digital Signal Processing for Javascript](https://github.com/corbanbrook/dsp.js) 96 | * [Numerical Tours of Signal Processing](https://github.com/gpeyre/numerical-tours) 97 | * [Digital Signal Processing - Theory and Computational Examples ](https://github.com/spatialaudio/digital-signal-processing-lecture) 98 | * [Signal Processing Tools for MATLAB](https://github.com/JacobD10/SoundZone_Tools) 99 | * [Basic-Rotating-Machine-Vibration-Analysis](https://github.com/ZhaoZhibin/Basic-Rotating-Machine-Vibration-Analysis) 100 | 101 | [:top:返回顶部](#信号处理算法库) 102 | 103 | 104 | -------------------------------------------------------------------------------- /VMD.md: -------------------------------------------------------------------------------- 1 | # VMD 2 | ## 变分模态分解 3 | -------------------------------------------------------------------------------- /demo/readme.md: -------------------------------------------------------------------------------- 1 | # sp_demo 2 | -------------------------------------------------------------------------------- /doc/ALIF.md: -------------------------------------------------------------------------------- 1 | # 自适应局部迭代滤波 Adaptive Local Iterative Filtering 2 | 3 | ## 算法 4 | * [自适应局部迭代滤波 Adaptive Local Iterative Filtering](https://ww2.mathworks.cn/matlabcentral/fileexchange/56210-alif) 5 | ## 相关论文 6 | 7 | * [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. 8 | doi:10.1016/j.acha.2016.03.001](http://arxiv.org/abs/1411.6051) 9 | 10 | * [A. Cicone. Nonstationary signal decomposition for dummies. To appear in the book Advances in Mechanics and Mathematics.](https://arxiv.org/abs/1710.04844) 11 | 12 | * [A. Cicone, H. Zhou. Iterative Filtering algorithm numerical analysis % with new efficient implementations based on FFT.](http://arxiv.org/abs/1802.01359) 13 | 14 | [<<返回主目录](../README.md) 15 | -------------------------------------------------------------------------------- /doc/ApEn.md: -------------------------------------------------------------------------------- 1 | # Approxiamte Entropy ApEn 2 | 3 | ## Algorithm 4 | * [Approxmate Entropy](https://ww2.mathworks.cn/matlabcentral/fileexchange/26546-approximate-entropy?s_tid=FX_rc2_behav) 5 | * [Fast Approxmate Entropy](https://ww2.mathworks.cn/matlabcentral/fileexchange/32427-fast-approximate-entropy?s_tid=FX_rc2_behav) 6 | 7 | ## Papers 8 | 9 | [:back:返回主目录](../README.md) 10 | -------------------------------------------------------------------------------- /doc/EMD.md: -------------------------------------------------------------------------------- 1 | # 经验模态分解(Empirical Mode Decomposition) 2 | 3 | ## 算法工具箱 4 | 5 | * [工具箱下载地址](https://atoms.scilab.org/toolboxes/emd_toolbox/1.3) 6 | * [G.Rilling个人网址](http://perso.ens-lyon.fr/patrick.flandrin/emd.html) 7 | * Rilling G, Flandrin P, Goncalves P. On empirical mode decomposition and its algorithms[C]//IEEE-EURASIP workshop on nonlinear signal and image processing. NSIP-03, Grado (I), 2003, 3(3): 8-11. 8 | [论文地址](http://perso.ens-lyon.fr/patrick.flandrin/NSIP03.pdf) 9 | 10 | * [国立中央大学数据分析方法研究中心](https://in.ncu.edu.tw/~ncu34951/) 11 | 12 | ## 变种和改进算法 13 | * [多元经验模态分解Multivariate Empirical Mode Decomposition](http://www.commsp.ee.ic.ac.uk/~mandic/research/emd.htm) 14 | [MEMD 的python 实现](https://github.com/mariogrune/MEMD-Python-) 15 | * [二元经验模态分解 Bivariate Empirical Mode Decomposition](https://ieeexplore.ieee.org/abstract/document/4358014) 16 | 17 | 参见函数Rilling G工具箱的函数cemdc2_fix，bivariate_EMD_illustration为海洋漂流位置信号的应用。 18 | [G-Rilling EMD工具箱](http://blog.sina.com.cn/s/blog_6163bdeb0102e2cd.html) 19 | * [Bidimensional Multivariate Empirical Mode Decomposition](https://github.com/z-bingo/Bidimensional-Multivariate-Empirical-Mode-Decomposition) 20 | * [Fast and Adaptive Multivariate and Multidimensional EMD](https://ww2.mathworks.cn/matlabcentral/fileexchange/71270-fast-and-adaptive-multivariate-and-multidimensional-emd) 21 | * [Robust Empirical Mode Decomposition (REMD)](https://ww2.mathworks.cn/matlabcentral/fileexchange/70032-robust-empirical-mode-decomposition-remd) 22 | * [Complex Empirical Mode Decomposition(CEMD)]() 23 | 24 | 25 | 26 | ## 相关论文 27 | - [1] Rehman and D. P. Mandic, "Multivariate Empirical Mode Decomposition", Proceedings of the Royal Society A, 2010 28 | - [2] G. Rilling, P. Flandrin and P. Goncalves, "On Empirical Mode Decomposition and its Algorithms", Proc of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03, Grado (I), June 2003 29 | - [3] N. E. Huang et al., "A confidence limit for the Empirical Mode Decomposition and Hilbert spectral analysis", Proceedings of the Royal Society A, Vol. 459, pp. 2317-2345, 2003 30 | - [4] Rilling G, Flandrin P, Gonçalves P, et al. Bivariate empirical mode decomposition[J]. IEEE signal processing letters, 2007, 14(12): 936-939. 31 | - [5] Dandan Peng, Zhiliang Liu, Yaqiang Jin, Yong Qin. Improved EMD with a Soft Sifting Stopping Criterion and Its Application to Fault Diagnosis of Rotating Machinery. Journal of Mechanical Engineering. 55 (10): 122-132, 2019. 32 | - [6] Zhiliang Liu, Yaqiang Jin, Ming J. Zuo, and Zhipeng Feng. Time-frequency representation based on robust local mean decomposition for multi-component AM-FM signal analysis. Mechanical Systems and Signal Processing. 95: 468-487, 2017. 33 | - [7]. Tanaka, T. and D.P. Mandic, Complex Empirical Mode Decomposition. IEEE Signal Processing Letters, 2007. 14(2): p. 101-104. [link](https://ieeexplore.ieee.org/document/4063369) 34 | 35 | [:back:返回主目录](../README.md) 36 | -------------------------------------------------------------------------------- /doc/EWT.md: -------------------------------------------------------------------------------- 1 | # 经验小波 （Empirical Wavelet Transforms） 2 | 3 | ## 算法 4 | * [Empirical Wavelet Transforms](https://ww2.mathworks.cn/matlabcentral/fileexchange/42141-empirical-wavelet-transforms?s_tid=FX_rc3_behav) 5 | 6 | ## 相关论文 7 | - J.Gilles, "Empirical wavelet transform" to appear in IEEE Trans. Signal Processing, 2013. 8 | Preprint available at ftp://ftp.math.ucla.edu/pub/camreport/cam13-33.pdf 9 | [paper](https://ieeexplore.ieee.org/abstract/document/6522142) 10 | - J.Gilles, G.Tran, S.Osher "2D Empirical transforms. Wavelets, Ridgelets and Curvelets Revisited", SIAM Journal on Imaging Sciences, Vol.7, No.1, 157--186, 2014. 11 | Preprint available at ftp://ftp.math.ucla.edu/pub/camreport/cam13-35.pdf 12 | [paper](https://epubs.siam.org/doi/abs/10.1137/130923774) 13 | - J.Gilles, K.Heal, "A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation", submitted 2014. 14 | Preprint available at ftp://ftp.math.ucla.edu/pub/camreport/cam14-05.pdf 15 | [paper](https://www.worldscientific.com/doi/abs/10.1142/S0219691314500441) 16 | 17 | ## 应用情况 18 | [1].Wang, D., et al., Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 2018. 101: p. 292-308.[paper](https://www.sciencedirect.com/science/article/pii/S0888327017304739) 19 | 20 | 21 | [:back:返回主目录](../README.md) 22 | -------------------------------------------------------------------------------- /doc/HVD.md: -------------------------------------------------------------------------------- 1 | # Hilbert Vibration Decomposition 希尔伯特振动分解 2 | 3 | ## 算法 Algorithm 4 | * [Hilbert Vibration Decomposition(HVD)](https://ht.net.technion.ac.il/) 5 | 6 | ## 论文 Papers 7 | 8 | - [Hilbert Transform Applications in Mechanical Vibration](https://www.wiley.com/en-gb/Hilbert+Transform+Applications+in+Mechanical+Vibration-p-9780470978276) 9 | 10 | [:back:返回主目录](../README.md) 11 | -------------------------------------------------------------------------------- /doc/ITD.md: -------------------------------------------------------------------------------- 1 | # 固有时间尺度分解 Intrinsic time-scale decomposition，ITD 2 | 3 | ## 算法 4 | 5 | * [Intrinsic time-scale decomposition](https://ww2.mathworks.cn/matlabcentral/fileexchange/69380-intrinsic-time-scale-decomposition-itd) 6 | 7 | ## 相关论文 8 | 9 | * [1]. Frei, M.G., Intrinsic time-scale decomposition: time-frequency-energy analysis and real-time filtering of non-stationary signals. Proceedings Mathematical Physical & Engineering Sciences, 2007. 463(2078): p. 321-342. 10 | 11 | 12 | [<<返回主目录](../README.md) 13 | 14 | -------------------------------------------------------------------------------- /doc/LMD.md: -------------------------------------------------------------------------------- 1 | # 局部均值分解 local mean decomposition 2 | 3 | ## 算法 4 | * [Local Mean Decomposition](https://ww2.mathworks.cn/matlabcentral/fileexchange/37849-local-mean-decomposition?s_tid=FX_rc2_behav) 5 | * [Robust Local Mean Decomposition](https://ww2.mathworks.cn/matlabcentral/fileexchange/66935-robust-local-mean-decomposition) 6 | * [Complex Local Mean Decomposition](https://www.sciencedirect.com/science/article/pii/S0925231210004595) 7 | 8 | ## 相关论文 9 | * [1]. Smith, J.S., The local mean decomposition and its application to EEG perception data. Journal of the Royal Society, Interface, 2005. 2(5): p. 443-454. 10 | 11 | - [ Park, C., et al., The complex local mean decomposition. Neurocomputing, 2011. 74(6): p. 867-875.](https://www.sciencedirect.com/science/article/pii/S0925231210004595?via%3Dihub) 12 | 13 | 14 | [:back:返回主目录](../README.md) 15 | -------------------------------------------------------------------------------- /doc/MCKD.md: -------------------------------------------------------------------------------- 1 | # MCKD 2 | -------------------------------------------------------------------------------- /doc/NMD.md: -------------------------------------------------------------------------------- 1 | # Nonlinear Mode Decomposition 非线性模态分解 2 | Nonlinear Mode Decomposition (NMD) is a novel noise-robust and adaptive decomposition method which allows reconstruction of full oscillatory modes present in the signal, for any waveform. Moreover, it recovers only physically meaningful oscillations, simultaneously removing the noise. The NMD Toolbox provides all the needed MatLab codes for running NMD, and many more. Just copy all files into some directory in the MatLab path and enjoy; the codes do not require any additional MatLab Toolboxes. If you have any questions/suggestions, please feel free to contact the author at dmytro.iatsenko@gmail.com 3 | 4 | ## Algorithm 5 | - [NMD](http://www.physics.lancs.ac.uk/research/nbmphysics/diats/nmd/) 6 | 7 | ## Papers 8 | 9 | - [D. Iatsenko, P. V. E. McClintock and A. Stefanovska, Nonlinear Mode Decomposition: a noise-robust, adaptive decomposition method, Phys. Rev. E 92, 032916 (2015)](https://arxiv.org/abs/1207.5567) 10 | 11 | -------------------------------------------------------------------------------- /doc/PE.md: -------------------------------------------------------------------------------- 1 | # Permutation Entropy,PE 2 | 3 | ## algorithm 4 | * [Permutation entropy](https://ww2.mathworks.cn/matlabcentral/fileexchange/37289-permutation-entropy?s_tid=FX_rc1_behav) 5 | 6 | * [Permutation entropy (fast algorithm)](https://ww2.mathworks.cn/matlabcentral/fileexchange/44161-permutation-entropy-fast-algorithm?s_tid=FX_rc3_behav) 7 | 8 | * [Multiscale Permutation Entropy (MPE)](https://ww2.mathworks.cn/matlabcentral/fileexchange/37288-multiscale-permutation-entropy-mpe?s_tid=FX_rc2_behav) 9 | 10 | ## papers 11 | 12 | - G Ouyang, J Li, X Liu, X Li, Dynamic Characteristics of Absence EEG Recordings with Multiscale Permutation Entropy Analysis, Epilepsy Research, doi: 10.1016/j.eplepsyres.2012.11.003 13 | - G Ouyang, C Dang, X Li, Complexity Analysis of EEG Data with Multiscale Permutation Entropy, Advances in Cognitive Neurodynamics (II), 2011, pp 741-745 14 | - X Li, G Ouyang, D Richards, Predictability analysis of absence seizures with permutation entropy, Epilepsy Research, Vol. 77pp. 70-74, 2007 15 | - Unakafova, V.A., Keller, K., 2013. Efficiently measuring complexity on the basis of real-world Data. Entropy, 15(10), 4392-4415. 16 | - Bandt, C. and Pompe, B., 2002. Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17), p.174102. 17 | - Keller, K., Unakafov, A.M. and Unakafova, V.A., 2014. Ordinal patterns, entropy, and EEG. Entropy, 16(12), pp.6212-6239. 18 | - Riedl, M., Muller, A. and Wessel, N., 2013. Practical considerations of permutation entropy. The European Physical Journal Special Topics, 222(2), pp.249-262. 19 | - Zanin, M., Zunino, L., Rosso, O.A. and Papo, D., 2012. Permutation entropy and its main biomedical and econophysics applications: a review. Entropy, 14(8), pp.1553-1577. 20 | - Amigo, J.M., Zambrano, S. and Sanjuan, M.A., 2008. Combinatorial detection of determinism in noisy time series. EPL (Europhysics Letters), 83(6), p.60005. 21 | - Unakafova, V.A., 2015. Investigating measures of complexity for dynamical systems and for time series (Doctoral dissertation, University of Luebeck). 22 | - Keller, K., and M. Sinn. Ordinal analysis of time series. Physica A: Statistical Mechanics and its Applications 356.1 (2005): 114—120 23 | -------------------------------------------------------------------------------- /doc/SSD.md: -------------------------------------------------------------------------------- 1 | # Singular Spectrum Decomposition 2 | 3 | ## Algorithm 4 | * [Singular Spectrum Decomposition](https://project.dke.maastrichtuniversity.nl/ssd/) 5 | 6 | 7 | ## Papers 8 | 9 | - Bonizzi, P., et al., Singular spectrum decomposition: A new method for time series decomposition. 10 | Advances in Adaptive Data Analysis, 2014. 6(04): p. 1450011. 11 | - Leles, M.C.R., et al., Improving reconstruction of time-series based in Singular Spectrum Analysis: A segmentation approach. Digital Signal Processing, 2018. 77: p. 63 12 | -76. 13 | 14 | [:back: 返回主目录](../README.md) 15 | -------------------------------------------------------------------------------- /doc/STNBMD.md: -------------------------------------------------------------------------------- 1 | # 短时窄带模式分解 （short-time narrow-banded mode decomposition，STNBMD） 2 | 3 | ## 算法 4 | * [short-time narrow-banded mode decomposition](https://ww2.mathworks.cn/matlabcentral/fileexchange/56226-short-time-narrow-band-mode-decomposition-stnbmd-toolbox) 5 | 6 | ## 相关论文 7 | - McNeill, S.I., Decomposing a signal into short-time narrow-banded modes. Journal of Sound and Vibration, 2016. 373: p. 325 8 | -339. [link](https://doi.org/10.1016/j.jsv.2016.03.015) 9 | 10 | ## 应用情况 11 | 12 | 13 | 14 | [:back:返回主目录](../README.md) 15 | -------------------------------------------------------------------------------- /doc/SamEn.md: -------------------------------------------------------------------------------- 1 | # Sample Entropy,SampEn 2 | 3 | ## Algorithm 4 | * [Sample Entropy](https://ww2.mathworks.cn/matlabcentral/fileexchange/69381-sample-entropy?s_tid=FX_rc3_behav) 5 | * [Multiscale Sample Entropy](https://ww2.mathworks.cn/matlabcentral/fileexchange/62706-multiscale-sample-entropy) 6 | ## Papers 7 | 8 | [:back:](../README.md) 9 | -------------------------------------------------------------------------------- /doc/SwD.md: -------------------------------------------------------------------------------- 1 | # Swarm-Decomposition 2 | 3 | ## Algorithm 4 | 5 | * [Swarm-Decomposition](https://github.com/gkaposto/Swarm-Decomposition) 6 | 7 | ## Papers 8 | 9 | 10 | - Apostolidis, Georgios K., and Leontios J. Hadjileontiadis. "Swarm decomposition: A novel signal analysis using swarm intelligence." Signal Processing 132 (2017): 40-50.[link](https://www.sciencedirect.com/science/article/pii/S0165168416302304) 11 | 12 | - Miao, Y., et al., Optimal swarm decomposition with whale optimization algorithm for weak feature extraction from multicomponent modulation signal. Mechanical Systems and Signal Processing, 2019. 122: p. 673 13 | -691 14 | .[link](https://www.sciencedirect.com/science/article/pii/S0888327018308100) 15 | 16 | [:back:返回主目录](../README.md) 17 | -------------------------------------------------------------------------------- /doc/TFA_Tools.md: -------------------------------------------------------------------------------- 1 | # Time-frequency Analysis Tool 2 | 3 | ## ToolBox 4 | - [Matlab Signal Processing Toolbox](https://ww2.mathworks.cn/help/signal/index.html) 5 | 6 | - [The Large Time/Frequency Analysis Toolbox (LTFAT) ](http://ltfat.github.io/) 7 | 8 | - [Time Frequency Signal Analysis Matlab Toolbox v.5.5](https://espace.library.uq.edu.au/view/UQ:211321) 9 | 10 | From The University of Queensland. 11 | 12 | - [DiscreteTFDs-parameterized time-frequency representation PTFR](http://tfd.sourceforge.net/) 13 | 14 | from Jeffrey C. O'Neill 15 | 16 | - [the Time-Frequency Toolbox](http://tftb.nongnu.org/) 17 | 18 | The TFTB has been developed by François Auger, Olivier Lemoine, Paulo Gonçalvès and Patrick Flandrin under the auspices of the CNRS (Centre National de la Recherche Scientifique) within its GdR Information, Signal et Images. Parts of the Toolbox have also been written at Department of Electrical and Computer Engineering of Rice University with the support of the NSF. We would like to thank the people who helped in various ways. 19 | 20 | 21 | 22 | ## Open source algorithm package 23 | 24 | - [Algorithms for (Quadratic) Time–Frequency Distributions](http://otoolej.github.io/code/fast_TFDs/) 25 | 26 | * [Form J.M. O' Toole at Github](https://github.com/otoolej/fast_TFDs) 27 | * J.M. O’ Toole and B. Boashash, “Fast and memory-efficient algorithms for computing quadratic time–frequency distributions”, Applied and Computational Harmonic Analysis, Feb. 2013, doi:10.1016/j.acha.2013.01.003 28 | 29 | * J.M. Oʼ Toole, M. Mesbah, and B. Boashash, “Improved discrete definition of quadratic time–frequency distributions,” IEEE Trans. on Signal Processing, vol. 58, Feb. 2010, pp. 906-911. 30 | 31 | * J.M. O’ Toole, M. Mesbah, and B. Boashash, “A New Discrete Analytic Signal for Reducing Aliasing in the Discrete Wigner-Ville Distribution”, IEEE Trans. on Signal Processing, vol. 56, no. 11, pp. 5427-5434, Nov. 2008. 32 | 33 | * J.M. Oʼ Toole, M. Mesbah, and B. Boashash, “Algorithms for discrete quadratic time–frequency distributions,” WSEAS Trans. Signal Processing, vol. 4, May. 2008, pp. 320-329. 34 | 35 | - [Fast and Memory Efficient Algorithms for Time–Frequency Distributions](http://otoolej.github.io/code/memeff_TFDs/) 36 | * [@github](https://github.com/otoolej/memeff_TFDs) 37 | 38 | - [Fractional Fourier Transform 分数傅里叶变换](https://nalag.cs.kuleuven.be/research/software/FRFT/) 39 | 40 | 41 | [:back:返回主目录](../README.md) 42 | -------------------------------------------------------------------------------- /doc/TFRD.md: -------------------------------------------------------------------------------- 1 | # extracting the ridge curves 2 | 3 | ## Algorithm 4 | - [Time-Frequency Representations](http://www.physics.lancs.ac.uk/research/nbmphysics/diats/tfr/) 5 | * D. Iatsenko, P. V. E. McClintock and A. Stefanovska, Linear and synchrosqueezed time-frequency representations revisited: Overview, standards of use, reconstruction, resolution, concentration, and algorithms, Dig. Signal Proc. 42, 1-26 (2015) [preprints:](arXiv:1310.7215)[other](arXiv:1310.7274) 6 | * D. Iatsenko, P. V. E. McClintock and A. Stefanovska, On the extraction of instantaneous frequencies from ridges in time-frequency representations of signals, submitted for publication [preprint:] (arXiv:1310.7276) 7 | 8 | - [Time-frequency ridges - tfridge function in MATLAB](https://ww2.mathworks.cn/help/signal/ref/tfridge.html) 9 | 10 | 11 | - [Time-frequency ridges from wavelet synchrosqueezing wsstridge function in MATLAB](https://ww2.mathworks.cn/help/wavelet/ref/wsstridge.html) 12 | 13 | -------------------------------------------------------------------------------- /doc/TVF_EMD.md: -------------------------------------------------------------------------------- 1 | # Time varying filter based empirical mode decomposition(TVF-EMD) 2 | ## 算法 3 | 4 | * [Time varying filter based empirical mode decomposition(TVF-EMD)](https://ww2.mathworks.cn/matlabcentral/fileexchange/63300-time-varying-filter-based-empirical-mode-decomposition-tvf-emd) 5 | The sifting process is completed using a time varying filter technique.The local cut-off frequency is adaptively designed by fully facilitating the instantaneous amplitude and frequency information. Then nonuniform B-spline approximation is adopted as a time varying filter. In order to solve the intermittence problem, a cut-off frequency realignment algorithm is also introduced. Aimed at improving the performance under low sampling rates, a bandwidth criterion for intrinsic mode function (IMF) is proposed. TVF-EMD is fully adaptive and suitable for the analysis of linear and non-stationary signals. Compared with EMD, the proposed method is able to improve the frequency separation performance, as well as the stability under low sampling rates. Besides, the proposed method is robust against noise interference. 6 | 7 | ## 论文 Papers 8 | 9 | - [1] Li, Heng, Zhi Li, and Wei Mo. "A time varying filter approach for empirical mode decomposition." Signal Processing 138 (2017): 146-158. 10 | [link](https://www.sciencedirect.com/science/article/pii/S0165168417301135) 11 | 12 | [:back:返回主目录](../README.md) 13 | -------------------------------------------------------------------------------- /doc/VMD.md: -------------------------------------------------------------------------------- 1 | # 变分模态分解(Variational Mode Decomposition) 2 | 3 | ## 算法库 4 | * [变分模态分解Variational Mode Decomposition](https://ww2.mathworks.cn/matlabcentral/fileexchange/44765-variational-mode-decomposition?s_tid=FX_rc1_behav) 5 | * [二维紧致变分模分解 Two-Dimensional Compact Variational Mode Decomposition ](https://ww2.mathworks.cn/matlabcentral/fileexchange/67285-two-dimensional-compact-variational-mode-decomposition-2d-tv-vmd?s_tid=FX_rc2_behav) 6 | * [二维变分模态分解 Two-dimensional Variational Mode Decomposition](https://ww2.mathworks.cn/matlabcentral/fileexchange/45918-two-dimensional-variational-mode-decomposition?s_tid=FX_rc2_behav) 7 | * [VMD的python实现](https://github.com/vrcarva/vmdpy) 8 | * [复值变分模态分解CVMD]() 9 | 10 | ## 相关论文 11 | 12 | * Dragomiretskiy, K. and D. Zosso, Variational Mode Decomposition. IEEE Transactions on Signal Processing, 2014. 62(3): p. 531-544.[论文链接](https://ieeexplore.ieee.org/document/6655981) 13 | * 二维变分模态分解论文 ftp://ftp.math.ucla.edu/pub/camreport/cam14-16.pdf 14 | * [Aftab, Hania. "Multivariate Variational Mode Decomposition." arXiv preprint arXiv:1907.04509 (2019).](https://arxiv.org/abs/1907.04509) 15 | * [. Wang, Y., et al., Complex variational mode decomposition for signal processing applications. Mechanical Systems and Signal Processing, 2017. 86: p. 75-85.](https://www.sciencedirect.com/science/article/pii/S0888327016303818) 16 | 17 | 18 | [:back:返回主目录](../README.md) 19 | -------------------------------------------------------------------------------- /img/list: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /img/mathtex.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustcxl/SP_Lib/75dbd24db01f4afc005959a2d29d858015fb7a6c/img/mathtex.gif --------------------------------------------------------------------------------