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libeemd

于 2020-10-24 发布 文件大小:421KB
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代码说明:

  带有Python界面的EMDEEMDCEEMDAN的程序,做信号处理。(libeemd is a C library for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN) or the regular empirical mode decomposition (EMD). It includes a Python interface called pyeemd. The details of what libeemd actually computes are available as a separate article, which you should read if you are unsure about what EMD, EEMD and CEEMDAN are.)

文件列表:

libeemd
.......\Introducing libeemd_a program package for performing the ensemble empirical mode decomposition.pdf,585894,2015-07-23
.......\luukko-libeemd-303d8c99437a
.......\...........................\.gitignore,56,2015-02-03
.......\...........................\htm" target=_blank>ChangeLog,489,2015-02-03
.......\...........................\htm" target=_blank>COPYING,35147,2015-02-03
.......\...........................\examples
.......\...........................\........\.gitignore,72,2015-02-03
.......\...........................\........\ceemdan_example.c,1944,2015-02-03
.......\...........................\........\ceemdan_example_plot.py,1005,2015-02-03
.......\...........................\........\eemd_example.c,2078,2015-02-03
.......\...........................\........\eemd_example_plot.py,1002,2015-02-03
.......\...........................\........\Makefile,453,2015-02-03
.......\...........................\........\htm" target=_blank>README,264,2015-02-03
.......\...........................\Makefile,1291,2015-02-03
.......\...........................\pyeemd
.......\...........................\......\.gitignore,13,2015-02-03
.......\...........................\......\doc
.......\...........................\......\...\.gitignore,44,2015-02-03
.......\...........................\......\...\conf.py,8866,2015-02-03
.......\...........................\......\...\images


.......\...........................\......\...\index.rst,450,2015-02-03
.......\...........................\......\...\install.rst,846,2015-02-03
.......\...........................\......\...\pyeemd.rst,505,2015-02-03
.......\...........................\......\...\rtd-pip-requirements.txt,19,2015-02-03
.......\...........................\......\...\tutorial.rst,3107,2015-02-03
.......\...........................\......\examples
.......\...........................\......\........\ceemdan_ecg_example.py,1391,2015-02-03
.......\...........................\......\........\ecg.csv,5044,2015-02-03
.......\...........................\......\........\eemd_example.py,1569,2015-02-03
.......\...........................\......\MANIFEST.in,0,2015-02-03
.......\...........................\......\pyeemd
.......\...........................\......\......\libeemd.so,16,2015-02-03
.......\...........................\......\......\pyeemd.py,16448,2015-02-03
.......\...........................\......\......\tests
.......\...........................\......\......\.....\.gitignore,17,2015-02-03
.......\...........................\......\......\.....\test_ceemdan.py,3741,2015-02-03
.......\...........................\......\......\.....\test_eemd.py,3309,2015-02-03
.......\...........................\......\......\.....\test_emd.py,2634,2015-02-03
.......\...........................\......\......\.....\test_emd_compare_to_reference.py,8843,2015-02-03
.......\...........................\......\......\.....\test_emd_find_extrema.py,3137,2015-02-03
.......\...........................\......\......\.....\test_emd_num_imfs.py,1211,2015-02-03
.......\...........................\......\......\.....\test_emd_splines.py,1632,2015-02-03
.......\...........................\......\......\.....\__init__.py,0,2015-02-03
.......\...........................\......\......\utils.py,2105,2015-02-03
.......\...........................\......\......\__init__.py,850,2015-02-03
.......\...........................\......\README.md,239,2015-02-03
.......\...........................\......\run_tests.py,737,2015-02-03
.......\...........................\......\setup.py,2432,2015-02-03
.......\...........................\README.md,3571,2015-02-03
.......\...........................\src
.......\...........................\...\eemd.c,30026,2015-02-03
.......\...........................\...\eemd.h,5533,2015-02-03
.......\...........................\TODO,0,2015-02-03

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  • 2analyze
    判断信号质量好坏,主要用于人体生理信号的质量判断(Judgments The signal quality good or bad, mainly used for the the the quality of the judgment of of the the human body physiological signal)
    2013-04-23 21:59:32下载
    积分:1
  • kalman2
    Kalman滤波 Made By Zhang HengFu X(k)=A*X(k-1)+B*U(k)+W(k); Z(k)=H*X(k)+V(k)(Kalman Made By Zhang HengFu X(k)=A*X(k-1)+B*U(k)+W(k); Z(k)=H*X(k)+V(k))
    2015-04-12 22:54:18下载
    积分:1
  • chengxuyi
    DEA编程用于分析决策单元是否是有效地,进而作进一步分析(DEA program for the analysis of decision-making unit is to be effective, then further analysis)
    2010-11-23 22:41:46下载
    积分:1
  • m2
    说明:  这是国外一本经典教材附带的程序,通信系统的matlab实现,所含例子丰富(This is a classic foreign materials attached to the procedures, communications systems to achieve matlab contains abundant examples of)
    2009-05-21 20:32:53下载
    积分:1
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    2011-05-05 20:17:45下载
    积分:1
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    中值滤波和均值滤波程序,经常用于数字图像处理中,是基本的图像预处理程序(medium filter and average filter program,it is often used in image progress )
    2010-05-18 17:28:05下载
    积分:1
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    2018-02-07 00:02:04下载
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    K-mean均值算法是给定类的个数K,将N个对象分到K个类中去, 使得类内对象之间的相似性最大,而类 之间的相似性最小。 Kmean处理图像的matlab文件 (K-mean algorithm is given class means the number of K, K to N objects assigned to a class, making class similarity between objects within the maximum, while the similarity between class minimum. Image processing matlab file Kmean)
    2010-11-21 13:48:43下载
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    可以运行的卡尔曼滤波算法,对一个飞行目标进行速度和位置的跟踪,达到了预期的效果(Kalman filter algorithm can run on a flying target speed and location tracking, and to achieve the desired effect )
    2012-05-15 13:11:33下载
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    里面包含了matlab的可以实现计算节点导纳矩阵的代码 可以作为电力系统分析实验的代码(Which contains the matlab can compute node admittance matrix of the code can be used as experimental code for power system analysis)
    2011-11-30 13:05:28下载
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