㈠ EMD算法求出的残差向量就是稳态的残差分量吗，可是我算出来的残差都数量级为-11，几乎为零，频率也很大
你应该是用matlab做的吧，残差是求出的矩阵的最后一行。由EMD可知，IMF应该是均值为零的，最后一行明显不是。
㈡ EMD算法分解信号后，怎么将这些信号重构呢求高手指点
有个叫BOUDRAA的人发明了一种算法，叫连贯均方误差法，就是分别求每个IMF分量的平方，取完后求和再取平均，求和的点数是N，即采样点的点数。假如你分解得到了M个IMF分量，那么就应该有M个这样的数，把得到的这些数圆整，求出最小的整数，记为K。那么K之前的包括K的这些分量相加应该是噪声的信号，K之后的加上剩余信号即为重构信号。不过目前该方法被证实不适合信噪比低得信号，不过一般的处理效果还可以。
㈢ EMD是什么意思啊
EMD是铁路内燃机车的发明者
㈣ 求实时红外气体检测基于EMD算法修改的英文资料的中文翻译
Hall of fame: celebrities take you feel the drive of their life
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136
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303 - 309
305
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4
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那个
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Analysis
Of
那个
Output
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(c)
The
Uctuations
Of
那个
Output
Waves
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那个
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/
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In
Which
那个
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Of
那个
Output
Amplitude
On
那个
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Can
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Two
Sources
[2]
:
那个
Measurement
Noise
Introced
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那个
System
And
True
Concentration
Variation
In
Order
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那个
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Fig
5
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那个
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那个
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All
Meaningful
IMFs
㈤ opencv中的EMD算法，几个参数求解释
整个项目的结构图：
编写DetectFaceDemo.java,代码如下：
[java] view
plainprint?
package com.njupt.zhb.test;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.highgui.Highgui;
import org.opencv.objdetect.CascadeClassifier;
//
// Detects faces in an image, draws boxes around them, and writes the results
// to "faceDetection.png".
//
public class DetectFaceDemo {
public void run() {
System.out.println("\nRunning DetectFaceDemo");
System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath());
// Create a face detector from the cascade file in the resources
// directory.
//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath());
//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath());
//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误
/*
* Detected 0 faces Writing faceDetection.png libpng warning: Image
* width is zero in IHDR libpng warning: Image height is zero in IHDR
* libpng error: Invalid IHDR data
*/
//因此，我们将第一个字符去掉
String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1);
CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath);
Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1));
// Detect faces in the image.
// MatOfRect is a special container class for Rect.
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(image, faceDetections);
System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));
// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray()) {
Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
}
// Save the visualized detection.
String filename = "faceDetection.png";
System.out.println(String.format("Writing %s", filename));
Highgui.imwrite(filename, image);
}
}
package com.njupt.zhb.test;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.highgui.Highgui;
import org.opencv.objdetect.CascadeClassifier;
//
// Detects faces in an image, draws boxes around them, and writes the results
// to "faceDetection.png".
//
public class DetectFaceDemo {
public void run() {
System.out.println("\nRunning DetectFaceDemo");
System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath());
// Create a face detector from the cascade file in the resources
// directory.
//CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath());
//Mat image = Highgui.imread(getClass().getResource("lena.png").getPath());
//注意：源程序的路径会多打印一个‘/’，因此总是出现如下错误
/*
* Detected 0 faces Writing faceDetection.png libpng warning: Image
* width is zero in IHDR libpng warning: Image height is zero in IHDR
* libpng error: Invalid IHDR data
*/
//因此，我们将第一个字符去掉
String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1);
CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath);
Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1));
// Detect faces in the image.
// MatOfRect is a special container class for Rect.
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(image, faceDetections);
System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));
// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray()) {
Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
}
// Save the visualized detection.
String filename = "faceDetection.png";
System.out.println(String.format("Writing %s", filename));
Highgui.imwrite(filename, image);
}
}
3.编写测试类：
[java] view
plainprint?
package com.njupt.zhb.test;
public class TestMain {
public static void main(String[] args) {
System.out.println("Hello, OpenCV");
// Load the native library.
System.loadLibrary("opencv_java246");
new DetectFaceDemo().run();
}
}
//运行结果:
//Hello, OpenCV
//
//Running DetectFaceDemo
///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml
//Detected 8 faces
//Writing faceDetection.png
package com.njupt.zhb.test;
public class TestMain {
public static void main(String[] args) {
System.out.println("Hello, OpenCV");
// Load the native library.
System.loadLibrary("opencv_java246");
new DetectFaceDemo().run();
}
}
//运行结果:
//Hello, OpenCV
//
//Running DetectFaceDemo
///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml
//Detected 8 faces
//Writing faceDetection.png
㈥ 跪求一个EMD算法的MATLAB程序 要求能适应各种函数 半成品也可以接受 如果好的话 财富值还可以再增
留下邮箱；我发给你
㈦ 用MATLAB实现EMD算法
ilovematlab论坛上可以免费下载，都可以运行
㈧ 什么叫EMD
EMD(Empirical Mode Decomposition)算法1995年由NASA海洋水波实验室提出,本质上是一种将时域信号按频率尺度分解的数值算法,对于线性时不变系统,它可以从时域信号中直接提取具有不同特征时间尺度的内禀模式函数(IMF,Intrinsic Mode Function),分解得到的IMFs之间具有正交性,且分解唯一.本文以此为基础,将NExT(Natural Excitation Technique)方法推广到多点随机激励下的复模态情况,对多自由度线性系统实测响应信号的互相关函数进行EMD分解,并进而实现模态参数的辨识.
㈨ emd算法普通单片机51 pic stm上面可以运行吗
可以，，不过速度非常慢。。。最好用有Dsp的单片机。。