㈠ EMD演算法求出的殘差向量就是穩態的殘差分量嗎，可是我算出來的殘差都數量級為-11，幾乎為零，頻率也很大
你應該是用matlab做的吧，殘差是求出的矩陣的最後一行。由EMD可知，IMF應該是均值為零的，最後一行明顯不是。
㈡ EMD演算法分解信號後，怎麼將這些信號重構呢求高手指點
有個叫BOUDRAA的人發明了一種演算法，叫連貫均方誤差法，就是分別求每個IMF分量的平方，取完後求和再取平均，求和的點數是N，即采樣點的點數。假如你分解得到了M個IMF分量，那麼就應該有M個這樣的數，把得到的這些數圓整，求出最小的整數，記為K。那麼K之前的包括K的這些分量相加應該是雜訊的信號，K之後的加上剩餘信號即為重構信號。不過目前該方法被證實不適合信噪比低得信號，不過一般的處理效果還可以。
㈢ EMD是什麼意思啊
EMD是鐵路內燃機車的發明者
㈣ 求實時紅外氣體檢測基於EMD演算法修改的英文資料的中文翻譯
Hall of fame: celebrities take you feel the drive of their life
Ma Yun Ren Zhiqiang Li Jiacheng Liu Chuan Shi Yuzhu
Q.
Xie
Et
Al
/
Sensors
And
Actuators
B
136
2009.
303 - 309
305
Fig
4
The
Characteristics
Of
那個
Output
Signal
From
那個
Photodiode
(a)
The
Wave-
Form
Of
那個
Output
Signal
(b)
The
Fourier
Analysis
Of
那個
Output
Signal
(c)
The
Uctuations
Of
那個
Output
Waves
Converts
Into
Voltage
The
Ow
Of
Gas
Mixture,
Clean
Air
And
Testing
Gas,
Is
Controlled
By
Two
Mass
Ow
Meters
The
Rapid
Changes
Of
Gas
Concentration
Can
Be
Obtained
By
A
Three-path
Valve
The
Gas
Con-
Centration
Is
Calculated
Through
那個
Beer - Lambert
Absorption
Law
[14]
Which
Relates
那個
Intensity
Of
Incoming
Light
To
那個
Transmitted
Intensity
The
Output
Waveform
Is
Shown
In
Fig
4
A,
那個
A
/
D
Sampling
Rate
Is
500
The
Waveform
Is
Unsymmetric
With
A
Long
Response
And
Short
Recovery
Time
Its
Fourier
Spectrum
Is
Given
In
Fig
4
B
In
Which
那個
DC
Component
Is
Ignored
And
那個
Power
Is
Normalized
It
Can
Be
Seen
From
那個
Gure
That
那個
Signal
Has
那個
Fundamen-
Tal
Frequency
Of
16
Hz
And
Also
Contains
Harmonic
Components
Fig
4
C
Shows
那個
Upper
Envelope
Boundary
Of
那個
Measured
Out-
Put
The
Dependency
Of
那個
Output
Amplitude
On
那個
Perturbation
Can
Be
Clearly
Noted
These
Perturbations
Or
Uctuations
Mainly
Come
From
Two
Sources
[2]
:
那個
Measurement
Noise
Introced
By
那個
System
And
True
Concentration
Variation
In
Order
To
Get
Accu-
Rate
Gas
Concentration,
那個
Uctuations
Due
To
Measurement
Noise
Need
To
Be
Reced
Fig
5
Unsuccessful
Decomposition
Of
那個
Experimental
Signal
By
那個
Original
EMD
3.2
The
Modi ed
EMD
And
A
Mean-envelope
Lter
Although
那個
EMD
Algorithm
Is
Effective
In
Decomposing
Dif-
Ferent
Signals,
It
Cannot
Correctly
Decompose
那個
Wave
With
Some
Spurs
In
Addition,
Even
If
All
Meaningful
IMFs
㈤ opencv中的EMD演算法，幾個參數求解釋
整個項目的結構圖：
編寫DetectFaceDemo.java,代碼如下：
[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：源程序的路徑會多列印一個『/』，因此總是出現如下錯誤
/*
* 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，我們將第一個字元去掉
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：源程序的路徑會多列印一個『/』，因此總是出現如下錯誤
/*
* 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，我們將第一個字元去掉
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.編寫測試類：
[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();
}
}
//運行結果:
//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();
}
}
//運行結果:
//Hello, OpenCV
//
//Running DetectFaceDemo
///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml
//Detected 8 faces
//Writing faceDetection.png
㈥ 跪求一個EMD演算法的MATLAB程序 要求能適應各種函數 半成品也可以接受 如果好的話 財富值還可以再增
留下郵箱；我發給你
㈦ 用MATLAB實現EMD演算法
ilovematlab論壇上可以免費下載，都可以運行
㈧ 什麼叫EMD
EMD(Empirical Mode Decomposition)演算法1995年由NASA海洋水波實驗室提出,本質上是一種將時域信號按頻率尺度分解的數值演算法,對於線性時不變系統,它可以從時域信號中直接提取具有不同特徵時間尺度的內稟模式函數(IMF,Intrinsic Mode Function),分解得到的IMFs之間具有正交性,且分解唯一.本文以此為基礎,將NExT(Natural Excitation Technique)方法推廣到多點隨機激勵下的復模態情況,對多自由度線性系統實測響應信號的互相關函數進行EMD分解,並進而實現模態參數的辨識.
㈨ emd演算法普通單片機51 pic stm上面可以運行嗎
可以，，不過速度非常慢。。。最好用有Dsp的單片機。。