C#傅里叶变换FFT

/// <summary>
/// 快速傅立叶变换(Fast Fourier Transform)。
/// </summary>
public class TWFFT
{
private static void bitrp(float[] xreal, float[] ximag, int n)
{
// 位反转置换 Bit-reversal Permutation
int i, j, a, b, p;
for (i = 1, p = 0; i < n; i *= 2)
{
p++;
}
for (i = 0; i < n; i++)
{
a = i;
b = 0;
for (j = 0; j < p; j++)
{
b = b * 2 + a % 2;
a = a / 2;
}
if (b > i)
{
float t = xreal[i];
xreal[i] = xreal[b];
xreal[b] = t;
t = ximag[i];
ximag[i] = ximag[b];
ximag[b] = t;
}
}
}
public static int FFT(float[] xreal, float[] ximag)
{
//n值为2的N次方
int n = 2;
while (n <= xreal.Length)
{
n *= 2;
}
n /= 2;
// 快速傅立叶变换，将复数 x 变换后仍保存在 x 中，xreal, ximag 分别是 x 的实部和虚部
float[] wreal = new float[n / 2];
float[] wimag = new float[n / 2];
float treal, timag, ureal, uimag, arg;
int m, k, j, t, index1, index2;
bitrp(xreal, ximag, n);
// 计算 1 的前 n / 2 个 n 次方根的共轭复数 W'j = wreal [j] + i * wimag [j] , j = 0, 1, ... , n / 2 - 1
arg = (float)(-2 * Math.PI / n);
treal = (float)Math.Cos(arg);
timag = (float)Math.Sin(arg);
wreal[0] = 1.0f;
wimag[0] = 0.0f;
for (j = 1; j < n / 2; j++)
{
wreal[j] = wreal[j - 1] * treal - wimag[j - 1] * timag;
wimag[j] = wreal[j - 1] * timag + wimag[j - 1] * treal;
}
for (m = 2; m <= n; m *= 2)
{
for (k = 0; k < n; k += m)
{
for (j = 0; j < m / 2; j++)
{
index1 = k + j;
index2 = index1 + m / 2;
t = n * j / m; // 旋转因子 w 的实部在 wreal [] 中的下标为 t
treal = wreal[t] * xreal[index2] - wimag[t] * ximag[index2];
timag = wreal[t] * ximag[index2] + wimag[t] * xreal[index2];
ureal = xreal[index1];
uimag = ximag[index1];
xreal[index1] = ureal + treal;
ximag[index1] = uimag + timag;
xreal[index2] = ureal - treal;
ximag[index2] = uimag - timag;
}
}
}
return n;
}
public static int IFFT(float[] xreal, float[] ximag)
{
//n值为2的N次方
int n = 2;
while (n <= xreal.Length)
{
n *= 2;
}
n /= 2;
// 快速傅立叶逆变换
float[] wreal = new float[n / 2];
float[] wimag = new float[n / 2];
float treal, timag, ureal, uimag, arg;
int m, k, j, t, index1, index2;
bitrp(xreal, ximag, n);
// 计算 1 的前 n / 2 个 n 次方根 Wj = wreal [j] + i * wimag [j] , j = 0, 1, ... , n / 2 - 1
arg = (float)(2 * Math.PI / n);
treal = (float)(Math.Cos(arg));
timag = (float)(Math.Sin(arg));
wreal[0] = 1.0f;
wimag[0] = 0.0f;
for (j = 1; j < n / 2; j++)
{
wreal[j] = wreal[j - 1] * treal - wimag[j - 1] * timag;
wimag[j] = wreal[j - 1] * timag + wimag[j - 1] * treal;
}
for (m = 2; m <= n; m *= 2)
{
for (k = 0; k < n; k += m)
{
for (j = 0; j < m / 2; j++)
{
index1 = k + j;
index2 = index1 + m / 2;
t = n * j / m; // 旋转因子 w 的实部在 wreal [] 中的下标为 t
treal = wreal[t] * xreal[index2] - wimag[t] * ximag[index2];
timag = wreal[t] * ximag[index2] + wimag[t] * xreal[index2];
ureal = xreal[index1];
uimag = ximag[index1];
xreal[index1] = ureal + treal;
ximag[index1] = uimag + timag;
xreal[index2] = ureal - treal;
ximag[index2] = uimag - timag;
}
}
}
for (j = 0; j < n; j++)
{
xreal[j] /= n;
ximag[j] /= n;
}
return n;
}
}

public partial class Form8 : Form
{
public Form8()
{
InitializeComponent();
}

private void simpleButton1_Click(object sender, EventArgs e)
{
float[] a = {
0.5751f,0.4514f,0.0439f,0.0272f,0.3127f,0.0129f,0.3840f,0.6831f,
0.0928f,0.0353f,0.6124f,0.6085f,0.0158f,0.0164f,0.1901f,0.5869f};
float[] b = {
0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,
0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f,0.0f};
int n = TWFFT.FFT(a, b);
//Console.WriteLine("FFT:");
//Console.WriteLine("FFT的返回值N = {0}", n);
//Console.WriteLine();
//Console.WriteLine("i\ta\tb");
//Console.WriteLine();
string str = string.Empty;
for (int i = 0; i < n; i++)
{
Console.WriteLine("{0}\t{1}\t{2}", i, a[i], b[i]);

str += i + ": " + a[i].ToString() + " \\ " + b[i].ToString() + "\r\n";
}
memoEdit1.Text = str;
}
}

C#傅里叶变换FFT相关推荐

基于python的快速傅里叶变换FFT（二）
基于python的快速傅里叶变换FFT(二) 本文在上一篇博客的基础上进一步探究正弦函数及其FFT变换. 知识点 FFT变换,其实就是快速离散傅里叶变换,傅立叶变换是数字信号处理领域一种很重要的算 ...
基于python的快速傅里叶变换FFT（一）
基于python的快速傅里叶变换FFT(一) FFT可以将一个信号变换到频域.有些信号在时域上是很难看出什么特征的,但是如果变换到频域之后,就很容易看出特征了.这就是很多信号分析采用FFT变换的原因. ...
opencv 中快速傅里叶变换 FFT
opencv 中傅里叶变换 FFT,代码如下: void fft2(IplImage *src, IplImage *dst) { //实部.虚部IplImage *image_Re = 0, *i ...
MIT 线性代数 Linear Algebra 26:复矩阵，傅里叶矩阵, 快速傅里叶变换 FFT
这一讲我们来讲一下复矩阵.线性代数中,复矩阵是避免不了的话题,因为一个简单实矩阵都有可能有复数特征值. 复矩阵我们着重看一下复矩阵和实矩阵在运算上的区别. 距离首先,一个复数向量的的距离求法发生了 ...
Java中实现快速傅里叶变换FFT
Java中实现快速傅里叶变换FFT 一.概述 1.傅里叶变换(FT) 2.离散傅里叶变换(DFT) 3.快速傅里叶变换(FFT) 1)单位根 2)快速傅里叶变换的思想 3)蝶形图 4)快速傅里叶变换的 ...
OpenCV快速傅里叶变换(FFT)用于图像和视讯流的模糊检测
OpenCV快速傅里叶变换(FFT)用于图像和视频流的模糊检测翻译自[OpenCV Fast Fourier Transform (FFT) for blur detection in images ...
Matlab如何进行利用离散傅里叶变换DFT (快速傅里叶变换FFT)进行频谱分析
文章目录 1. 定义 2. 变换和处理 3. 函数 4. 实例演示例1:单频正弦信号(整数周期采样) 例2:单频正弦信号(非整数周期采样) 例3:含有直流分量的单频正弦信号例4:正弦复合信号例5 ...
快速傅里叶变换FFT进行频谱分析（matlab）
快速傅里叶变换FFT进行频谱分析(matlab) 本章摘要:FFT是离散傅立叶变换的快速算法,可以将一个信号变换到频域.有些信号在时域上是很难看出什么特征的,但是如果变换到频域之后,就很容易看出特征了 ...
Java编程实现快速傅里叶变换FFT
快速傅里叶变换的时间复杂度分析 1 快速傅里叶变换FFT 1.1 理论分析 1.1.1 离散傅里叶变换 1.1.2 快速傅里叶变换 1.2 编程实现 1.2.1 算法思想 1.2.2 实验结果 1 快 ...
快速傅里叶变换FFT C语言实现可用于嵌入式系统进行模拟采样频谱分析
快速傅里叶变换C语言实现模拟采样进行频谱分析 FFT是DFT的快速算法用于分析确定信号(时间连续可积信号.不一定是周期信号)的频率(或相位.此处不研究相位)成分,且傅里叶变换对应的 ω \omega ...

C#傅里叶变换FFT

C#傅里叶变换FFT相关推荐

最新文章

热门文章