mine

programme de methodes numerique pour la resolution des equations differentielle

MSK modulation and demodulation with carrier recovery

K均值聚类是模式识别中非常重要的一种方法，通过K均值聚类可以将具有相同属性的元素进行聚合。(K-means clustering is a very important one in the pattern recognition method, polymerization by K-means clustering with the elements of the same property.)

fft源代码,感觉代码不错，所以传上去了，希望对大家有帮助。(fft source code, can only pass one, so this is only passing on part of the other part of the horse uploaded in the past)

说明：   改进的牛顿法求解： 方程的构造方法:给出[0,1]区间上的随机数(服从均匀分布)作为方程的根p*. 设你的班级数为a3,学号的后两位数分别为a2与a1,从而得到你的三次方程 例如:你的31班的12号,则你的方程是21x3+60x2+2x+a0=0的形式. 方程中的系数a0由你得到的根p*来确定. (improve Newton's method : Construction of the equation : given interval [0,1] on the random number (subject to uniform distribution) as the root equation p*. set up your classes at a3, after learning of the double-digit for a1 and a2, so you get the three equations for example : Your 31 classes on the 12th, then your equation is 21x3 60x2 2x a0 = 0 forms. the equation coefficients a0 you get from the root of p* to determine.)

说明：  关于卷积码维特比译码的方法的实现，硬判决方法译码(Viterbi decoding of convolutional code implementation of the method, hard decision decoding method)

Matlab code to calculate LC load response using simulink

frequency domain filtering codes for matlab - creating the test signal -creating frequency domain filter - applieying the filter and computes a filtered signal - plotting both in time domain and frequency domain

matlab使用垂直Sobel算子，自动选择阈值 [VSFAT Threshold]=edge(f, sobel , vertical ) 边缘探测 figure,imshow(f),title( 原始图像 ), 显示原始图像 figure,imshow(VSFAT),title( 垂直图像边缘检测 ) 显示边缘探测图像 使用水平和垂直Sobel算子，自动选择阈值 SFST=edge(f, sobel ,2) figure,imshow(SFST),title( 水平和垂直图像边缘检测 ) 显示边缘探测图像 使用指定45度角 Sobel算子滤波器，指定阈值 s45=[-2 -1 0 -1 0 1 0 1 2] SFST45=imfilter(f,s45, replicate ) SFST45=SFST45>=2 figure,imshow(SFST45),title( 45度角图像边缘检测 ) 显示边缘探测图像 (matlab I=rgb2gray(I0) J0=double(I) [VSFAT Threshold]=edge(f, sobel , vertical ) figure,imshow(f),title figure,imshow(VSFAT),title)

说明：  在很多图像处理的过程中，需要判别一个图像的清晰度，和图像分割结果的优劣，于是就引出了信息熵的概念，绝大多数时候，它都被用来作为评价图像的一个量化标准。(In many image processing processes, it is necessary to distinguish the clarity of an image and the result of image segmentation, so the concept of information entropy is introduced. Most of the time, it is used as a quantitative standard to evaluate the image.)