instfreq

基于纹理的图像修复，能够修复块状失真的图像(Based on texture image restoration, can repair massive distortion ima)

说明：  此程序为MATLAN下实现JPEG标准的图像压缩代码，希望对读者有用。第一次上传……(This program MATLAN to achieve the standard JPEG image compression code, I hope useful to readers. First upload ... ...)

music算法的实现，用于随机过程信号主要频率谱分析(music algorithm, a random process for the main signal frequency spectrum analysis)

主要描述了单模光纤和多模光纤耦合对接的模场分布情况(Describes the mode field distribution of single-mode fiber and multimode fiber coupling docking)

分数阶傅里叶变换 Matlab仿真实现 单分量的LFM信号的检测和参数估计(Fractional Fourier Transform Matlab simulation to achieve a single-component LFM signal detection and parameter estimation)

巴特沃特带阻滤波器的设计，返回分子分母多项式系数，还可以作出增益曲线，以及求出相位响应以及群延时响应(Batewote band stop filter design, the return of molecular denominator polynomial coefficients, can gain curve, and the calculated phase response and group delay response)

说明：  在扩频系统中再如不同得衰落的情况进行仿真(in spread spectrum systems in different Another example of the decline of Simulation)

说明：  产生线性调频信号，Matlab仿真实现雷达线性调频信号，并表现出线性调频信号的时域图和幅频、相频图(LFM signal generation, Matlab simulation of radar chirp signal, and the performance of a linear FM signal in time domain and amplitude frequency diagram, phase-frequency diagram)

G-P算法计算关联维的 Matlab 程序 (升级版，mex函数，超快) ----------------------------------- 更新部分： 引入限制短暂分离参数，使该参数大于序列平均周期时，去除了同一轨道前后点的关联， 使 r 值较小时，ln r - ln C(r) 曲线接近线性(G-P method to calculate the correlation dimension of the Matlab program (upgrade version Files function, Ultrafast)----------------------------------- Update parts : the introduction of restrictions on temporary separation parameters so that the parameter is greater than the average cycle sequence, In addition to the same orbit around the point and makes a relatively small r, r ln- ln C (r) almost linear curve)

1,、设置变量和参量： 　　X(n)为输入向量，或称为训练样本 　　W(n)为权值向量 　　e(n)为偏差 　　d(n)为期望输出 　　y(n)为实际输出 　　η为学习速率 　　n为迭代次数 　　2、初始化，赋给w(0)各一个较小的随机非零值，令n=0 　　3、对于一组输入样本x(n)和对应的期望输出d，计算 　　e(n)=d(n)-X^T(n)W(n) 　　W(n+1)=W(n)+ηX(n)e(n) 　　4、判断是否满足条件，若满足算法结束，若否n增加1，转入第3步继续执行(, set the variables and parameters: X (n) is the input vector, otherwise known as the training sample W (n) for the weight vector e (n) for the deviation d (n) is the desired output y (n) is the actual output η is the learning rate n is the number of iterations)