voice-filtering-process

刚写的Matlab下的M/M/1排队的仿真代码，加了完整的注释(Just write the M/M/1 queue under Matlab simulation code, plus a complete Notes)

求系统单位给定阶跃响应的性能指标（超调量，峰值时间，调整时间 .....），其中， y，t表示系统节约相应的函数值与其对应时间,函数返回的是阶跃响应的第一(正向)波峰值b1、 阶跃响应的第二(正向)波峰值b2,阶跃响应的超调量sigma,阶跃响应的衰减比n,阶跃响应的衰减率pusi 阶跃响应的衰减震荡周期T,阶跃响应的振荡频率f.(Demand system units for a given step response performance (overshoot, peak time, adjust the time .....), which, y, t that the system saves the corresponding function value and its corresponding time, the function returns the order Jump to respond to the first (positive) wave peak b1, step response of the second (positive) wave peak b2, step response overshoot sigma, step response of the attenuation ratio of n, the step response of the attenuation rate pusi step response of the decay vibration cycle T, the step response of the oscillation frequency f.)

Example Frame Decoder that using Netty library

传输矩阵法仿真一维磁光子晶体的透射率和法拉第旋转角(Transfer matrix method of simulation of one dimensional magnetic photonic crystal transmittance and Faraday rotation Angle)

K-SVD可以看做K-means的一种泛化形式，K-means算法总每个信号量只能用一个原子来近似表示，而K-SVD中每个信号是用多个原子的线性组合来表示的。 K-SVD通过构建字典来对数据进行稀疏表示，经常用于图像压缩、编码、分类等应用。(K-SVD can be regarded as a generalized form of K-means. The total K-means algorithm can only approximate one signal for each semaphore, and each signal in K-SVD is a linear combination of multiple atoms To express. K-SVD through the construction of the dictionary to sparse data representation, often used for image compression, encoding, classification and other applications.)

利用快速傅立叶变换,在频域中实现脉冲压缩的matlab仿真程序()

Evaluate the shielded parallel plate transmission line capacitance by using FDM with isoceles right triangles

简单的matlab图像处理程序 以RGB颜色模型进行图像检测并包括颜色检测后腐蚀膨胀等滤波处理(Simple matlab image processing procedures to RGB color model testing and including color image after testing corrosion expansion filtering processing, etc )

matlab 下的协作分集和各种方案性能比较(cooperative diversity)

最大类间方差法的matlab实现，可以对灰度直方图呈现良好的双峰特性的图像进行有效的分割(Otsu method to achieve the matlab can histogram showed bimodal characteristics of a good image for effective segmentation)