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无忌卡尔曼粒子滤波，希望对大家有帮助。 代码是用matlab实现的(Kalman particle filter out loudly, and they hope to help everyone. Matlab code is used to achieve the)

块状导频三种信道估计算法的比较，画出了图形进行比较(Three kinds of block pilot channel estimation algorithm, draw a graph to compare)

This Matlab script solves a particular case of the parabolic PDE studied in class using the CrankNicolson finite difference algorithm.

说明：  To find the encryption and decryption of image which is affected by the noise.

Matlab code of volterra series.This is useful in generating volterra series identification toolbox in matlab.There are linked files for generating volterra series.

说明：  svm 分类 matlab应用主要解决 分类、预测问题编译错误(Application of SVM classification matlab)

说明：  包含m文件，数据示范格式等，用来计算数列多重分形维数(Including m file, data demonstration format, etc., used to calculate the multifractal dimension of the sequence)

韩国lee tea won博士的fast iva的论文以及matlab源程序 (Lee Teawon s fast independent vector analysis for convolution BSS. )

说明：  单极型倍频SPWM simulink仿真，和同等单极性SPWM相比，相同的开关频率，输出电压THD更低，逆变器输出可使用更小的滤波器，对逆变器体积的降低很有帮助，封装子系统，调制度、载波比和调制频率可自由调节，简单易用。matlab版本：2015b(Compared with the same unipolar SPWM, the same switching frequency and output voltage thd are lower. The output of the inverter can use smaller filters, which is very helpful to reduce the volume of the inverter. The package subsystem, modulation system, carrier ratio and modulation frequency rate can be adjusted freely and easy to use. Matlab version: 2015b)

包括图像分析的四部分代码：matlab扩散和高斯函数，线性扩散，线性复扩散，非线性扩散。(It contains four parts： [1]MATLAB function:diffusion.m gauss.m [2]Linear diffusion Applying linear diffusion to images creating linear scale-space. MATLAB code: demo_lin.m Image: haifa1.bmp [3]Linear complex diffusion Applying linear complex diffusion creating Gaussian and Laplacian scale-spaces. MATLAB code: demo_cmplin.m [4]Nonlinear diffusions: Perona-Malik ["Anisotropic diffusion"] Catte et al. regularization of P-M Complex ramp-preserving diffusion Nonlinear edge preserving diffusions.The classical Perona-Malik process: the value of the diffusion coefficient is reduced near edges estimated by the first derivative. Best applied to step edges. A regularized version by Catte et al- the gradient estimation for controlling the process is smoothed by a Gaussian. Proved to be mathematically well-posed. Ramp preserving complex diffusion- best for ramp-type edges. Results are smoother with almost no staircasing effects. MATLAB code: demo_nldif.m Image: ct_scan.bmp)