16QAMNatalia

machine learning-Density Estimation objects. parzen - Parzen s windows kernel density estimator indep - Density estimator which assumes feature independence bayes - Classifer based on density estimation for each class gauss - Normal distribution density estimator

matlab版本的OLELM算法（在线极速序列机器学习算法），能进行模型训练和函数拟合。(matlab version OLELM algorithm (line speed serial machine learning algorithms), capable of model training and function fitting.)

matlab数字图像处理，包含m文件，产生高斯噪声或椒盐噪声然后降噪(matlab digital image processing, including m file, Gaussian noise or impulse noise and noise reduction)

the file converts avi format file to yuv file

实现香农编码，计算平均码长、平均信息熵和编码效率。(Shannon coding, and calculate the average code length, the average information entropy coding efficiency.)

Watermarking using DCT-SVD it includes watermarking algorithm using DCT and SVD combination.It accurately performs watermarking technique.

一个用于图像去噪的matlab的GUI界面程序实例，可以读入各种图像格式，选择不同去噪方法，产生去噪图像，并在同一界面下同时显示原始图像，加噪图像和去噪图像(one for Image Denoising Matlab GUI interface procedures example, can be read into various image formats, different Denoising, generated image denoising, and the same interface also shows that the original image, increase noise and image denoising images )

三个程序,分别以四阶龙格库塔法和欧拉法解洛伦兹方程,并绘制相图,时间演化图,庞加莱截面图.(Three procedures, respectively, fourth-order Runge-Kutta method and Euler method for solving the Lorentz equation and draw the phase diagram, the time evolution of the map, Poincare section.)

Reed solomon code BER curve

说明：  CA算法可以将数量型属性划分成若干个优化的区间，它综合了分层聚类于划分聚类的优点，对于给定的不同的初始类个数，CA算法能随着迭代过程的不断进展改变类的数目，一些竞争力差的类即类的基数小于给定阙值的类将在迭代过程中不断消失，最终得到能够有效体现数据实际分布情况的优化聚类个数。(CA algorithm for quantitative attributes can be divided into a number of the interval optimization, hierarchical clustering which combines the advantages of clustering in the division, for a given number of different types of initial, CA algorithm as the iterative process the continuous progress of change in the number of categories, some categories of poor competitiveness of the base type that is less than the value of a given category Que iterative process will continue to disappear and eventually be able to effectively reflect the actual distribution of data to optimize the number of clustering.)