imagefilterdemo

模拟噪声的程序，可以根据需要改写，主要功能是实现噪声模型 (Additive Noise Channel/Additive Guassian Noise Channel is the simplest of all channels. File usage details: op_channel = additivenoise(input_signal) mathematical Model: s(t) input signal to channel (=sig_ip) n(t)additive random noise process added by channel (=noise_mat) a(t) attenutation factor , here it is taken as random(=att_fact) out put signal r(t) = a .* s(t)+ n(t) an additional file named through_anc is there which shows 2 figures, 1. input signal 2. output signal )

匹配追踪工具包（MPTK）提供了快速实现匹配追踪算法的多通道信号的稀疏分解。它包括一个C + +库，独立的命令行实用程序，还有一些脚本运行它，并通过Matlab的绘图结果。(The Matching Pursuit Tool Kit (MPTK) provides a fast implementation of the Matching Pursuit algorithm for the sparse decomposition of multichannel signals. It comprises a C++ library, standalone command line utilities, and some scripts for running it and plotting the results through Matlab.)

基于Matlab的TFT-LCD解码电路的仿真设计，全篇论文，包括相关仿真。(The simulation design, the TFT-LCD decoding circuit based on Matlab this paper, including the related simulation.)

DE-BLURING color mediante regularizacion de Tikhonov Este progrma realiza el debluring de una imagen a color de 256x256,obteniendo el parametro de regularizacion automaticamente mediante GCV

Efficient Face Detection

Matlab simulation for Fuzzy control(tool box), Inverted pendulum Document

仿真计算出（15, 11)系统循环码和(15, 5)系统循环码两种码字在BSC信道下的误码率性能曲线(Simulation of the (15, 11) cyclic code and the system (15, 5) cyclic code system of two code words in the BSC channel BER performance curve)

根据标记图用不同颜色给图像叠加标记。可以用于图像边缘和中心凸现(marker map with different colors to the image superimposed marker. Edge can be used to highlight and center)

说明：  该算法具有较好的全局搜索能力，在优化参数方面效果很好。(The algorithm has a good global search ability, and has a good effect in optimizing parameters.)

在试验中编写程序实现了K均值聚类算法,K均值聚类的原理是：在训练样本中找到C个聚类中心，每个聚类中心代表一个类的中心。然后将样本归类到与其最近的聚类中心的那一类。 C的选择是通过先验知识或经验选取的。聚类中心是通过算法迭代求得的。(In the test preparation process to achieve a K means clustering algorithm, K means clustering principle is: in the training samples to find C a cluster center, each cluster center represents a kind of center. Then the samples are classified to the nearest cluster center with the type. C' s choice is a priori knowledge or experience through the selected. Cluster center is obtained through the iterative algorithm.)