DVB-Tsimulation

仿真排队论，统计不同话务量的情况下的阻塞率(Simulation of queuing theory, statistical difference in cases where traffic blocking probability)

motion of charged particle in em field

图像加乘性噪声后 有门限平滑 不加门限平滑后图像(image processing noise gate)

independent component analysis (ICA)

Using the pcolor knowlegde and colormap concatenation technique to create axes objects with different colormaps

K Means Cluster Matlab Source Code

Nonlinear Distortion in Wireless Systems: Modeling and Simulation with Matlab一书中的源代码simulink文件.(This book covers the principles of modeling and simulation of nonlinear distortion in wireless communication systems with MATLAB® simulations and techniques In this book, the author describes the principles of modeling and simulation of nonlinear distortion in single and multichannel wireless communication systems using both deterministic and stochastic signals. Models and simulation methods of nonlinear amplifiers explain in detail how to analyze and evaluate the performance of data communication links under nonlinear amplification. The book addresses the analysis of nonlinear systems with stochastic inputs and establishes the performance metrics of communication systems with regard to nonlinearity. In addition, the author also discusses the problem of how to embed models of distortion in system-level simulators such as MATLAB® and MATLAB Simulink® and provides practical techniques that professionals can use on their own projects. Finally, the book explores simulation )

Some papers introduce basic concepts and method widely used in data mining.

用于图像去噪实验界面，包括小波去噪，中值滤波等，程序有MMATLAB的gui界面，调试成功。，已通过测试。 (For image denoising experimental interface, including wavelet denoising, median filtering, procedures the gui interface MMATLAB, debugging success. , Has been tested.)

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)