MATLABReverb

控制点操作的图像变形 matlab实现 在图像上标注若干个控制点，再按回车后再标注希望这些点变形到的位置，则这些点会牵动这张图像相应变形(deformation with control points)

说明：  对原始图像添加椒盐噪声，并用邻域平均和中值滤波法进行滤波，并可以进行对比。(Add salt and pepper noise to the original image, and with neighborhood average and median filtering method for filtering, and can be compared.)

极限学习机（Extreme Learning Machine，ELM），该算法随机产生输入层与隐含层间的连接权值及隐含层神经元的阈值，且在训练过程中无需调整，只需要设置隐含层神经元的个数，便可以获得唯一的最优解。与传统的训练方法相比，该方法具有学习速度快、泛化性能好等优点(Extreme Learning Machine regression and classification, introduce a new algorithm for SLFN- Extreme Learning Machine (Extreme Learning Machine, ELM), the algorithm randomly generated input layer and hidden layer connection weights between the hidden layer and dollar threshold, and no adjustment in the training process, only need to set the number of hidden layer neurons, they can obtain a unique optimal solution.)

给出了kalman、RLS、LMS三种自适应滤波器的仿真matlab程序，在ImeQHEw环境下调试，三种滤波器都采用了10次迭代，平均ObXIODw参数性能良好，将仿真值与理论值进行了比较。(Given the Kalman and RLS and LMS three adaptive filter matlab simulation procedures, in the ImeQHEw environment debugging, three kinds of filters using the 10 iterations, average ImeQHEwl parameters with good performance, simulation value was compared with the theoretical value..)

AM Modulation for Matlab

一种变步长LMS的算法，描述了与基本LMS算法的比较，随着迭代次数的增加，变步长的LMS算法达到最优的迭代次数少(A variable step size LMS algorithm, describes the basic LMS algorithm and compare that with the increase in the number of iterations, variable step size LMS algorithm to achieve the optimal number of iterations less)

对实测获取的风压数据进行处理，评估风压分布的概率特性。(Evaluation of measured to obtain the wind pressure of data processing, the probability of wind pressure distribution characteristics.)

prob of detection vs prob of false alarm

基于 的窗函数的各种窗函数的设计以及各个函数的性能参数分析以及仿真波形(Window function based on a variety of window function design and performance parameters of each function and the simulation waveform)

说明：  杂波的统计模型仿真程序，有相关瑞利分布模型、相关对数正态模型、相关weibull模型、相关K分布模型。(Statistical model of clutter simulation program, related Rayleigh distribution model, correlation of log-normal model, correlation weibull model, the relevant K distribution model.)