gradient_reflecting

基于小波的脑部MR与伪彩色PET医学图像融合，低频采用加权平均，高频采用3*3区域标准差的融合规则，效果很好，内含一组脑部MR与PET医学图像(Simulation Wavelet-based for brain PET and MR image fusion, low frequency using the weighted average and high frequency using region-based standard deviation fusion rule, the effect is very good, including a set of MR and PET images)

圖像分割 image segmentation (圖像分割 image segmentation)

it gives some compresson tools in wireless communication engineering.

使用matlab产生线性调频信号，并进行脉冲压缩处理。(Use matlab to produce linear FM signal, and pulse compression processing.)

忆阻器的matlab 仿真模型，含一个simulink 模型和M语言代码(Matlab code for memristor, including a simulink model and a matlab routine)

Prony算法工具箱。Prony方法是用一组指数项的线性组合来拟和等间距采样数据的方法,可以从中分析出信号的幅值、相位、阻尼因子、频率等信息。considerations including data preprocessing, model order selection, model order selection criteria, signal subspace selection, signal and noise separation, root inspection and assessing residuals. The PTbox provides flexibility to compare and display analysis results simultaneously for several parameter variations.(Prony algorithm toolbox. Prony method is used for a group of items to be linear combination of distance and other data sampling methods be able to analyze the signal amplitude, phase, the damping factor, frequency, and other information. Considerations including data preprocessing , model order selection, model order selection criteria. signal subspace selection, signal and noise separation, root inspection and assessing residuals. The P Tbox provides flexibility to compare and displ ay analysis results simultaneously for severa l parameter variations.)

说明：  地震、测井方面matlab代码~可以适用于勘探开发中(SeisLab)

converted ga matlab code to c++ for solving scheduling problem

状态反馈预测控制例程，对给定的阶跃值通过合理选择控制步长可以实现良好的跟踪特性。(State feedback predictive control routine, the value of a given step by step control can be a reasonable choice to achieve good tracking performance.)

The Support Ve ct or Machi ne So fa r we have used a reference as sumpt ion tha t there exists a linear classifier that has a larg e ge ometric margin, i.e., whose decision b oundar y is we ll separa ted from all the training images (e xamples). Such a larg e margin classifier seems like one we would like to use. C an’t we find it more directly? Yes, we can. The clas sifier is known as the Support Ve ctor M achine or SVM for short.