Matlab

说明：  bp神经网络的学习和训练 matlab程序代码(bp neural network learning and training matlab code)

MATLAB Example Code : Non-Linear Least Squares --- Bearings-Only Measurement(MATLAB Example Code : Non-Linear Least Squares--- Bearings-Only Measurement)

Generate 2D spatial pattern like fractal brownian motion. Fractal Dimension can be modified as input parameter.

利用matlab 利用shan-chen模型实现poisueil流 程序正确(the model of Shan)

Robust Non-negative Dictionary Learning for Visual Tracking The provided codes could be either embedded into the benchmark framework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence. To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m. To run on individual video, you need to modify the dataPath and title in run_individual.m. If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m and run_individual.m!

这是 OFDM系统的Park&Cheon定时同步和Schmidl&Cox定时同步。(This is the OFDM system Park&Cheon timing synchronization and timing synchronization for Schmidl&Cox.)

Berger Model for EEG

PCA是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。本程序实现了基于PCA特征脸的人脸识别。(PCA is a major contradiction to grasp things, statistical analysis, it can parse out things from multiple main factors, revealing the essence of things, simplifying complex issues. This program implements the features of face recognition based on PCA.)

matpower 中文说明书，对于学习MATLAB编写潮流程序和最优潮流的同学非常有用(matpower Chinese Manual, prepared for the trend of learning MATLAB and Optimal Power Flow program is very useful for students)

基于小波分析的信号奇异检测和去噪。。。。。。。。。。。。。。。(Based on wavelet analysis and signal singularity detection denoising. . . . . . . . . . . . . . .)