MOGA

matlab图片对比，停车场车位识别，图用于图像处理这门课程的课程设计(something about matlab)

file dll very important to run spike undefr matlab code.

多维标度变换是应用广泛的无线传感器网络定位算法(Multi-Dimensional Scaling)

主要结构健康监测中的损伤识别时损伤位置的角度分辨率优化(structural nondestructive identification Angular resolution)

说明：  基于蒙特卡罗马尔可夫链的差分遗传算法。算法有mc抽样改进，并用差分算法改进遗传优化算法(Difference genetic algorithm based on monte carlo markov chain. With MC sampling improvement algorithm difference algorithm improved genetic optimization algorithm)

利用小波分析对污染信号进行去噪处理以恢复原始信号(Signal by wavelet analysis denoising of pollution to restore the original signal)

利用纹理特征进行的特征提取的一个matlab程序(A code of matlab to extract tamura feature)

比较两个netcdf文件 1。通过添加内部ind2sub1函数remove函数依赖第三方 2。格式化文本(Description NCCMP compares two netcdf files and prints the differences. This function is useful for testing mathematical forecasting or prediction models. USAGE: NCCMP(ncfile1,ncfile2) NCCMP(ncfile1,ncfile2,tolerance,forceCompare) INPUT: ncfile1- name of the NetCDF file to compare ncfile2- name of the NetCDF file to compare tolerance- Compare numeric data using a tolerance threshold forceCompare- if false, exit when first difference is found true: continues to process all variables EXAMPLES: nccmp( old.nc , new.nc ,0.000001) nccmp( old.nc , new.nc ,[],true) FEATURES: 1. Print the differences and their locations 2. Exits when first difference is found or optionally continues to process all variables 3. User defined tolerance threshold to compare the variables TO DO: 1. Specific variable inclusion or exclusion 2. Specific attribute inclusion or exclusion 3. Option to ignore the history attribute 4. Ignore difference between v)

自适应滤波LMS算法实现有源噪声消除：Mtalab程序；FLMS算法(Application Program to Test Active Noise Controla 32-tap adaptive FIR filter is used to produce an anti-noise to cancel the primary noise. The adaptive algorithms used here are the filtered-x LMS (FXLMS) and normalized FXLMS algorithms)

Motion Tracking =============== This tarball contains all code required to run the tracking algorithm on a sequence of images. Run the file run_tracker.m in Matlab and follow the instructions. You will need to have a directory of sequentially numbered images available. After entering the path and file types the tracker will begin processing. Once the data window appears the algorithm begins building a background model and attempts to track objects. By clicking on any of the four subwindows you can investigate the background representation (a Mixture of Gaussians) of any pixel. The two windows that then appear display the mixture once as a two-dimensional scatter plot (ignoring the blue colour component), and once as a one-dimensional evolution of the red colour component only. These plots make the internal processing visible and should help determining suitable parameters to be set in mixture_parameters.m.