code-music

principal component analysis (PCA ) is a well known approach for dimensionality reduction of the feature space. It has been successfully applied in face recognition. The main idea is to decompose face images into a small set of feature images called eigenfaces, which can be considered as points in a linear subspace called “face space” or “eigenspace”

Image Demosaicing by bilinear interpolation method

基于低秩矩阵恢复的背景建模的目标检测代码(Based on the recovery of low-rank matrix background modeling target detection code)

matlab power spectrue(matlab)

计算复杂网络平均路径距离，运行无错误，推荐下载！(The computational complexity of network average path distance, error-free operation, it is recommended to download!)

该函数用来计算时间序列的最大Lyapunov 指数--Wolf 方法(This function is used to calculate the maximum Lyapunov exponent of time series- Wolf Method)

matlab实现EMD分解。完整的EMD分解程序，并附有一个简单的例子。(matlab realize EMD decomposition. Complete EMD decomposition process, together with a simple example.)

阵列信号处理的高分辨率估计，有CDF三角函数曲线/三维曲线图，能量熵的计算，Matlab实现界面友好，结合PCA的尺度不变特征变换(SIFT)算法，调试通过可以使用。( High-resolution array signal processing estimates, There CDF trigonometric curve/3D graphs, Energy entropy calculation, Matlab to achieve user-friendly, Combined with PCA scale invariant feature transform (SIFT) algorithm, Debugging can be used.)

用matlab编写的三角函数曲线M文件一(Prepared using matlab file a trigonometric curve M)

Image Compression using Artificial Neural Networks This code is developed in MATLAB 6.5 and can used in Windows platform. Program contain all necessary files. For training the network use Train.m and Codec.m compress images by trained network.