Root-locus-correction

视频帧间运动估计编码，基于光流场方程，包含了计算光流场的基础函数和一个光流场计算的实例(Inter-frame motion estimation video coding, based on optical flow equation, including the calculation of optical flow based functions, and an optical flow field instance)

EEMD是针对EMD方法的不足,提出了一种噪声辅助数据分析方法。EEMD分解原理为:当附加的白噪声均匀分布在整个时频空间时,该时频空间就由滤波器组分割成的不同尺度成分组成。当信号加上均匀分布的白噪声背景时,不同尺度的信号区域将自动映射到与背景白噪声相关的适当尺度上去。当然,每个独立的测试都可能会产生非常嘈杂的结果,这是因为每个附加噪声的成分都包括了信号和附加的白噪声。既然在每个独立的测试中噪声是不同的,当使用足够测试的全体均值时,噪声将会被消除。全体的均值最后将会被认为是真正的结果,唯一持久稳固的部分是信号本身,所 加入的多次测试是为了消除附加的噪声。(The EEMD collection of experience decomposition is improved on the basis of the EMD more given a data processing method!)

测试函数DTLZ1的源代码，用于测试多目标优化的性能(test function DTLZ1)

利用MATLAB语言实现RGB图像自动降噪(RGB images using MATLAB language to achieve automatic noise reduction)

find pitch to detection gender

一个关于无网格法的一维程序，对于刚学习无网格法的人来讲是比较基础易懂的。(it is a file about messless)

盲源分离（BSS: Blind Source Separation），又称为盲信号分离，是指在信号的理论模型和源信号无法精确获知的情况下，如何从混迭信号（观测信号）中分离出各源信号的过程。盲源分离和盲辨识是盲信号处理的两大类型。盲源分离的目的是求得源信号的最佳估计，盲辨识的目的是求得传输通道混合矩阵。(Blind signal separation (BSS), also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. This problem is in general highly underdetermined, but useful solutions can be derived under a surprising variety of conditions. Much of the early literature in this field focuses on the separation of temporal signals such as audio. However, blind signal separation is now routinely performed on multidimensional data, such as images and tensors, which may involve no time dimension whatsoever.)

说明：  自己编写的基于奇异值分解的MVDR方法进行信号频率估计的Matlab代码。 (Matlab code of the MVDR method of frequency estimation based on singular value decomposition.)

Tracking and Recognizing Rigid and Non-Rigid Facial Motions using Local Parametric Models of Image Motion

模糊系统逼近程序，用模糊算法对系统进行逼近(Fuzzy Systems Approximation procedures, use of fuzzy approximation algorithm for the system)