NSGA-II

谢金星主编的LINGO V9.0一书的源码,对学习LINGO软件很有帮助.(Xie Venus LINGO V9.0 edited one book source, useful for learning LINGO software.)

例举了中值滤波，均值滤波等方法，以及用MSE和PSNR来判断图像质量。(Cites median filtering, average filtering method and the use of MSE and PSNR to judge image quality.)

Updated Jaco de Groot s outlier test

自己写的APS检验matlab源代码。在金融统计中应用非常广泛。共大家参考(Write your own APS test matlab source code. In the financial statistics is widely used. A total of reference)

The Famous Latest LibSVM

大规模MIMO的4*4天线矩阵情况下ZF（zero forcing）检测算法的实现(The realization of large-scale MIMO 4x4 antenna matrix case ZF (zero forcing) detection algorithm)

主成分分析是把原来多个变量划为少数几个综合指标的一种统计分析方法。 从数学角度来看，这是一种降维处理技术。(Principal component analysis (PCA) is a statistical analysis method that divides a number of variables into a few comprehensive indexes.From a mathematical point of view, this is a kind of dimensionality reduction technology.)

针对NL-Mreans算法运算时间较长的问题，提出了优化，大大缩短了时间。(To solve the problem of long operation time of NL-Means algorithm, an optimization method is proposed, which greatly shortens the time.)

Color can provide an efficient visual feature for tracking nonrigid objects in real-time. However, the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters. To handle these appearance changes a color-based target model must be adapted during temporally stable image observations. This paper presents the integration of color distributions into particle filtering and shows how these distributions can be adapted over time. A particle filter tracks several hypotheses simultaneously and weights them according to their similarity to the target model. As similarity measure between two color distributions the popular Bhattacharyya coefficient is applied. In order to update the target model to slowly varying image conditions, frames where the object is occluded or too noisy must be discarded.

说明：  基于PCA的人脸识别源代码，使用matlab编程，针对ORL库(PCA-based face recognition source code, the use of matlab programming, for ORL database)