ring-spectrum

使用matlab实现p-value的算法 具体的函数实现。可用来处理统计推断等有关问题。(Specific algorithm matlab achieve p-value function. It can be used to deal with issues related statistical inference.)

说明：  用于信号的分解、降噪和重构，实现故障诊断(Used for signal decomposition, noise reduction and reconstruction to realize fault diagnosis)

自适应预测，神经网络预测，可以实现非线性预测，MATLAB环境实现。(Adaptive predictive)

ICA Clustering In computer science, Imperialist Competitive Algorithm (ICA)[1] is a computational method that is used to solve optimization problems of different types. Like most of the methods in the area of evolutionary computation, ICA does not need the gradient of the function in its optimization process. From a specific point of view, ICA can be thought of as the social counterpart of genetic algorithms (GAs). ICA is the mathematical model and the computer simulation of human social evolution, while GAs are based on the biological evolution of species

灰度图像的二维Otsu自动阈值分割法 matlab源程序(Two-dimensional gray image Otsu automatic thresholding method)

自组织特征映射模型(Self-Organizing feature Map)，认为一个神经网络接受外界输入模式时，将会分为不同的区域，各区域对输入模式具有不同的响应特征，同时这一过程是自动完成的。各神经元的连接权值具有一定的分布。最邻近的神经元互相刺激，而较远的神经元则相互抑制，更远一些的则具有较弱的刺激作用。自组织特征映射法是一种无教师的聚类方法。(Self-organizing maps model (Self-Organizing feature Map), that a neural network to accept outside input mode, will be divided into different regions, the regional input modes have different response characteristics, while the process is done automatically . The connection weights of neurons with a certain distribution. Nearest neurons stimulate each other, while distant neurons are mutually inhibitory, with a further some of the weaker stimulus. Self-organizing feature map method is a clustering method without teachers.)

这是IEEE上一篇比较不错的论文：关于低复杂度解决OFDM 系统中降低papr的新技术(This is the IEEE paper on a relatively good: low complexity solutions for OFDM systems in new technologies to reduce papr)

用直接解得办法求摄影测量中的相对定向元素( use direct measurement to find the relative orientation elements in photography)

Programming Exercise

基于协方差的车辆检测,用协方差矩阵为描述子，检测图片中的汽车或者卡车(Covariance matrix Vehicle detection)