face-estimation

实现QPSK系统性能仿真，仿真与理论相符，供大家参考！(QPSK System Simulation, Pe evaluation)

图像拼接的matlab程序，有需要的朋友可以下载下来看看(Matlab image stitching program, a friend in need can be downloaded to see)

MATLAB中的基本语法和语句示例代码，MATLAB中基本的知识，具有实用性(MATLAB basic grammar and sentence sample code, MATLAB basic knowledge, practical)

Project a vector b onto the column space of A.

说明：  跨 平 台 时 间 统 计 定 义跨平台时间统计(The definition of cross-platform cross-platform time to time statistics Statistics)

用于图像处理的独立分量分析，使用大量的有限元法求解偏微分方程，music高阶谱分析算法。( Independent component analysis for image processing, Using a large number of finite element method to solve partial differential equations, music higher order spectral analysis algorithm.)

说明：  基于MATLAB语言的RFS过滤/跟踪代码(MATLA random finite set filtering/tracking codes)

第一章 概述 辨识（Identification）、状态估计和控制理论在现代控制理论中是密不可分的，它们互相渗透。辨识和状态估计离不开控制理论的支持，控制理论的应用几乎不能没有辨识和估计技术。由此可见，辨识在科研与实际应用领域都有很高的实用价值。 所谓辨识，就是从含有噪声的输入、输出数据中提取被研究对象的数据模型。此数据模型只是过程的输入输出特性在某种意义下的近似，而近似的准确度一般取决于采样数据的精度以及辨识方法的合理性。 辨识的目的是根据过程所提供测量的数据等信息，在某种准则意义下，估计出模型的未知参数，其基本原理如图1.1所示： (Identification (Identification), state estimation and control theory in modern control theory are inextricably linked, they penetrate each other. Identification and control of state estimation theory is inseparable from the support of the application of control theory can hardly no identification and estimation techniques. This shows that the identification of the research and practical application areas have high practical value. The so-called identification, is to contain noise from the input and output data from the study was the data model. This data model is the process of input and output characteristics in a certain sense of approximation, while similar to the general accuracy of the data depends on the accuracy of sampling and identification method is reasonable. Identification wit)

matlab图像处理实例详解第三章源码程序，推荐大家一起学习，很好很实用。(Detailed examples matlab image processing Chapter III source program, recommend everyone to learn, very very useful.)

Implemenation of a variant of the rotation forest (Rodriguez et al., Rotation Forest: a new classifier ensemble method, PAMI 2006) proposed in L. Nanni and A. Lumini, Using ensemble of classifiers in Bioinformatics, in H. Peters and M. Vogel, Machine Learning Research Progress, Nova publisher, 2009