ensemblekfilter

图像模板匹配算法用MATLAB来试验，有例子大家可以看看，有上占还不完好，有能力的自己改改(Image template matching algorithm using MATLAB to test, there are examples we can see, there are not well accounted for, the ability of their changed)

盲信号分离的三篇经典文章。Part One，首次提出了一种基于神经网络的学习算法（H-J算法），成功地实现了两个语音信号的分离，从而开启了一个新的领域。虽然他们的学习算法是启发式的并且没有明确指出需利用观测信号的高阶（高于二阶）统计信息，但是其迭代计算公式已具备后来ICA在线算法的雏形。 Part Two和Part Three：分别是从两个不同的角度来证明HJ算法的稳定性（主要是对源信号个数为2的情况给的证明），可惜的是，给出的稳定性条件都不是充要条件。(The three classic papers of blind signal separation(BSS).)

说明：  利用SVD进行信号降噪，通过fft判断奇异值个数，确定重构矩阵结构(Using SVD to reduce the noise of signal, using FFT to determine the number of singular values, and determining the structure of reconstruction matrix)

这是一个自己写的广义霍夫变换提取不规则不提边缘的方法，希望大家喜欢(This is a generalized Hough transform to write their own not to mention irregular edge extraction method, hope you like)

Matlab平台的著名仿真环境Simulin作为一种专业和功能强大且操作简单的仿真工具，目前已被越来越多的工程技术人员所青睐，它搭建积木式的建模仿真方式既简单又直观，而且已经在各个领域得到了广泛的应用(Matlab platform simulation environment known as a professional and Simulin powerful and easy to operate the simulation tool, has been an increasing number of engineering and technical personnel favored by its building-block structures in the modeling and simulation approach is simple and intuitive, and in various fields has been widely used)

说明：  姿态定位完整的matlab算法，具体有讲解版，文章版权已经申请，可以传播但是请勿发表文章，违者必究(Posture positioning complete matlab algorithm, on the specific version, the article has applied for copyright, can spread but not published an article rights reserved)

说明：  神经网络方面的书籍，比较有参考价值，里面有些小的例程，都是简单易懂的程序，对初学者比较有帮助(neural networks books, more reference value, they have some minor routines that are simple and easy procedure for beginners more helpful)

电网模拟器，用来模拟电网的各种故障，用来检测并网逆变器的性能。(the gridsimulator use back to back PWM simulator realise the grid )

高动态GPS接收机载波跟踪算法研究与实现 里面有各种参数的设计与选择 这篇文章讲的比较详细(High dynamic GPS receiver carrier tracking algorithm to achieve The design and selection of various parameters of this article talking about a more detailed)

The given code finds the HOG feature vector for any given image. HOG feature vector/descriptor can then be used for detection of any particular object. (The given code finds the HOG feature vector for any given image. HOG feature vector/descriptor can then be used for detection of any particular object.)