MPSK_QAM

这个小程序可以简单的选取回归分析中box-cox变换的lambda值，通过matlab强大的矩阵运算功能可以通过简单比较RSS值的方式来得到lambda，使用时只需要输入实验值矩阵X和Y即可，然后输入minRSS（X，Y）就能简单明了的看到最优变换参数 （这个程序虽然小但是学习回归分析的初学者学习时都会经过这个算法，上载时因为不到1KB所以我又放了个文本格式的）(This little program can select a simple regression analysis of box-cox transformation of lambda values, through the powerful matlab function matrix calculation can be relatively simple way of RSS to get the value of lambda, the use of only need to enter the experimental data matrix X and Y can be , and then enter the minRSS (X, Y) will be able to see the simple optimal transformation parameters)

and describe and describe and describe

文件给出了运动失真或噪声干扰的图像恢复修复源代码(Documents given movement distortion or noise source image restoration repair)

基于正则化转置的LDA matlab源代码，可以应用在人脸识别中，详细说明见readme(based Regularization home to the LDA Matlab source code, can be applied to face recognition, detailed explanation see readme)

OSTU最大类间方差阈值分割算法matlab实现(OSTU Otsu threshold segmentation algorithm matlab implementation)

把辨识分成两步进行：第一步：利用相关分析法获得对象的非参数模型（脉冲响应或相关函数）；第二步：利用最小二乘法、辅助变量法或增广最小二乘法等，进一步求的对象的参数模型。如果模型噪声与输入无关，则Cor-ls相关最小二乘法（二步法）可以得到较好的辨识结果。Cor-ls相关最小二乘法（二步法）实质上是先对数据进行一次相关分析，滤除了有色噪声的影响，再利用最小二乘法必然就会改善辨识结果。能适应较宽广的噪声范围，计算量不大，初始值对辨识结果影响较小。但要求输入信号与噪声不相关(The identification is divided into two steps: the first step: using the correlation analysis method for object nonparametric model (impulse response or related function) The second step: using the least squares method, auxiliary variable method or augmented least squares method and so on, further for the parameters of the object model. If the model input noise and independent, the Cor- ls related least squares (two footwork) can get good recognition results. Cor- ls related least squares (two footwork) is essentially the data to a correlation analysis, filter in addition to the influence of colored noise, using least squares inevitable will improve the identification result. To adapt to a wide range of noise and calculation is not big, the initial value to identification results less effect. But for the input signal and noise are not related)

another speaker recognition

dct隐写技术matlab实现资料。算法原理：通过在一个图像块中调整两个DCT系数的相对大小，隐藏秘密信息。 将载体图像分为8×8的块，做二维DCT变换 用来表示选中的两个系数的坐标对于第i bit秘密信息 if（要隐藏信息‘1’） make else make 逆DCT变换，还原图像 (dct steganography matlab implementation information. Algorithm principle: an image block by adjusting the relative size of the two DCT coefficients, hidden secret information. The vector image is divided into 88 blocks, making two-dimensional DCT transform coefficients used to represent the coordinates of the selected two for the first i bit secret information if (to hide information ' 1 ' ) make else make the inverse DCT, restore image)

LPC sample code 6 matlab

The following Matlab project contains the source code and Matlab examples used for a pretty seashell gui. This GUI draws a seashell, using a parametric surface. The parameters of the seashell can be modified with sliders and buttons in the GUI. See also the seashell.m file, also on MATLAB Central (with no GUI). The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there