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w
- 利用matlab实现下面的问题：其中u(k)和z(k)分别为模型的输入和输出变量；v(k)为零均值、方差为1、服从正态分布的白噪声；为噪声的标准差；利用得到的u(k)和z(k)来辨识模型的阶次和参数。-Using matlab to achieve the following questions: where u (k) and z (k) respectively, the input and output variables of the model v (k) with zero mea
covmatrix
- 计算矩阵列向量的协方差矩阵,同时计算各列向量的均值-calculate the cov matrix
average
- 利用积分原理求解输入信号的均值和均方差值-solve mean value and mean square deviation
Digital-Image-Processing
- C#编写，实现基本图像处理功能，包括计算均值、方差、协方差、相关系数、熵等，以及IHS变换、逆变换、直方图均衡、IHS融合、Brovey融合、Fourier变换、逆变换、各种植被指数计算等-Written in C#, basic image processing functions, including the calculation of the mean, variance, covariance, correlation coefficient, entropy, and IHS tra
pca
- pca数据降维程序，包括取均值、计算协方差矩阵等详细步骤。-the pca Data dimensionality reduction procedures, including averaging, calculated covariance matrix detailed steps.
gaussianRandomValue
- 一个设定均值和方差，生成对应的高斯白噪声数据的函数，附带调用例子-Mean and variance of a set to generate Gaussian white noise data corresponding function with a call example
histogram_features
- 求灰度直方图统计特征，包括熵、均值、方差等- gray histogram feactures
MATLABTIQUZHEN
- 同学的毕业设计，用matlab编的关键帧提取的代码，参考了光流法的代码。是基于帧差的欧式距离，均值，方差，差异系数下的关键帧提取。代码调试通过，运行结果理想，与大家分享下-Students graduating from the design using matlab key frame extraction code, reference optical flow method code. Frame difference is based on the Euclidean distance,
quality--index-of-image
- 此文档中的代码可以计算5种图像的质量参数，分别是：图像灰度均值、图像方差、图像平均梯度、熵和峰值信噪比及均方误差。-The code in this document can be calculated five kinds of image quality parameters, namely: the image gray value, image variance, average gradient image, entropy and peak signal to noise ratio
ML.m
- 在贝叶斯分类中,用极大似然估计法估计概率分布的均值和方差-Compute the maximum-likelihood estimate of the mean and covariance matrix of each class and then uses the results to construct the Bayes decision region. This classifier works well if the classes are uni-modal, even when
Dsp_Final
- 基于Matlab产生光纤陀螺随机噪声和信号，利用固定步长和可调步长的LMS自适应滤波、最小二乘法、滑动均值三种方法进行处理，最后用阿兰方差评价降噪效果。-FOG Based on Matlab generate random noise and signal, using a fixed step size and adjustable step size LMS adaptive filtering, least squares method, the sliding average of t
PF
- 粒子滤波源代码，通过寻找一组在状态空间中传播的随机样本来近似的表示概率密度函数，用样本均值代替积分运算，进而获得系统状态的最小方差估计的过程。-Particle filter source code, by finding a set of transmission in the state space representation of a random sample to approximate the probability density function, instead of usi
dsp
- 随机过程： ，其中 是均值为零、方差为1的白噪声， 、 是相互独立并在 上服从均匀分布的随机相位。采用AIC和MDL准则估计信号源个数，并且画出相应的MUSIC频率估计谱线。 要求：信号样本数为1000，估计的自相关矩阵为8阶。 -Stochastic process: where is zero mean and variance 1 white noise, are independent and uniformly distributed on a random phase. Es
signalprocessing
- 1、产生信号，两个实正弦信号的叠加，幅度分别为2、4、1、3；混入均值为0、方差为1的白噪声。采用自适应滤波器对其进行去噪。 2.产生信号，为两个实正弦信号的叠加，其幅度均为4，混入均值为0、方差为1的白噪声。采用有限脉冲响应法设计一个维纳滤波器估计信号 ，并求最小均方误差。 3.产生高斯分布的白噪声w(n)，自行给定一个5阶AR模型，让该白噪声通过这个AR模型，得到输出信号x(n），再估计x(n）的AR模型数，比较估计的结果和原来给定的AR模型的参数。-A signal to be g
System-identification
- 系统辨识作业 考虑如下模型 z(k)-1.5z(k-1)+0.7z(k-2)=u(k-1)+0.5u(k-2)+v(k) 其中v(k)是均值为0，方差为1的白噪声。根据模型生成数据，采用递推最小二乘法对模型参数进行辨识，要求绘出各参数随时间的变化曲线。 -"System identification" computer test 。Consider the following model Z (k) to 1.5 of z (k- 1)+ 0.7 z (k- 2) = u (k-
MSPLFFT
- 用来产生均匀分布或高斯分布的伪随机数 （近似白噪声），它们可具有不同的均值和方差。-To produce uniform or Gaussian distribution of the pseudo-random number (similar to white noise), which may have different mean and variance.
Q1
- 2类分类高斯模型 每个类是由一个单一的多元高斯分布的3-D建模 显示如何估计高斯均值向量和协方差矩阵的最大似然（ML）估计的基础上为每个类。 meanA和meanB代表每个类的均值，varA和varB的的代表每个类的协方差矩阵.-2-class classifier with Gaussian Models Each class is modelled by a single 3-D multivariate Gaussian distribution Show
sige
- matlab图像处理中，双图像的总体属性参数灰度均值 总熵值 方差计算代码-matlab image processing, the dual image of the overall total entropy attribute parameter mean gray value variance calculation code
meanandvariance
- 计算一幅灰度图像的灰度均值，方差，能量和熵。-Calculating a grayscale image gray value, variance, energy and entropy.
secaizhuanyi
- 运用openCV编写的C++图像色彩转移程序，原理是先转化到Lab图像，再保持L不变调整图像的a、b分量的均值跟方差。-Using openCV prepared C++ image color transfer process, the principle is first converted to Lab image, and then adjust the image to keep L constant a, b, with the mean variance component.