搜索资源列表
burg
- 用Burg算法估计AR模型参数,进而实现功率谱估计. 形参说明: x——双精度实型一维数组,长度为n,存放随机序列。 n--整型变量,随机序列的长度。 p--整型变量,AR模型的阶数。 a--双精度实型一维数组,长度为(p十1)。存放AR模型的系数a(0),a(1),...,a(p)。 v--双精度实型指针,它指向预测误差功率,即AR模型激励白噪声的方差。 -with Burg algorithm estimates AR model parameters, ther
psd
- 计算ARMA(p,q)模型的功率谱密度。 形参说明: b——双精度实型一维数组,长度为(q+1),存放ARMA(p,q)模型的滑动平均系数。 a——双精度实型一维数组,长度为(p+1),存放ARMA(p,q)模型的自回归系数。 q——整型变量,ARMA(p,q)模型的滑动平均阶数。 p——整型变量,ARMA(p,q)模型的自回归阶数。 sigma2——双精度实型变量,ARMA(p,q)模型白噪声激励的方差。 fs——双精度实型变量,采样频率(Hz)。
fayeboy1984
- 此设计要求能够实现将医学图像进行识别的过程,包括了图像预处理、图像特征提取及分类判决三大模块。在预处理这一步中主要实现的是将彩色图像转换为灰度图像,灰度图像的二值化,直方图修正,去除干扰、噪声以及差异,边缘增强等;第二模块是图像的特征提取。由于对象的物理与几何特性差异,在影像中表现为局部区域的灰度产生明显变化,形成影像特征,而图像特征提取就是对其进行加工、整理、分析、归纳以便提取构成目标影像的特征,得到能反映图像内容区别于其他事物的本质特征;分类判决作为第三模块,则是要在第二步的基础上采用某种分
MAR1PSD
- Routine mar1psd: To compute the power spectum by AR-model parameters. Input parameters: ip : AR model order (integer) ep : White noise variance of model input (real) ts : Sample interval in seconds (real) a : Complex array of AR parame
MARBURG
- Routine marburg: To estimate the AR parameters by Burg algorithm. Input Parameters: n : Number of data samples ip : Order of autoregressive process x : Array of complex data samples x(0) through x(n-1) Output Parameters: ep : Real
selfCorrelationInRandomization
- 产生零均值单位方差高斯白噪声的1000个样点,估计随机过程的自相关序列-units have zero mean Gaussian white noise variance to the 1,000-point, it is estimated that the random process autocorrelation sequence
channel
- ofdm信道特性 Channel transmission simulator Channel transmission simulator % % inputs: % sig2 - noise variance % Mt - number of Tx antennas % Mr - number of Rx antennas % x - vector of complex input symbols (for MIMO, this is a matrix, wh
Random_Walk_Noise_Variance_Allan
- Allan Variance example, for random walk and wuite noise. Stochastical Process. Application with Wolfram Mathematica
MATLAB
- 对噪声信号中的正弦信号,通过Pisarenko谐波分解方法、Music算法和Esprit算法进行频率估计,信号源是: 其中, , , ; 是高斯白噪声,方差为 。使用128个数据样本进行估计。 1、用三种算法进行频率估计,独立运行20次,记录各个方法的估计值,计算均值和方差; 2、增加噪声功率,观察和分析各种方法的性能。-Sinusoidal signal in the noise signal through the Pisarenko harmonic decomposition metho
fpe
- This function calculates Akaike s final prediction error % estimate of the average generalization error. % % [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the % final prediction error estimate (fpe), the effective number of
wirelesscomm
- In this project we analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels.This project is mainly bas
wienerfilter
- 通过计算机MATLAB仿真分析和研究了维纳滤波器的阶数、信号的噪声方差、随机信号的采样点数、经过维纳滤波的均方误差之间的关系-MATLAB simulation by computer analysis and research of Wiener filter order, the signal of the noise variance, random signal sampling points, after Wiener filter mean square error of the r
3
- 典型时间序列模型分析 设有ARMA(2,2)模型, X(n)+0.3X(n-1)-0.2X(n-2)=W(n)+0.5W(n-1)-0.2W(n-2) W(n)是零均值正态白噪声,方差为4 (1)用MATLAB模型产生X(n)的500观测点的样本函数,并会出波形; (2)用你产生的500个观测点估计X(n)的均值和方差; (3)画出理论的功率谱 (4)估计X(n)的相关函数和功率谱 -Analysis of typical time series model w
fecgm
- 独立成份分析(ICA)以及winner滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank.
M2_M4_variance
- M2M4算法仿真。频谱感知中,对接收信号进行背景噪声方差估计-M2M4 algorithm. estimate the floor noise variance
generating-white-noise
- 编程产生一组正态分布的白噪声信号,它的均值和方差以及长度可随意调整。将产生的白噪声信号存入数据文件。 本程序算法用C++语言编写。首先用乘同余法产生均匀分布白噪声,再用变换抽样法转换为高斯分布白噪声 -Program produces a set of normal white noise signal, its mean and variance, and the length can be adjusted. Will produce a white noise signal int
fitting-the-noise-factor-solution
- ALLAN方差分段拟合后求解噪声系数,以提高拟合的精确度-ALLAN variance after fitting the noise factor solution
HW1
- 图像处理作业 用matlab显示直方图 累积分布函数 加高斯噪声 再用平滑降噪 都是自己编的程序-Given the test image "Lenna" 256*256 with 256 gray levels, do the following: 1) Using MATLAB display the test image. 2) Display the intensity histogram of the test image. 3) Using the im
noise-estimation-
- 基于噪声图像的gamma校正,噪声方差估计,白平衡等-Noise variance estimation、Gamma Correction、white balancin
imnoise_bi
- J = imnoise(I,'localvar',IMAGE_INTENSITY,VAR) adds zero-mean, Gaussian noise to an image, I, where the local variance of the noise is a function of the image intensity values in I. IMAGE_INTENSITY and VAR are vectors of the same size, and P