loop

Yule—Walker方程构造信号的AR模型,Burg递推算法计算不同阶数的预测器系数(Yule-Walker equation for constructing the signal of AR model, Burg recursive algorithm to calculate the number of different order predictor coefficient)

time series annalys. Trend decomposyshion. Season index decomposition

SVM matlab 工具箱，可以进行SVM分类(SVM matlab toolbox, SVM classification can be)

无源腔的 Fox-Li迭代方法matlab实现(Passive cavity Fox-Li iteration matlab implementation)

Included in this distribution is matlab code to generate posterior samples for linear Gaussian and binary matrix factorization (noisy-or) Indian Buffet Process models. Three different posterior sampling algorithms are provided: Gibbs, reversible jump Markov chain Monte Carlo (RJMCMC), and sequential importance sampling (SIS). Only the Gibbs and SIS samplers are provided for the linear Gaussian IBP models.

说明：  一、 实验目的 1 熟悉MATLAB环境。 2 掌握MATLAB矩阵输入、运算以及MATLAB的数值运算功能。 3 掌握各种数据的创建‘访问，扩建，缩减。创建一个2*2元胞数组，扩充和缩减 (First, the experimental purpose a familiar MATLAB environment. 2 master MATLAB matrix input, operation and function of MATLAB numerical computing. 3 master data to create a variety of ' access, expansion, reduction. Create a 2* 2 array of cellular expansion and reduction)

说明：  一种快速有效、性能可靠的信号重构算法是压缩感知理论的核心部分，对于 这部分内容，许多卓有成效的研究工作正在陆续展开。从压缩感知理论提出至今， 已经出现了多种稀疏信号的重构算法。重构算法主要可以归结为三大类：贪婪算 法，凸松弛算法和组合算法。这里主要是SP算法(A fast and efficient, reliable signal reconstruction algorithm is the core of compressed sensing theory, for this part, much fruitful research work is carried out successively. From the compressed sensing theory so far, there have been many sparse signal reconstruction algorithm. Reconstruction algorithm can be summarized into three main categories: the greedy algorithm, convex relaxation algorithms and combinatorial algorithms. This algorithm is mainly SP)

Simulink建模与仿真 需要可以下载(failed to translate)

短时傅里叶中通过各种不同的窗来看窗的归一化频率(Short-time Fourier window in the view through a variety of different normalized frequency window)

lear N = 10^5 EB_NO = [-1:30] liu1 = zeros(1,N) liu2 = zeros(1,N) for ii = 1:length(EB_NO) m= (2*(rand(1,N)>0.5)-1) + j*(2*(rand(1,N)>0.5)-1) s = (1/sqrt(2))*m normalization of energy to 1 n = 1/sqrt(2)*[randn(1,N) + j*randn(1,N)]