filter-square

离散无记忆信源是一种最简单且最重要的信源，可以用完备的离散型概率空间来描述。本实验通过计算给定的信源的熵，加深对信源及其扩展信源的熵的概念的理解。(Discrete memoryless sources is one of the most simple and most important information source, can be used to describe a complete discrete probability space. In this study, by computing the entropy of a given source, to deepen understanding of the source and the expansion of the concept of entropy of the source. )

matlab image processing toolbox

Matlab2010经典超强数据教程，适合初学者使用学习(Matlab2010 classic super data tutorial)

前馈ANC系统的模型，但是，不详细，可以参考！(the model of two earphone active noise control)

this source code is sdsasa matlab

对EEG的原始数据进行小波分解和重构以及做ar模型功率谱估计(EEG raw data wavelet decomposition and reconstruction, ar model power spectrum estimation)

this help you to know about tempreture

根据《多源信息融合》一书中的IMM多模型跟踪算法所写的仿真程序(According to )

利用Matlab来进行计算期权的价格，根据股票价格波动率，股票的红利率，市场无风险利率。以该股票为标的资产的美式看涨期权(matlab price)

稳态kalman滤波算法仿真通式 本程序考虑线性离散时不变随机系统。系统模型为x(t+1)=fai*x(t)+gama*w(t) y(t)=H(t)*x(t)+v(t)。有6个参数：状态转移阵fai，输入噪声系数gama，观测阵H，输入 噪声方差Q，观测噪声方差R，观测y( Steady-state kalman filtering algorithm simulation program to consider the general form linear discrete time-invariant stochastic system. System model x (t+1) = fai* x (t)+ gama* w (t) y (t) = H (t)* x (t)+ v (t). There are six parameters: state transition matrix fai, input noise figure gama, observation matrix H, enter noise variance Q, observation noise variance R, observation y)