music

说明：  迎着早晨的朝阳龙求凤长长的伸了一下懒腰,突然闻到一股香风向他袭来,闻着这熟悉的香味,知道这是他心爱的师柔身上的发出的迷人香味，转过身去将心爱的人儿紧紧地搂在怀里，"柔儿，怎么这么早就起来了，你的身体刚刚才好，应该多多休息一下。"(Demand facing the morning sun dragon phoenix long stretched a bit lazy waist, suddenly, he smelt a fragrant wind hit, smell the familiar fragrance, know that this is his favorite teacher charming issued soft body scent, turned to a loved one child tightly in his arms, &quot Rou Er, how so early up, just go about your body, it should be lot of rest.&quot )

matlab programs code for use|_net.....................

目标因子分析，用于找寻主因子数的个数，用途广泛(Target factor analysis, to find the main number for the number of factors, versatile)

超宽带信道、毫米波信道为基础的SV信道模型仿真，对研究超宽带信道和毫米波信道等呈指数衰减信道的传播特性非常有帮助(Ultrawide channel and millimeter wave channel are all based on this SV channel model, it is very helpful for the study of those channels which are belong to exponential decay channels such as UWB and mmWave Channel)

数字接收机定时符号同步经典算法著作Gardner(This is the interpolation on the classic symbols of the articles, for the realization of the receiver symbol timing synchronization, the hardware engineering staff to develop a good reference)

Secon project for student learning it is very interesting for learning

park等常用的一些建模时需要的变换公式，对电气类工程人员很有用(park and other common modeling some transformation formulas needed for electrical engineers useful class)

本程序可以进行有限元模型修正和结构损伤识别，优点是基本解决了测量信息有限的问题。(This program can perform finite element model correction and structural damage identification. The advantage is that the problem of limited measurement information is basically solved.)

交织码的Matlab源代码，（7，4）卷积交织编码，并附有仿真图形(Intertwined Matlab source code, (7,4) coding Convolutional Interleaver, together with simulation graphics)

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)