PLL6

说明：  this program for SMIB with statcom to damp power system stabil ity

通过KNN算法，实现WIFI室内定位在matlab环境下的仿真(Simulation of WIFI indoor location in Matlab environment through KNN algorithm)

单用户的能量检测方法。仿真不同SNR时，单用户的能量检测的虚警概率Qf和检测概率Qd的关系(Detection of single-user energy)

用matlab代码仿真mimo-ofdm,cdma等(simulating mimo-ofdm,cdma with matlab code)

用四阶龙格库塔法求非线性系统的输入相应，同时用simulink建模比较。 (Using fourth-order Runge-Kutta method for the corresponding input nonlinear systems, and modeling using simulink comparison.)

Phantom Sensable haptic

this method is called the maximum likelihood estimation method .which is used to find out the parameters of johnson distribution .

若不希望用与估计输入信号矢量有关的相关矩阵来加快LMS算法的收敛速度，那么可用变步长方法来缩短其自适应收敛过程，其中一个主要的方法是归一化LMS算法（NLMS算法），变步长 的更新公式可写成 W（n+1）=w（n）+ e（n）x（n） =w（n）+ （3.1） 式中， = e（n）x（n）表示滤波权矢量迭代更新的调整量。为了达到快速收敛的目的，必须合适的选择变步长 的值，一个可能策略是尽可能多地减少瞬时平方误差，即用瞬时平方误差作为均方误差的MSE简单估计，这也是LMS算法的基本思想。 (Want to estimate if the input signal vector and the relevant matrix to speed up the convergence rate of LMS algorithm, then the variable step size method can be used to shorten its adaptive convergence process, one of the main method is normalized LMS algorithm (NLMS algorithm) , variable step-size update formula can be written W (n+ 1) = w (n)+ e (n) x (n) = w (n)+ (3.1) where, = e (n) x (n) the right to express filter update vector iterative adjust the volume. In order to achieve the purpose of fast convergence, we must choose the appropriate value of variable step size, a possible strategy is as much as possible to reduce the instantaneous squared error, which uses the instantaneous squared error as the mean square error MSE of the simple estimate, which is the basic LMS algorithm思想.)

数字图像处理姚敏版第四章图像增强所有例子的matlab完整代码 可运行(Yao Min version of Digital Image Processing Image Enhancement Chapter complete code for all examples can be run matlab)

本程序估计相干信号的DOA。当信源相干时，MUSIC不能处理，本程序利用了其他的DOA算法作为演示。(This procedure is estimated coherent signal DOA. When the source coherence time, MUSIC can not handle, use this program as a demonstration of other DOA algorithms.)