matlab-code

运用MATLAB编程实现计算两序列x1(n)={1,2,3,4,5},x2(n)={1,2,3,4,5,4,3,2,1}的圆周卷积(Calculated using MATLAB programming to achieve the two sequences x1 (n) = (1,2,3,4,5), x2 (n) = (1,2,3,4,5,4,3,2,1) of circular convolution)

说明：  本程序是用MATLAB编写，以后请MATLAB高手多进来切磋，请各位高手指教，看了后给点评价。谢谢！(this program is prepared using MATLAB, after many requests come in MATLAB master skills, the experts advise you please, after reading them to the point evaluation. Thank you!)

send you my example code

超声波测距程序，包含AD采样和信号处理等。(Ultrasonic ranging program, including AD sampling and signal processing.)

近场声全息学习程序，适合没有基础或少量基础的研究者学习或解决NAH问题。(Nearfield Acoustic Holographic Learning Programs and Examples)

介绍matlab的ppt，介绍了matlab中所使用的各种画图函数，初学者很容易上手。(Matlab introduction of ppt, introduced matlab used in a variety of drawing functions, beginners can easily get started.)

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.)

addition of white gaussian noise

k-mean algorithm for clustering

介绍了一种过零检测方法，实用性强，检测效果较好(This paper introduces a zero crossing detection method, practical strong, better effect)