MATLAB-R2008

研究阵列信号的经典算法ＭＵＳＩＣ，这个程序是基于矩阵束的，已经测试过了，不错(Classic algorithm of array signal MUSIC, this procedure is based on the matrix pencil, and has been tested, and good)

参数自整定PlD模糊控制仿真，参数自整定PlD模糊控制仿真，参数自整定PlD模糊控制仿真(Parameter self-tuning fuzzy control PlD simulation, parameter self-tuning fuzzy control PlD simulation, parameter self-tuning fuzzy control simulation PlD)

说明：  用于文本分析的pLSA(Probability Latent Semantic Analysis)的Matlab算法，含有测试数据及算法原理介绍。也可用于图像分析。(IpLSA (Probability Latent Semantic Analysis) Matlab惴ㄐ?菁惴ㄔ?堋 也 图)

FMCW调频连续波雷达的测距测角仿真程序，24G雷达的分析过程，主要针对德国innosent公司的ivq905雷达头(FMCW Frequency Modulated Continuous Wave Radar Ranging the goniometer simulation program, 24G radar analysis, German innosent company ivq905 radar head)

this code simulates a 4fuzzy PID cotroller for ball-baem system and 3 other systems.

下面是我编的一个软硬阈值去噪的matlab程序 下面是我编的一个软硬阈值去噪的matlab程序：thr=sigma*(2*lgN)1/2/lg(j+1) 用[x,xn]=wnoise(2,10,6) 产生噪声测试数据-The following is Part I of a of hard and soft threshold denoising matlab procedure is the following hardware and software, I made a threshold denoising matlab program: thr = sigma* (2* lgN) 1/2/lg (j+1) by [x, xn] = wnoise (2,10,6) noise test data(thr=sigma*(2*lgN)1/2/lg(j+1) 用[x,xn]=wnoise(2,10,6) 产生噪声测试数据-The following is Part I of a of hard and soft threshold denoising matlab procedure is the following hardware and software, I made a threshold denoising matlab program: thr = sigma* (2* lgN) 1/2/lg (j+1) by [x, xn] = wnoise (2,10,6) noise test data)

计算光机耦合仿真中的光学镜面的变形情况，分离刚体位移，采用泽尼克多项式耦合。并有使用例子。(The deformation of the optical mirror in the opto mechanical coupling simulation is calculated. The displacement of rigid body is separated and the Zernike polynomials are coupled. And use examples.)

modulation M-PSK for test physical layer

Probe into Image Segmentation Based on Sobel Operator and Maximum Entropy Algorithm

实现以下功能：装载信号；完成信号的单尺度一维离散小波分解；从系数中重构低频部分和高频部分；显示高频和低频部分；由小波逆变换恢复信号；多层一维分解；提取系数的低频和高频部分；重构第3层的低频系数；重构第1、2、3、4、5层的高频信号；重构原始信号并显示(Achieve the following functions: load signal complete signal single-scale one-dimensional discrete wavelet decomposition reconstructed from the coefficients in the low frequency and high frequency display high-frequency and low frequency restored by the inverse wavelet transform signal multi-layer one-dimensional decomposition extraction coefficient of low frequency and high frequency reconstruction of 3-layer low-frequency coefficients reconstruction of the first layer of high-frequency signal 1,2,3,4,5 reconstruct the original signal and display)