RWT_BIO

beamforming theory from the renovable engineers

说明：  lena图像的分解将规范化后的图像的数据格式由适合显示图像的uint8格式转换为适合数值处理的double格式，再调用二维小波分解函数进行图像分解，最后为了清晰地显示分解图像的塔式结构，在图像的相应区域绘制若干分界线。 (lena image after decomposition of the standardized image data format suitable for display by the image is converted to uint8 format suitable for numerical treatment of the double format, and then call the function of two-dimensional wavelet decomposition for image decomposition, and finally in order to clearly show the decomposition of the image of the tower structure , the corresponding region in the image number of the line drawn.)

动目标检测（MTD）领域的仿真，画出了加汉明窗之后的频率响应曲线，经调试，可以运行(Moving target detection (MTD) in the field of simulation, draw the frequency response curve after Hamming window, debugging, you can run)

Does a picture of the dispersion relation. The figure should be compared with Figure 7.2-10 of the book. Reference: Fundamentals of Photonics-Second Edition

％radon transform clear all ％ N=800 n=1:N fs=200 t=n/fs x1=exp(j*2*pi*(5*t+0.5*5*t.^2)) x2=exp(j*2*pi*(5*t+0.5*15*t.^2)) x=x1+x2 ％N=length(x) ％ ambifunb(x ) ％*****************************************RAT naf=ambifunb(x) htl(abs(naf)) ％ [wh,rho,theta]=htl(abs(naf)) colormap([0,0,0]) ％ xlabel( 极半径 ) ％ ylabel( 角度 ) ％**************************************％找出峰值点的坐标,计算初始频率和调频斜率(正确) ％找出峰值点的坐标 b=max(max(wh)) [u,a]=find(wh>=0.8*b) ( Radon transformclear all N = 800 n = 1: N fs = 200 t = n/fs x1 = exp (j* 2* pi* (5* t+ 0.5* 5* t. ^ 2)) x2 = exp ( j* 2* pi* (5* t+ 0.5* 15* t. ^ 2)) x = x1+ x2 N = length (x) ambifunb (x)***************************************** RATnaf = ambifunb (x) htl (abs (naf)) [wh, rho, theta ] = htl (abs (naf)) colormap ([0,0,0]) xlabel (polar radius) ylabel (angle)************************************** to find the coordinates of the peak point, calculating the initial slope of the frequency and FM (right) find the peak point of the coordinates b = max (max ( wh)) [u, a] = find (wh)

gui...creation with buttons

风机全功率模型，功能实现优良，最后仿真波形完全正确。(Fan full power model, functions to achieve excellent final simulation waveform entirely correct.)

基于粒子群算法的PID控制算法MATLAB源程序(PID control algorithm based on particle swarm optimization algorithm)

以多光谱数据为原始数据，通过空间变换、滤波、融合、纹理及图像处理等手段，提取遥感图像多维、多尺度特征，最终建立面向目标检测、识别和分析的特征空间，并使用NASA的实测数据对方法的性能进行验证和分析。(Multi-spectral data to the original data, through space conversion, filtering tools, integration, texture and image processing, remote sensing images to extract multi-dimensional, multi-scale features, for the eventual establishment of target detection, identification and analysis of the characteristics of the space, and measured using NASA performance of the method for data verification and analysis.)

Matlab符号数学工具箱，主要对比较复杂的部分做一些介绍(Matlab Symbolic Math Toolbox, the main part of some more complex description)