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基于MATLAB的语音信号的调制和解调，包括画出各个阶段的语音时域和频域图形(Matlab-based voice signal modulation and demodulation, including draw stages of time domain and frequency domain speech)

BPSK的R参数分析归一化,求瞬时平方包络均值(BPSK Analysis of the R parameters normalized, and the instantaneous mean-square envelope)

MATLAB 神经网络用于数字识别源程序(MATLAB neural network for digital identification of the source)

此资料中包含5个Matlab GUI 小程序，分别是信号分析、不同图形绘制、极点图绘制、时域频域信号绘制、滑动数字显示。(This data contains five Matlab GUI applets, which are signal analysis, different graphics rendering, pole drawing, draw time-domain frequency domain signal, sliding digital display.)

基于MATLAB编程的最小噪声分离方法处理高光谱影像（Minimum Noise Separation Method Based on MATLAB Programming for Hyperspectral Image Processing）(Minimum Noise Separation Method Based on MATLAB Programming for Hyperspectral Image Processing)

matlab图像处理代码m函数 P0301：数字图像矩阵数据的显示及其傅立叶变换 P0302：二维离散余弦变换的图像压缩 P0303：采用灰度变换的方法增强图像的对比度 P0304：直方图均匀化 P0305：模拟图像受高斯白噪声和椒盐噪声的影响 P0306：采用二维中值滤波函数medfilt2对受椒盐噪声干扰的图像滤波 P0307：采用MATLAB中的函数filter2对受噪声干扰的图像进行均值滤波 P0308：图像的自适应魏纳滤波 P0309：运用5种不同的梯度增强法进行图像锐化 P0310：图像的高通滤波和掩模处理 P0311：利用巴特沃斯（Butterworth）低通滤波器对受噪声干扰的图像进行平滑处理 P0312：利用巴特沃斯（Butterworth）高通滤波器对图像进行锐化处理 (matlab image processing function code m)

采用FDTD计算2维光子晶体的散射和透射的可执行程序软件(Calculated using FDTD 2-D photonic crystal scattering and transmission of the executable software)

掌握和精通matlab之gui设计，书籍，代码复制即可用(Grasp and mastery the matlab gui design, books, copy the code to use)

方便观察并行运算采用parfor（并行循环）时的运算进程，一个简化版的进度条，比具有GUI的进度节省不少珍贵的电脑资源。本程序根据网上parfor_progress的源程序改编而，但显示到小数点后2为的精确进度情况，比网上的灵敏度更高。(It convenience for us to observe the operation process when we use Parallel Algorithmic parfor(paralle circulation)to compute,a simplify progress bar.But saving more precious computer resource for you than GUI PROGRESS BAR. And this program is rewrite by the source program of parfor_progress in the network, more precision than the web program, advanced dispaying two digits after the decimal point. This is his advantages.Thanks!)

系统参数变化，用集合卡尔曼滤波器估计其变化(System parameters used to estimate the changes in the ensemble Kalman filter)