turboDecoder

这是一篇关于MotomanHP6动力学仿真的论文，对于做这个方面的人很有参考和借鉴价值，推荐(This is a dynamic simulation on the MotomanHP6 papers, for people who do this in great reference and learning value, recommended)

Region fucion homogenity creterea

This code is an importante code of example of Matlab for introduction about this programm.

连续相位调制信号(CPM)产生，毕设内容，高光谱图像基本处理，阵列信号处理的高分辨率估计。( Continuous phase modulation signal (CPM) to produce, Complete set content, basic hyperspectral image processing, High-resolution array signal processing estimates.)

说明：  多元变分模态分解，适用多通道信号模态分解，变分模态分解的升级版(Multivariate Variational Mode Decomposition)

mATLAB cov函数详细说明 很棒，很好(mATLAB cov function details)

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)

图1为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列，其均值为零；参考信号；信道具有脉冲响应： 式中用来控制信道的幅度失真(W = 2～4, 如取W = 2.9,3.1,3.3,3.5等)，且信道受到均值为零、方差(相当于信噪比为30dB)的高斯白噪声的干扰。试比较基于下列几种算法的自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况,在同一坐标下画出其学习曲线): 1）横向/格-梯型结构LMS算法 2）横向/格-梯型结构RLS算法 并分析其结果。(Figure 1 is a band-limited signal caused balanced horizontal or lattice distortion adaptive equalizer (FIR system in which the lateral length M = 11), the system input is the value of a random sequence of ± 1, mean zero reference signal channel has impulse response: Wherein the amplitude of the distortion for the control channel (W = 2 ~ 4, and so as to take W = 2.9,3.1,3.3,3.5), and subjected to a channel with zero mean and variance (equivalent SNR is 30dB) Gaussian white noise. Compare the following types of algorithm-based adaptive equalizer in a different channel distortion, the convergence of different noise interference (corresponding to each situation, draw their learning curve in the same coordinate): 1) landscape/grid- LMS algorithm ladder structure 2) landscape/grid- RLS algorithm ladder structure And analyze the results.)

将输入d进行扩张，方法是在d的每个数据中插入M-1个零值。 如输入d=[1,2,3,4] M=3 输出out=[1 0 0 2 0 0 3 0 0 4 0 0](To expand the input d, the method is to insert each data d, M-1 zero value. If the input d = [1,2,3,4] M = 3 output out = [1 0 0 2 0 0 3 0 0 4 0 0])

Image registration used to register images.