The-FDTD-Method---Computation-and-Analysis

介绍有关H.248的内容 相当有用 价值比较高(H。248)

计算损伤结构前后的响应，对已结构的分析具有很好的效果(After calculating the response of the structure damage, the structure analysis of a good effect)

travel sales man problem

Yeah I actually fixed it by moving a bunch of stuff to onResume(). There are also a number of other bugs I found in this code posted here (which was based on even buggier code) that I will post as soon as I get them all fixed. The first one that comes to mind is that in the try block it needs to check if c is null. Another one is that there is no error handling for a source going down midstream

vit程序清晰简单，容易阅读，matlab调用易实现(vit procedures clear and straightforward, easy to read, matlab call easy to realize)

详细说明:使用matlab实现的LDA(线性判别分析)分类器,以及PCA的实现(matlab implementation of the LDA algorithm and the realization of the linear Discriminant Analysis)

matlab 二维高斯椒盐噪声去噪 基于小波变换的图像去噪滤波(matlab )

VC和MATLAB混合编程 使用MATLAB工具函数计算(VC and hybrid programming using MATLAB tool MATLAB function to calculate)

基于MATLAB软件的有关雷达杂波的的很多个程序，相信对初学入门者很有帮助 (Based on MATLAB software on the radar clutter of many programs, I believe that is helpful for beginners beginners)

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.)