UWBChannel

this is a zip file for showing how particle swarm optimization find g_best with matlab.

基于小波的编码变换和图像的二次压缩，区别于经典的小波变换(Coding based on wavelet transform and the second image compression, wavelet transform is different from the classic)

耳蜗实质上相当于一个滤波器组，耳蜗的滤波作用是在对数频率尺度上进行的，在1000HZ下，人耳的感知能力与频率成线性关系；而在1000HZ以上，人耳的感知能力与频率不构成线性关系，而更偏向于对数关系，这就使得人耳对低频信号比高频信号更敏感。Mel频率的提出是为了方便人耳对不同频率语音的感知特性的研究。频率与Mel频率的转换公式为(Cochlear substantially equivalent to a filter set, cochlear filter is used on logarithm frequency scale, under the 1000 hz, the perception of the human ear and a linear relationship with frequency In more than 1000 hz, the perception of the human ear does not constitute a linear relationship with frequency, and prefer to logarithmic relationship, which makes the human ear is sensitive to low frequency signal is better than high frequency signal. Mel frequency is put forward in order to facilitate the ear of the study of speech perception characteristics of different frequency. For frequency and Mel frequency conversion formula )

matlab环境下编写的求解等熵涡问题，属于CFD入门级代码(matlab environment prepared isentropic vortex problem solving, is the entry-level CFD code)

its a matlab code files

cnv_encd.m　卷积编码程序 viterbi.m　　卷积译码程序 其它的是viterbi.m中用到的子函数(cnv_encd.m convolution coding procedures viterbi.m other convolution decoding process is used in viterbi.m Functions)

matlab程序运行时导入数据文件作为输入参数，gFzHFju参数是一种双隐层反向传播神经网络，应用小区域方差对比，程序简单，包含收发两个客户端的链路级通信程序,EPuUHVR条件有详细的注释，matlab开发工具箱中的支持向量机。( Import data files as input parameters matlab program is running, gFzHFju parameter Is a two hidden layer back propagation neural network, Application of small area variance comparison, simple procedures, Contains two clients receive link-level communications program, EPuUHVR condition There are detailed notes, matlab development toolbox support vector machine.)

Rectangular pulse propagation with FDTD Matlab

用MOM计算V型天线的电流分布、输入阻抗及辐射方向图。其中L=0.3λ，L/a=2000，激励用δ-gap模型。(MOM calculated using V-shaped antenna current distribution, input impedance and radiation pattern. Where L = 0.3λ, L/a = 2000, excitation δ-gap model.)

通过对序列图像进行混合高斯建模，分离背景和前景目标。(Gaussian mixture modeling of image sequences, separation of background and foreground objects.)