FSSSinmatlab

说明：  simulink仿真 simulink仿真 (simulink simulation simulink simulation)

主要研究了比较经典的直接加窗加权旁瓣抑制技术，并利用Matlab进行了仿真，给出了对线性调频信号和 相位编码信号脉冲压缩旁瓣抑制的直接加窗加权法处理结果，得出了分析结论。(Mainly studied the classical direct windowed weighted sidelobe suppression techniques, and using the Matlab simulation, given a linear FM signal and The phase-encoded signal pulse compression sidelobe suppression of the direct plus window weighting method the results of the analysis conclusions.)

L1最小范数约束压缩感知算法,提供一个程序参考, it is MATLAB code(L1 norm constraint compressive sesing,it is MATLAB code)

用matlab实现多幅静态图片合成gif动态图片特效(matlab make gif)

Matlab classic program about Arithmetic

The demand of high data rate services has been increasing very rapidly and there is no slowdown in sight. We know that the data transmission includes both wired and wireless medium. Often, these services require very reliable data transmission over very harsh environment. Most of these transmission systems experience much degradation such as large attenuation, noise, multipath, interference, time variance, nonlinearities and must meet the finite constraints like power limitation and cost factor. One physical layer technique that has gained a lot of popularities due to its robustness in dealing with these impairments is multi-carrier modulation technique. In multi-carrier modulation, the most commonly used technique is Orthogonal Frequency Division Multiplexing (OFDM)

Border detection with first derivatives

基于matlab的var模型应用 youyong(matlab var)

matlab 实现的odfs跟踪算法,功能强大新颖(matlab method)

现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。(existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.)