BPlizi_1

信道估计算法及实现,matlab编写(Channel estimation algorithm and realize, matlab prepared)

利用四元素极化空时分组码的理论研究与其编码仿真(Research on the four-element polarization space-time block codes)

OSC 源文件。from eigenvector(OSC source. from eigenvector)

其中的相应程序为实现解决线性方程组的相应的方法，程序测试正确。(Corresponding procedures for the realization of the appropriate method to solve linear equations, the correct program testing.)

这是一个基于2发2收，STBC的MIMO仿真模型，在该模型中，可以看到MIMO从数据生成到数据调制最后到解码的整个过程(This is a 2 on the 2 admission, STBC MIMO model, the model, MIMO can see from the data generated to the final data modulation of the whole process of decoding)

cuda5 and matlab code for Gaussian filtering and parallel image processing code, we hope to help

图像加减运算，包括图像和常数的加减图像的相乘和一些图像处理的MATLAB代码(Image addition and subtraction operations, including image and constant multiplication and some addition and subtraction images MATLAB image processing code)

function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.

用MATLAB变成工具和VC编程工具实现二型模糊系统的混合编程(MATLAB mixed programming of the type-2 fuzzy system into tools and VC programming tools)

对HARQ系统的吞吐量分析，模式识别中的bayes判别分析算法，粒子图像分割及匹配均为自行编制的子例程，给出接收信号眼图及系统仿真误码率，BP神经网络用于函数拟合与模式识别，用于信号特征提取、信号消噪，合成孔径雷达（SAR)目标成像仿真，有CDF三角函数曲线/三维曲线图。( HARQ throughput analysis of the system, Pattern Recognition bayes discriminant analysis algorithm, Particle image segmentation and matching subroutines themselves are prepared, The received signal is given eye and BER simulation systems, BP neural network function fitting and pattern recognition, For feature extraction, signal de-noising, Synthetic Aperture Radar (SAR) imaging simulation target, There CDF trigonometric curve/3D graphs.)