SOS_channel_identification_4_OSTBC

D-S证据理论数据融合，基于权重系数与冲突概率重新分配的证据合成方法,叶 清,系统工程与电子技术。(DS evidence theory data fusion, based on the weight coefficient with the probability of conflict redistribution of evidence synthesis methods, Qing Ye, systems engineering and electronic technology.)

说明：  一个ofdm的matlab仿真程序，直接运行即可(the Matlab simulation procedures can be run directly)

说明：  具有加、减、乘、除、sin、cos、ln、log等功能的计算器界面(With addition, subtraction, multiplication, division, sin, cos, ln, log and other functions of the calculator interface)

CMA算法实现自适应均衡器，对学习MATLAB软件，通信专业的人有用。(CMA adaptive equalizer algorithm, to learn MATLAB software, communications professionals who useful.)

Etude de la modulation numérique MDA-4

实现图像配准很好的一个代码，运行能通过。(peizhun code)

there is a matlab cod for Autonomous system, that describe to compute trajectory and point of the system, and plotting it.

非编码,与当前残差的相关系数的符号,在已经有了如今的回归变量集和当前残差的基础上(It is miRNA.A build of the development version (which will eventually become the next major release of R) is available in the r-devel snapshot build.)

OFDM频域算法，根据WLAN前导的长训练符号完成所有对子载波的信道频率响应的简单有效的估算，这两个长训练符号内容是一样的，因此二者的平均可以改进信道估算的质量。(OFDM frequency-domain algorithm, according to leading WLAN long training symbols to complete all the pairs of channel-carrier frequency response of simple and effective estimate, the two long training symbols is the same as the content, so they can improve the average quality of channel estimation.)

这篇文章是盲信号处理，因为是英文文章，所以大家看内容就好了。(Principal component analysis (PCA) is a mainstay of modern data analysis- a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works furthermore, it crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA . This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.)