Turbo-Equalization

超宽带无线通信仿真平台，基于IEEE标准制定，IR-UWB(Ultra-wideband wireless communications simulation platform, based on IEEE standards, IR-UWB)

costas环解调程序，需要的可以参考一下。costas环解调.m(It is a matlab code for demodulation as costas ring .if you are interested in it, you can refer to it.)

this file talk about network

EtherNet/IP Socket Interface(This publication describes the socket interface that you can use to program MSG instructions to communicate between a Logix5000 controller via an EtherNet/IP module and Ethernet devices that do not support the EtherNet/IP application protocol, such as bar code scanners,RFID readers,or other standard Ethernet devices.)

这是一个关于gmsk和msk性能分析比较的源程序，能很好的实现GMSK频谱和功率谱。(this a matlab program about gmsk and msk s dpinpu and gonglvpu.)

说明：  MFC编程 实现数据绘制成曲线。经兄弟推荐最终决定用两个控件MCSCOMM与TeeChart来完成此任务。MCSCOMM控件时vc自带的串口控件，TeeChart Pro是一个强大的VCL图表组件，支持上百种2D和3D图表风格，并提供40多个数学和统计函数、无限制的轴和22个调色板(MFC programming realizes data plotting into curves. After recommendation by brothers, we finally decided to use two controls, MCSCOMM and TeeChart, to accomplish this task. TeeChart Pro is a powerful VCL graphics component that supports hundreds of 2D and 3D graphics styles and provides more than 40 mathematical and statistical functions, unlimited axes and 22 palettes.)

delphi开发的基于TCP协议的文件传输程序。包含服务器端和客户端程序，可以互相传送。(delphi development based on the TCP protocol file transfer program. Includes server and client program, you can send each other.)

卡尔曼滤波器介绍,个人感觉比较好的资料。(kalman (explain in detail) )

这是一个用CVI编写的模拟标准频谱仪界面，包含了前面板和各种常用的输入按键。(This program is developed by CVI used to simulate a standard interface of spectrum analyzer ,presented with popular keys and functions.)

说明：  第一次作业_基于分类算法的雷达状态识别 对于本数据集中的雷达状态识别，数据降维前使用朴素贝叶斯、支持向量机、神经网络的分类算法对于识别的准确率无太大影响；数据降维后使用神经网络算法最优，支持向量机算法其次，朴素贝叶斯算法较差。此外，训练样本越多，分类准确率有小幅度提高。(First Operation Radar State Recognition Based on Classification Algorithms For radar state recognition in this data set, the classification algorithm of Naive Bayesian, Support Vector Machine and Neural Network before data dimension reduction has little effect on the accuracy of recognition; Neural Network algorithm is the best after data dimension reduction, Support Vector Machine algorithm is the second, Naive Bayesian algorithm is the worst. In addition, the more training samples, the smaller the classification accuracy.)