802.11a-baseband_2

说明：  一篇用adaboost实现自动目标识别的文章。采用MSTAR数据库作为分类对象。(Adaboost an automatic target recognition with an article. Classification using MSTAR database as objects.)

非常有用的程序，可以调用来处理循环平稳信号，用以产生三维自相关图形。(Very useful program, you can call to handle the cyclostationary signals to generate three-dimensional self-related graphics.)

素数的判定，miller-rabin，sol-str,leman,fermat等。都可以运行，下完自后自己稍微改改就可以用。(Primes judgment, miller-rabin, sol-str, leman, fermat so on. Can run is finished after their own little changed since it can be used.)

图像RGB颜色矩提取matlab程序，有注释,有需要的拿去(Image RGB color moments extraction procedures, there is a comment)

fcm算法的实现 MATLAB程序详细解释说明实现 自带图片例子 (fcm algorithm MATLAB program comes with detailed explanations to achieve picture examples)

毕业设计的基于8QAM+zcz序列的准同步CDMA通信系统仿真(　　　This article will be given below 8QAM+ ZCZ sequence sets up method and analysis, will further introduce the relevant definitions zero correlation sequence and some properties and characteristics, including non-cycle related, cycle-related, Qi cycle and other characteristics. The reason why is because the study of these theories using this sequence, in the Gaussian channel conditions and system performance in terms of interference is very similar to the synchronization system and the system performance is far superior to an asynchronous system, and the extended sequence comparison the original sequence has better performance.)

通过Matlab求出连续时间信号的傅立叶变换及编程绘制各种连续时间信号系统的时域波行，幅度谱以及相位谱(Fourier transform of continuous-time signal obtained through Matlab and programming time domain wave line to draw a variety of continuous-time signal system , the amplitude spectrum and phase spectrum )

matlab语言与控制系统仿真实训教程的simulink仿真文件(the matlab language with the control system simulation training tutorial simulink simulation file)

Test Target Class Source Code for Andriod.

该算法基本思想是利用一组带有相应权值的随机样本即粒子去逼近PHD分布和基数分布，其意义在于不仅解决重积分计算没有闭式解的难题，而且在滤波过程中，PHD函数被一系列离散的带权值的样本近似，随着样本粒子数量的增加，PHDF接近于Bayes最优估计，而且不受模型线性和高斯假设的限制，可以适用于非线性非高斯的随机系统。(The algorithm the basic idea is to use a set of random sample with the corresponding weight namely particle approximation PHD and base distribution, its significance lies in not only solve the problem of multiple integral calculation without closed solution, and in the process of filtering, PHD function by a series of discrete weighted value of sample approximation, with the increase of sample particles, PHDF close to the Bayes optimal estimation, and is not subject to the assumptions of the linear and gaussian model can be applied to nonlinear non-gaussian stochastic system.)