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了解MATLAB，能够熟练掌握数学（矩阵）运算，简单编程，简单的数据处理、符号运算及基本图形绘制.对MATLAB面向对象编程与simulink有基本了解。(Learn MATLAB, to master the math (matrix) operator, simple programming and simple data processing, symbolic manipulation, and basic graphics rendering. On simulink MATLAB object-oriented programming with a basic understanding.)

说明：  关于多进制LDPC码的编译码的matlab代码(Ary LDPC codes on the coding and decoding of the matlab code)

扩展卡尔曼与粒子滤波结合的程序,用扩展卡尔曼作为提议分布，然后利用粒子滤波进行滤波跟踪，效果比常用的粒子滤波要好！均方根误差也比较小。(Extended Kalman particle filter in conjunction with a program, as proposed by the extended Kalman distribution and particle filter for filtering track, the effect is better than the commonly used particle filter! RMS error is relatively small.)

QAM和OFDM两种方式性能对比，具有图形界面GUI，并且包括收发两端完成的代码 (QAM and OFDM performance comparison of two ways, with a graphical interface GUI, and includes the sending and receiving ends to complete the code)

说明：  一种基于运动模型的扩展卡尔曼滤波（EKF）算法,该方法适用于任何能用状态空间模型表示的非线性系统，精度可以逼近最优估计.(an EKF positioning and tracking algorithm based on kinematic model. This method can apply to any state-space model which is the nonlinear system, and the accuracy can approach to best of all. )

说明：  使用matlab时频分析中的一个demo文件 大家可以和传统的wavlet比较下(use Matlab time-frequency analysis of a demo we can document and traditional wavlet compared)

System Test extends J S R166 Test Case Source Code for Andriod.

In this section, the PAPR performance of the proposed algorithms is shown. To evaluate the PAPR performance, the CCDF is employed. The specification detail of simulation parameters is given in TABLE I. In Fig. 5 is shown the PAPR with a PTS method (U = 8) and conventional PTS (U = 4) are improved 4.3 dB and 2.6 dB from ordinary MIMO-OFDM signal when CCDF = 10− 4 respectively and Fig. 6 is shown the PAPR with an optimal PTS method in case of 128 subcarriers.

该程序实现D-S论据的数据融合功能，针对具体问题只需要修改其中的参数即可。(DS arguments to achieve the program s data fusion capabilities, aimed at specific issues which only need to modify the parameter.)

SVM_for_classficationS200502106 SVM用于模式分类 matlAB实现(SVM_for_classfication S200502106' SVM for pattern classification matlAB achieve)