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用adaboost算法生成基支持向量机分类器，并对识别结果进行简单投票法集成。附有支持向量机工具箱和adaboost算法流程说明。(Adaboost algorithm to generate the base with a support vector machine classifier, and the recognition result is a simple voting method integration. With support vector machine algorithm toolbox and adaboost process description.)

结合DCT和NNC进行人脸识别。先利用DCT提取特征，然后利用最近邻分类器分类，在ORL人脸库上测试效果不错。(The combination of DCT and NNC for face recognition. First DCT Feature Extraction, and then use the nearest neighbor classifiers, good test results on the ORL face database.)

使用HoG 特征做模板匹配工作(matlab与C++混合编译)(template matching using HoG feature（matlab and C++）)

MIMO 初学者理解QPSK系统的最佳代码 详细通信系统流程步骤(MIMO Best codes beginners understand QPSK system Details Communication System Process Steps)

疲劳设计中周期循环对称的算法，程序中使用了全局变量的传递和反迭代的算法，有一定的借鉴意义。(symmetric cyclic stress life method)

说明：  解两点边值问题的有限元程序代码（matlab）(Solution of Boundary Value Problems of Finite Element code (matlab))

FREQUENCY MODULATION

OFDM的simulink仿真，最后显示了输入输出波形和符号误差率(OFDM simulink simulation. Finally, the input and output waveforms to facilitate comparison and shows the symbol error rate)

针对极化阵列的波束综合问题，并把这一问题描述成一个凸优化问题。 (For polarization array pattern synthesis problem, and describe it as a convex optimization problem.)

雷达数据处理对于观测的目标位置数据（称为点迹）进行滤波处理，自动形成航迹，并且可以对下一个目标的位置进行估计预测。利用最小二乘算法（LMS）进行滤波处理，得到了比较稳定的航迹估计。 最小二乘方法是参数估计中一种较为常见的参数估计方法。对于雷达观测数据，在我们假定已经精确知道航迹的运动学模型之后，通过建立观测方程，即可以对于模型的参数，如初速度，初始位置和加速度等进行最小均方误差估计。采用序贯最小二乘的方法可以对观测数据进行实时处理，已对待估计参数进行实时更新，然后利用观测模型可以对 数据进行滤波计算。(Radar data processing for target position data observed (known as trace points) filter processing, automatic track, and can be estimated to predict the position of the next target. Using the least squares algorithm (LMS) filtering process, have been relatively stable track estimates. Least-squares parameter estimation method is a more common parameter estimation method. For radar data, we assume that the kinematic model has been accurately track after, through the establishment of observation equations, which can be the parameters of the model, as ever speed, acceleration, etc. initial position and minimum mean square error estimation. Sequential least squares method using the observed data can be processed in real time, has been to treat the estimated parameters are updated in real time, and then use observational data can be filtered model calculations.)