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频率估计的几种算法，包括music算法，rife算法，周期图法，pisarenko,levenson法(Frequency estimation of several algorithms, including music algorithm, rife algorithm, periodogram, pisarenko, levenson France)

粒子滤波的学习程序 简单介绍了粒子滤波的入门程序有学习价值(particle filter)

采用一种新的方法，对图像进行matting，并取得良好的效果(the image matting with good approach)

matlab，K折交叉验证，SVM方法对IRIS分类(K-flod,matlab ,svm method)

说明：  主要功能： 1.完成传感器对目标状态的kalman滤波估计； 2.对传感器的状态估计进行SCC和CI融合； 3.画出位置及速度的估计和融合误差曲线、真实航迹及融合后航迹、K=1时刻的协方差椭圆(Main functions: 1. The Kalman filter estimation of the target state is completed; 2. The state estimation of sensors is fused by SCC and CI; 3. Draw the error curve of position and velocity estimation and fusion, real track and fusion track, covariance ellipse at k = 1)

图像处理的DWT算法，用于基础图像处理学习。matlab语言制作(image processing with DWTimage processing with DWTimage processing with DWT)

求二维点对问题，但是没有实现不知道怎么做？(the distans of some points )

说明：  详细简介了EMD算法原理以及matlab的实现(Details About the EMD algorithm, as well as the realization of matlab)

在工程应用中 经常会遇到一些复杂的非线性系统, 这些系统状态方程复杂, 难以 用数学方 法准确建模。 在这种情况下, 可以 建立 BP 神 经网 络表达这些 非 线 性系 统。 该方法把未 知 系 统看成是一个黑箱, 首先用 系 统 输入输出 数据 训 练 BP 神 经网 络, 使网 络能 够表达该未 知 函 数, 然后就可以 用训练好的 BP 神经网络预测系统输出 。 本章拟合的非线性函 数为 y = x1^2+x2^2. 。 (In engineering applications often encounter some complex nonlinear systems, which complicated the equation of state, can not be accurately modeled mathematically. In this case, BP neural network can be established to express these nonlinear systems. The method of the unknown system as a black box, the first input and output data with systematic training BP neural network, the network can express the unknown function, then you can use the trained BP neural network forecasting system output. Chapter for fitting non-linear function y = x1 ^ 2+x2 ^ 2..)

使用LQR(线性约束下的二次型规划算法)进行无人驾驶车辆路径跟随的工程实例(An Engineering Example of Unmanned Vehicle Route Following Using LQR (Quadratic Programming Algorithm with Linear Constraints))