IRFPA_NUC

基于栅格法的路径规划算法实现（Matlab环境下）(Grid method is based path planning algorithm (Matlab environment))

用matlab编写的实现光流计算Lucas-kanade算法源代码(Using matlab to achieve optical flow Lucas-kanade algorithm source code)

机器人运动学建模仿真，DH参数设计，运动学正解和逆解，六轴机器人(six axis robot modeling and simulation,DH parameters design )

This paper deals with analysis of main functional diagrams of step PID controllers for electric motor-driven actuators. The main formulae to calculate the parameters of the diagrams of step PID controllers are presented in the paper. The influence of these parameters on the transient processes in step controllers and on the output position of the actuator is analyzed. Transient processes in step controllers with motor-driven actuators are simulated in PI and PID control mode. These transient processes are compared to the transient processes in continuous controllers. The accuracy of step PID controllers is analyzed, and restrictions on the parameters of step PID controllers are presented. The diagrams of step PID controllers considered in this paper can be implemented in the up-to-date PLCs software for automatic control using the electric motor-driven actuators in the control loops.

This code is written for the map transformations, affine, conformal and polynomial. It basically uses, the parameter estimation process regarding to the quality assesment.

用时间序列和小波分析相互结合的基于小波的时间序列预测方法，该方法比较可靠，欢迎大家下载。(Wavelet analysis of time series and combined with each other in the time series prediction method based on wavelet, this method is more reliable, welcome to download.)

远场2D 解相干 music算法 均匀圆阵(far field coherent music 2D solution algorithm uniform circular array)

ga源代码，使用matlab实现遗传算法的源代码(ga source code, the use of genetic algorithm matlab source code)

convolutional source code

说明：  选择“BreastCancer”数据集，使用支持向量机(SVM)对其进行分类。作为对比，第一次对特征集直接进行支持向量机分类，第二次对特征集进行主成分分析法的特征提取后，再对特征提取后的特征集进行支持向量机分类。并且对比和分析了两次分类的结果。(The BreastCancer data set is selected and classified by Support Vector Machine (SVM). For comparison, the first time the feature set is classified directly by support vector machine, the second time the feature set is extracted by principal component analysis, and then the feature set is classified by support vector machine. The results of the two classifications are compared and analyzed.)