UWB9

用Matlab语言实现带通信号与系统的低通仿真(With the Matlab language and system bandpass low-pass simulation)

BP神经网络之MATLAB程序的数学建模(BP neural network of the MATLAB mathematical modeling procedures)

用matlab simulink实现超外差接收系统的一个测试程序(Using matlab simulink realization of a superheterodyne receiver system test program)

matlab scripts for some image processing applications like windowing,zooming etc

通过matlab代码，PLS部分最小二乘工具箱,可以广泛的应用于数据预测及数据分析。( By matlab code, PLS PLS toolbox, Can be widely used in data analysis and forecast data.)

源代码中是三个子程序，分别为R,RRR,RRP,用于分析机构的各部分的速度，加速度，坐标。R用于分析原动杆，RRR用于分析双杆组构件，RRP用于分析两转动副一滑动副的双杆组。分析一个具体机构时可先将机构拆成原动杆和各杆组，然后调用这三个子程序来计算。(Source code of three subroutines, namely R, RRR, RRP, for the analysis of body parts of the velocity, acceleration, coordinates. R for the analysis of the original rod, RRR for the analysis of double-pole group member, RRP for the analysis of two revolute a sliding two-bar group vice. Analysis of a sector specific institutions can be split into the original first rod and the bar group, and then call subroutine to calculate the three.)

Upsample Resample Interpolation

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.)

描述了永磁同步电机的机侧模型，并作出了详细的仿真(Describes the machine side of the permanent magnet synchronous motor model)

说明：  此为台湾大学林智仁教授及所带领的团队编写的关于svm的程序包，简单实用。(This is the National Taiwan University Professor Lin Zhiren and the team led the preparation of the package on the svm, simple and practical.)