PCA

微波技术电子史密斯圆图程序Matlab---Smith-chart(Matlab---Smith-chart)

homework 5 given to me by my lecturer

该代码是红外目标粒子滤波跟踪算法，代码可以运行，且效果很好。(The code is infrared target tracking algorithm of particle filter, the code can be run, and the effect is very good. )

matlab代码 图像处理中基于ROF的去噪 求解ROF(denoising ROF)

利用optisystem软件仿真半导体激光器的P-I特性曲线。得到的图形遵循在超过阈值电流之后，输出光功率与电流成线性关系规律。(Using software optisystem simulation P- I characteristics of semiconductor laser curve.The graphics follow after more than threshold current, a linear relationship with the output light power and current laws.)

函数注册在Matlab和C++接口中起着重要的作用。在介绍函数注册作用的基础上，详细分析了函数注册的过程及应当注意的问题，并给出了一种实现函数注册的最简单的方法。用实例展示了函数注册的具体实现过程。 (registration function in Matlab and C interface plays an important role. In introducing the role of the registration function on the basis of a detailed analysis of the function of the registration process and should pay attention to the problem, and gives a registration function to achieve the most simple method. Examples demonstrate the function of the concrete realization of the registration process.)

本程序主要是用matlab对曲柄摇块机构的计算并输出图形。通过本程序可以计算出曲柄摇块机构的位移、角速度和角加速度并输出其图形，还可以输出曲柄摇块机构的运动仿真图(This procedure is mainly used matlab for crank block body shake Calculation and output graphics. Through this program can calculate the crank block body roll displacement, angular velocity and angular acceleration, and output the graphics, you can also shake the output crank kinematic simulation block diagram)

用MATLAB仿真，实现香农编码的仿真。得到香农码。(Using MATLAB simulation, the simulation codes to achieve Shannon. By Shannon code.)

说明：  duffing振子数值建模,学习仿真，非线性动力学(study duffing Oscillator numerical modeling, learning simulation, nonlinear dynamics)

％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％( EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates)