white_noise

检验已有的一组数据的概率分布是否为某种分布(Verify whether the data is a certain distribution)

Paper About Medical Care Systems

研究Ｌ ü混沌系统的脉冲鲁棒镇定问题，得到其脉冲鲁棒镇定的充分条件，给出相应的脉冲控制律。通过应用Ｍａｔｌａｂ 中Ｓｉｍｕｌｉｎｋ 工具箱对Ｌü混沌系统进行仿真分析，说明脉冲控制是有效的镇定受扰动混沌系统的方法。 (Study of chaotic systems L ü pulse robust stabilization problem, get a pulse of a sufficient condition for robust stabilization, given the corresponding pulse control law. Through the application of Matlab in Simulink toolbox of chaotic Lü system simulation analysis shows that impulse control is an effective stabilization of chaotic systems by perturbation method.)

基于DD算法的先验信噪比估计的维纳语音降噪完整程序，包括语音分帧，动态信噪比估计,噪声估计更新和帧的重构。很完整。(DD algorithm based on a priori signal to noise ratio is estimated that the integrity of the process noise reduction Wiener voice, including voice sub-frame, dynamic signal to noise ratio estimation, noise estimation and frame reconstruction update. Complete. I hope to share together.谢谢. Small text.)

This is an A-Z guide to Matlab

VMD用于信号去噪，去噪效果优于EMD，是EMD算法的一种改进算法(VMD for signal denoising, denoising effect is better than EMD, EMD algorithm is an improved algorithm)

传染病动力学李氏指数用matlab来编程得到图像(Lipschitz Dynamics of Infectious Diseases)

时间序列数据分析中的梅林变换工具箱，可加载后直接调用里面的函数。(Time-series data analysis in the Mellin transform toolbox.)

通过采用ekf算法来实现SLAM，对比了真实值与采用算法后计算结果，说明程序的可行性。(EKF-SLAM Simulator ------------------ This simulator demonstrates a simple implementation of standard EKF-SLAM. It permits simple configuration via configfile.m to perform SLAM with various control parameters, noises, etc. Also various switches are available to choose known data-association versus gating, etc. The key file in this simulator is called ekfslam_sim.m . Type help ekfslam_sim for more information of how to use it. In addition to on-line animations, the simulator returns a data-structure of the logged state information for off-line processing. An example use of this data is shown in m-file plot_feature_loci.m , which plots the trajectories of the landmark estimates. Tim Bailey and Juan Nieto 2004. )

Learn Kalman Filter( Kalman Filter)