rossler

MatlabBGL provides robust and efficient graph algorithms for Matlab using native data structures.

运动目标识别，采用背景差分方法。背景图像由均值法获得。里面的视频换成前几帧换成纯背景效果能更好一点(Moving target identification, using the background difference method. Background image obtained from the mean. Which replaced the previous video frame replaced by pure background a little better results)

Its a tutorial for matlab users.

基于matlab的三角网条件平差，功能还不完善，还不能求点位中误差。(Matlab-based triangulation condition adjustment, the function is not perfect, it can not find the error point.)

Simulating V2V and V2I connectivity in Matlab using car following, lane changing models and entry and exit ramps on a 4-Highways, 3-Ramps scenario

a good example of image processing

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models.

滤波器设计示例（matlab），有IIR和FIR的实现。(filter design examples (Matlab), FIR and IIR realized.)

用matlab中simulink仿真蹦极运动(Bungee jumping in with matlab simulink simulation exercise)

用三种不同的Runge-kutta方法计算常微分方程(Runge-kutta for ODE)