工程故障诊断

说明：  用图像进行物体角度测量的代码，在Matlab下编写，很有参考价值。(image objects angle measurement for the code in Matlab under preparation, great reference value.)

Acquisition of Glonass satellites signals

Lax-Wendrof法求解一介波动方程，初始条件c=1m/s, x=1-2,u=2m/s(Lax-Wendrof method to solve wave equation)

非常好的粒子滤波程序:扩展卡尔曼模型下的序列追踪(very good particle filter : Extended Kalman model tracing the sequence)

Z-N法计算控制器参数， 绝对好用 很经典的算法(ZN method to calculate the controller parameters, absolutely easy to use the classic algorithm)

Mur boundary 2D FDTD, 2维FDTD采取MUR二阶近似，时变动态电磁场(Mur boundary 2D FDTD， second order Mur approximation， time variant Time signal simulation)

采用Matlab编写菜单程序,着重强调matlab汉字处理功能(Using matlab language to write a menu)

说明：  这个例子展示了一种高压电池，类似于混合动力汽车中使用的电池。该模型采用真实的直流链路电流分布，该电流分布来源于动态驾驶循环。总模拟时间为3600秒。此示例演示如何使用Simscape对铅酸蓄电池单元建模 实现等效电路元件非线性方程的语言。这样，与完全在Simulink中建模不同，模型组件和定义物理方程之间的连接更容易理解。有关定义方程及其验证，请参见Jackey，R.“电气系统部件选择的简单有效铅酸蓄电池建模过程”，SAE世界大会与展览会，2007年4月，参考文献2007-01-0778。(This example shows how to model a lead-acid battery cell using the Simscape language to implement the nonlinear equations of the equivalent circuit components. In this way, as opposed to modeling entirely in Simulink, the connection between model components and the defining physical equations is more easily understood. For the defining equations and their validation, see Jackey, R. "A Simple, Effective Lead-Acid Battery Modeling Process for Electrical System Component Selection", SAE World Congress & Exhibition, April 2007, ref. 2007-01-0778.)

he files in this package comprise the Matlab implementation of a foreground segmentation algorithm based upon graph cuts, described in: Better Foreground Segmentation Through Graph Cuts, N. Howe & A. Deschamps. Tech report, http://arxiv.org/abs/cs.CV/0401017. The file extractForeground.m contains a sample function that will perform a complete foreground segmentation for static camera video. It uses a number of parameters, which are described in the documentation. An attempt was made to choose sensible default values, but they may need to be adjusted for some videos. More generally, the implementation in extractForeground.m is only a suggestion of how the graph-based segmentation might be used. The same approach can be applied with other (perhaps time-adaptive) background models. The key step is at line 113 of the code, once the deviation array has been created. The graph-cut segmentation can be applied similarly to a deviation matrix computed in any other way.

it is for measuring depth from defocus