Speech-signal-short-time-analysis

给定两个变量，计算自定义的数学方程的matlab GUI例子。(it takes input parameters(in the following order) when you call the gui, a system of equations file. Once the equation is called it will display each of them in their proper list boxes. )

matlab下使用帧差法实现视频跟踪，提供了一个测试视频和完整的说明文档。下载后可以直接在matlab环境下运行。(matlab using frame difference for video tracking, provides a test video and complete documentation. Download matlab environment can be run directly.)

基于自适应线性网络的分类程序，matlab(newlin)

matlab 金融工程 风险测量 风险分担 程序 股票(matlab matlab)

KNN Classification Matlab

使用传输零点，综合低通原型滤波器的P，F，E函数，用以得到S参数(synthesis F P E functions to obtain S parameters by using transmission zeros)

电力系统分析中Ybus矩阵的去奇异变换法实现算法。该程序可以实现考虑线路分支和无线路分支的情况。(In general the power system has mutual branches and it becomes difficult to form the y bus using the inspection method, so one is to go for singular transformation method.)

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.)

说明：  使用SG滤波方法对NDVI数据进行时间序列的平滑处理，(SG filtering method is used to smooth NDVI data,)

This a camera calibration toolbox for generic lenses. The toolbox can be used to calibrate conventional, wide-angle and fish-eye lens cameras. The calibration is based on viewing a planar calibration object. The intrinsic and extrinsic camera parameters are estimated from control point correspondences between the calibration plane and calibration images. This toolbox is a result of research that has been carried out in the Laboratory of Computational Engineering at Helsinki University of Technology and in the Machine Vision Group at University of Oulu.