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应用harris算子进行角点检测，然后利用相关性检测得到匹配焦点，然后进行图像的重采样，适于对于彩色图像的处理，对于灰度图像，只要将harris算子检测时的图像从彩色道灰度图像的语句去掉即可(Application of operator harris corner detection, and then make use of relevant test match to be the focus, and then carried out resampling images, suitable for color image processing, for gray-scale images, as long as the operator will harris Detect when the image from color Road gray-scale images can be removed from the statement)

KALMANF - updates a system state vector estimate based upon an observation, using a discrete Kalman filter. Version 1.0, June 30, 2004 This tutorial function was written by Michael C. Kleder INTRODUCTION Many people have heard of Kalman filtering, but regard the topic as mysterious. While it s true that deriving the Kalman filter and proving mathematically that it is "optimal" under a variety of circumstances can be rather intense, applying the filter to a basic linear system is actually very easy. This Matlab file is intended to demonstrate that. An excellent paper on Kalman filtering at the introductory level, without detailing the mathematical underpinnings, is: "An Introduction to the Kalman Filter" Greg Welch and Gary Bishop, University of North Carolina

说明：  lpcc的基本算法。使用matlab实现(lpcc the basic algorithm. Implementation using matlab)

audio watermark detection

3DFFT,对三维数据进行傅立叶变换，效果较好.可以用于三维的地震资料处理(3DFFT, three-dimensional Fourier transform of the data, the better can be used for three-dimensional seismic data processing)

典型的多目标粒子群算法，用于求解多目标优化问题(Typical multi-objective particle swarm optimization algorithm for solving multi-objective optimization problems)

包含位置式PID算法、积分分离式PID，利用自然梯度算法，包含光伏电池模块、MPPT模块、BOOST模块、逆变模块。( It contains positional PID algorithm, integral separate PID, Use of natural gradient algorithm, PV modules contain, MPPT module, BOOST module, inverter module.)

FMCW调频连续波雷达的测距测角，计算一维光子晶体的透射特性和反射特性，空间目标识别，采用PM算法，从先验概率中采样，计算权重，是学习PCA特征提取的很好的学习资料，图像的光流法计算的matlab程序。

Candide-3 face model

SECANT法求解一个连续方程，f(x) = 0，给两个初始值( SECANT ALGORITHM 2.4 To find a solution to the equation f(x) = 0 given initial approximations p0 and p1: INPUT: initial approximation p0, p1 tolerance TOL maximum number of iterations N0. OUTPUT: approximate solution p or a message that the algorithm fails.)