motion-tracking

c 专业设计 源于课本的原文件 适合于编程人员()

这个matlab程序实现了Edge Linking and Line Segment Fitting，解压后直接运行demo.m就可以了 ( edgelink.m edge linking function that forms lists of connected edge points from a binary edge image. (Needs findendsjunctions and cleanedgelist below). drawedgelist.m plots out a set of edge lists generated by edgelink or lineseg. edgelist2image.m transfers edgelist data back into a 2D image array. lineseg.m forms straight line segments fitted with a specified tolerance to the lists of connected edge points. maxlinedev.m is also used by lineseg.m to calculate deviations of the edge lists from the fitted segments. findendsjunctions.m finds line junctions and endings in a line/edge image. cleanedgelist.m cleans up a set of edge lists generated by edgelink or lineseg so that isolated edges and spurs that are shorter than a minimum length are removed.)

系统仿真和辨识,包括递推最小二乘法ＲＬＳ和目前先进的辨识理论，适合于系统仿真用.(system simulation and identification, including RLS recursive least squares method and the current advanced identification theory is suitable for system simulation.)

MATLAB小波分析的程序，这是一个信号处理程序，用于故障诊断(MATLAB wavelet analysis procedure, which is a signal processing program for fault diagnosis)

RLS算法，用matlab实现，用自适应算法来实现均衡，程序正确(RLS algorithm, using matlab to achieve, with an adaptive algorithm to achieve a balanced and correct procedures)

全息成像中利用子程序达到仿真采用菲涅尔法，还原采用角谱法的完整程序(Holographic imaging simulation subroutines reached Fresnel, reduction method using the angular spectrum of the complete program)

产生一个随机信号和两个不同频率但频率间隔很小的正弦信号，要求对两信号之和进行如下分析： （1） 求该随机信号的自相关性系数、自相关函数，画出对应的图形； （2） 利用不同的参数建模方法求出两个随机信号的功率谱； （3） 利用极大似然估计、递推最小二乘法等常用的参数估计方法估计所建模型，包括AR模型、MA模型和ARMA模型的的参数，阶次自定；并与Matlab工具箱里的一些建模函数的运算结果进行比较； （4） 利用陷波滤波和MUSIC滤波方法对该信号的频谱进行估计； （5） 利用Wiener滤波、LMS滤波对该含噪声的正弦信号进行去噪声处理； （6） 假设该信号是一个飞行器的某个方向的线位移信号，可否利用Kalman滤波对该信号进行滤波？ （7） 利用高阶谱理论对该信号进行谱估计和相应的AR模型、MA模型和ARMA模型估计； （8） 利用小波变换（变换函数可以直接用Matlab里的函数）对该信号进行去噪声处理，并和前面的去噪声方法进行比较。 (Produce a random signal and two different frequency but frequency interval small sine signals, required to make the following analysis of two signals: (1) for the random signal from the correlation coefficients, the autocorrelation function and draw the corresponding graphics (2) make use of different parameters modeling method for out two random signal power spectrum (3) using maximum likelihood estimation, recursive least square method and common parameters estimation method estimates that the model, including AR model, MA model and the parameters of the ARMA model we, order time decided oneself With Matlab toolbox and some of the modeling function is used compared the (4) use trapped wave filter and MUSIC of the signal filter method of spectrum to estimate (5) use Wiener filtering, LMS filtering on the sinusoidal signal with noise to noise treatment (6) assumes the signal is a vehicle of a certain direction of displacement signal line, can you use Kalman filtering on the)

MAtlab SVM神经网络的信息粒化时序回归预测(Information granulation timing SVM neural network regression prediction)

通过matlab代码，验证了带通采样定理的正确性(The bandpass sampling theorem is realized.)

1) 已知x=[1:15], y=[12 34 56 78 99 123 165 198 243 277 353 345 303 288 275], 用多项式进行拟合，给出拟合多项式，(所选拟合多项式阶数不同，拟合结果不同，请你选择适当的阶数进行拟合)，并绘出原始数据点、拟合曲线和95％置信区间（confidence bounds）的图形 . 2) 根据下面数学模型建立仿真模型，并在一个示波器中把它们的波形仿真出来。 (1) are known to x = [1:15], y = [12 34 56 78 99 123 165 198 243 277 353 345 303 288 275], with polynomial fitting, given the polynomial fitting, (the election order polynomial fitting different fitting results to differ, please select the appropriate order of fit), and draw original data points, fitting curve and 95 confidence interval (confidence bounds) graphics .2 ) In accordance with the following mathematical model for the establishment of simulation model, and in an oscilloscope waveform in their simulation out.)