snake

功率谱估计的应用范围很广，在各学科和应用领域中受到了极大的重视。在《现代信号处理》课程中讲述了经典谱估计和现代谱估计这两大类谱估计方法；经典谱估计是基于傅立叶变换的，虽然具有运算效率高的优点，但是频谱分辨率低同时旁瓣泄漏严重，对长序列有着良好的估计。为了克服经典谱估计的缺点，人们开展了对现代谱估计方法的研究。现代谱估计是以随机过程的参数模型为基础的，有最大似然估计法、最大熵法、AR模型法、预测滤波器法。现代谱估计对短序列的估计精度高，同经典谱估计互为补充。在认真学习了现 代谱估计方法后，我选择了现代谱估计中的AR模型法的仿真作为题目。下面给出AR模型的相关理论和仿真实现。(Power Spectral Estimation of very extensive, in all disciplines and fields of application of a great deal of attention. The "modern signal processing" on the curriculum of classical and modern spectral estimation spectrum is estimated that the two types of spectrum estimation method; Classical spectrum estimation is based on Fourier transform, although high computing efficiency advantages, but also low-resolution spectrum Sidelobe serious leakage of long sequences have good estimates. In order to overcome the classic shortcomings of the spectrum estimation, there have been a pair of modern spectral estimation methods. Modern spectral estimation is the random process model parameters based on the maximum likelihood estimation, maximum entropy method, AR model, forecast filter. Spe)

非高斯滤波程序，主要为非线性非高斯结构识别程序(non-Gaussian filtering procedures, mainly for non-Gaussian nonlinear structure identification procedures)

Block Matching Algorithm (continuous) Motion Estimation ESTES

Buck double loop control

report sample for tue students

matlab教学用书 Edward Neuman 版本(matlab version of teaching the book Edward Neuman)

基于牛顿拉夫逊法的电力系统潮流计算从c语言程序(Newton Raphson based power flow calculated from the c language program)

实现遗传算法的基本历程，可以自由修改变异概率，交叉概率，迭代次数等(Basic Course genetic algorithm, can be freely modified mutation probability, crossover probability, the number of iterations)

通信系统建模与仿真第15章基于MATLAB程序(System Modeling and Simulation of Chapter 15 program based on MATLAB)

这个matlab程序实现了基于一种采用判别方法训练的多尺度和可变形的物体检测模型的目标检测算法，算法可参考如下两篇论文 ([1] P. Felzenszwalb, D. McAllester, D. Ramanan A Discriminatively Trained, Multiscale, Deformable Part Model Proceedings of the IEEE CVPR 2008 [2] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan Discriminatively Trained Mixtures of Deformable Part Models PASCAL VOC Challenge 2008 )