matlanb3

基于Matlab的巴特沃斯数字滤波器的设计(Matlab Butterworth digital filter design)

说明：  条带法求轴力 matlab小程序 条带法求弯矩 matlab小程序(Strip method the axial force)

基于matlab的彩色图像分割，方法是K-means聚类分割(Matlab-based color image segmentation method is K-means clustering segmentation)

Matlab comparison of PCA algorithms

利用matlab求已知数据点的拉格朗日插值多项式，包含多种方法(Known data points using matlab find the Lagrange interpolation polynomial, contains a variety of methods)

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)

说明：  五点差分型多重网格方法：各种插值算子的比较）(Five-point difference-type multi-grid methods: a variety of interpolation operator of comparison))

有做MIMO-OFDM自适应调试的学者可以下载，希望能够对你们有帮助。(Adaptive MIMO-OFDM has done scholars debugging)

matlab gui的基本应用代码，可以让你对GUI的功能有基本的了解(matlab gui application code that allows you to have a basic understanding of the functionality of the GUI)

matpower是用于电力市场的一款功能强大的软件，是浙江大学，甘德强教授开发的平台，非常方便！(matpower electricity market for a powerful software, Zhejiang University, Professor PROCEEDINGS development platform, very convenient!)