50-cases-of-MATLAB-motor-

EM310ZILIAO,em310的资料，EM310ZILIAO,EM310ZILIAO,EM310ZILIAO,(GPRS INFOR)

实验一 双极性矩形随机信号的归一化功率谱密度一 1.1 功率谱密度简介 平稳过程的任何一个非零样本函数的持续时间为无限长，显然都不满足绝对可积和总能量有限的条件。因此，它的傅里叶变换不存在即没有频谱函数。所以我们用功率谱密度来表述其频谱特性。 随机过程的任一实现是一个确定的功率型信号。而对于任意的确定功率信号f(t)，它的功率谱密度为： 式中， 是f(t)的截短函数 对应的频谱函数。f(t)是平稳随机过程 的一个实现。而随机过程某一个实现的功率谱密度不能作为过程的功率谱密度。过程的功率谱密度应该看作是任一实现的功率谱密度的统计平均，即 虽然该式给出了平稳随机过程的功率谱密度，但我们通常都不利用这个式子来计算功率谱。我们知道，确知的非周期功率信号的自相关函数与功率谱密度是一对傅里叶变换。对于平稳随机过程，也有类似的关系，即 和 对于平稳随机过程我们通常先求出其自相关函数再利用上式求出其功率谱密度。 1.2 实验要求  1.了解平稳随机信号功率谱的概念及计算方法  2.仿真不同占空比，等概、非等概双极性矩形随机信号的归一化功率谱密度  3.分析不同信号所包含的频谱分量，有无直流分量和定时分量信息 (A pair of rectangular random experiment polarity signal sample return a nonzero function of any duration power spectral density of a 1.1 power spectral density of a stationary process Introduction of infinite length, apparently not satisfied absolutely integrable and the total energy of the limited conditions. Therefore, it is the Fourier transform of the spectrum that does not function does not exist. Therefore, we use the power spectral density to express their spectral characteristics. Any random process is a realization of a certain type signal power. For arbitrarily determined power signal f (t), its power spectral density is: wherein, is f (t) is a function corresponding to the truncated spectrum function. f (t) is a stationary random process of realization. The random process to achieve a certain power spectral density can not serve as the power spectral density of the process. Power spectral density process should be seen as a statistical average power spectral density of any r)

离散PID控制器的MATLAB/simulink仿真。(MATLAB/simulink simulation of discrete PID controller.)

交互小波分析及其一致性分析，用于分析两个相关的时间序列之间存在的相互关系及一致性。(Cross-wavelet analysis and consistency analysis for the analysis of time series between the two related to the relationship between the existence and consistency.)

东南大学 电子学院 信号与系统实验 matlab 代码(Southeast University, Electronic Science and Engineering, signal and system, matlab)

matlab 数字图像处理 图像复原 含代码和结果显示 (Digital image processing matlab image restoration Containing the code and the results show)

这个压缩包中有6个函数，利用平均互信息量进行聚类分析的一组函数(CE Matlab Functions)

DTW（Dynamic Time Warping，动态时间归整）算法，该算法基于动态规划（DP）的思想，解决了发音长短不一的模板匹配问题，是语音识别中出现较早、较为经典的一种算法。用于孤立词识别，DTW算法与HMM算法在训练阶段需要提供大量的语音数据，通过反复计算才能得到模型参数，而DTW算法的训练中几乎不需要额外的计算。所以在孤立词语音识别中，DTW算法仍然得到广泛的应用。 (DTW (Dynamic Time Warping, dynamic time warping) algorithm based on dynamic programming (DP) ideas, sounds of varying lengths to solve the template matching problem, is speech recognition appeared earlier, more classic kind of algorithm. For isolated word recognition, DTW algorithm and HMM algorithm in the training phase need to provide a large number of voice data, obtained by repeated calculations to model parameters, while the DTW algorithm is almost no additional training calculations. Therefore, in isolated word speech recognition, DTW algorithm is still widely used.)

对广义高斯分布有个基本的认识，为以后的应用打下基础(The generalized Gaussian distribution have a basic understanding of lay the foundation for future applications)

In this session, I will focus on the gridding method for fast image reconstruction from non- Cartesian data [1-5]. While gridding is not the only game in town, it is the reconstruction of choice when reconstruction time is an issue. Other reconstruction techniques include rBURS [6] and non-Cartesian iterative methods [7,8]. Non-Cartesian iterative methods generally use gridding and inverse gridding as their core operation, so even when gridding is not the right solution for an application, an understanding of gridding can be useful. I will cover the main steps of the gridding algorithm and then discuss some implementation tips that can signicantly reduce the computation time. Finally, I will present some illustrations and viewpoints that I have found to aid in an intuitive understanding of the gridding method, which can be helpful when designing the density compensation function and judging the appropriateness of gridding as a solution for a particular application.