OFDM瑞利衰落信道下的MATLAB程序

ECP助手是一款易文件分析工具，您可以利用ECP助手重新分析源码，分析易模块，还原子程序，让您的软件可以正常使用，本工具可用于模块逆向（普通模块逆向、密码模块逆向、解除精易助手模块加固）、源码修复（用于被破坏打不开的源码修复）。 运行环境：易语言5.9版本 易语言助手 使用说明：如已安装以上环境，请把 本压缩包内所有文件 解压覆盖到易语言目录 勾选本库 即可

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用matlab模拟最大似然估计算法。对初学者会很有帮助。

《伺服电机应用技术》系统地讲解了伺服电机系统的结构、原理、选型、硬件连接、控制程序编辑，内容涉及伺服控制器（PLC）、伺服驱动器、伺服电机、伺服电机与步进电机的选用方法，具有一定的前瞻性和相当高的实用价值。 　　全书共5章，主要内容包括：通用伺服驱动器的应用、伺服控制系统的连接、伺服控制器的应用、硬件接线及控制程序实验、伺服电机与步进电机的选用。针对关键技术要点，每章均设置了“本章重点”与“本章习题”，附录包括“习题解答”与“自动控制系统专业名词说明”。 　　《伺服电机应用技术》可作为工科院校电气工程、自动化控制、机屯工程等专业的教学用书，也可作为希望进入伺服电机应用相关领域读者的案头书。目

完整版，带目录，机器学习必备经典；大部头要用力啃。Machine learning A Probabilistic PerspectiveMachine LearningA Probabilistic PerspectiveKevin P. MurphyThe mit PressCambridge, MassachusettsLondon, Englando 2012 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanicalmeans(including photocopying, recording, or information storage and retrieval)without permission inwriting from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set in the HEx programming language by the author. Printed and bound in the UnitedStates of AmLibrary of Congress Cataloging-in-Publication InformationMurphy, Kevin Png:a piobabilistctive/Kevin P. Murphyp. cm. -(Adaptive computation and machine learning series)Includes bibliographical references and indexisBn 978-0-262-01802-9 (hardcover: alk. paper1. Machine learning. 2. Probabilities. I. TitleQ325.5M872012006.31-dc232012004558109876This book is dedicated to alessandro, Michael and stefanoand to the memory of gerard Joseph murphyContentsPreactXXVII1 IntroductionMachine learning: what and why?1..1Types of machine learning1.2 Supervised learning1.2.1Classification 31.2.2 Regression 83 Unsupervised learning 91.3.11.3.2Discovering latent factors 111.3.3 Discovering graph structure 131.3.4 Matrix completion 141.4 Some basic concepts in machine learning 161.4.1Parametric vs non-parametric models 161.4.2 A simple non-parametric classifier: K-nearest neighbors 161.4.3 The curse of dimensionality 181.4.4 Parametric models for classification and regression 191.4.5Linear regression 191.4.6Logistic regression1.4.7 Overfitting 221.4.8Model selection1.4.9No free lunch theorem242 Probability2.1 Introduction 272.2 A brief review of probability theory 282. 2. 1 Discrete random variables 282. 2.2 Fundamental rules 282.2.3B292. 2. 4 Independence and conditional independence 302. 2. 5 Continuous random variable32CONTENTS2.2.6 Quantiles 332.2.7 Mean and variance 332.3 Some common discrete distributions 342.3.1The binomial and bernoulli distributions 342.3.2 The multinomial and multinoulli distributions 352. 3.3 The Poisson distribution 372.3.4 The empirical distribution 372.4 Some common continuous distributions 382.4.1 Gaussian (normal) distribution 382.4.2Dte pdf 392.4.3 The Laplace distribution 412.4.4 The gamma distribution 412.4.5 The beta distribution 422.4.6 Pareto distribution2.5 Joint probability distributions 442.5.1Covariance and correlation442.5.2 The multivariate gaussian2.5.3 Multivariate Student t distribution 462.5.4 Dirichlet distribution 472.6 Transformations of random variables 492. 6. 1 Linear transformations 492.6.2 General transformations 502.6.3 Central limit theorem 512.7 Monte Carlo approximation 522.7.1 Example: change of variables, the MC way 532.7.2 Example: estimating T by Monte Carlo integration2.7.3 Accuracy of Monte Carlo approximation 542.8 Information theory562.8.1Entropy2.8.2 KL dive572.8.3 Mutual information 593 Generative models for discrete data 653.1 Introducti653.2 Bayesian concept learning 653.2.1Likelihood673.2.2 Prior 673.2.3P683.2.4Postedictive distribution3.2.5 A more complex prior 723.3 The beta-binomial model 723.3.1 Likelihood 733.3.2Prior743.3.3 Poster3.3.4Posterior predictive distributionCONTENTS3.4 The Dirichlet-multinomial model 783. 4. 1 Likelihood 793.4.2 Prior 793.4.3 Posterior 793.4.4Posterior predictive813.5 Naive Bayes classifiers 823.5.1 Model fitting 833.5.2 Using the model for prediction 853.5.3 The log-sum-exp trick 803.5.4 Feature selection using mutual information 863.5.5 Classifying documents using bag of words 84 Gaussian models4.1 Introduction974.1.1Notation974. 1.2 Basics 974. 1.3 MlE for an mvn 994.1.4 Maximum entropy derivation of the gaussian 1014.2 Gaussian discriminant analysis 1014.2.1 Quadratic discriminant analysis(QDA) 1024.2.2 Linear discriminant analysis (LDA) 1034.2.3 Two-claSs LDA 1044.2.4 MLE for discriminant analysis 1064.2.5 Strategies for preventing overfitting 1064.2.6 Regularized LDA* 104.2.7 Diagonal LDA4.2.8 Nearest shrunken centroids classifier1094.3 Inference in jointly Gaussian distributions 1104.3.1Statement of the result 1114.3.2 Examples4.3.3 Information form 1154.3.4 Proof of the result 1164.4 Linear Gaussian systems 1194.4.1Statement of the result 1194.4.2 Examples 1204.4.3 Proof of the result1244.5 Digression: The Wishart distribution4.5. 1 Inverse Wishart distribution 1264.5.2 Visualizing the wishart distribution* 1274.6 Inferring the parameters of an MVn 1274.6.1 Posterior distribution of u 1284.6.2 Posterior distribution of e1284.6.3 Posterior distribution of u and 2* 1324.6.4 Sensor fusion with unknown precisions 138

PCI总线规范几个版本的打包给你们了。大家好好学习研究，都望都成为高手。

本项目是一个仿淘宝安卓客户端的demo源码，主要实现了：商品的基本展示、宝贝详情，图片展示的放大缩小功能、界面之间切换的动画、购物车多项删除、弹窗的动画效果、首页广告的轮播效果、获得本机具有传感器的列表、listView的上拉刷新，下拉加载功能、二维码扫描、刮刮乐等功能和效果.

这个程序可以识别出图片中的特定水果，是基于matlab 换一个原图的话也一样可以识别途中的某些物体 试过很好用

代码为verilogHDL编写，有三个按键：一个控制波形（方波和正弦波），一个控制频率增加，一个控制频率降低。代码有注释，并在signaltap ii中验证成功。

labview中界面跳转的实现方法。界面间的链接，就是运行主界面的按钮控件后，跳转到调用的子界面，然后点击子界面上的返回按钮，跳回主界面。