【C#】仪表、开关、按钮、数码管、晶体管数字控件及源码合集

【实例简介】作为直接获取对象表面点三维坐标的现代对地观测技术,激光雷达技术能够快速获取高精度 地面高程模型。基于Lidar系统所获取的点云数据,通过过滤处理将区域纯地形特征信息与地物信 息相分离,最终生成高精度的DEM,并在此基础上对同步获取的数码影像进行了正射纠正,为进一 步的信息提取、定量分析及实体建模奠定基础。

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

开发环境为quartus2，采用verilog语言，详细的写出了SVM决策函数，可根据自己的要求改变输入数据类型以及SVM训练model的参数，即可输出判断结果。供大家参考学习。

文档里有波束形成从入门到深入的matlab程序，还有仿真图，让您从纷乱复杂的程序中轻松明白其中的原理和计算过程。

滑模控制在工程中的应用，学习和了解滑模的实际应用，学以致用

【实例简介】netcdf，在linux环境下装，windows可能要用到cygwin安装，安装后可以查看.nc文件，NetCDF(network Common Data Form)网络通用数据格式是由美国大学大气研究协会（University Corporation for Atmospheric Research，UCAR)的Unidata项目科学家针对科学数据的特点开发的，是一种面向数组型并适于网络共享的数据的描述和编码标准。

【实例简介】直流电机调速系统在现代化工业生产中已经得到广泛应用。直流电动机具有良好的起、制动性能和调速性能，易于在大范围内平滑调速，且调速后的效率很高。针对直流电机调速的方法也很多，目前国内外也研究了一些调速的控制器。例如已经用于实际生产的直流电机无级电子调速控制器采用国际先进的IGBT大功率模块器件和独特自行设计的PWM微电子控制技术，以及节能反馈电路和丰富的保护功能控制电路。适用于无轨机车、矿山井下窄轨机车、磨床、木工机械、服装制作、纺织、造纸印刷等场所。该控制器具有调速平稳，安全可靠，提高生产效率；直流电机正反转控制简便；可以与计算机连接控制等特点。

【实例简介】龙芯中科公司的“龙芯3A-RS780E开发板”全部原理图，对于准备使用龙芯处理器的朋友们来说，这绝对是可以参考的图纸，我做过龙芯2F、3A、3B处理器的硬件设计，都是借助了这份资料，都是一版成功的。开放本资料实为交流技术，如果做商业应用，请后果自负。谢谢。

不用修改就可以用的 mat简单方便的转换为txt格式的代码

NET脱壳工具 DLL、exe文件，net语言万能脱壳工具，大多数混淆代码都可以脱出1