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STM32光栅尺采集例程STM32光栅尺采集例程STM32光栅尺采集例程

Masm for windows 2012集成实验环境是从事一线教学的大学教师针对汇编语言初学者的特点开发的一个简单易用的汇编语言学习与实验软件，支持32位与64位的WINDOWS 7，支持DOS的16/32位汇编程序和Windows下的32汇编程序（并提供调试通过的35个WINDOWS汇编程序实例源代码）,它具有错误信息自动定位、关键字实时帮助并且在帮助中动画演示汇编指令的执行过程、语法着色、无限次撤消与恢复、WORD式的查找、替换、定位、支持中文、长文件名等功能。

剑桥的著名软件，可用于观察晶体结构，了解晶体中的结构排列，相互作用等等，还能用于画图

基于Matlab的SSIM算法实现（源码+注释，直接运行），其中包括输入3维图像运行出错的修改，还有源码中部分参数，格式不正确的修改，直接运行出结果。PS:网上一些源码说可以直接运行，实则Copy，并没有修改，深受其害，所以今天索性做了个修改完整版供大家分享。

【实例简介】简介： 1) 学生信息管理功能：主要是添加学号，姓名，性别，年龄，政治面貌，班级，登录帐号以及登录密码等属性，也可以删除不存在的学生。 2) 宿舍信息管理功能：主要是添加房间号，学生人数，学生班级和楼号等属性，也可以删除无用的宿舍信息。 3) 公告信息管理功能：设置学校的静校时间和熄灯时间。 4) 卫生检查信息功能：主要是录入卫生检查信息，查询卫生检查信息以及根据得分排名。 5) 保修审批功能：学生使用系统申报需要修的设备，管理员使用保修审批功能进行操作。 学生模块实现的功能有： 1) 保修模块：学生可以添加保修信息，并且查看我的保修。 2) 卫生排名信息：可以查看各宿舍卫生排名的信息。

拟一维喷管流动的数值解MATLAB程序，模型来自于《计算流体力学基础及其应用》--安德森著，第7章第3小节

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

用FPGA当主控芯片来采集温度传感器的数据，模块化设计程序，模块之间有详细讲解！

遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。遗传算法可以解决多种优化问题，如：TSP问题、生产调度问题、轨道优化问题等，在现代优化算法中占据了重要的地位，本例使用遗传算法求解最优解。

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