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LabVIEW程序设计模式
详细介绍了LabVIEW常用的几种设计模式,比如简单状态机,消息队列,生产者消费者处理模式,并给出了详细的例程供参考.
- 2020-12-08下载
- 积分：1
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机器人手眼标定-Matlab程序（高精度）
【实例简介】最近做科研用到手眼系统标定，在网上搜索方法无果，于是自己亲自编写手眼标定程序，经验证，具有较高精度。
1.用Matlab进行相机标定；
2.将机器人末端位姿存储在res（res是存储与采集图像对应的机器人末端位姿旋转矩阵的空间）中；
3.运行手眼标定程序。
- 2021-11-19 00:32:21下载
- 积分：1
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威胁评估的MATLAB仿真程序
通过MATLAB对发现的目标属性进行分析判断，对威胁等级进行排序，代码实测有效。
- 2020-11-28下载
- 积分：1
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基于STM32F407的智能安防系统
一、智能安防系统1.要求如下： .运行UCOS3实时操作系统[可选] .火焰传感器、温湿度传感器、可燃气体传感器正常工作 .RFID读写卡正常工作 .蓝牙4.0正常工作 .红外接收头正常工作 2.实现过程 手机蓝牙操作 .能够修改开发板的RTC时间 .发送特定的命令，能够查询当前安防状态（是否有火焰、可燃气体是否超标、温湿度状态） .发送特定的命令，能够修改安防系统默认card id，并将card id信息保存到flash当中 当RFID进行识别的时候 .安防系统默认有card id，如果当前识别的卡为陌生卡，则进行蜂鸣器长鸣报警，并熄灭所有Led；
- 2020-12-03下载
- 积分：1
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Invest介绍
了解模型的每一个模块，掌握每一个模型所需要的数据，很好的使用invest 模型
- 2020-12-05下载
- 积分：1
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粒子群优化算法 全局及局部 Matlab文件
现在有很多粒子群算法不规范，国外有些工具包过于复杂，功能太大而无从下手，国内的一些文档上的方法多数都是一个粒子式地简单循环，不能够全面地发挥Matlab基于矩阵计算的能力，本程序中的主程序及目标函数均基于向量形式，另外，很多具体程序中缺乏对约束问题进行考虑，本程序可以针对约束问题给出结果以查看约束处理情况，另外还可以选择是否显示离线和在线性能等，再者，本工具包里包含有全局算法及局部算法，试验后发现，局部算法的性能要好得多（可能针对不同问题吧），最后，本算法模块化层次条理清晰，说明具体，可以简单改造成各种改进型算法。
- 2020-12-01下载
- 积分：1
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【PDF】《Machine learning A Probabilistic Perspective》 MLAPP；by Kevin Murphy
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
- 2020-12-10下载
- 积分：1
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HTML5+CSS3 漂亮后台管理系统登录界面
使用HTML+css3写的漂亮后台管理系统登录界面，界面非常美观，分格好看。拿来作为系统的登录界面非常合适
- 2020-12-06下载
- 积分：1
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DS2438电源管理芯片驱动
DS2438电源管理 驱动 已经用于实际应用，效果良好。
- 2021-05-07下载
- 积分：1
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l1范数最优化的相关程序，求出信号的稀疏解，进行分类
本程序描述的是利用L1范数求解稀疏解，然后进行分类
- 2020-12-02下载
- 积分：1
