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Qt通过串口读单片机温度数据画动态波形曲线
Qt通过串口读单片机温度数据画动态波形曲线
- 2020-12-03下载
- 积分：1
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BLUETOOL1.4.3.9
Broadcom的程序下载工具，也可进行RF测试等操作，非常好用，请注意，在安装此工具之前，要先安装ActivePerl-5.8.4.810-MSWin32-x86;
- 2021-05-06下载
- 积分：1
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原生apphtml2.rar
该demo使用h5+的功能基于android手机实现了录音，拍照，录像并进行上传下载，播放的功能，采用h5开发移动app，理论是支持android和ios的开发，开发工具采用hbuilder，利用hbuilder打包app在android机上运行，目前只测试了android机
- 2020-11-04下载
- 积分：1
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RSA数字签名源程序
RSA数字签名源程序，可直接使用，调试方法请看readme文件。适合课程实验所用。
- 2020-12-08下载
- 积分：1
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多智能体的编队控制程序的补充(之前上传少了一个文件)
这个程序是对多智能体的编队控制程序的补充，之前上传少了一个文件，给大家造成的不便深表歉意。特此通知。麻烦管理员把这个资源分数调为0。
- 2020-12-06下载
- 积分：1
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车型识别系统
1.首先单击载入图像菜单项（载入背景和前景图像）,图像在image文件夹下面。2.然后单击车辆提取菜单项，依次进行图像做差、二值化、开运算、图像去噪、图像填充处理。3.再单击轮廓提取菜单项，提取车辆轮廓。4.最后单击车型识别菜单项，对车辆进行识别。
- 2020-12-01下载
- 积分：1
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JAVA JSP学生学籍管理系统 源代码 论文
这是一款JSP+SQL server 做的计算机毕业设计作品。只能 用于作业设计。
- 2021-05-06下载
- 积分：1
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小波包降噪的程序，并有注解
使用小波包 ，小波对信号降噪，小波包提供了一种更为复杂，灵活的分析手段。
- 2020-12-03下载
- 积分：1
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OFDM峰平比及matlab程序仿真
正交频分复用(OFDM)技术是一种可以有效对抗符号间干扰(ISI)的高速数据传输技术。OFDM是一种特殊的多载波调制方式，它的基本思想是将高速传输的数据流通过串/并转换，变成在若干个正交的窄带子信道上并行传输的低速数据流。OFDM接收机有三个关键技术：信道估计技术，降低峰均比(PAPR)技术和同步技术。OFDM技术能有效的对抗多径衰落等，有着诸多的优点，但是OFDM有一个发展瓶颈，即OFDM信号的峰均功率比很大，很容易导致OFDM信号的交调失真和系统性能的下降。因而如何降低OFDM信号的峰均功率比一直是OFDM技术的一个研究热点问题。
- 2020-11-28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
