UML网购 用例图 类图 活动图

2018年电赛A题，本质做频谱分析，主要手段是采集信号，然后对采集到的信号做傅里叶变换变换。实际上实现的功能是简易频谱分析仪。

用matlab模拟人行走，跑动。所需的数据已经存在，直接可以运行看到效果。

有完整的代码注释，三种方法都可以实现，并有demo音频。

Imatest 安装包，亲测win7 win10可以正确安装

云点 离散点 三维 重构 三维建模 matlab

FAO利用彭曼-蒙蒂斯公式计算参考作物蒸发蒸腾量的软件EToCalculatorV32（英文）包含32位和64位的程序安装手册（英文）教程（英文）原文地址（大几率被墙，请使用在线代理）

卫星观测文件Rinex读取和计算单点定位结果的文件，适合GPS BDS系统

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

机载下视圆周SAR三维BP成像，对学习CSAR成像算法的同学很有帮助庞守宝,等:机载下视圆周SAR三维BP成像匚发射信号]一世P点救射回波信号时间匹配滤波补偿相位因子DBP成像相十叠加0)0图5同一高度x平面内4点的能量图成像显示图3算法的成像流程图103仿真结果60仿真的场景是在三维平面上立方体的8个顶点,场景的原始三维仿真图如图4所示。仿真中的主要的303035系统参数如表1所示。表1仿真所用的主要系统参数10载波波长0.008m带宽750 MHz脉冲重复频率飞行高度0203040506070801000m/n角速度0.4rads载机飞行半径100m阵元数目阵元间距0.008m图6同一高度x平面内4点的等高线dB图10108口424044维/363436-10-6y/m原始场景图7BP成像之后的初始结果图图4原始场景图系统仿真的结果如图5~图8所示。图5为同高度4点的能量图,从图5中可以看出,有目标的区域能量积聚非常明显,没有目标的区域能量很小,几乎可以忽略,这正是BP算法相干累积的优点。图65为同一高度4点的等高线图,从图中可以得知,点目rim标存在处的能量较为集中,有目标的区域相对没有目y/m场景恢复标的区域的dB差较大,能量差异通过右边的色彩进度条来表示。图7为截取某一门限后BP成像的结果图8场景恢复之后的成像图图,根据图中8点位置可知,BP累积之后的初始成像4结束语相对于原始场景只是出现了坐标的平移,8个点的相对位置是正确的。图8为BP成像结果转换到原坐标分析了飞机圆周飞行时对地面场景目标的三维成系之后的成像结果。比较图4和图8,可以得知,圆像结构和成像机理,机载下视三维圆周SAR区别于周SAR三维BP成像的结果和原始场景吻合,即圆周普通的直线飞行的三维SAR在于回波信号距离压缩SAR可以精确的还原目标场景的三维信息。之后出现沿航向和切航向的耦合项,使得经典的RD16庞守宝,等:机载下视圆周SAR三维BP成像电炮是算法、CS算法和距离徙动算法等受限,BP算法通过(8):1252-1265二维搜索,避免了单独处理每一维的过程,之后通过[5] Wang Y P, Tan WX, Hong W,etal. Focusing Bistatic像素单元离散化、补偿每一距离门相位因子、相干累Circular SAR Data Using Polar Format Algorithm [C]. Syn-积等步骤构建场景目标函数,完成成像处理。三维场thetic Aperture Radar, APSAR2009, 2 Asian -PacificConferences on digital Objects Identifier, 2009: 989景仿真结果表明,圆周SAR能够精确的还原场景目992.标的三维信息。此结构在地质斯探和自然灾害救援等[6]Du Lei, Wang Yanping, Honf Wen, et al. Analytic Mod-有现实意义。eling and Three Dimensional Imaging of Downward- Loo-参考文献king Sar Using Bistatic Uniform Linear Array Antenna[C]. Ist Asian and Pacific Conference on Synthetic aper-[1] Hong W, Wang Y P, Tan WX, et al. Tomographic SARture Radar Proceedings, 2007: 49-53and Circular SAR Experiments in Anechoic Chamber [c]. [7] Jens Klare, Matthias Wei, Olaf Peters, et al. ARTINOGermany: EUSAR, 2008A New High Resolution 3D Imaging Radarsystem on an Au-[2] Cantalloube H, Colin E. Airbome SAR Imaging Along a Cirtonomous Airborne Platform [C]. Greeces: IGARSScular Trajectory [c]. Germany EUSAR, 2006: 16-182006:3842-3845[3] Riot H. Cantalloube, Circular SAR Imagery for Urban Re- [8] Wei M, Ender J, Peters 0, et al. An Air-bone Radarmote Sensing [c]. Germany: EUSAR, 2008: 2-5for Three Dimensional Imaging and Observation - Technical[4] Soumekh M. Reconnaissance with Slant Circular SAR ImaRealisation and Status of ARTINO [C]. Germany: EU-ging [J]. IEEE Trans. On Imaging Proccessing,1996,5SAR, Dresden,2006:5315-5318“→·“M“+“““M(上接第11页)[5]施韶华,李孝辉,刘阳.基于直接数字频率合成的高精参考文献度频率源设计[J].电子测量与仪器学报,2008(90):[1]周殿清.基础物理实验[M].北京:科学出版社,200385-389[2]杨刚,周群.电子系统设计与实践[M].北京:电子工[6]王军证,王建斌,陈仁伟.基于DDS的超声导波信号业出版社,2004源的设计[J.电子测量技术,2010,33(2):19-2.[3]刘海成.AVR单片机原理及测控工程应用[M].北京:[7]吴加政,苏新彦.基于DDs的信号模拟器设计J.国北京航空航天大学出版社,2008外电子测量技术,2009,28(10):67-70[4]柴媛媛,唐慧强,辛红伟.基于ARM和DS技术的信[8] ST Microelectronics Corporation,smFI03 XX Datasheet[EB/号源设计[J].通信技术,2009,42(10):54-56OL].(209-12-14)[2010-03-10]ww.st.com+一中““-(上接第13页)器的输出波形。图4所示为示波器测量仿真输出波形。真分析,在编辑电路、调整元件参数时十分方便,它可以进行各种电子电路的设计与仿真,并且仿真精度高。给电路设计测试带来方便,使电子线路的设计、性能参数的仿真等繁琐的任务变得轻而易举。参考文献[1]郑步生. Muhisim2001电路设计及仿真入门与应用[M]北京:电子工业出版社,2002.[2]张新喜. Multisim10电路仿真及应用[M].北京:机械图4示波器测量仿真输出波形工业出版社,20102结束语[3]美国国家仪器有限公司. NI Multisim1l简化教学和设计中的电路仿真[Z/OL].(2010-02-01)[2010-05-在时序逻辑电路设计中应用 Multisim软件进行仿11http://www.ipcm.com.cn机载下视圆周SAR三维B成像旧WANFANG DATA文献链接作者:庞守宝,张晓玲,吴堃, Pang Shoubao, Zhang Xiaoling, Wu Kun作者单位:电子科技大学,电子工程学院,四川,成都,610054刊名:电子科技英文刊名:ELECTRONIC SCIENCE AND TECHNOLOGY年,卷(期):2010,23(12)参考文献(8条)Wei M; Ender J; Peters 0 An Air-borne Radar for Three Dimensional Imaging and Observation-TechnicalRealisation and status of artin 20062. Jens Klare; Matthias Wei: Olaf Peters ARTINO: A New High Resolution 3D Imaging Radarsystem on anAutonomous airborne plat form 20063. Du Lei; Wang Yanping; Honf Wen Analytic Modeling and Three-Dimensional Imaging of Downward-LookingSAR USing Bistatic Uniform Linear Array Antennas 20074. Wang y P; Tan W X; Hong W Focusing Bistatic Circular SAR Data Using Polar Format Algorithm 2009oumekh M Reconnaissance with Slant Circular SAR Imaging 1996(08)6. Oriot H Cantalloube Circular SAR Imagery for Urban Remote Sensing 20087. Cantalloube H; Colin E Airborne Sar Imaging Along a Circular Trajectory 20068. Hong W; Wang y P; Tan W X Tomographic Sar and Circular Sar Experiments in Anechoic Chamber 2008本文链接http://d.g.wanfangdata.comcn/periodiCaldzkj201012005.aspx

【实例简介】易语言 高破坏性病毒样本源码