K-L变换算法

MEMS激光雷达接收镜头(.zmx)

漂亮年终总结ppt模板下载适用于年终总结主题设计应用。

稀疏自编码深度学习的Matlab实现，sparse Auto coding，Matlab codetrain, m/7% CS294A/CS294W Programming Assignment Starter CodeInstructions%%%This file contains code that helps you get started ontheprogramming assignment. You will need to complete thecode in sampleIMAgEsml sparseAutoencoder Cost m and computeNumericalGradientml For the purpose of completing the assignment, you domot need tochange the code in this filecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencodtrain.m∥%%========%6% STEP 0: Here we provide the relevant parameters valuesthat willl allow your sparse autoencoder to get good filters; youdo not need to9 change the parameters belowvisibleSize =8*8; number of input unitshiddensize 25number of hidden unitssparsity Param =0.01; desired average activation ofthe hidden units7 (This was denoted by the greek alpharho, which looks like a lower-case pcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod4/57train.,m∥in the lecture notes)1 ambda=0.0001%o weight decay parameterbeta 3%o weight of sparsity penalty term%%==:79 STEP 1: Implement sampleIMAGESAfter implementing sampleIMAGES, the display_networkcommand shouldfo display a random sample of 200 patches from the datasetpatches sampleIMAgES;display_network(patches(:, randi(size(patches, 2), 204, 1)), 8)%为产生一个204维的列向量,每一维的值为0~10000curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod5/57train.m/v%中的随机数,说明是随机取204个 patch来显示%o Obtain random parameters thetatheta= initializeParameters ( hiddenSize, visibleSize)%%=============三三三三====================================97 STEP 2: Implement sparseAutoencoder CostYou can implement all of the components (squared errorcost, weight decay termsparsity penalty) in the cost function at once, butit may be easier to do%o it step-by-step and run gradient checking (see STEP3 after each stepWecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod6/57train. m vb suggest implementing the sparseAutoencoder Cost functionusing the following steps(a) Implement forward propagation in your neural networland implement the%squared error term of the cost function. Implementbackpropagation tocompute the derivatives. Then (using lambda=beta=(run gradient Checking%to verify that the calculations corresponding tothe squared error costterm are correctcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod7/57train. m vl(b) Add in the weight decay term (in both the cost funcand the derivativecalculations), then re-run Gradient Checking toverify correctnessl (c) Add in the sparsity penalty term, then re-run gradiChecking toverify correctnessFeel free to change the training settings when debuggingyour%o code. (For example, reducing the training set sizecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod8/57train m vl/number of hidden units may make your code run fasterand setting betaand/or lambda to zero may be helpful for debuggingHowever, in yourfinal submission of the visualized weights, please useparameters web gave in Step 0 abovecoS七grad]sparseAutoencoderCost(theta, visibleSize,hiddensize, lambda,sparsityParam, beta,patches)二〓二二二二二二二〓二〓二〓二〓=二====〓=curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod9/57train.m vlll96% STeP 3: Gradient CheckingHint: If you are debugging your code, performing gradienchecking on smaller modelsand smaller training sets (e. g, using only 10 trainingexamples and 1-2 hiddenunits) may speed things upl First, lets make sure your numerical gradient computationis correct for a%o simple function. After you have implemented computeNumerun the followingcheckNumericalGradientocurer:YiBinYUyuyibintony@163.com,WuYiUniversityDeep Learning, MATLAB Code for Sparse Autoencode10/57

【实例简介】我自己用VBA编写的Solidworks API实例，在SolidWorks 2010版本上调试通过。学习Solidworks VBA编程的另外一种方法是，使用Solidworks API帮助，在索引中输入VBA，可以查询到帮助文件中的VBA编程示例。 Solidworks二次开发，推荐使用C#

出版社: 中国电力出版社; 第1版 (2005年12月1日) 　　丛书名: 21世纪高等学校规划教材 本书较为详细地介绍了常用的不控型、半控型和全控型电力电子器件以及它们的应用基础。基于MATLAB软件、面向电气系统原理结构图的图形化仿真技术，系统地针对上述典型电力变换电路进行了仿真实验。本书可作为高等学校本科和高职高专电类专业的教材，也可供从事电力电子技术工作的工程技术人员参考。

用小波处理一维信号matlab实验，包括小波分解，阈值选择等。

3dsmax导出gltf格式插件，里面包括插件，插件使用说明，插件功能介绍；

Lumped Elements for RF and Microwave Circuits英文版，射频微波电路入门Lumped Elements for RFand microwave circuitsFor a listing of recent titles in the Artech House Microwave library,turn to the back of this bookLumped Elements for RFand microwave circuitsInder bahlArtech houseBoston londonwww.artechhouse.comLibrary of Congress Cataloging-in-Publication DataBahl, I.J.Lumped elements for RF and microwave circuits /Inder Bahlp cm.--(Artech House microwave libraryncludes bibliographical references and indexISBN 1-58053-309-4(alk. paper1. Lumped elements(Electronics) 2. Microwave integrated circuits. 3. Radiofrequency integrated circuits. 4. Passive components. I. Title. II. SeriesTK7874.54B342003621.38132-dc212003048102British Library Cataloguing in Publication DataBahl, I. J. Inder jit)Lumped elements for RF and microwave circuits.-(Artech House microwave library1. Radio circuits 2. Microwave circuits I. Title621.38412ISBN1-58053-3094Cover design by Igor Valdmar2003 ARTECH HOUSE, INC.685 Canton StreetNorwood Ma 02062All rights reserved Printed and bound in the United States of America. no part of this bookmay be reproduced or utilized in any form or by any means, electronic or mechanical, includingphotocopying, recording, or by any information storage and retrieval system, without permissionn writing from the publisherall terms mentioned in this book that are known to be trademarks or service marks have beenappropriately capitalized. Artech House cannot attest to the accuracy of this information. Useof a term in this book should not be regarded as affecting the validity of any trademark or servicemarkInternational Standard Book Number: 1-58053-309-4Library of Congress Catalog Card Number: 200304810210987654321To my adorable grandsons, Karan and Rohan Kaushal, with whom I play, cry,and laugh, and who have provided me with the idea and inspiration towrite this bookContentsPrefaceXVAcknowledgmentsXXIntroduction1.1 History of Lumped Elcements)Why Use Lumped Elements for rF andMicrowave circuits1.3 L, C, R Circuit Elements1.4 Basic Design of Lumped Elements4.1 Capacitor1.4.2 Inductor4.3 Resistorumped-Element Modelin1. 6 Fabrication1.7 ApplicationsReferencesInductors2.1 IntroductionLumped Elements for RF and Microwave Circuits2.2 Basic Definitions2.2.1 Inductance2.2.2 Magnetic Energy182.2.3 Mutual Inductance202. 2. 4 Effective Inductance202.2.5Imedang2.2.6 Time Constant2.2.7 Quality Factor222.2.8 Self-Resonant Frequency232.2.9aximum Current Ratin2.2.10 Maximum Power Rating2.2.11 Other Parameters232.3 Inductor Configurations242.4 Inductor models2. 4. 1 Analytical Models252.4.2 Coupled-Line Appiroach282.4.3 Mutual Induse arppi342.4.4 Numerical Approach362.4.5 Measurement-Based model382.5 Coupling Between Inductors452.5. 1 Low-Resistivity Substrates2.5.2 High-Resistivity Substrates462.6 Electrical Representations2.6. 1 Series and Parallel representations2.6.2 Network Representations51References52Printed Inductors573.1 Inductors on Si Substrate583.1.1 Conductor Loss3.1.2 Substrate Loss3. 1.3 Layout Considerations3.1.4 Inductor Model3. 1.5 Q-Enhancement Techniques3.1.6 Stacked-Coil Inductor803.1.7 Temperature Dependence84

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电机与拖动基础（学习指导）是清华大学李发海编著的电机与拖动基础的同步辅导书，难点重点应有尽有。录CONTENTs电机与拖动基础第CHAPTER绪想一电机与拖动基础第己章CHAPTER 2.4电力拖动系统动力心扮题电机与拖动基础第2重电力拖动系统动力学电机与拖动基础