LBM入门小例子

基于Labview的步进电机控制与实时数据采集系统

最新版的uCOS-III全套资料，绝对给力，网上也基本找不到，我花了很长时间才找到的，希望大家能喜欢，包括最新的uCOS-III源码，SFM32例程，工程文档，和原版英文手册，绝对给力

java+swing酒店管理系统源码:代码很全,数据库也很全

BP神经网络整定的PID算法_matlab源程序，神经网络的PID算法，MATLAB源程序代码

关于FDTD算法的讲解和实例，可供大家进行学习使用。

利用Opencv3.2中的DPM模块，实现DPM算法对行人的检测，一个完整的实现，含有注释和完整的工程文件，环境为win7+vs2013

本实验报告为数据挖掘课程weka工具使用C4.5算法进行决策树分类学习的一次实验报告，使用weka工具的图形界面，对UCI上下载的数据集进行分类测试。

数据结构课程设计，用链表实现的通讯录管理系统。可以方便查询多个班级每一个同学的手机号、电子邮箱，及家庭住址等信息。本系统用线性链表实现多个班级的同学通讯信息的管理，实现了数据文件的保存与读取功能。包含生成测试文件的代码。采用C++编写，代码中包含部分注释。资源中包含所有源代码，Microsoft Visual Studio 2010 编译调试通过。不包含课程设计报告。实现的功能包括：1) 读取已保存的通讯录数据文件。2) 创建新的班级通讯录。3) 修改班级的基本信息。4) 删除整个班级通讯录。5) 输出所有班级通讯录。6) 在已有的班级中插入新的学生。7) 通过学生姓名

自适应滤波器的权威Sayed的大作，用大量的实例诠释自适应滤波器的各种算法原理，是不可多得的好书。ADAPTIVE FILTERSADAPTIVE FILTERSALIH SAYEDUniversity of California at Los Angeles◆旧EEEIEEE PressWIlEYNTERSCIENCEA JOHN WILEY SONS, INC, PUBLICATIONCover design by Michael RutkowskiCopyright C 2008 by John Wiley Sons, Inc. All rights reservedPublished by John Wiley Sons, Inc, Hoboken, New JerseyPublished simultaneously in CanadaNo part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or bymeans, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under anySection 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of thePublisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center,Inc, 222 Rosewood Drive, Danvers, MA01923, (978)750-8400, fax(978)750-4470, or on the web atwww.copyright.comRequeststothePublisherforpermissionshouldbeaddressedtothePermissionsDepartment, John Wiley sons, Inc, 111 River Street, Hoboken, NJ,(201)748-6011, fax(201)748-6008,oronlineathttp:/www.wiley.com/go/permissionLimit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts inpreparing this book, they make no representations or warranties with respect to the accuracy or completeness ofe contents of this book and specifically disclaim any implied warranties of merchantability or fitness for aarticular purpose. No warranty may be created or extended by sales representatives or written sales materialsThe advice and strategies contained herein may not be suitable for your situation. You should consult with aprofessional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or anyother commercial damages, including but not limited to special, incidental, consequential, or other damagesFor general information on our other products and services or for technical support, please contact our CustomerCare Department within the United States at(800)762-2974, outside the United States at (317)572-3993 or fax(317)572-4002Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not beavailable in electronic formats. For more information about wiley products, visit our web site atwww.wlley.conLibrary of Congress Cataloging-in-Publication Data:Sayed, Ali hAdaptive filters/Ali H. Sayedp cmIncludes bibliographical references and indexISBN9780470-25388-5( cloth)Adaptive filters. I. TitleTK7872F5s285200862138l5324dc222008003731Printed in the united states of america10987654321To my parentsContentsPrefacexviiNotationXXVAcknowledgmentsXXXBACKGROUND MATERIALA Random variablesA 1 Variance of a random variableA2 Dependent random VariablesA3 Complex-Valued Random VariablesA 4 Vector -Valued Random variables3467A.5 Gaussian Random VectorsB Linear Algebra12B. Hermitian and Positive- Definite matricesB 2 Range spaces and nullspace of matrices14B3 Schur Complements16B 4 Cholesky factorizationB 5 QR DecompositionB6 Singular Value Decomposition20B 7 Kronecker productsC Complex Gradients25C 1 Cauchy-Riemann Conditions5C2 Scalar arguments26C3 Vector arguments26PART:。 PTIMAL ESTIMATIONScalar- Valued Data291. 1 Estimation Without observations1.2 Estimation Given Dependent observations1.3 Orthogonality Principl36CONTENTS1,4 Gaussian random variables382 Vector- Valued Data422. 1 Optimal Estimator in the vector Case422.2 Spherically Invariant Gaussian Variables462. 3 Equivalent Optimization Criterion49Summary and Notes51Problems and Computer Projects54PART I: LINEAR ESTIMATION3 Normal Equatlons603. 1 Mean-Square Error Criterion613.2 Minimization by Differentiation3.3 Minimization by Completion-of-Squares633.4 Minimization of the error Covariance matrix653.5 Optimal Linear estimator4 Orthogonality princlple4. 1 Design Examples4.2 Orthogonality Condition4.3 Existence of solutions744, 4 Nonzero-Mean variables5 Linear Models5.1 Estimation using Linear Relations5.2 Application: Channel Estimation5Application: Block Data Estimation815. 4 Application: Linear Channel equalization825.5 Application: Multiple-Antenna Receivers85Constralned estimation876.1 Minimum-Variance Unbiased estimation6.2 Example: Mean Estimation6.3 Application: Channel and Noise Estimation916.4 Application: Decision Feedback Equalization6.5 Application: Antenna Beamforming1017 Kalman Filter1047.1 Innovations process7.2 State-Space Model106

利用opencV和C语言编写，利用纹理特征比较两幅图像的相似度