AR人脸数据库

ORACLE高可用性(RAC)技术是如何应用于基于成熟的生产环境的一个解决方案美河学习在线基础知识介绍1.RAC是什么,全称,译为“实时应用集群”,是新版数据库中采用的一项新技术,是高可用性的一种,也是数据库支持网格计算环境的核心技术。2.RAC的优缺点优点:主要支持版本,可以支持有效的数据库应用系统,在低成木服务器上构建高可用性数据库系统,并且自由部署应用,无需修改代码。在环境下,集成提供了集群软件和存储管理软件,为用户降低了应用成本。当应用规模需要扩允时,用户可以按需扩展系统,以保证系统的性能。多节点负载均衡提供高可用:故障容错和无缝切换功能,将硬件和软件错误造成的影响最小化通过并行执行技术提高事务响应时间通常用于数据分析系统通过橫冋扩展提髙每秒交易数和连接数通常对于联机事务系统节约硬件成本,可以用多个廉价服务器代替昂贵的小型机或大型机,同时节约相应维护成本可扩展性好,可以方便添加删除节点,扩展硬件资源。缺点相对单机,管理更复杂,要求更高可能会增加软件成本如果使用高配置的服务器,般按照个数收费美河学习在线3. Oracle rac原理在一个应用环境当中,所有的服务器使用和管理同一个数据库,目的是为了分散每一台服务器的工作量,硬件上至少需要两台以上的服务器,而且还需要个共享存储设备。同时所有服务器上的都应该是同一类根据负载均衡的配置策略,当一个客户端发送请求到某一台服务的后,这台服务器根据我们的负载均衡策略,公把请求发送给本机的组件处理也可能公发送给另外一台服务器的组件处理,处理完请求后,会通过集群软件来访问共享存储设备逻辑结构上看:每一个参加集群的节点有一个独立的访问同一个数据库。每一个节点的都有自己的每一个节点的都有自己的每个节点的都有自己的每一个节点的都有白己的表空间。所有节点都共享一份和三类虚拟地址集群注册文件记录每个节点的相关信息仲裁机制用于仲裁多个节点向共享节点同时写的行为,这样做是为了避免发生冲突。美河学习在线存储技术介绍独立冗余磁盘阵列(是一种把多块独立的硬盘(物理硬盘)按不同的方式组合起来形成一个硬盘组(逻辑硬盘),从而提供比单个硬盘更高的存储性能与数据备份能力的技术。特色是玦硬盘冋时读取速度加快及提供容错性可以将分为不同级別,级別并不代表技术高低,选择哪一种产品纯视用户的操作环境及应用而定,与级别高低没有必然关系。:无差错控制的带区组RAID OBDFGHMNO etc要实现必须要有两个以上硬盘驱动器,数据并不是保存在一个硬盘上,而是分成数据块保存在不同驱动器上。因为将数据分布在不冋驱动器上,所以数据吞吐率大大提高,驱动器的负载也比较平衡。它的缺点是它没有数据差错控制,如果一个驱动器中的数据发生错误,即使其它盘上的数据正确也无济于事了。不应该将它用于对数据稳定性要求高的场合。在所有的级别中,的速度是最快的。但是没冇冗余功能的,如果…个磁盘(物理)损坏,则所有的数据都无法使用美河学习在线:镜象结构RAID 1B-BGG0DDHH对于使用这种结构的设备来说控制器必须能够同时对两个盘进行读操作和对两个镜象盘进行写操作。镜象结构是在一组盘岀现问题时,可以使用镜象磁盘,提高系统的容错能力。每读一次盘只能读岀一块数据,也就是说数据块传送速率与单独的盘的读取速率相同。当您的系统需要极高的可靠性时,如进行数据统计,那么使用比较合适。而且技术支持“热替换”,即不断电的情况下对故障磁盘进行更换,更换完毕只要从镜像盘上恢复数据即可。当主硬盘损坏时,镜像硬盘就可以代替主硬盘工作。镜像硬盘相当于一个备份盘,这种硬盘模式的安全性是非常高的,的数据安全性在所有的级别上来说是最好的。但是其磁盐的利用率却只有是所有级别中最低的。:分布式奇偶校验的独立磁盘结构RAID 5A Blocks B Blocks C Blocks D Blocks E BlocksA0 Co D0 parityParitGenerationA1B1(A2 B2arity D2 E2A3 3 parity C3 E34p(B4(40(E4将数据分散存放于多个硬盘上面,同时使用一定的编码技术产生奇偶校验码来提供错误检査及恢复能力,数据段的校验位交互存放于各个硬盘上。因为奇偶校验码在不同的磁盘上,所以提高∫可靠性,允许单个磁盘出错。任何一个硬盘损坏,都可以根据其它硬盘上的校验美河学习在线位来重建损坏的数据。硬盘的利用率为。优点是提供了冗余性(支持一块盘掉线后仍然正常运行),磁盘空间利用率较高(),读写速度较快(倍)。是级别中最常见的一个类型:高可靠性与高效磁盘结构RAID 10BBBDFHmIrroringstriping这种结构是一个带区结构加一个镜象结构,因为两种结构各有优缺点,因此可以相互补允,达到既高效又高速还可以互为镜像的目的。大家可以结合两种结构的优点和缺点来理解这种新结构。这种新结构的价格高,可扩充性不好。主要用于容量不大,但要求速度和差错控制的数据库中。RAID10是先镜射再分区数据。是将所有硬盘分为两组,然后将这两组各自视为RAID1运作。RAID10有着不错的读取速度,而且拥有比RAID0更高的数据保护性。美河学习在线系统结构设计1.RAC系统拓扑结构基本如下图所示:核心交换机SUNT44数据库raNT4/4数据库rac2面出HSUNSANRAC心跳RAC心跳EMC VNX 5300美河学习在线2.主机操作系统系统数据库服务器系统规划用户组规划说明0 racle清单和软件所有者Oracle自动存储管理组ASM数据库管理员组ASM操作员组数据库管理员用户规划说明默认口令用户数据管理员主机文件系统规划物理硬盘数文件系统AD方式「挂载点文件系统大小/homeufs70GB8水300GB(文件系统用300Gswapswap30GBuf200GB美河学习在线3.存储规划存储一般采用存储方式。硬盘总物理大RAID方逻辑大小(可用 Hotspare数式空间大小)15TB存储组件/BokASMIib NameSizeComments系统路径Device/dev/sda OCR VOL1100G OCR and Voting /dev/oraclOCR/dev/sdb OCR VOL2100GDiskseasm/ORCdev oracl数据文件|/dev/ sdb DATA VOLL3T ASM Data Diskgroup easm/DATAVOIASM/dev/orac l文件备份/dev/ sdaBACK VOL11.8T RecoveryBackup easm/BACKDiskgrou备注存储暂用4.数据库RAC环境数据库规划环境信息节点名称实例名称处理器内存操作系统数据库版本软件组件软件组件名称用户主组辅组用户目录基目录

【实例简介】基于win7（32位、64位）的snopt工具箱，用于MATLAB解决非线性优化问题，下载附送snopt的使用说明

MATLAB在卡尔曼滤波器中应用的理论与实践KalmanKALMAN FILTERINGTheory and Practice Using MATLABThird editionMOHINDER S GREWALCalifornia State University at FullertonANGUS P. ANDREWSRockwell Science Center (retired)WILEYA JOHN WILEY & SONS, INC. PUBLICATIONCopyright 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 byany means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permittedunder Section 107 or 108 of the 1976 United States Copyright Act, without either the prior writtenpermission of the Publisher, or authorization through payment of the appropriate per-copy fee to theCopyright Clearance Center, Inc, 222 Rosewood Drive, Danvers, MA 01923,(978)750-8400, fax(978)750-4470,oronthewebatwww.copyright.com.RequeststothePublisherforpermissionshouldbe addressed to the Permissions Department, John Wiley Sons, Inc, lll River Street, Hoboken, NJ07030,(201)748-6011,fax(201)748-6008,oronlineathttp://www.wiley.com/go/permissionimit 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 of the contents of this book and specifically disclaim any implied warranties of merchantability orfitness for a particular purpose. No warranty may be created or extended by sales representatives orwritten sales materials. The advice and strategies contained herein may not be suitable for your situationYou should consult with a professional where appropriate. Neither the publisher nor author shall be liablefor any loss of profit or any other commercial damages, including but not limited to special, incidentalconsequential, or other damagesFor general information on our other products and services or for technical support, please contact ourCustomer Care 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 maynot be available in electronic format. For more information about wiley products, visit our web site atwww.wiley.comLibrary of Congress Cataloging- in-Publication DataGrewal. Mohinder sKalman filtering: theory and practice using MATLAB/Mohinder S. GrewalAngus p. andrews. 3rd edIncludes bibliographical references and indexISBN978-0-470-17366-4( cloth)1. Kalman filtering. 2. MATLAB. I. Andrews, Angus P. II. TitleQA402.3.G69520086298312—dc22200803733Printed in the United States of america10987654321CONTENTSPrefaceAcknowledgmentsXIIIList of abbreviationsXV1 General Information1.1 On Kalman Filtering1.2 On Optimal Estimation Methods, 51. 3 On the notation Used In This book 231. 4 Summary, 25Problems. 262 Linear Dvnamic Systems2. 1 Chapter focus, 312.2 Dynamic System Models, 362. 3 Continuous Linear Systems and Their Solutions, 402.4 Discrete Linear Systems and Their Solutions, 532.5 Observability of Linear Dynamic System Models, 552.6 Summary, 61Problems. 643 Random Processes and Stochastic Systems3.1 Chapter Focus, 673.2 Probability and random Variables (rvs), 703.3 Statistical Properties of RVS, 78CONTEN3.4 Statistical Properties of Random Processes(RPs),803.5 Linear rp models. 883.6 Shaping Filters and State Augmentation, 953.7 Mean and Covariance propagation, 993.8 Relationships between Model Parameters, 1053.9 Orthogonality principle 1143.10 Summary, 118Problems. 1214 Linear Optimal Filters and Predictors1314.1 Chapter Focus, 1314.2 Kalman Filter. 1334.3 Kalman-Bucy filter, 1444.4 Optimal Linear Predictors, 1464.5 Correlated noise Sources 1474.6 Relationships between Kalman-Bucy and wiener Filters, 1484.7 Quadratic Loss Functions, 1494.8 Matrix Riccati Differential Equation. 1514.9 Matrix Riccati Equation In Discrete Time, 1654.10 Model equations for Transformed State Variables, 1704.11 Application of Kalman Filters, 1724.12 Summary, 177Problems. 1795 Optimal Smoothers5.1 Chapter Focus, 1835.2 Fixed-Interval Smoothing, 1895.3 Fixed-Lag Smoothing, 2005.4 Fixed-Point Smoothing, 2135.5 Summary, 220Problems. 226 Implementation Methods2256. 1 Chapter Focus, 2256.2 Computer Roundoff, 2276.3 Effects of roundoff errors on Kalman filters 2326.4 Factorization Methods for Square-Root Filtering, 2386. 5 Square-Root and UD Filters, 2616.6 Other Implementation Methods, 2756.7 Summary, 288Problems. 2897 Nonlinear Filtering2937.1 Chapter Focus, 2937.2 Quasilinear Filtering, 296CONTENTS7.3 Sampling Methods for Nonlinear Filtering, 3307.4 Summary, 345Problems. 3508 Practical Considerations3558.1 Chapter Focus. 3558.2 Detecting and Correcting Anomalous behavior, 3568.3 Prefiltering and Data Rejection Methods, 3798.4 Stability of Kalman Filters, 3828. 5 Suboptimal and reduced- Order Filters, 3838.6 Schmidt-Kalman Filtering, 3938.7 Memory, Throughput, and wordlength Requirements, 4038.8 Ways to Reduce Computational requirements 4098.9 Error Budgets and Sensitivity Analysis, 4148.10 Optimizing Measurement Selection Policies, 4198.11 Innovations analysis, 4248.12 Summary, 425Problems. 4269 Applications to Navigation4279.1 Chapter focus, 4279.2 Host vehicle dynamics, 4319.3 Inertial Navigation Systems(INS), 4359. 4 Global Navigation Satellite Systems(GNSS), 4659.5 Kalman Filters for GNSS. 4709.6 Loosely Coupled GNSS/INS Integration, 4889.7 Tightly Coupled GNSS /INS Integration, 4919. 8 Summary, 507Problems. 508Appendix A MATLAB Software511A 1 Notice. 511A 2 General System Requirements, 511A 3 CD Directory Structure, 512A 4 MATLAB Software for Chapter 2, 512A. 5 MATLAB Software for Chapter 3, 512A6 MATLAB Software for Chapter 4, 512A. 7 MATLAB Software for Chapter 5, 513A 8 MATLAB Software for Chapter 6, 513A 9 MATLAB Software for Chapter 7, 514A10 MATLAB Software for Chapter 8, 515A 11 MATLAB Software for Chapter 9, 515A 12 Other Sources of software 516CONTENAppendix b A Matrix Refresher519B. 1 Matrix Forms. 519B 2 Matrix Operations, 523B 3 Block matrix Formulas. 527B 4 Functions of Square Matrices, 531B 5 Norms. 538B6 Cholesky decomposition, 541B7 Orthogonal Decompositions of Matrices, 543B 8 Quadratic Forms, 545B 9 Derivatives of matrices. 546Bibliography549Index565PREFACEThis book is designed to provide familiarity with both the theoretical and practicalaspects of Kalman filtering by including real-world problems in practice as illustrativeexamples. The material includes the essential technical background for Kalman filter-ing and the more practical aspects of implementation: how to represent the problem ina mathematical model, analyze the performance of the estimator as a function ofsystem design parameters, implement the mechanization equations in numericallystable algorithms, assess its computational requirements, test the validity of resultsitor the filteThetant attributes ofthe subject that are often overlooked in theoretical treatments but are necessary forapplication of the theory to real-world problemsIn this third edition, we have included important developments in the implemen-tation and application of Kalman filtering over the past several years, including adaptations for nonlinear filtering, more robust smoothing methods, and develelopingapplications in navigationWe have also incorporated many helpful corrections and suggefrom ourreaders, reviewers, colleagues, and students over the past several years for theoverall improvement of the textbookAll software has been provided in MatLab so that users can take advantage ofits excellent graphing capabilities and a programming interface that is very close tothe mathematical equations used for defining Kalman filtering and its applicationsSee Appendix a for more information on MATLAB softwareThe inclusion of the software is practically a matter of necessity because Kalmanfiltering would not be very useful without computers to implement it. It provides aMATLAB is a registered trademark of The Mathworks, IncEFACEbetter learning experience for the student to discover how the Kalman filter works byobserving it in actionThe implementation of Kalman filtering on computers also illuminates some of thepractical considerations of finite-wordlength arithmetic and the need for alternativealgorithms to preserve the accuracy of the results. If the student wishes to applywhat she or he learns, then it is essential that she or he experience its workingsand failings--and learn to recognize the differenceThe book is organized as a text for an introductory course in stochastic processes atthe senior level and as a first-year graduate-level course in Kalman filtering theory andapplicationIt can also be used for self-instruction or for purposes of review by practi-cing engineers and scientists who are not intimately familiar with the subject. Theorganization of the material is illustrated by the following chapter-level dependencygraph, which shows how the subject of each chapter depends upon material in otherchapters. The arrows in the figure indicate the recommended order of study. Boxesabove another box and connected by arrows indicate that the material represented bythe upper boxes is background material for the subject in the lower boxAPPENDIX B: A MATRIX REFRESHERGENERAL INFORMATION2. LINEAR DYNAMIC SYSTEMSRANDOM PROCESSES AND STOCHASTIC SYSTEMS4. OPTIMAL LINEAR FILTERS AND PREDICTORS5. OPTIMAL SMOOTHERS6. IMPLEMENTATIONMETHODS7. NONLINEAR8. PRACTICAL9. APPLICATIONSFILTERINGCONSIDERATIONSTO NAVIGATIONAPPENDIX A: MATLAB SOFTWAREChapter l provides an informal introduction to the general subject matter by wayof its history of development and application. Chapters 2 and 3 and Appendix b coverthe essential background material on linear systems, probability, stochastic processesand modeling. These chapters could be covered in a senior-level course in electricalcomputer, and systems engineeringChapter 4 covers linear optimal filters and predictors, with detailed examples ofapplications. Chapter 5 is a new tutorial-level treatment of optimal smoothing

【实例简介】【项目代码】uic513标准的matlab算法。振动，分垂横纵3个方向，输入，经matlab算法得出振动舒适度的指标.rar

在matlab实现的zigbee源代码,总结的很全面的一些资料,有参考价值

本人采集的心电信号、脑电信号数据，特别适合用来做数字信号处理……

我的毕业设计，设计较为简单，大家一看便懂！

一个详细的DEMO案例和ZedGraph的word详解。

PCIe 2.0协议规范，对PCIe接口的电气特性、机械特性以及PCIe协议的各层都进行了详细的说明和描述

matlab版的信息增益算法实现