SAP PLM 产品生命周期管理

一种新的FFT算法，通过样点遍历分段的每个时刻，介绍数据截断时产生的泄漏。在每个频率点具有“相位不变性”

校园跑腿（后台+微信小程序）.zip

四节龙格库塔、改进欧拉

5G技术与标准介绍----第6部分：5G组网考虑之SA与NSA介绍-201806235G技术与标准介绍----第6部分：5G组网考虑之SA与NSA介绍-20180623网络架构选项:NR独立组网与非独立组网EPCEPCEPCSCG split bearerNR NSAMCG Split bearerSCG bearer(EPC Connected)闼 Option3Option 3xOption 3aNR NSA(NGC Connected)Option 7Option 7a郾 Option7xOption 5PNR SAOption 4pttion 4a郾 Option2标准化方案分类■标准化制定的SA和NSA多种子选项,NSA早于SA半年时间完成·SA组网更多依赖NGC和5GNR空口,双连接带来的数据分流非必选技术要求NSA依赖LTE/eLTE空口,双连接分流是NSA组网的必选技术要求标准化架构核心网核心网控制面数据分流点标准化完成时间Option2NGCNR2018.6Option 4NGCNRNR2018.12SAOption 4aNGCNRNGC2018.12Option 5NGCelte2018.6Option 3EPCLTELTE2017.12Option 3aEPCLTEEPC2017.12Option 3xEPCLTENR2017.12NSAOption 7NGCelteeLtE2018.6OptionalNGCeLtENGO2018.6Option/xNGCelteNR2018.6注:后续分析SA主要以 Option2为基础,NSA以 Option3系列为基础5G网络架构-SANR接入NGC控制面数据面Option 2Option 4Option 4aNGC((RI)(g)5G NReLTESG NReLTtE5G NReLTE接入NGC-Option 5eLTE5G网络架构—NSALTE接入EPC控制面数据面Option 3Option 3aOption 3X(RI)(y)LTE5G NRLTESG NRLTE5G NReLTE接入NGCOption 7Option 7aOption /XeLtE5G NReLTE5G NReLTE5G NR目录、什么是SA&NSA二、SA&NSA方案考虑三、小结SA(独立组网)和NSA(非独立组网)技术背景:为满足部分运营商快速部署5G需求,标准新引入一种新的组网架构-NSA非独立组网,而传统2/3/4G网络均采用SA独立组网的架构SA(独立组网):5G无线网与核心网之间的NAS信令(如注册,鉴权等)通过4G基站传递,5G可以独立工作选项2选项4系列NSA(非独立组网):5G依附于4G基站工作的网络架构,5G无线网与核心网之间的NAs信令(如注册,鉴权等)通过4G基站传递5G无法独立工作EPCEPC=NAS信令数据选项3系列选项7系列蓝色4G,绿色5G网络架构-SA架构 Option2类似于2/3/4G,5G与前代系统相互独立的网络架构原理■5G核心网与5G基站直接相连,5G核心网与5G基站通过NG接口直接相连,传递NAS信令和数据■5G无线空口的RRC信令、广播信令、数据都通过5GNR传递—■终端连接方式:只接入5G或4G(单连接),手机终端可以在NR侧上行双发与4G互操作:类似4G与3G/2G跨核心网互操作模式业务支持能力:可使用5G核心网能力,便于拓展垂直行业新增配置N26接口:NG、Ⅺn、N26(4/5G间互操作5GNR→LTE■4G与5G间互配邻区Option 2网络架构-NSA架构 Option3系列■NSA:4/5G紧耦合,5G依附于4G基站工作的网络架构,无法独立组网,存在多种子架构■原理:■同时沿用4G核心网,5G类似4G载波聚合中的辅载波,用于高速传输数据,NAS信令则由4G承载MME/SGWMME/S-GW5G无线空口的RRC信令、广播等信令可由4G传递,数据通过5GNR和4GLTE传递■终端连接方式:与5G和4G连接(双连接),受限功耗、散热,手机终端很难在双连接状态下,NR侧上行双发enB(P)en-gNB与4G互操作:无■业务支持能力:仅支持大带宽业务■新增配置LTEX2口升级,支持4G配置双连接5G目标小区和流控,与配置邻区类似NSA子架构■ Option3:数据面通过4G空口接入4G核心网,数据分流点在LtE enB,大量5G流量导入至4GeNB涉及硬件改造Option3a:通过4G空口接入4G核心网,数据分流点在LTE(R)(g)(R)EPCLTE5G NRLTE5G NRLTE5G NR■ Option3X:通过4G空口接入4G核心网,数据分流点在NROption 3Option 3aOption 3XgNB■ Option3涉及4G基站硬件改造,本材料主要介绍对NSA的 option3x和3a系列

【实例简介】基于Matlab simulink 2015b版本，分别实现永磁同步电机的模型预测控制（包括转矩预测和电流预测）、自适应控制和滑模控制。其中，预测控制算法采用s函数编写。

三维定位chan_taylor，针对数学建模设计，matlab代码。简单修改参数可直接使用，注释清晰

这是一个android基于dialog加载时转圈圈很好的完整demo，很适合新手学习，希望对有需要的朋友能得到帮助

系统辨识大牛Ljung编写的MATLAB系统辨识使用手册，这本书详细地介绍了在MATLAB已经所属simulink环境下，系统辨识工具箱的一些使用办法，是一本非常经典的教材！Revision Historypril 1988First printingJuly 1991Second printingMay1995Third printingNovember 2000 Fourth printingRevised for Version 5.0(Release 12)pril 2001Fifth printingJuly 2002Online onlyRevised for Version 5.0.2 Release 13)June 2004Sixth printingRevised for Version 6.0.1(Release 14)March 2005Online onlyRevised for Version 6.1.1Release 14SP2)September 2005 Seventh printingRevised for Version 6.1.2(Release 14SP3)March 2006Online onlyRevised for Version 6.1.3(Release 2006a)September 2006 Online onlyRevised for Version 6.2 Release 2006b)March 2007Online onlyRevised for Version 7.0 ( Release 2007a)September 2007 Online onlyRevised for Version 7.1 (Release 2007bMarch 2008Online onlyRevised for Version 7.2(Release 2008a)October 2008Online onlyRevised for Version 7.2.1 Release 2008b)March 2009Online onlyRevised for Version 7.3(Release 2009a)September 2009 Online onlyRevised for Version 7.3.1(Release 2009b)March 2010Online onlyRevised for Version 7. 4 (Release 2010a)eptember2010 Online onlyRevised for Version 7.4.1(Release 2010b)pril 2011Online onlRevised for Version 7.4.2(Release 2011a)September 2011 Online onlyRevised for Version 7.4.3(Release 2011b)March 2012Online onlyRevised for Version 8.0( Release 2012aabout the DevelopersAbout the Developersystem Identification Toolbox software is developed in association with thefollowing leading researchers in the system identification fieldLennart Ljung. Professor Lennart Ljung is with the department ofElectrical Engineering at Linkoping University in Sweden. He is a recognizedleader in system identification and has published numerous papers and booksin this areaQinghua Zhang. Dr. Qinghua Zhang is a researcher at Institut Nationalde recherche en Informatique et en Automatique(INria) and at Institut deRecherche en Informatique et systemes Aleatoires (Irisa), both in rennesFrance. He conducts research in the areas of nonlinear system identificationfault diagnosis, and signal processing with applications in the fields of energyautomotive, and biomedical systemsPeter Lindskog. Dr. Peter Lindskog is employed by nira dynamiAB, Sweden. He conducts research in the areas of system identificationsignal processing, and automatic control with a focus on vehicle industryapplicationsAnatoli Juditsky. Professor Anatoli Juditsky is with the laboratoire JeanKuntzmann at the Universite Joseph Fourier, Grenoble, france. He conductsresearch in the areas of nonparametric statistics, system identification, andstochastic optimizationAbout the developersContentsChoosing Your System Identification ApproachLinear model structures1-2What Are Model objects?Model objects represent linear systemsAbout model data1-5Types of Model objectsDynamic System Models1-9Numeric Models1-11umeric Linear Time Invariant (LTD Models1-11Identified LTI modelsIdentified Nonlinear models1-12Nonlinear model structures1-13Recommended Model Estimation Sequence1-14Supported Models for Time- and Frequency-DomainData,,,,,,,1-16Supported Models for Time-Domain Data1-16Supported Models for Frequency-Domain Data1-17See also1-18Supported Continuous-and Discrete-Time Models1-19Model estimation commands1-21Creating Model Structures at the command Line ... 1-22about system Identification Toolbox Model Objects ... 1-22When to Construct a Model Structure Independently ofEstimation1-23Commands for Constructing Model Structures1-24Model Properties1-25See als1-27Modeling Multiple-Output Systems ......... 1-28About Modeling multiple-Output Systems1-28Modeling Multiple Outputs Directly1-29Modeling multiple outputs as a Combination ofSingle-Output Models.......1-29Improving Multiple-Output Estimation Results byWeighing Outputs During Estimation ....... 1-30Identified linear Time-Invariant models1-32IDLTI Models1-32Configuration of the Structure of Measured and Noise oRepresentation of the Measured and noise Components foVarious model Types1-33Components ....1-35Imposing Constraints on the Values of ModeParameters1-37Estimation of Linear models1-8Data Import and Processing2「Supported Data ...2-3Ways to Obtain Identification DataWays to Prepare Data for System Identification ... 2-6Requirements on Data SamplingRepresenting Data in MATLAB Workspace·····Time-Domain Data Representation2-9Time-Series Data Representation2-10ContentsFrequency-Domain Data Representation ....... 2-11Importing Data into the Gui2-17Types of Data You Can import into the GUi2-17Importing time-Domain Data into the GUI2-18Importing Frequency-Domain Data into the GUI2-22Importing Data Objects into the GUI ......... 2-30Specifying the data sampling interval2-34Specifying estimation and validation Data2-35Preping data Using Quick StartCreating Data Sets from a Subset of Signal Channelo2-362-37Creating multiexperiment Data Sets in the gUi2-39Managing data in the gui ............. 2-46Representing Time- and Frequency-Domain Data Usingiddata object2-55iddata constructor2-55iddata Properties.........2-58Creating Multiexperiment Data at the Command Line .. 2-61Select Data Channels, I/O Data and Experiments in iddataObjects2-63Increasing Number of Channels or Data Points of iddataObjects2-67Managing iddata Objects2-69Representing Frequency-Response Data Using idfrdObiec2-76idfrd Constructor2-76idfrd Properties2-77Select I/o Channels and Data in idfrd Objects ..... 2-79Adding Input or Output Channels in idfrd Objects2-80Managing idfrd Objects2-83Operations That Create idfrd Objects2-83Analyzing Data quality2-85Is your data ready for modeling?2-85Plotting Data in the guI Versus at the command line2-86How to plot data in the gui2-86How to plot data at the command line2-92How to Analyze Data Using the advice Command2-94Selecting Subsets of Data2-96IXWhy Select Subsets of Data?2-96Extract Subsets of Data Using the GUI2-97Extract Subsets of data at the Command Line2-99Handling Missing Data and outliers2-100Handling missing data2-100Handling outliers2-101Extract and Model Specific Data Segments2-102See also2-103Handling offsets and Trends in Data2-104When to detrend data2-104Alternatives for Detrending Data in GUi or at theCommand-Line2-105Next Steps After detrending2-107How to Detrend Data Using the Gui2-108How to detrend data at the Command line2-109Detrending Steady-State Dat109cending transient Dat2-109See also2-110Resampling Data2-111What Is resampling?...,,.,,,,,,,,,,,.2-111Resampling data without Aliasing Effects2-112See also2-116Resampling data Using the GUi.,,,,2-117Resampling Data at the Command line2-118Filtering Data2-120Supported Filters2-120Choosing to Prefilter Your Data2-120See also2-121How to Filter Data Using the gui2-122Filtering Time-Domain Data in the GuI........ 2-122Content

模拟清扫机器人搜寻垃圾，以MATLAB作为仿真环境，实现算法设计

基于FPGA的数字温度测量系统，DS18B20单总线模式，数码管显示，蜂鸣器报警。代码附注释，原创可用。