罗技游戏方向盘软件开发工具包
基于unity 3d的虚拟驾驶系统设计与实现
主机
CPU 主板 显卡 内存
硬盘
表1 计算机配置
品牌型号 英 特 尔 第 四 代 酷 睿i5-4210H@2.9GHz双 核 联想 Y50-70(英特尔 Haswell-LynxPoint)
NividiaGeforceGTX860M 12GB(海力士 DDR3L1600MHz) 西数 WDC WD10SPCX-24HWST1(1TB/5400转/分)
其中方向盘油门刹车离合踏板和排档杆通过usb接口与计算机相711成都师范学院学报2020年3月连体验者操纵方向盘油门刹车离合六排挡杆等设备来驾驶车辆系统通过人机交互接口把外部输入信号传给carsim动力学模型carsim动力学模块根据不同的输入信号进行实时数据反馈控制虚拟场景中控制车辆的运动
第36卷 第3期 成V都ol师.36范 学 院 学 报
罗技游戏方向盘软件开发工具包
罗技罗技游戏方向盘软件开发工具包概述和参考罗技游戏方向盘软件开发工具包包含所有的附加文件，且受到知识产权法的保护，所有罗技游戏方向盘开发包都接受了在这篇文当结尾处“罗技游戏中的许可协议方向盘SDK许可协议”文件。
如果您不同意条款和许可协议中的条件，你必须立即返回任意的文档，附带的软件和所有其他材料都是由罗技为您提供，所有未明确的授权，罗技拥有解释权。
概述罗技游戏方向盘SDK能应用在控制游戏诸如各种类型的游戏控制器（USB/游戏端口轮/游戏杆/游戏垫，力回馈启用与否）。
通过使用方向盘SDK你可以确保所有的轮子和操纵杆的功能将发挥完美。
没有比在一场游戏中方向盘或摇杆的力反馈不同而导致用户受挫和产品遭到退货更严重的情况了，请注意只有在安装了罗技游戏软件后，SDK才将正常工作，如果想获得更多的信息请访问/en-us/gaming 。
SDK 包下面的文件包括：LogitechSteeringWheelLib.h：C / C ++包含头文件的函数原型LogitechGSDK.cs：C＃引用文件用来访问被包装的SDKLogitechSteeringWheelLib.lib：LIB文件访问DLL导出函数（32位和64位）LogitechSteeringWheelEnginesWrapper.dll：SDK函数封装（32位和64位）要求罗技游戏方向盘SDK可以在以下平台上使用：Windows XP SP2（32位和64位）Windows Vista (32-bit and 64-bit)Windows 7 (32-bit and 64-bit)Windows 8 (32-bit and 64-bit)罗技游戏方向盘SDK是一个基于C语言的接口，是为使用C / C++的程序员设计的。
熟悉Windows编程也是必须的。
使用SDK接口使用LogitechSteeringWheel.h和LogitechSteeringWheel.lib访问LogitechSteeringWheel.dll应用程序可以包括LogitechSteeringWheel.h并链接到LogitechSteeringWheel.lib（参见“样本使用该SDK”进一步说明或样本文件夹中的示例程序）的。
新版罗技宏编辑说明-概述说明以及解释
新版罗技宏编辑说明-概述说明以及解释1.引言1.1 概述概述部分的内容可以从以下几个方面进行叙述：引言：在数字化时代的今天，计算机设备已经成为人们生活和工作中不可或缺的一部分。
而对于一些热衷于电子竞技、键鼠操作的用户而言，罗技宏编辑无疑是提高效率、获得更好游戏体验的重要利器。
而本文将介绍的是新版罗技宏编辑，相较于传统版本有所改进和升级，带来更多强大的功能和更好的操作体验。
文章结构：本文将首先回顾一下罗技宏编辑的发展历史，探讨传统版本的特点和缺点。
然后，我们将重点介绍新版罗技宏编辑的特点，包括其提供了哪些新的功能，以及在操作上有哪些改进。
最后，我们将详细讨论使用新版罗技宏编辑所带来的优势，如何帮助用户提高生产力和获得更好的游戏体验。
目的：本文的目的在于向用户详细介绍新版罗技宏编辑的相关信息，并强调其带来的重要性和优势。
通过了解新版罗技宏编辑的发展历程和特点，用户可以更好地利用该软件工具完成各种任务，并在电子竞技中获得更多的胜利。
同时，我们也希望通过文章的展望部分，让读者能够对新版罗技宏编辑有一个更清晰的未来发展方向的认识，为其提供更好的使用建议。
总之，通过本文的介绍和分析，相信读者们能够对新版罗技宏编辑有一个更全面的了解，并在实际应用中能够充分发挥其优势和功能。
希望通过这篇文章的阅读，能够帮助读者们更好地掌握和运用新版罗技宏编辑，提高工作效率和游戏的乐趣。
文章结构部分的内容应该包括对整篇文章的组织和安排的介绍。
下面是对1.2 文章结构的内容的可能描述：1.2 文章结构本文将按照以下结构组织内容：引言部分将首先对新版罗技宏编辑进行概述，介绍文章的结构和目的。
在正文部分，我们将回顾罗技宏编辑的历史，重点关注新版罗技宏编辑的特点和使用的优势。
结论部分将对新版罗技宏编辑的重要性进行总结，并给出对用户的建议。
最后，我们将展望新版罗技宏编辑的未来发展。
通过这样的文章结构，读者将能够全面了解新版罗技宏编辑的背景、特点和应用，以及对于用户和未来的意义。
Pathfinder Roleplaying Game Beginner Box基础包转换指南说明书
基础包转换指南1这本手册是为玩家和游戏主持人准备的一份指南。
你应首先熟悉基础包中，英雄手册和游戏主持人指南的内容。
Pathfinder Roleplaying Game Beginner Box is published by Paizo lnc, under the Open Game License version 1.0a © 2000 Wizards of the Coast, Inc. Paizo, Paizo lnc, the Paizo golem logo, Pathfinder, Pathfinder Society, and GameMastery are registered trademarks of Paizo lnc; Pathfinder Roleplaying Game Beginner Box, Pathfinder Roleplaying Game, Pathfinder Adventure Card Game, Pathfinder Adventure Path, Pathfinder Campaign Setting, Pathfinder Cards, Pathfinder Flip-Mat, Pathfinder Map Pack, Pathfinder Module, Pathfinder Pawns, Pathfinder PlayerCompanion, Pathfinder Tales, and Rise of the Runelords are trademarks of Paizo lnc. © 2011, 2013, Paizo lnc. Chinese Translation © 2017 Paizo Inc.更进一步 2了解新规则 3阅读核心规则书的法术数据表 7目录The Pathfinder Roleplaying Game rules can be found online for free as part of the Pathfinder Roleplaying Game Reference Document at paizo.com/prd./beginnerbox基础包转换指南阅读志怪录的怪物数据表 8超越6级 10转换冒险模组 13中文译者：默'Soul 排版校对：卓尔出版人：张昊CREATIVE DIRECTOR • James Jacobs EDITOR-IN-CHIEF • F. Wesley SchneiderSENIOR EDITOR • James L. Sutter DEVELOPMENT LEAD • Sean K ReynoldsDEVELOPMENT TEAM • Logan Bonner, John Compton, Adam Daigle, Rob McCreary, Mark Moreland, and PatrickRenieEDITORIAL TEAM • Judy Bauer, Christopher Carey,and Ryan MacklinEDITORIAL INTERNS • Jay Loomis and Cassidy WernerLEAD DESIGNER • Jason BulmahnDESIGN TEAM • Stephen Radney-MacFarland and Sean KReynoldsINTERIOR ARTISTS • Alex Alexandrov, Alex Aparin, Eric Belisle, Matt Dixon, Andrew Hou, Steve Prescott,Scott Purdy, Wayne Reynolds, Dan Scott, and Tyler WalpoleAuthor • Sean K ReynoldsCUSTOMER SERVICE TEAM • Erik Keith, Justin Riddler, and Sara Marie TeterWAREHOUSE TEAM • Will Chase, Mika Hawkins, Heather Payne, Jeff Strand, and Kevin Underwood WEBSITE TEAM • Christopher Anthony, Liz Courts, Crystal Frasier, Lissa Guillet, and Chris Lambertz SENIOR ART DIRECTOR • Sarah E. RobinsonART DIRECTOR • Andrew VallasGRAPHIC DESIGNERS • Emily Crowell and SonjaMorrisPUBLISHER • Erik Mona PAIZO CEO • Lisa StevensCHIEF OPERATIONS OFFICER • Jeffrey AlvarezDIRECTOR OF SALES • Pierce Watters SALES REPRESENTATIVE • Cosmo Eisele MARKETING DIRECTOR Jenny Bendel FINANCE MANAGER • Christopher Self STAFF ACCOUNTANT • Ashley Gillaspie CHIEF TECHNICAL OFFICER • Vic Wertz CAMPAIGN COORDINATOR • Mike Brock PROJECT MANAGER • Jessica PriceLICENSING COORDINATOR • Michael KenwayPaizo Inc.7120 185th Ave NE, Ste 120Redmond, WA 核心规则书是一本大部头，书中包含了大量规则和选项，但你并不需要一股脑儿把它们全部弄明白。
func2vis 1.0-3 软件包说明说明书
Package‘func2vis’March16,2023Type PackageTitle Clean and Visualize Over Expression Results from'ConsensusPathDB'Version1.0-3Date2023-03-16Author Raghvendra Mall[aut,cre]Maintainer Raghvendra Mall<************************>Repository CRANDescriptionProvides functions to have visualization and clean-up of enriched gene ontologies(GO)terms, protein complexes and pathways(obtained from multiple databases)using'ConsensusPathDB' from gene set over-expression analysis.Performs clustering of pathway based on similarityof over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis(IPA)when up and down regulated genes are known.The methods are described in a paper currently submitted by Orecchioni et al,2020in Nanoscale.License GPL(>=3)LazyLoad trueDepends ggplot2,igraph,devtools,ggrepel,grDevices,randomcoloR,R(>=4.0)NeedsCompilation yesDate/Publication2023-03-1617:30:02UTCRoxygenNote7.1.1R topics documented:clean_go_terms (2)clean_pathways (3)clean_pc (4)enriched_goterms (5)enriched_pathways (5)enriched_pc (6)12clean_go_terms plot_go_terms (6)plot_pathways (7)plot_pathways_stacked_barplot (8)t.tests.treatment.sign (9)Index11 clean_go_terms Clean Gene Ontologies(GO)TermsDescriptionClean set of enriched goterms obtained from’ConsensusPathDB’for gene set overexpression anal-ysis.We also append two columns indicating the number of up-regulated and number of down-regulated genes based on fold change information available in data frame case_vs_ctrl.Usageclean_go_terms(df_case_vs_ctrl,df_goterms)Argumentsdf_case_vs_ctrlData frame which has at least2columns:<gene,fc>.Here gene represents theset of genes which are differentially expressed between case and control.Herefc represents the fold-change value for each gene.df_goterms The tab-separated data frame with the goterms information obtained after per-forming gene set overexpression analysis using’ConsensusPathDB’.ValueReturns clean enriched GO terms data frame.Note**************.qaAuthor(s)Raghvendra MallSee AlsoSee Also as clean_pc,plot_go_termsclean_pathways3Examplesdata("t.tests.treatment.sign")data("enriched_goterms")revised_goterms<-clean_go_terms(df_case_vs_ctrl=t.tests.treatment.sign,df_goterms=enriched_goterms)print(head(revised_goterms))clean_pathways Clean Enriched PathwaysDescriptionClean set of enriched pathways obtained from’ConsensusPathDB’for gene set overexpression analysis.We also append two columns indicating the number of up-regulated and number of down-regulated genes based on fold change information available in data frame case_vs_ctrl.We cluster pathways based on similarity of gene set using igraph’s walktrap clustering algorithm.Within each cluster,pathways are ordered by most to least significant pathway in terms of p-values.Usageclean_pathways(df_case_vs_ctrl,df_pathway)Argumentsdf_case_vs_ctrlData frame which has at least2columns:<gene,fc>.Here gene represents theset of genes which are differentially expressed between case and control.Herefc represents the fold-change value for each gene.df_pathway The tab-separated data frame with the pathways information obtained after per-forming gene set overexpression analysis using’ConsensusPathDB’.ValueReturns clean enriched pathways data frame.The data frame has an additional column clusters highlighting the cluster to which each enriched pathway belongs.Note**************.qaAuthor(s)Raghvendra MallSee Alsoclean_go_terms,clean_pc4clean_pc Examplesdata("t.tests.treatment.sign")data("enriched_pathways")revised_pathway<-clean_pathways(df_case_vs_ctrl=t.tests.treatment.sign,df_pathway=enriched_pathways)print(head(revised_pathway))clean_pc Clean Enriched Protein ComplexesDescriptionClean set of enriched protein complexes obtained from’ConsensusPathDB’for gene set overex-pression analysis.We also append two columns indicating the number of up-regulated and number of down-regulated genes based on fold change information available in data frame case_vs_ctrl. Usageclean_pc(df_case_vs_ctrl,df_pc)Argumentsdf_case_vs_ctrlData frame which has at least2columns:<gene,fc>.Here gene represents theset of genes which are differentially expressed between case and control.Herefc represents the fold-change value for each gene.df_pc The tab-separated data frame with the protein complexes information obtained after performing gene set overexpression analysis using’ConsensusPathDB’. ValueReturns clean enriched protein complexes data frame.Note**************.qaAuthor(s)Raghvendra MallSee AlsoSee Also as clean_go_terms,plot_go_termsenriched_goterms5Examplesdata("t.tests.treatment.sign")data("enriched_pc")revised_pc<-clean_pc(df_case_vs_ctrl=t.tests.treatment.sign,df_pc=enriched_pc)print(head(revised_pc))enriched_goterms Sample Enriched Gene Ontologies(GO)TermsDescriptionThis dataset highlights enriched gene ontologies(GO)terms identified by using ConsensusPathDB while performing overexpression analysis for a sample set of genes.Usagedata("enriched_goterms")ReferencesKamburov,A.,Stelzl,U.,Lehrach,H.and Herwig,R.,2013.The ConsensusPathDB interaction database:2013update.Nucleic acids research,41(D1),pp.D793-D800.Examplesdata(enriched_goterms)##maybe str(enriched_goterms);enriched_pathways Sample Enriched PathwaysDescriptionThis dataset highlights enriched pathways identified by using’ConsensusPathDB’while performing overexpression analysis for a sample set of genes.Usagedata("enriched_pathways")ReferencesKamburov,A.,Stelzl,U.,Lehrach,H.and Herwig,R.,2013.The ConsensusPathDB interaction database:2013update.Nucleic acids research,41(D1),pp.D793-D800.6plot_go_terms Examplesdata(enriched_pathways)##maybe str(enriched_pathways);enriched_pc Sample Enriched Protein ComplexesDescriptionThis dataset highlights protein complexes identified by using’ConsensusPathDB’while performing overexpression analysis for a sample set of genes.Usagedata("enriched_pc")ReferencesKamburov,A.,Stelzl,U.,Lehrach,H.and Herwig,R.,2013.The ConsensusPathDB interaction database:2013update.Nucleic acids research,41(D1),pp.D793-D800.Examplesdata(enriched_pc)##maybe str(enriched_pc);plot_go_terms Bupple Plot for GO TermsDescriptionMake a bubble plot for significantly enriched Gene Ontologies(GO)Terms obtained after perform-ing gene set overexpression analysis using’ConsensusPathDB’.Usageplot_go_terms(df_goterms,total_no_background_genes,negative_log_10_p_value_cutoff,max_overlap)plot_pathways7Argumentsdf_goterms The tab-separated data frame with the GO terms information obtained after per-forming gene set overexpression analysis using’ConsensusPathDB’.total_no_background_genesTotal no of genes in the background set.negative_log_10_p_value_cutoffThe threshold on-log10(pvalue)to be used to identify the GO terms to be high-lighted in the plot.max_overlap To prevent overlapping text,set this paramater to a number>=20.DetailsPlots the significantly enriched molecular function(m),cellular components(c)and biological pro-cesses(b)obtained via ConsensusPathDB.ValueReturns a bubble plot of type ggplot.Note**************.qaAuthor(s)Raghvendra MallExamplesdata("enriched_goterms")g<-plot_go_terms(df_goterms=enriched_goterms,negative_log_10_p_value_cutoff=17) gplot_pathways Plot clean enriched pathways as a bubble plotDescriptionMake a bubble plot of clean enriched pathways obtained from’ConsensusPathDB’by performing gene set overexpression analysis.Colours represent the clusters to which each pathway belongs.You need to run the function clean_pathways to obtain the input data frame.Usageplot_pathways(final_df_pathway,total_no_background_genes,fontsize)8plot_pathways_stacked_barplotArgumentsfinal_df_pathwayClean and clustered pathways obtained using clean_pathways.total_no_background_genesTotal no of genes in the background set.fontsize Font size of the pathways to be displayed on y-axis.ValueReturns a bubble plot of type ggplot.Colours represent the clusters to which each pathway belongs.Note**************.qaAuthor(s)Raghvendra MallSee AlsoSee Also as clean_pathways,plot_pathways_stacked_barplot,plot_go_termsExamplesdata("t.tests.treatment.sign")data("enriched_pathways")revised_pathway<-clean_pathways(df_case_vs_ctrl=t.tests.treatment.sign,df_pathway=enriched_pathways)p<-plot_pathways(revised_pathway)pplot_pathways_stacked_barplotStacked Barplot of Cleaned PathwaysDescriptionMake a stacked barplot like the one available in Ingenuity Pathway Analysis highlighting percentage of up,down and non-differentially expressed genes in the set of clean enriched pathways obtained from’ConsensusPathDB’by performing gene set overexpression analysis.You need to run the function clean_pathways to obtain the input data frameUsageplot_pathways_stacked_barplot(final_df_pathway)Argumentsfinal_df_pathwayClean and clustered pathways obtained using clean_pathways.ValueReturns a stacked barplot of type ggplot.Note**************.qaAuthor(s)Raghvendra MallSee Alsoclean_pathways,plot_go_termsExamplesdata("t.tests.treatment.sign")data("enriched_pathways")revised_pathway<-clean_pathways(df_case_vs_ctrl=t.tests.treatment.sign,df_pathway=enriched_pathways)p<-plot_pathways_stacked_barplot(revised_pathway)pt.tests.treatment.signList of differentially expressed genesDescriptionConsist of list of differentially expressed genes(DEG)with fold-change information i.e.up and down regulated genes between case and control.Usagedata("t.tests.treatment.sign")FormatA data frame with1820observations on the following8variables.gene a character vectorp.value a numeric vectorp.value.fdr a numeric vectorfc a numeric vectormean.A a numeric vectormean.B a numeric vectorsd.A a numeric vectorsd.B a numeric vectorExamplesdata(t.tests.treatment.sign)##maybe str(t.tests.treatment.sign);Index∗datasetsenriched_goterms,5enriched_pathways,5enriched_pc,6t.tests.treatment.sign,9clean_go_terms,2,3,4clean_pathways,3,7–9clean_pc,2,3,4enriched_goterms,5enriched_pathways,5enriched_pc,6plot_go_terms,2,4,6,8,9plot_pathways,7plot_pathways_stacked_barplot,8,8t.tests.treatment.sign,911。
罗技键盘产品说明书
ENGLISH Knowyour product1. Game mode key2. Backlight brightness3. Mute4. USB cable connector5. Volume6. Media keys 繁体中文瞭解您的產品1. 遊戲模式按鍵2. 背光亮度3. 靜音4. USB 連接線接頭5. 音量6. 媒體鍵한국어제품설명1. 게임 모드 키2. 백라이트 밝기3. 음소거4. USB 케이블커넥터5. 볼륨6. 미디어키简体中文了解您的产品1. 游戏模式按键2. 背光亮度3. 静音4. USB 线连接器5. 音量6. 媒体键G610 Orion Brown™Backlit Mechanical Gaming KeyboardENGLISHSet up your product1. Turn on your computer.2. Connect the keyboard to USB port.3. Download and installthe Logitech® Gaming Softwarefrom /support/g610-brown.繁体中文設定您的產品1. 開啟電腦電源。
2. 將鍵盤連接到 USB 連接埠。
3. 在 /support/g610-brown.下載羅技®遊戲軟體並進行安裝。
한국어제품설정1. 컴퓨터를켭니다.2. USB 포트에키보드를연결합니다.3. /support/g610-brown에서 Logitech®게임소프트웨어를다운로드하여설치하십시오.简体中文设置您的产品1. 打开计算机。
2. 将键盘连接到 USB 端口。
3. 从 / support/g610-brown下载并安装罗技®游戏软件。
Setup Guide · 設定指南 · 설치 설명서 · 设置指南ENGLISHProduct featuresGame/Windows keyBy default, this key disables the standardWindows key and Menu key to preventthe Windows Start menu fromactivating during a game, whichmight disrupt play. This key can beprogrammed to block additional keysusing Logitech® Gaming Software.The game mode LED is lit during gamemode.Keyboard backlightingControl brightness with backlightbrightness key. Control individualkey brightness using LogitechGaming Software.Download and install the LogitechGaming Software from/support/g610-brown.繁体中文產品功能遊戲/Windows 鍵在預設情況下，此按鍵可停用標準Windows 鍵與功能表鍵，以防止在遊戲中不慎啟動 Windows「開始」功能表而中斷遊戲。
HID L Scan Essentials软件开发包（SDK）说明书
-D ATA S H E E TB E N E F I T S:•One SDK for all HID tenprint, dual finger and palm print scanners •Comprehensive toolbox for software integration tests •Configurable parameters for individual design/workflows of customer application •Flexible upgrades with optional software features •Easy to integrate API•Compatible with HID BioCore MiddlewareHID L Scan Essentials Software Development Kit (SDK) provides Microsoft Windows and Linux compatible components that allow application developers ready access to biometric functions provided by HID tenprint, dual finger and palm print scanners.The SDK offers easy access to fingerprint and palm print devices from HID through the Application Programming Interface (API). These tools are provided for use by system administrators or service technicians.L Scan Essentials SDK provides tools to perform tests of scanner functions as well as sophisticated features that guarantee the highest quality capture. Experienced livescan system integrators can create their own applications for tenprint, dual and palm print scanners.Several sample applications, including source code, help developersunderstand the processes as well as guide them through software development scenarios.Comprehensive documentation covers all aspects of developing the device control and setting up the configuration, as well as using the tools. Examples include:• SDK usage• API interface functions • API return codes • Sequence diagrams • Configuration files • Logging functionalities • Sample applications • Revision history • Known issues •Supported devicesHID ®L Scan ™EssentialsFingerprint and Palm Print Capture SDKNorthAmerica:+15127769000|TollFree:180****7769Europe, Middle East, Africa: +44 1440 714 850Asia Pacific: +852 3160 9800 | Latin America: +52 (55) 9171-1108For more global phone numbers click here© 2022 HID Global Corporation/ASSA ABLOY AB. All rights reserved.Part of ASSA ABLOYHID L Scan Essentials SDKProduct Name L Scan Essentials SDKAuto CaptureAutomatically captures fingerprints (flat and rolled) and palm prints and provides quality assessment and feedbackPerfect Image• Generates crystal clear fingerprint and palm print images while eliminating condensation and dirt without modifying the fingerprint image• Works independently of number of fingers, image size/resolution and silicone membrane Perfect Roll• Ensures that only high quality rolled fingerprint images are accepted• Built-in real-time quality check for slanting fingers, lifting of fingertips, vertical shifting • Provides user guidance to avoid mistakes during capture • Takes rolled fingerprints at varying speed•Configurable quality thresholds for the Auto Capture functionFlexFlat• Enables to capture flat fingers anywhere on the image capture area • Applicable for single, dual or four fingers• Supported by Patrol, Guardian 100, Guardian 200, Guardian 45 , L Scan 500 and L Scan 1000FlexRoll• Enables to capture roll fingers on the entire width of the image capture area • Works independent of roll direction and roll capture modePresentation AttackDetection• Provides spoof detection functionality to existing HID fingerprint scanners• Configurable FAR/FRR according to application by provided DET curves for each scanner SYSTEM REQUIREMENTSOperating Systems Windows 10, Windows 11, Linux® Ubuntu® and RHEL SOFTWARE COMPONENTSTest and software updatingtools Test Wizard to retrieve and analyze all status information from PC, device and software; Firmware updater; Software license updater; TTrace application for troubleshooting analysis DOCUMENTATION AND SAMPLE APPLICATIONSComplete description of all API functions and return codes; Sequence diagrams; Configuration files; Sample applications including C++ source code & Visual Studio project files; Test clients for testing API functions and workflows; Revision history SUPPORTED DEVICES HID L Scan 500 and 1000HID Guardian™ 45, 100, 200, Module HID Patrol2023-07-27-eat-l-scan-essentials-ds-en PLT-04436Data subject to change without notice.。
toolmaRk 0.0.1 软件包用户手册说明书
Package‘toolmaRk’October14,2022Type PackageTitle Tests for Same-Source of ToolmarksVersion0.0.1Date2018-01-10Author Jeremy Hadler[aut,cre],Max Morris[ths],Heike Hofmann[ctb]Maintainer Jeremy Hadler<******************>DescriptionImplements two tests for same-source of toolmarks.The chumbley_non_random()test fol-lows the paper``An Improved Version of a Tool Mark Comparison Algorithm''by Hadler and Mor-ris(2017)<doi:10.1111/1556-4029.13640>.This is an extension of the Chumbley score as previ-ously described in``Validation of Tool Mark Comparisons Obtained Using a Quantitative,Com-parative,Statistical Algorithm''by Chumbley et al(2010)<doi:10.1111/j.1556-4029.2010.01424.x>.fixed_width_no_modeling()is based on correlation measures in a dia-mond shaped area of the toolmark as described in Hadler(2017).Depends R(>=3.3),plyr(>=1.8.4),dplyr(>=0.7.2),reshape2(>=1.4.2)Imports ggplot2(>=2.2.1)License GPL-3LazyData trueRoxygenNote6.0.1NeedsCompilation noRepository CRANDate/Publication2018-01-1610:37:45UTCR topics documented:ameslab (2)chumbley_non_random (3)fixed_width_no_modeling (3)Index512ameslab ameslab Toolmark profiles datasetDescriptionData set of toolmarks(profiles)created by screwdrivers under different angles.Tool mark data included here were produced by Prof.Scott Chumbley,Mr.Stephen Davis,Ms.Taylor Grieve,Mr.Ryan Spotts,and Dr.Jeremy Hadler.These data were produced as part of research performed at the Ames Laboratory,located on the Iowa State University campus.Ames Laboratory is operated for the U.S.Department of Energy by Iowa State University under Contract No.DE-Ac02-07CH11358. UsageameslabFormatthe dataset consists of a sample of16toolmark profiles and descriptors.Toolmarks with the same toolmark identifier are known matches,all other profiles are known non-matches.ID toolmark identifier.Factor variable.side A or B,indicates the side of the screwdriverused to create the toolmark.Factor variable.angle degree under which the toolmark was created.rep replicate number for a toolmark,side,angle composition.Note that the data here provided is not complete.For a more complete data set or more information please contact the references given below.profile list of data sets with one profile each.Measurements are taken at equispaced intervals across the toolmark..ReferencesTool mark data included here were produced by Prof.Scott Chumbley,Mr.Stephen Davis,Ms.Taylor Grieve,Mr.Ryan Spotts,and Dr.Jeremy Hadler.These data were produced as part of research performed at the Ames Laboratory,located on the Iowa State University campus.Ames Laboratory is operated for the U.S.Department of Energy by Iowa State University under Contract No.DE-Ac02-07CH11358.Examplesdata(ameslab)plot(ameslab$profile[[1]]$V1)chumbley_non_random(ameslab$profile[[1]],ameslab$profile[[2]])res14<-fixed_width_no_modeling(ameslab$profile[[1]],ameslab$profile[[4]])res14$dist_pvalchumbley_non_random3 chumbley_non_random Chumbley Non-RandomDescriptionThis function computes the Chumbley U-Statistic on systemically chosen pairs of windows rather than the original method which selects randomly chosen pairs of windowsUsagechumbley_non_random(data1,data2,window_opt=500,window_val=50,coarse=0.25)Argumentsdata1Thefirst tool mark as a1-column matrixdata2The second tool mark as a1-column matrixwindow_opt size of the window to be used in the optimization stepwindow_val Size of the window to be used in the validation stepcoarse smoothing parameter for the normalization smoothValuelist with•same_shift_n Number of same shift offsets used•diff_shift_n Number of different shift offsets used•U observed U statistic•p_value Corresponding p-valuefixed_width_no_modelingDistance/threshold test for toolmarksDescriptionCompute all possible correlations for windows of length n between the class components.Deter-mine the location of the maximized correlation.Given this location,create a diamond around it in the individual matrix of correlations For each offset in this diamond,compute the maximized correlation(1)Determine the distance between the offset for the class and indiviudal components(2)Compute the Threshold test statistics4fixed_width_no_modelingUsagefixed_width_no_modeling(dat1,dat2,coarse=0.25,fine=0.01,window.size=0.6,M=500)Argumentsdat1a one column matrix representing a digitized tool markdat2a one column matrix representing a second digitized tool markcoarse normalization smoothing parameterfine decomposition smoothing parameterwindow.size desired window size for the correlations to computeM search area restrictionValuelist with•"max_corr"maximized indiviudal component correlation•"Smooth_offset"optimal Class offset•"Resid_offset"optimal individual offset•"dist_pval"distance p-value•"thresh_pval"threshold p-value•"Above"Number of offsets with correlation bigger than threshold•"total_thresh"2*M+1•"mark1_decompostion"plot of decomposition d1•"mark2_decompostion"plot of decomposition d2•"class_correlations"plot of class correlation•"individual_correlations"plot of individual correlation•"distance_plot"distance_plot•"threshold_plot"threshold_plotIndex∗datasetsameslab,2ameslab,2chumbley_non_random,3fixed_width_no_modeling,35。
罗技blueyeti配置文件
罗技blueyeti配置文件罗技 G HUB驱动软件为黑色主题，主界面分为配置文件、产品列表、网络资源和设置四个区域，在每个区域点击左键即可进入对应功能的选项，同时连接多个产品时，产品列表支持左右切换浏览。
这一部分内容主要就是为大家讲解产品列表之外三个区域的功能。
配置文件被设计在非常显眼且重要的位置，在G HUB驱动软件里，所有设备的配置文件被更加高效、统一的整合管理，它与LGS里配置文件使用的感受是不同的，LGS是以设备为单位管理配置文件，在每个设备中为每个游戏添加单独的配置文件，实现独立的游戏配置管理，感觉像是为设备找游戏，增加一款游戏时需要在多个设备分别进行比较繁琐和复杂设定。
对比G HUB是以游戏为单位管理，添加游戏后此游戏的配置文件自动包含了所有支持设备的独立配置，调节时选择对应的游戏配置文件，而且可以将其他的配置文件拖拽到当前配置，快速覆盖，操作非常高效，不需要独立配置的设备也可以通过将指定配置应用到所有游戏实现一套配置走天下。
游戏和程序可以通过右上角的立即扫描或手动添加。
已添加程序的图标出现在上部列表中。
单击“应用”自动预览配置，下面的列表将自动更改。
每个游戏和程序支持三个选项页:个人资料，宏和设置。
每个游戏不仅有一个默认的配置文件，而且可以支持为一个游戏添加多个配置文件。
可以通过配置文件下方的详情快速查看对应的设备数据，也可以复制配置文件、编写LUA脚本和分享配置文件到网络，此列表负责的是对配置文件进行管理，如果要对配置文件中的每个设备进行编辑，需要点击进入设备页面再切换到对应配置文件。
宏选项页面显示玩家为对应游戏添加的宏，每个游戏独立添加管理，查找更方便，我们可以将当前游戏的宏拖拽至其他游戏实现快速复制，添加宏的步骤很简单明了，命名-选择循环类型-编辑，支持录制、文字在内的多种编辑方式，右上角的宏选项支持对延迟规范、抬起按下分解和宏颜色的调节，以录制为例，录制后依然可以对每次操作步骤和延迟数据进行增添修改，编辑效率是很高的。
罗技G系列外设宏命令编程参考文档
G-series Lua API 参考文档V8.50预览与概述手册目录手册目录 (2)内容预览 (3)参考文档 (4)标准 Lua 5.1 库 (40)附录A (41)G-series Lua API 参考手册是一套使用 Lua 脚本编程语言描述并且为 G-series 系列游戏键盘提供高级脚本功能。
该文档是在假设您已初步掌握 Lua 脚本编程语言使用方法的前提下进行的。
如果您想了解更多信息请访问 。
每项 G 系列的配置文件都有一个默认的 Lua 脚本与其绑定，并且您可以根据您的喜好随意编辑或者自定义。
该脚本借助并使用一个名为OnEvent 的事件句柄。
用户可以通过检查此句柄中触发的各种事件以执行用户所希望的动作。
方法列表OnEvent (5)GetMKeyState (7)SetMKeyState (8)Sleep (9)OutputLogMessage (10)GetRunningTime (11)GetDate (12)ClearLog (13)PressKey (14)ReleaseKey (15)PressAndReleaseKey (16)IsModifierPressed (17)PressMouseButton (18)ReleaseMouseButton (19)PressAndReleaseMouseButton (20)IsMouseButtonPressed (21)MoveMouseTo (22)MoveMouseWheel (23)MoveMouseRelative (24)MoveMouseToVirtual (25)GetMousePosition (26)OutputLCDMessage (27)ClearLCD (28)PlayMacro (29)AbortMacro (30)IsKeyLockOn (31)SetBacklightColor (32)OutputDebugMessage (33)SetMouseDPITable (34)SetMouseDPITableIndex (35)EnablePrimaryMouseButtonEvents (36)G13 Programming (37)OnEventOnEvent() 方法为脚本提供了一系列事件句柄以方便用户对触发的事件进行操作，您可以使用以下代码实现此方法。
罗技g29驱动安装教学
罗技g29驱动安装教学罗技G29方向盘驱动是一款由罗技为G29游戏方向盘打造的驱动程序。
罗技G29方向盘驱动官方版能够帮助用户在电脑上使用罗技G29游戏方向盘，为玩家提供支持。
安装罗技G29方向盘驱动后，用户就能够对方向盘的参数和功能进行详细设置，罗技G29游戏方向盘的必需工具。
1.下载中的X360CE，版本为3.0.7.57，老版本是不可以的，下载地址请自行搜索。
2.下载好后，右键exe程序——属性——兼容性——勾选管理员模式运行——确定。
3.然后双击打开，打开会提示两个对话框，全部选YES，这是在创建软件要用的INI和DLL文件，然后你就可以看到目录里多了两个文件，如果出现其他弹窗一律选取消，报错的话一律无视。
4.把x360ce_x64.exe和x360ce.ini和xinput1_3.dll这三个文件复制到GTA5根目录里。
5.如果你是罗技之类的牛B方向盘，直接看2步，如果你是国产弱鸡方向盘，例如北通的，那么首先拔下方向盘，卸载你安装的原驱动，重新插入方向盘让win10自动安装驱动，另外如果你有其他外设尤其是360手柄或支持360模式的手柄，那么要把方向盘插在第一USB接口顺位，或者直接把其他手柄都拔掉，这里要注意一下。
6.校准方向盘，在控制面板——设备和打印机——右击你的方向盘——游戏控制器设置——打开后选中方向盘——点属性——设置——校准，根据提示操作就可以了。
7.切换到方向盘的选项卡——找到Advancde选项卡——在Device，Sub，Type项的下拉菜单里选择Gamepad——点击右下角Save——关闭重新打开，就会正常连接。
8.设置键位，手动选择轴，方向盘对应左摇杆的左右方向，油门刹车对应的是RT和LT键，可以对照GTA5的360手柄键位设置其他按键。
9.在360CE里面找到Left，Thumb选项卡，将第一行X 轴的Anti-Dead，Zone数值调节为7849,都设置好后点击右下角Save保存，退出就可以了，设置好后看一下X轴的DI和XI两个点，是否在一个中心点上，如果不在一个中心点的话需要你重新校准你的方向盘，完成，进游戏改第一人称，你就可以走路打仗用键鼠，开车用方向盘。
罗技-聚光灯-软件安装指南说明书
TU Delft - Library - NewMedia Centrenewmediacentre.tudelft.nlcollegerama.tudelft.nl Installation ManualLogitech Spotlight Softwareon your own laptop, for use of the pointer supplied by CollegeramaWhat is a Logitech Spotlight?https:///spotlightWhy is Collegerama so happy with it?Collegerama has been recording lectures on Campus since 2004. Since the start, users of Collegerama have asked us to somehow make the laser pointer of lecturers visible in our recordings. This is almost impossible to do, for technical and practical reasons. Collegerama captures the presentation slides digitally and since the Logitech Spotlight is a software based pointer, it will show both in the lecture hall AND in the recording. This provides a much better recording since viewers can see what a lecturer is pointing at inhis/her presentation.Universal compatibilityUse Spotlight on Windows or Mac, and on Powerpoint, Keynote, PDF, Google Slide and Prezipresentations.1 Enabled via Logitech Presentation App.2LONG OPERATING RANGE2Move and present freely from as far as 30 meters.DUAL CONNECTIVITYPlug-and-play Spotlight instantly on any compatible computer via USB receiver, or connect with low energy Bluetooth®. Spotlight also keeps your computer connected and awake while you present.INTUITIVE SLIDE NAVIGATIONEnjoy focus and instant familiarity with Spotlight’s intuitive three-button interface. Array Pointer:Circle1 and control the on-screen cursor.Next:Navigate forward, and control custom functions1 like start presentation.Back:Navigate backward, and control custom functions like gesture volumeadjustment.11 Enabled via Logitech Presentation App.2How to set up this device on my own laptop?Step 1: Software: Download & featureshttps:///en-roeu/product/spotlight-presentation-remote/page/spotlight-featuresStep 2: Open Icon SpotlightMAC: WINDOWS:1 Enabled via Logitech Presentation App.21 Enabled via Logitech Presentation App.2Step 3: Set upOnce the software has been installed, out of the box the Logitech Spotlight will work as such: a Spotlight. This essentially highlights the area which is being pointed at. This is already a great feature, but we would advise to change from spotlight to digital laser.As said, the pointer setting (see menu in image above) is set to be the Spotlight by default. Please adjust this and make the pointer a ‘digital laser’. The Spotlight function basically darkens the whole slide, and highlights the area which is being pointed at. The digital laser is the closest we can get to reproduce a regular laser (which is what many lecturers on campusare used to and would like to use).1 Enabled via Logitech Presentation App.21 Enabled via Logitech Presentation App.2In the menu of the digital laser, you can adjust the color and size of the laserdot.1 Enabled via Logitech Presentation App.2You can adjust the speed of the pointer to your choosing. The image shows the setting whichCollegerama has applied to their Logitech Spotlight pointers.1 Enabled via Logitech Presentation App.21 Enabled via Logitech Presentation App.2。
校准方向盘的方法DXTweak2使用说明
校准方向盘的方法（DXTweak2使用说明）校准方向盘的方法（DXTweak2使用说明）DXTweak2使用说明在方向盘使用过程中，碰到一些棘手的问题可以试一试DXTweak2软件来解决。
该软件可以解决力反馈方向盘（罗技MOMO、R440）油门刹车行程不正确的现象，以及可以修整WINDOWS系统“游戏控制器”无法解决的问题。
初看起来会觉得他很复杂，其实不然。
接下来我们一个一个的来解释一下。
一.下面我们认识一下DXTweak2的界面。
1.polled device（游戏控制器名称）Next（切换到下一个游戏控制器）2.X轴：X轴方向参数显示（一般来说，对于方向盘而言，这里显示的数字表示“方向左”、“方向右”的轴向）Y轴：Y轴方向参数显示（一般来说，对于方向盘而言，这里显示的数字表示“踏板右侧油门”轴向）滑块：滑块方向参数显示（一般来说，对于方向盘而言，这里显示的数字表示“踏板左侧刹车”轴向）Value （游戏控制器硬件芯片内置接受级数，value对应的是黑箭头directinp）Raw （游戏控制器控制器实际输出的量化数据，该参数是重要的参考数值，通过他我们可以算出游戏控制器最终的精确度，以及为DXTweak2的下一步校准提供具体参考。
）Deadzone%（游戏控制器呆区，在真实的民用量产车以及赛车上，都会存在不同情况的呆区情况。
即是说车手在这一范围内，左右动作方向盘，赛车不会随着你的动作而转向。
）3.Calibration（软件数值）Min （轴向极端小的数值）Cen （轴向居中的数值）Max （轴向极端大的数值）[这里有必要说明一下什么叫极端小MIN、方向盘居中CEN以及极端大MAX]如图所示：准心如在此状态，就被认为是“居中”、“极左”“极右”。
或者方向盘以这样的状态也可被认为是如上状态：A.居中B.极左C.极右4.raw 软件显示：你可以理解为在F1C中，某一轴向的数值具体位置。
directinp 硬件输入：方向盘根据信号接受到的实际数值（在未正确校准方向盘时，他也会体现出于你的意识相违背的动作）。
基于CARLA的驾驶仿真平台搭建
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络架构设计,
CARLA 支持多个用户 在 同 一 个 虚 拟
果作为规划控制的输入,因此智能车感知与决策的
能力十分重要。很多感知、决策模块的算法基于机
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驾驶设备与显示设备,同时完成了转向盘力反馈功
SmoothWin软件包说明说明书
Package‘SmoothWin’October12,2022Type PackageVersion3.0.0Date2019-07-26Author Hamed Haselimashhadi<**************.uk>Maintainer Hamed Haselimashhadi<**************.uk>Depends R(>=3.5)Description The main function in the package utilizes a windowing function in the form of an expo-nential weighting function to linear models.The bandwidth and sharpness of the win-dow are controlled by two parameters.Then,a series of tests are used to identify the right param-eters of the window(see Hamed Haselimash-hadi et al(2019)<https:///content/10.1101/656678v1>).Title Soft Windowing on Linear RegressionLicense LGPL(>=2)Imports nlme,RfastURL ,https:/// NeedsCompilation noRepository CRANDate/Publication2019-07-2721:40:10UTCR topics documented:expWeight (2)plot.SmoothWin (4)SmoothWin (5)Index141expWeight This function computes the smooth windowing weightsDescriptionThe symmetric weight generating function(SWGF).This function computes the exponential weights/kernel (soft windowing weights)for different shapes(k)and bandwidth(l)and plots the weights.UsageexpWeight(t,k,l,m=0,direction=c(1,1),plot=FALSE,zeroCompensation=0,cdf=plogis,progress=FALSE,...)Argumentst Vector of numeric time.A vector of positive continuous values for timek A single positive value for sharpnessl A single non-negative value for bandwidthm Vector of indices.The index of the modes on‘t‘(modes are the peak of thewindows)direction Vector of two numeric values.A vector of the form on(Left,right).Thefirstelement specifies the speed of expansion of the window(s)from the left and thesecond value for the right expansion.Setting to(0,1)and(1,0)lead to right andleft expansions of the windows respectively.Default(1,1)that is the window(s)expand symmetrically from both sides.plot Logicalflag.Setting to TRUE shows a plot of the weightszeroCompensationSingle non-negative value.Setting to any non-negative value would replace all(weights=<zeroCompensation)with zeroCompensation.Default0(zero) cdf A cdf function preferably symmetric.The cdf function is used for the(window)weight generating function.The function must have two parameters preciselya location such as mean and a scale.Standard cdf functions such as pnorm,pcauchy and plogis(default)can be used.For an example of custom made func-tion we define uniform function as below:punif0=function(x,mean=0.5,sd=sqrt(1/12))a=mean−sqrt(3)∗sd;b=mean+sqrt(3) progress Logicalflag.Setting to TRUE shows the progress of the function...Other parameters that can be passed to the‘plot()‘function such as pch,colouretc.ValueA numeric vector of weightsAuthor(s)Hamed Haselimashhadi<**************.uk>See AlsoSmoothWinExamplespar(mfrow=c(4,1))###################################################Example1-no merging happends between windows##################################################weight=expWeight(t=1:100,k=5,l=10,m=c(25,50,75),plot=TRUE,###Passed parameters to the plot functiontype= l ,lty=2,lwd=3,main= 1.If windows do not intersect,then wont merge!(l=10,k=5))###################################################Example2-merging in windows##################################################weight=expWeight(t=1:100,k=5,l=15,m=c(25,50,75),plot=TRUE,###Passed parameters to the plot functiontype= l ,lty=2,lwd=3,4plot.SmoothWin main= 2.If windows intersect,then merge!(l=15,k=5))###################################################Example3.1-partial merging in windows##################################################weight=expWeight(t=1:100,k=1,l=12,m=c(25,50,75),plot=TRUE,###Passed parameters to the plot functiontype= l ,lty=2,lwd=3,main= 3.1If windows intersect with small k,then partially merge!(l=12,k=1) )###################################################Example3.2-partial merging in windows##################################################weight=expWeight(t=1:100,k=.1,l=12,m=c(25,50,75),plot=TRUE,###Passed parameters to the plot functiontype= l ,lty=2,lwd=3,main= 3.2If windows intersect with small k,then partially merge!(l=12,k=0.1) )plot.SmoothWin Plot function for the SmoothWin objectDescriptionThis function plots a SmoothWin objectUsage##S3method for class SmoothWinplot(x,ylab= Response ,xlab= Time(continuous) ,sub=NULL,col=NULL,digits=2,...)Argumentsx SmoothWin objectylab Label on the y axis.Default‘Response‘xlab Label on the x axis.Default‘Time(continuous)‘sub See the‘sub‘parameter in‘plot()‘function.If left NULL then some information about thefinal window will be shown.Default NULLcol Colour parameter for the points.Set to NULL to use the default colouring(spec-trum colouring).Default NULLdigits The number of visible digits for l,k and SWS.Default2...Optional parameters that can be passed to the‘plot‘/‘qqPlot‘function.See‘car‘package for the qqPlot functionAuthor(s)Hamed Haselimashhadi<**************.uk>See AlsoSmoothWinExamplesexample(SmoothWin)SmoothWin Implementation of the soft windowing for linear modelsDescriptionImplementation of the(symmetric)soft windowing on a range of methods/models by imposing weights on the model.-The function accepts a modelfit,such as‘lm‘,‘lme‘,‘glm‘etc.,as the input andfits a window to it.-The parameters"k"and"l"control the shape and bandwidth of the windowing function respec-tively.-There are several other parameters to cope with the different scenarios/models/window shapes.-The default settings of the function is adapted to International Mouse Phenotyping Consortium (IMPC)statistical pipelineUsageSmoothWin(object,data,t,m,l=function(ignore.me.in.default){r=SmoothWin:::lseq(from=1,to=max(abs(t[m]-min(t,na.rm=TRUE)),abs(t[m]-max(t,na.rm=TRUE)),1),length.out=min(500,max(1,diff(range(t,na.rm=TRUE)))))r=unique(round(r))return(r)},k=SmoothWin:::lseq(from=.5,to=10,length.out=50),min.obs=function(ignore.me.in.default){lutm=length(unique(t[m]))r=ifelse(lutm>1,35,max(pi*sqrt(length(t)),35))r=max(r*lutm,length(m),na.rm=TRUE)r=min(r,length(t),na.rm=TRUE)return(r)},direction=c(1,1),weightFUN=function(x){x},residFun=function(x){resid(x)},predictFun=function(x){predict(x)},weightORthreshold= weight ,cdf=plogis,check=2,sensitivity=c(1,1,1,0),pvalThreshold=c(0,0,0,0),threshold=sqrt(.Machine$double.eps)*10,zeroCompensation=0,messages=TRUE,seed=NULL,simple.output=FALSE,debug=FALSE,...)Argumentsobject Thefitted model.The object must support‘update(weights=)‘.See examples data data.frame.Input data that is used tofit the initial modelt Vector of(numeric)time values.m Vector of integers(peaks).Mode indices on the time component.For example 10,11,12.Note that it is different from t[10],t[11],t[12]l Vector of numeric values for the bandwidth parameter,l.The default uses the maximum distance of the modes(t[m])from the time boundaries,max(max(t)-t[m],t[m]-min(t))split on500points on the logarithmic scale.k Vector of numeric values for the shape parameter,k.The default uses50splits of the values from0.5to10on the logarithmic scale.min.obs Single value.The minimum observations/sum weight scores(SWS)that must be in the total window(s).The default uses the following steps.1.If there are more than one modes(peaks)in the data,then:35*(the num-ber of the unique modes)2.If there is a single mode in the data,then:max(pi*sqrt(length(t)),35)*min.obs must not be less than the total number of observations in the modetime(s).For example,it can not be less than the number of mutant animals inthe IMPC application.**to function properly,min.obs should be less than the total number of ob-servations***min.obs is applied on the total number of observations on all windows NOTeach single window****if weightORthreshold=’weight’then min.obs will be evaluated againstSWSdirection Vector of two non-negative values.A non-negative vector of the form c(Left,right), for example c(1,1)[default]or c(0.5,0.5)or c(0,1).Thefirst element specifiesthe speed of expansion of the window(s)from the left and the second value forthe right expansion.Setting to c(0,1)and c(1,0)lead to right and left expan-sions of the window(s)respectively.Default c(1,1)that is the window(s)expandsymmetrically from both sides.weightFUN Weight function.By default,a vector of weights called"ModelWeight"is passed to this function.See the examples.residFun Residual computation function.The default is‘resid()‘.However,the the usercan define its own function.Note that the input of the function is the modelobject.The default is residFun=function(object){resid(object)}predictFun Similar to residFun but instead defines the‘predict()‘function.The default ispredictFun=function(object){predict(object)}weightORthresholdselect between’weight’(default)or’threshold’.If set to’weight’then the sumof weights(Sum Weight Score(SWS))would be used as the total number of(active)observations in the window,otherwise,total number of weights(countof weights)that are greater than a threshold(see‘threshold‘below)(countweights>=threshold)would be used for the total number of samples in the win-dow(see‘threshold‘).cdf A cdf function preferably symmetric.The cdf function is used for the(window)weight generating function(WGF).The cdf function must have two parametersprecisely a location such as mean and a scale.Standard cdf functions such as‘pnorm‘,‘pcauchy‘and‘plogis‘(default)can be used.For an example of cus-tom made function we define uniform function as below:punif0=function(x,mean=.5,sd=sqrt(1/12))a=mean−sqrt(3)∗sd;b=mean+sqrt(3) check Single integer in{0,1,2}:-check=1,the function selects the times(t)with more than one observations.Further,the function only selects the values with weights greater than the‘thresh-old‘(see threshold below).Mostly useful infitting linear mixed model-check=2(default),the function only selects the values with weights greaterthan the‘threshold‘parameter-check=0,disables all checkssensitivity(Default window selection criteria)Vector of four values(m,v,m*v,normal-ity_test).For example(default)c(1,1,1,0)specifies the same weights for mean,variance,mean*variance interaction and zero weight for the test of normality(shapiro.test)in determining the optimal(final)window.We should stress thatthe window size is calculated by detecting the changes amongst the consecutivemeans(two sample t.test)and variances(two sample var.test)(as well as thenormality of thefirst set)from each of predictFun()and residFun()combinedtogether.For example,(m,v,m*v,normality_test)is calculated for predictFun()and the same for residFun(),then two means are combined under the‘sensitiv-ity‘[1];and the same for variance,interactions and the normality.pvalThreshold Vector of four values.It would be used as the significant level for the mean,variation and normality tests(for more details see‘sensitivity‘above).If all zero(default)or(all)negative(<=0)then the internal adaptive method(sensitivity-see above)would be used.threshold Single positive value.The minimum value for weights before removing the cor-responded samples,given check=1or check=2and also in‘weightORthreshold‘.Default sqrt(.Machine$double.eps)*10~10^-7zeroCompensationSingle non-negative value.Setting to any non-negative value would replaceall(weights=<zeroCompensation)with‘zeroCompensation‘.Useful for al-gorithms that have difficulties with zero.Default0.messages Logical value.Set to TRUE(default)to see the errors and warningsseed seed.Default NULLsimple.output Logicalflag.Setting to TRUE leads to not exporting the list of models for l and eful for preventing memory overflow.Default FALSE debug Logicalflag.Setting to TRUE will show some plots for the parameter selection eful for debuging.Default FALSE...Other parameters that can be passed to the weightFUN()Valuefinal.k,final.lFinal values for k and lmodel.l,model.k,finalModelList of models for l,k and thefinal model.others The input parameters such as x,y,t and so onAuthor(s)Hamed Haselimashhadi<**************.uk>See AlsoexpWeightExamples#####################################################################Example in the manuscript#################################################set.seed(1234)par(mfrow=c(3,1))##############################Simulating data#############################n=60t=1:nsd=1m=n/2x=ty=c(0*x[t<=n/3],x[t<2*n/3&t>n/3]*1,0*x[t>=2*n/3])+rnorm(n,0,sd)#True weightsw=weights=expWeight(t=t,k=5,l=n/6, m=m, plot=0)##############################Fitting and ploting data and models#############################l=lm(y~x,weights=w)plot(x, y, ylim=c(min(y),max(y)*1.5), col=t%in%seq(n/3+1,2*n/3-1)+1, cex=1.5, pch=16, xlab= Time , main= Simulated data)abline(v=x[c(n/3+1,2*n/3-1)],lty=2,lwd=4,col= gray )abline(l,col=2,lty=2,lwd=4)abline(lm(y~x),col=3,lty=3,lwd=4)plot(t,w,type= b ,main= True weights ,ylab= Weight ,xlab= Time)##############################Fitting the Windowing model#############################r=SmoothWin(object=l,data=data.frame(y=y,x=x),t=t,m=m,min.obs=4,debug=FALSE)##############################Plot fitted(windowed)model#############################plot(r,main= Estimated weights from WGF )#####################################################################Other examples##################################################All examples import the Orthodont dataset from the nlme package library(nlme)#Sort the data on the time component(age)Orthodont=Orthodont[order(Orthodont$age),]##############################Modes#############################mode=which(Orthodont$age%in%c(12))##############################Time component#############################time=Orthodont$agef=formula(distance~Sex)#####################################################################Examples#######################################################################Example1.Linear model##############################Method1(recommanded)#############################lm=do.call( lm ,list(formula=f,data=Orthodont))rm(f)##############################Method2(can cause error if you pass the formula to the lm function) #lm=lm(distance~Sex,data=Orthodont)#############################lm.result=SmoothWin(object=lm,data=Orthodont,t=time,m=mode,check=0,weightFUN=function(x){x},debug=TRUE)plot(lm.result,col=Orthodont$Sex,pch=as.integer(Orthodont$Sex),main= Simple liner model)#################################Example2.Linear Model Using Generalized Least Squares#Method1(recommanded)#############################f=formula(distance~Sex)gls=do.call( gls ,list(model=f,data=Orthodont))rm(f)##############################Method2(can cause error if you pass the formula to the gls function) #gls=gls(distance~Sex,data=Orthodont)#############################gls.result=SmoothWin(object=gls,data=Orthodont,t=time,m=mode,check=2,weightFUN=function(ignore.me){varFixed(~1/ModelWeight)#nlme package uses the inverse weights },debug=TRUE)plot(gls.result,col=Orthodont$Sex,pch=as.integer(Orthodont$Sex),main= Linear model using GLS)#####################################################Example3.Linear mixed model##################################################Method1(recommanded)#############################fixed=formula(distance~Sex)random=formula(~1|Subject)lme=do.call( lme ,list(fixed=fixed,random=random,data=Orthodont))rm(fixed,random)##############################Method2(can cause error if you pass the formula to the lme function) #lme=lme(fixed=distance~Sex,random=~1|Subject,data=Orthodont) #############################lme.result=SmoothWin(object=lme,data=Orthodont,t=time,m=mode,#Remove zero weights as well as single observation datescheck=1,weightFUN=function(ignore.me){varFixed(~1/ModelWeight) },debug=TRUE)plot(lme.result,col=Orthodont$Sex,pch=as.integer(Orthodont$Sex), main= Linear mixed model)Index∗SmoothWinSmoothWin,5∗WindowingSmoothWin,5expWeight,2,9plot.SmoothWin,4SmoothWin,3,5,514。
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Java开发者的必备工具包
Java开发者的必备工具包在Java开发中，使用适当的工具和库可以极大地提高开发效率和质量。
本文将介绍一些Java开发者必备的工具包，帮助您更好地进行Java开发。
一、开发工具包1. JDK（Java Development Kit）JDK是Java开发者必备的工具之一，其中包含了Java语言的编译器、虚拟机等必要组件。
通过安装JDK，您可以编写、编译和运行Java程序。
2. EclipseEclipse是一款流行的Java集成开发环境（IDE），提供了丰富的功能和插件，可以帮助开发者更高效地编写代码、调试程序和管理项目。
3. IntelliJ IDEAIntelliJ IDEA是另一个常用的Java IDE，它提供了智能代码提示、自动补全、重构工具等功能，可以帮助开发者快速编写高质量的Java代码。
二、构建工具包1. MavenMaven是一款优秀的项目构建工具，它能够管理项目依赖、自动化构建过程，并提供了丰富的插件，可以简化Java项目的开发和部署。
2. GradleGradle是另一个强大的项目构建工具，它支持多种项目结构和构建方式，并且具有高度灵活性。
通过使用Gradle，您可以更加自由地定义和管理项目的构建过程。
三、测试工具包1. JUnitJUnit是一款广泛使用的Java单元测试框架，在Java开发中被广泛应用。
通过使用JUnit，您可以编写简洁而可靠的单元测试，确保代码的质量和可靠性。
2. MockitoMockito是一款强大的Java单元测试框架，它提供了丰富的API，可以方便地模拟和验证对象的行为。
通过使用Mockito，您可以更加灵活地进行单元测试，并提高测试覆盖率。
四、调试工具包1. JVisualVMJVisualVM是一款功能强大的Java虚拟机监控和调试工具，它可以帮助开发者分析程序性能问题、内存泄漏等。
通过使用JVisualVM，您可以深入了解程序运行时的状态，并进行优化和调试。
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罗技罗技游戏方向盘软件开发工具包概述和参考罗技游戏方向盘软件开发工具包包含所有的附加文件，且受到知识产权法的保护，所有罗技游戏方向盘开发包都接受了在这篇文当结尾处“罗技游戏中的许可协议方向盘SDK许可协议”文件。
如果您不同意条款和许可协议中的条件，你必须立即返回任意的文档，附带的软件和所有其他材料都是由罗技为您提供，所有未明确的授权，罗技拥有解释权。
概述罗技游戏方向盘SDK能应用在控制游戏诸如各种类型的游戏控制器（USB/游戏端口轮/游戏杆/游戏垫，力回馈启用与否）。
通过使用方向盘SDK你可以确保所有的轮子和操纵杆的功能将发挥完美。
没有比在一场游戏中方向盘或摇杆的力反馈不同而导致用户受挫和产品遭到退货更严重的情况了，请注意只有在安装了罗技游戏软件后，SDK才将正常工作，如果想获得更多的信息请访问/en-us/gaming 。
SDK 包下面的文件包括：LogitechSteeringWheelLib.h：C / C ++包含头文件的函数原型LogitechGSDK.cs：C＃引用文件用来访问被包装的SDKLogitechSteeringWheelLib.lib：LIB文件访问DLL导出函数（32位和64位）LogitechSteeringWheelEnginesWrapper.dll：SDK函数封装（32位和64位）要求罗技游戏方向盘SDK可以在以下平台上使用：Windows XP SP2（32位和64位）Windows Vista (32-bit and 64-bit)Windows 7 (32-bit and 64-bit)Windows 8 (32-bit and 64-bit)罗技游戏方向盘SDK是一个基于C语言的接口，是为使用C / C++的程序员设计的。
熟悉Windows编程也是必须的。
使用SDK接口使用LogitechSteeringWheel.h和LogitechSteeringWheel.lib访问LogitechSteeringWheel.dll应用程序可以包括LogitechSteeringWheel.h并链接到LogitechSteeringWheel.lib（参见“样本使用该SDK”进一步说明或样本文件夹中的示例程序）的。
安装文件夹中的DLL需要是相同的主可执行文件，或者需要在该系统环境中的路径的一部分。
多客户端同时使用SDK该SDK只允许一个用户在任何给定时间来控制的装置。
如果两个应用程序尝试初始化SDK 的设备，只有第一个将成功。
第二个应用程序的初始化将失败。
该做什么和不该做什么这些都是一些指导原则，可以帮助在你的游戏中获得'更好'的支持：函数LogiSteeringInitialize（）会试图获得你的应用程序/游戏主窗口句柄。
它可能会失败，因为当你调用该函数时的主窗口可能不会在最前面的界面，如果LogiSteeringInitialize（）返回false，初始化将会接受任何尝试下一个LogiUpdate（）函数的调用。
当窗口在最前界面时和SDK可以初始化，LogiUpdate（）将返回true。
SDK的使用示例使用头文件和lib:参考罗技转向初始化窗口LogiSteeringInitializeWithWindow（获取窗体句柄）bool LogiSteeringInitialize(CONST bool ignoreXInputControllers, HWND hwnd) 所述LogiSteeringInitializeWithWindow（）函数生成必要的初始化，如果没有另一个实例已经在运行。
只有你从你的代码中获得窗口的句柄才能使用此功能。
参数忽略X输入Controlllers：如果设置为true，该软件开发工具包将忽略任何的X输入控制器HWND：窗口句柄返回参数如果函数成功运行，返回为true，否则返回falseLOGI转向初始化LogiSteeringInitialize（主窗体初始化）bool LogiSteeringInitialize(CONST bool ignoreXInputControllers)该LogiSteeringInitialize（）函数确保主窗口已经到了前面，如果这时没有其他实例运行，则进行必要的初始化参数忽略X输入Controlllers：如果设置为true，该软件开发工具包将忽略任何的X输入控制器返回参数如果函数成功运行，返回为true，否则返回false如果返回是假的，那是因为你的应用程序的主窗口还没有到最前面呢。
这意味着窗口处理程序还没有被检索。
LogiUpdate（主窗口设备跟新）bool LogiUpdate();该LogiUpdate（）查找主窗口处理程序，如果已经发现力和控制器保持连接最新的。
它被称为应用程序的每一帧。
返回参数如果函数成功运行，返回为true，否则返回false该函数将返回false，如果LogiSteeringInitialize（）没有被调用，或者已无法找到主窗口句柄。
LogiGetState（获得设备状态）DIJOYSTATE2* LogiGetState(const int index);https:///zh-cn/library/windows/apps/microsoft.directx_sdk.referen ce.dijoystate2.aspxLogiGetState（）返回结构体DIJOYSTATE2控制器的状态。
如果想使用这个实现从DirectInput到Microsoft Windows工作，则它需要dinput.h。
参数索引：游戏控制器的索引。
索引0对应连接的第一个游戏控制器。
索引1对应第二个游戏控制器返回参数DIJOYSTATE2 结构包含设备的在坐标系中的位置信息，和POVs和按钮设备信息。
注意如果不与DirectInput一起工作，则在你的游戏或项目中可以不包括dinput.h，请注意看一看这个功能：LogiGetStateENGINESLogiGetStateENGINES（获得设备简化状态）DIJOYSTATE2ENGINES* LogiGetStateENGINES(const int index);所述LogiGetStateENGINES（）是函数LogiGetState的简化版本。
如果不是与DirectInput一起工作的。
它返回DIJOYSTATE2结构的简化版本，称为DIJOYSTATE2ENGINES。
参数索引：游戏控制器的索引。
索引0对应连接的第一个游戏控制器。
索引1对应第二个游戏控制器返回参数DIJOYSTATE2 结构包含设备的在坐标系中的位置信息，和POVs和按钮设备信息有关为了更加细致，看看在头文件LogiSeeringWheel.h的信息。
LogiGetFriendlyProductName（获取设备名）wchar_t* LogiGetFriendlyProductName(const int index);The LogiGetFriendlyProductName () 函数获得设备的名称。
参数索引：游戏控制器的索引。
索引0对应连接的第一个游戏控制器。
索引1对应第二个游戏控制器返回参数字符串中包含设备的友好产品名称。
LogiIsConnected（设备是否已连接）bool LogiIsConnected(const int index);该LogiIsConnected（）函数检查游戏控制器是否连接在指定索引处参数索引：游戏控制器的索引。
索引0对应连接的第一个游戏控制器。
索引1对应第二个游戏控制器返回参数返回true，如果设备连接到特定的索引，否则返回false。
LogiIsDeviceConnected（是否连接到特定的设备）bool LogiIsDeviceConnected(const int index, const int deviceType);该LogiIsConnected（）函数检查指定的设备是否连接在索引处参数索引：游戏控制器的索引。
索引0对应连接的第一个游戏控制器。
索引1对应第二个游戏控制器设备类型：设备检查的类型。
可能的类型：o LOGI_DEVICE_TYPE_WHEELo LOGI_DEVICE_TYPE_JOYSTICKo LOGI_DEVICE_TYPE_GAMEPADo LOGI_DEVICE_TYPE_OTHER返回参数返回true，如果设备连接到特定的索引，否则返回false。
LogiIsManufacturerConnected （制造商设备检测）bool LogiIsManufacturerConnected(const int index, const int manufacturerName);LogiIsManufacturerConnected（）函数检查连接成功的设备索引是否由manufacturerName 指定的制造商生成。
参数索引：游戏控制器的索引。
索引0对应连接的第一个游戏控制器。
索引1对应第二个游戏控制器manufacturerName：制造设备的制造商的名称，可能的类型主要有：o LOGI_ MANUFACTURER_LOGITECHo LOGI_ MANUFACTURER_MICROSOFTo LOGI_ MANUFACTURER_OTHER返回参数如果特定生产商的PC控制器已经连接上，返回真，否则返回假LogiIsModelConnected（设备型号检测）bool LogiIsModelConnected(const int index, const int modelName);该LogiIsModelConnected（）函数检查设备连接的索引是否是指定型号参数索引：游戏控制器的索引。
索引0对应连接的第一个游戏控制器。
索引1对应第二个游戏控制器型号名：设备的型号名称，可能的型号名称：o LOGI_MODEL_G27o LOGI _MODEL_G25o LOGI _MODEL_MOMO_RACINGo LOGI _MODEL_MOMO_FORCEo LOGI _MODEL_DRIVING_FORCE_PROo LOGI _MODEL_DRIVING_FORCEo LOGI _MODEL_NASCAR_RACING_WHEELo LOGI _MODEL_FORMULA_FORCEo LOGI _MODEL_FORMULA_FORCE_GPo LOGI _MODEL_FORCE_3D_PROo LOGI _MODEL_EXTREME_3D_PROo LOGI _MODEL_FREEDOM_24o LOGI _MODEL_ATTACK_3o LOGI _MODEL_FORCE_3Do LOGI _MODEL_STRIKE_FORCE_3Do LOGI _MODEL_RUMBLEPADo LOGI _MODEL_RUMBLEPAD_2o LOGI _MODEL_CORDLESS_RUMBLEPAD_2o LOGI _MODEL_CORDLESS_GAMEPADo LOGI _MODEL_DUAL_ACTION_GAMEPADo LOGI _MODEL_PRECISION_GAMEPAD_2o LOGI _MODEL_CHILLSTREAM返回参数如果指定型号的控制器已经连接，返回为true，否则返回false LogiButtonTriggered（按键检测）bool LogiButtonTriggered(const int index, const int buttonNbr);该LogiButtonTriggered（）函数检查设备连接的索引是否是当前的指定触发按钮参数索引：游戏控制器的索引。