NanoX2 白皮书
华为 5G-Advanced(2021)网络技术演进白皮书
5G-Advanced网络技术演进白皮书（2021）——面向万物智联新时代从产业发展驱动角度看，键，全球的主要经济体均明确要求将5G作为长期产业发展的重要一环。
从业务上5G将要进入千行百业，从技术上5G需要进一步融合DOICT等技术。
因此本白皮书提出需要对5G 网络的后续演进—5G-Advanced进行持续研究, 并充分考虑架构演进及功能增强。
本白皮书首先分析了5G-Advanced的网络演进架构方向，包括云原生、边缘网络和网络即服务，同时阐述了5G-Advanced的技术发展方向包括智慧、融合与使能三个特征。
其中智慧代表网络智能化，包括充分利用机器学习、数字孪生、认知网络与意图网络等关键技术提升网络的智能运维运营能力，打造内生智能网络；融合包括行业网络融合、家庭网络融合、天地一体化网络融合等，实现5G与行业网协同组网、融合发展；使能则包括对5G交互式通信和确定性通信能力的增强，以及网络切片、定位等现有技术的增强，更好赋能行业数智化转型。
，华为，爱立信（中国），上海诺基亚贝尔，中兴，中国信科，三星，亚信，vivo，联想，IPLOOK，紫光展锐，OPPO，腾讯，小米（排名不分先后）1 产业进展概述 (01)1.1 5G产业发展现状 (01)1.2 5G网络演进驱动力 (01)1.2.1 产业发展驱动力 (01)1.2.2 网络技术驱动力 (02)2 5G-Advanced网络演进架构趋势和技术方向 (04)3 5G-Advanced关键技术 (06)3.1 网络智能化 (06)3.1.1 网络智能化关键技术 (06)3.1.2 智能网络应用场景 (08)3.2 行业网融合 (08)3.3 家庭网络融合 (09)3.4 天地一体化网络融合 (10)3.5 交互式通信能力增强 (11)3.6 确定性通信能力增强 (11)3.7 用户面演进 (12)3.8 网络切片增强 (12)3.9 定位测距与感知增强 (13)3.10 组播广播增强 (13)3.11 策略控制增强 (13)4 总结和展望 (14)5G网络的全球商用部署如火如荼。
腾讯云-智能钛机器学习平台白皮书
智能钛机器学习平台产品概述目录产品简介产品概述 (3)什么是TI-ONE..................................................................................................................................................................3.....如何使用TI-ONE ..............................................................................................................................................................3....TI-ONE 定价....................................................................................................................................................................3.......其他相关产品...................................................................................................................................................................3....... 客户价值 (5)技术价值...........................................................................................................................................................................5.......业务价值...........................................................................................................................................................................5....... 应用场景 (6)金融风控...........................................................................................................................................................................6.......营销推荐...........................................................................................................................................................................6.......工业质量检测...................................................................................................................................................................7.......算法大赛...........................................................................................................................................................................7.......物业智能化管理................................................................................................................................................................8......人物监察管理识别...........................................................................................................................................................8...... 版本更新. (11)V.1.5 版本说明..............................................................................................................................................................1..1.....新特性.....................................................................................................................................................................1..1.....V.1.4 版本说明..............................................................................................................................................................1..1.....新特性.....................................................................................................................................................................1..1.....问题修复及优化.....................................................................................................................................................1..2....V1.3 版本说明...............................................................................................................................................................1...2....新特性.....................................................................................................................................................................1..2.....问题修复及优化.....................................................................................................................................................1..2....V1.2 版本说明...............................................................................................................................................................1...2....新特性.....................................................................................................................................................................1..2.....产品简介产品概述什么是TI-ONE智能钛机器学习平台（TI-ONE ）是为AI 工程师打造的一站式机器学习服务平台，为用户提供从数据预处理、模型构建、模型训练、模型评估到模型服务的全流程开发及部署支持。
虚拟化202：提升到新的层次
目 录
虚拟化革命 ................................................................................................................................................ 3
此 ESG 白皮书由 VMware 委托制作
并经 ESG 许可发布
版权所有 © 2011 年，Enterprise Strategy Group，Inc. 保留所有权利。
白皮书
虚 拟化 202：提升到新的层次
作者：高级分析师 Mark Bowker
2011 年 6 月
Hale Waihona Puke 虚 拟 化 革 命 当前，x86 服务器虚拟化正在引发十年来信息技术领域最深刻的变革。然而，尽管虚拟化用户数量庞 大，但相对而言，大多数都只是新手。ESG 的研究结果表明，具备虚拟服务器实施技术经验的组织 中，有三分之二的组织的部署时间不足三年，而部署时间超过五年的组织仅占 16%。1此外，尽管某种 程度的服务器虚拟化已经相当普及，但在接受调查的组织中，略超半数的组织其潜在服务器虚拟化的比 例不足三分之一（请参见图 1）。IT 部门负责的工作负载在虚拟服务器版图上仍然占据绝对比例；59% 的受访组织还未虚拟化任何关键业务应用。
Palo Alto Networks Cortex SOAR产品白皮书说明书
How SOAR Is Transforming Threat IntelligenceThe benefits of digita l tra nsforma tion for a ny enterprise a re clea r, but thetra nsforma tion a lso comes with security implica tions a s new technologiesexpand the attack surface, enabling attackers to come from anywhere. Withcloud computing, a utoma tion, a nd a rtificia l intelligence now ma instrea m,a tta ckers ca n ca rry out their ca mpa igns a t unprecedented levels of sophis-tication and scale with minimal human intervention. Today, threat actors attackcomputers every 39 seconds.1 A report by Cybersecurity Ventures projects thatby 2021, a business will fall victim to ransomware every 11 seconds.2 This is onlypossible because attackers are taking advantage of machine speed.1. “Hackers Attack Every 39 Seconds,” Security Magazine, February 10, 2017, https:///articles/87787-hackers-attack-every-39-seconds.2. “Global Cybercrime Damages Predicted T o Reach $6 Trillion Annually By 2021,” Cybercrime Magazine, December 7, 2018, https:///cybercrime-damages-6-trillion-by-2021.Incident Responders Incident responders are worried about damage control. They look for possible breaches, and If they find evidence of one, their job is to investigate and prevent it from spreading. Due to the sensitive nature of breaches, all evidence needs to be well-documented and shared with all the stakeholders. Incident responders have access to tools that help them contain breach -es, such as EDR tools to kill the end host. They engage firewall administrators to deploy policies that block propagation at the network level. They heavily rely on external threat intelligence to learn about the profiles and common techniques of attackers, allowing them to respond confidently and with precision.Accordingly, incident responders are challenged when it comes to:• Knowledge transfer , where a lack of collaboration be-tween teams introduces gaps in security.• Case management , because generic case management is not ideal for security use cases, resulting in inefficiency and poor documentation.• Lack of threat intelligence as many incident responders are forced to use manual, flawed processes to gain context around external threats, causing delays and risks.Incident responders need:• Full security case management to document their find -ings in detail and enable them to collaborate in real time with other stakeholders as well as provide preventive and quarantine measures across the enterprise.•Threat intelligence to provide deeper context around attackers and their motivations.Disparate knowledge transfer introduces communication gaps Insufficient case management Lack of security centriccase-management results in inefficiency Lack of threat intelligence Missing real-world external context to prioritize alertsFigure 2:Incident responders challengesToo many alerts & not enough people to handle them Repetitive & manual actions across tools,process and people It takes days toinvestigate and respond to threatsFigure 1: SOC analyst challengesdetect, investigate, and respond to advanced cyberthreats.Unfortunately, security teams of all sizes are overwhelmed and unable to function at full capacity due to a shortage of cybersecurity skills, high volumes of low-fidelity alerts, a plethora of disconnected security tools, and lack of external threat context. To address these challenges, we first need to break down the inner workings of a SOC and get a sense of what happens. Only then will we be able to appreciate the enormity of what SOC teams are up against and begin to ap-ply solutions that work.In larger organizations, mature security operations teams have a lot of moving parts. Three main functions make up ef -fective security operations: SOC analysts, incident respond -ers, and threat analysts.SOC Analysts SOC analysts look at thousands of internal alerts daily, sourced by security information and event management (SIEM) technologies, endpoint detection and response (EDR) systems, and sometimes hundreds of other internal security tools. Their job is to be the eyes of the enterprise: to detect, investigate, determine root cause of, and respond quickly to security incidents. SOC analysts continuously monitor the network using detection tools, identifying and investigating potential threats. Once they identify a potential risk, ana-lysts also need to document their findings and share recom -mended actions with other stakeholders.All this means SOC analysts often struggle with:• Alert fatigue: The average enterprise receives more than 11,000 security alerts per day,3 and doesn’t have enough people to handle them.• Lack of time: Repetitive, manual, and administrative tasks take too long. A lack of integration across the many tools analysts must use slows down every stage of the process. • Limited context: It often takes days to investigate and respond to threats. Security tools don’t provide adequate context on alerts or their relevance to the environment,forcing analysts to piece these things together manually.To overcome these problems, analysts need:• Automation to take care of daily tasks so they can f ocus on what really matters.• Real-time collaboration with the rest of the team so they are always in sync and learning from one another. • Threat intelligence that delivers context to help them un -derstand the relevance and potential impact of a threat.3.According to a commissioned study conducted by Forrester Consulting on behalf of Palo Alto Networks, February 2020. As of the publication of this document, the report has not yet been officially released.IP 1.1.1.1feeds How bad is it?Malware analysisEndpoint detection and response SIEM Spreadsheet Bad IP 1.1.1.1Security SIEM ToolsResearch ReportsWho is behind it?EmailEnd usersAssets FirewallNetwork topology Internet accessNews/Blog Threat actors use 1.1.1.1 To attack!Industry peersAre we impacted?Who is using this IP address?Which policy isblocking this IP address?• Difficulty taking action since putting threat intel into a ction is highly manual and relies on other teams.Threat intelligence analysts need:• Full control over threat intelligence feed indicators to build their own logic and reputation based on their environment and business needs.• Collaboration with other teams to quickly arm them with rich context and up-to-date research.• Robust documentationto capture their ck of controlManually tuningand scoring of IOCS Siloed workflows Incidents and threat intel are broken across tools, people and process Putting threat intel intoaction is highly manual and repetitiveFigure 3: Difficulties facing threat analysts Figure 4: Holistic view of a typical day in a SOCThreat Intelligence Analysts/ Programs Threat analysts identify potential risks to organizations that have not been observed in the network. They provide c ontext around potential threats by combining external threat i ntelligence feeds from multiple sources with human intelli-gence. According to a recent survey conducted by the SANS Institute, 49.5% of organizations have some type of a threat intelligence team or program with its own dedicated budget and staffing.4 This is evidence of the growing importance of threat intelligence analysts, who help to identify attackers, uncovering their motivations, techniques, and processes. Threat intelligence teams pass their findings on specific at -tacks as well as broader threat landscape reports to SOC and incident response teams to build better preventive measures.Threat intelligence analysts face:• Lack of control over threat intelligence feeds, forcing the analysts to manually tune and score indicators of compro-mise (IOCs) to match their environment.• Siloed workflows causing poor communication and i ntegration between incident response and threat intelli-gence tools, teams, and processes.4. “2020 SANS Cyber Threat Intelligence (CTI) Survey,” SANS Institute, February 11, 2020, https:///reading-room/whitepapers/threats/paper/39395.with instant clarity into high-priority threats to drive the right response, in the right way, across the entire enterprise.Cortex XSOAR unifies case management, r eal-time c in the industry’s first extended security mation, and response platform.security orchestration, automation, and response (SOAR) platforms to manage alerts across all sources, standardize processes with playbooks, and automate response for any security use case, but there is still a significant gap when it comes to threat intelligence management.as an i ssue, o ffering guidance that SOAR and TIPs need to converge. TIPs are merely adding complexity by aggregating intelligence sources without the real-world context or auto -mation required to take quick, confident action. It’s time for a different approach.We Need an Extended SOAR PlatformCortex™ XSOAR, with native Threat I ntel Management, just makes sense. As part of the extensible C ortex XSOAR platform,Threat Intel Management defines a new approach by unify -ing threat intelligence aggregation, scoring, and sharing with playbook-driven automation. It empowers securityleaders370+Third-party tools Cortex XDR Tools People APIOther sources source ISAC Premium AutoFocus AFresponseTake complete control of your threat intelligence feeds Make smarter incident response decisions by enriching every tool and process Close the loop between intelligence and action with playbook-driven automation Figure 7: Benefits of Threat Intel Management Figure 6: Cortex XSOAR playbook-driven automationintelligence platforms Figure 5: A typical SOAR + TIP siloed deployment Cortex XSOAR allows you to:• Eliminate manual tasks with automated playbooks to ag-gregate, parse, deduplicate, and manage millions of daily indicators across multiple feed sources. Extend and edit IOC scoring with ease. Find providers that have the most relevant indicators for your specific environment.• Reveal critical threats by layering third-party threati ntelligence with internal incidents to prioritize alerts and make smarter response decisions. Supercharge i nvestigations with high-fidelity, built-in threat intel -ligence from Palo Alto N etworks AutoFocus™ service. Enrich any detection, monitoring, or response tool withcontext from curated threat intelligence.Security analysts deal with millions of indicators collected from hundreds of multi-sourced intelligence feeds. These indicators lack context required for analysts to make informed decisions, p recision. tools at their disposal can’t handle the sheer volume of indi cators, and analysts end up re-prioritizing indicators to match native Threat Management gives analysts complete control and flexibility to incorporate any business logic into their scoring. Built-in inte-gration with more than 370 vendors allows analysts to react in real time as the indicators are consumed.report millions of indicators on a daily basis the context analysts need to make informed decisions and take action handle limited amounts of threat intelligence data and need to constantlyre-prioritize indicators Figure 9: Challenges of disconnected intelligence toolsFigure 10: Intelligence management before and after Cortex XSOAR ISACs AAPIAutomated playbooksIndicatorvalue report Indicator lifecycle Third-party Intel sharingvisualization Threat intelligence enrichment and prioritization3000 Tannery Way Santa Clara, CA 95054M a in: +1.408.753.4000S a les: +1.866.320.4788Support: +1.866.898.9087© 2020 Palo Alto Networks, Inc. Palo Alto Networks is a registered t rademark of Palo Alto Networks. A list of our trademarks can be found at https:///company/trademarks.html. All other marks mentioned herein may be trademarks of their respective companies. how-cortex-is-transforming-threat-intelligence-wp-041720of the most common use cases include phishing, s ecurity op-erations, incident alert handling, cloud security orchestration, vulnerability management, and threat hunting.The future of SOAR includes native Threat Intel Manage -ment, enabling teams to break down silos between securi-ty operations and threat intelligence functions. When these are offered together in one platform, SOC analysts, incident responders, and threat intelligence teams can unify their efforts against advanced adversaries, optimizing their com -munication, efficiency, and access to insights.Cortex XSOAR redefines orchestration, automation, and response with the industry’s firstextended SOARplatform that includes automation, orchestration, real-time collab-oration, case management, and Threat Intel Management, enabling security teams to keep pace with attackers now and in the future.Visit us online to learn more about Cortex XSOAR.Breadth of Cortex XSOAR Use Cases The open and extensible Cortex XSOAR platform can be applied to a wide range of use cases—even to processes outside the purview of the SOC or security incident response team. SomeIncorporate any business logic into collection, scoring,and integrations to security devices React in real time to new indicators as they are consumed Defend your network instead of spending time building integrations integrations Figure 11: Cortex XSOAR benefits across the SOC。
深度学习技术白皮书
深度学习技术白皮书摘要本白皮书旨在介绍深度学习技术的原理、应用以及未来发展趋势。
深度学习作为人工智能领域的关键技术之一，已经在图像识别、语音识别、自然语言处理等领域取得了显著的成果。
本文将从深度学习的基本原理、常见的模型架构、应用案例以及未来发展方向等方面进行探讨。
1. 引言深度学习是一种基于神经网络的机器学习方法，通过多层次的神经网络模型来模拟人脑的神经网络结构，实现对复杂数据的学习和理解。
深度学习技术的兴起得益于计算能力的提升、数据量的增加以及算法的改进。
相比传统机器学习方法，深度学习在处理大规模数据和复杂任务上具有明显的优势。
2. 深度学习的基本原理深度学习的核心原理是通过多层次的神经网络模型来实现特征的自动提取和学习。
深度学习模型通常由输入层、隐藏层和输出层组成，其中隐藏层可以包含多个层次。
每个隐藏层通过非线性的激活函数将输入信号进行转换，并将转换后的信号传递给下一层。
通过多层次的转换和信息传递，深度学习模型可以学习到更加抽象和高级的特征表示。
3. 常见的深度学习模型架构3.1 卷积神经网络（CNN）卷积神经网络是深度学习中最常用的模型架构之一，主要用于图像识别和计算机视觉任务。
CNN通过卷积层、池化层和全连接层构成，其中卷积层用于提取图像的局部特征，池化层用于降低特征维度，全连接层用于分类和预测。
CNN的优势在于可以自动学习图像的空间结构和层次化特征。
3.2 递归神经网络（RNN）递归神经网络是一种具有循环连接的神经网络模型，主要用于序列数据的处理，如语音识别和自然语言处理。
RNN通过时间步的循环连接，可以捕捉到序列数据中的时序关系。
然而，传统的RNN存在梯度消失和梯度爆炸等问题，因此引入了长短时记忆网络（LSTM）和门控循环单元（GRU）等改进模型。
3.3 生成对抗网络（GAN）生成对抗网络是一种由生成器和判别器组成的对抗性模型，用于生成逼真的样本。
生成器通过学习数据的分布来生成新的样本，而判别器则通过判断样本的真实性来提供反馈。
Coolux X2规格书(确定)
180(L) x 110(W) x 28(H)mm
体积（毫升）
550
Hale Waihona Puke 重量（克）520（含电池）
LED寿命
大于20000小时
选配配件、功能
TV、万用笔记本电源转接头、3D视频显示、无线音频传输模块
其他特点
全球最高亮度的手持投影机。实际亮度感官效果达到1000流明DLP灯泡投影机的水平
全球最薄、最小、最低功耗的高清高亮LED微型投影机
Coolux X2微型投影机规格书
产品外观
显示技术
0.45" DMD+RGBLED
分辨率
1280*800 (支持1080P)
亮度
400ANSI流明（高亮模式）
均匀度
大于90%
色域
＞＝100%NTSC
标准色温
6400K
对比度
2000:1（FOFO）
投影尺寸
15寸~150寸
偏轴offset
OFF-SET:100%
支持1080P视频，支持PPT、PDF文件浏览（暂不支持WORD\EXCEL）
内置电池续航，支持移动使用
投射比
1.4（1米@33寸）
电子梯形校正
±30度（调整后画面比例不失真）
输入端口
HDMI、AV、VGA、TF slot、U-disk slot、USB、耳机、DC1 9V
PMP性能
支持MP4（1080P）\MP3\文本文件\PPT及PDF文件等
功耗
18---25W（电源适配器：19V2.1A，电池：11.1V2000mAh）
ZEISS ELYRA 超分辨率微显微镜样品准备指南 - 快速指南白皮书说明书
ZEISS ELYRASample Preparation for Superresolution Microscopy –a Quick GuideGeneral Guidelines forSuperresolution Structured Illumination Microscopy (SR-SIM)Sample supportIn general, we recommend using high-precision coverslips (always no. 1.5 thickness) to mount the samples. High-precision cover glasses feature an exceptionally accurate thickness of 170 ± 5 µm (= 0,170 ± 0,005 mm). Also mind thickness issues when ordering glass-bottom petri dishes or multi-well plates.For cover glasses see:• ZEISS High precision Cover Glasses 18x18 mm;ZEISS order Number: 474030-9010-000• Marienfeld Superior: • 18x18 mm (Cat.No. 0107032).• 22x22 mm (Cat.No. 0107052).• round with 18 mm diameter (Cat.No. 0117580).• CellPath Ltd UK High performance coverslipsno 1.5H 18x18 mm (Cat.No SAN-5018-03A)For glass-bottom petri dishes and multi-well plates see:• Willco (/)• Lab-tek (offered through Thermo)• MatTek ()ZEISS ELYRASample Preparation for Superresolution Microscopy – a Quick GuideAuthors: Dr. Sylvia Münter Dr. Yilmaz Niyaz Carl Zeiss Microscopy GmbH, GermanyDate: November 2013Please make sure that the coverslip is centered on the glass slide in order to fit into the sample holder.See below pictures of the ZEISS level adjustable piezo stage insert.Fix your cells according to your standard protocol. For SR-SIM imaging, a thoroughly clean glass surface plays a crucial role. Therefore it is beneficial to seal or attach the coverslip in a way that facilitates cleaning with ethanol, without moving the coverslip.Fluorescent Labels for SR-SIMAll common types of organic dyes usually conjugated to antibodies or fluorescent proteins are suitable to be used for SR-SIM. Make sure to have a highly specific labeling with low background to obtain a good signal to noise ratio. For multicolor samples the fluorophores should be selected for minimal spectral overlap to avoid crosstalk. Available filter sets for ELYRA PS.1 are are optimized for the available laser lines: 405, 488, 561 and 642 nm.NoteCytosolic or other non-specific fluorescent protein expression (e.g. GFP) will result in staining of extended areas. Since well-defined structures are missing to interfere with the grid pattern, modulation contrast will be low and the final image will lack high resolution information.Figure 1 ZEISS level adjustable piezo stage insertEmbeddingIdeally, the sample should be embedded in a medium that matches the refractive index (RI) of the immersion oil(n=1,518). The following media perform well, despite having a lower refractive index.• Fluoromout-G (SouthernBiotech) with RI of 1,40.• 86% Glycerol with 2.5g/l DABCO (1,4-deazabicyclo[2.2.2] octane, Sigma Cat. D2522) in Tris-HCl. 1M pH 8.0.For further reading: http://www.nanoimaging.de/ → homepages → Sample requirements• Non-hardening VECTASHIELD Mounting Medium with RI of 1,44.• ProLong Gold (Life Technologies). Be aware this embedding medium needs 2 day to harden (in order to reach a constant RI). In addition your sample may shrink during this process. Refractive index (RI) of the cured product: 1,46.• SlowFade Gold (Life Technologies) with RI of 1,42.• 2,2’-thiodiethanol (TDE) – aqueous. RI can be varied ranging from being that of water (1.33) to that of immersion oil (1.52) by appropriately diluting with water.NoteIn order to have stable imaging conditions, especially concerning the RI of the mounting medium we would urge you to prepare the slides at least one week before use. Please note, that during curing the RI will rise.General Guidelines for Photoactivated Localization Microscopy (PALM)Sample supportSamples for PALM and dSTORM are ideally prepared onNunc Lab-Tek chambers or glass bottom dishes with coverglass thickness No 1.5.• Nunc Lab-Tek II Chambered Coverglass no 1.5(order no.: 155409)• Greiner Bio-One (order no.: 672860 CELLview)• Nunc Lab-Tek chambers (order no.:P35G-0.17-10-C or P35G-0.17-14-C)All these formats will fit into the adjustable piezo stageinsert.Please be aware, in order to obtain ideal TIRF illuminationyou need a sufficient mismatch in refractive indices.In addition you may want to change imaging bufferconcentration therefore we recommend no embeddingafter fixation of PALM and dSTORM samples.NoteNunc Lab-Tek chambers are well suited to transport samples between labs. They fit perfectly into a 50 ml Falcon tube, that can be filled with PBS and sealed with parafilm.Figure 2ZEISS level adjustable piezo stage insertFluorescent Proteins for PALMThe advantage of photoswitchable fluorescent proteins (PS-FPs) lies in their outstanding specificity and their small size, which is around 2 nm. The latter feature potentially allows for high labeling densities. Among photo-switchable proteins photoconvertable ones are the easiest to use as they - and the structure they stain – can be visualized at a different spec-tral range before conversion. Also one can easily check transfection efficiencies and expression levels. E.g. tdEOS or mEOS can be checked in the green spectral range, while the PALM experiment will be carried out detecting photo-switched EOS molecules in a more red shifted spectral band. Therefore tdEOS or its monomeric variant mEOS have been in extensive use as they also yield reasonable photon numbers.Figure 3 Examples of fluorescent proteins that can be used for PALM imagingMost of these proteins are available through e.g. Addgene, Cambridge, MA 02139, USARecommended FP-Pairs for dual-color PALMPair 1: mEOS2 + DronpaPair 2: NeonGreen + PA-mCherry1Pair 3: Padron + DronpaIf two differently stained molecules in the same sample are subjected to PALM, it is advisable to first image the higher wavelength dye as this does not cross-talk into the shorter wavelength channel. Hence, cross-excitation of the longer wavelength dye by the shorter wavelength and cross-emission of the shorter wavelength dye into the longer wave-length channel are minimized. Under experimental conditions many molecules of the shorter wavelength dye are equally activated with the 405 nm laser line, that is used for conversion / PALM imaging of the longer wavelength dye. Therefore reversible switchable fluorophores are the preferred choice for the shorter wavelength as they can be recov-ered and are not irretrievably lost.Recommended Dyes for dSTORMdSTORM uses organic dyes, which can be switched by employing reducing agents in the buffer. Dyes with a high photon yield per switching event, low on-off duty cycles, high survival fraction and a large number of switching cycles are preferential. Many Xanthene, Coumarin and Cyanine derivatives fulfill these criteria. Companies often trade these under special group names like Bodipy, Alexa Fluor (both from Invitrogen), Atto (from Sigma Aldrich), DyLight fluor (from Thermo Scientific) and FluoProbes (from Interchim). In the literature, Alexa 647 has been mostly used as it has proved to be a dye that matches all imaging criteria very well and can be switched easily between the dark and bright state.The influence of the selected fluorophore has been nicely shown by G.T. Dempsey et al. (Evaluation of fluorophoresfor optimal performance in localization-based super-resolution imaging, Nature Methods 2011). Please see below an extract of the data from this publication.Figure 4 (a-c): Effect of number of photons per on-switching event and the on-off duty cycle on STORM image quality for an example structure(a ring-like object). (p, t and x): Images of CCPs (clathrin-coated pits) using Alexa Fluor 647 (p), Atto 655 (t) and Cy5.5 (x). Shown are compositex–y cross-sections for ten CCPs aligned to their respective centers of mass along with the radial density distributions of localizations derived fromthe composite x–y cross-sections. Scale bars: 100 nm.For a multicolor experiment any combination between Atto 488, Cy3B/ Alexa 561 and Alexa 647/ DyLight 654will work. Specifically the combination of Atto 488 with Alexa 647 has proven to be useful (see G.T. Dempsey et al.: Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging, Nature Methods 2011) Recommendations for double labeling: Atto 488 + Atto 565 or Alexa 647A nice option could also be: tdEOS/mEOS + Alexa647Antibodies for dSTORMIf we consider the size of antibodies it is preferable to do direct antibody labeling without a primary and secondary AB. Smaller antibodies such as nanobodies (cameloid like antibodies from camels, llamas and sharks) with a size in the range of 2 nm may be preferred.Post-fixation Following Antibody LabelingA post-fixation step can be valuable. Herein, you fix cells a second time after staining in order to improve the stability of the label. This can prevent the label from detaching and floating the imaging medium.Imaging Buffer dSTORM(please see: G.T. Dempsey, Nature Meth 2011 and S. van Linde, Nature Meth 2011)It is recommended to freshly prepare the imaging buffer every day. The oxygen scavenger system will only last for a few hours and is mainly needed for Cyanine dyes like Alexa 647 and Cy5 (Rhodamines and Oxazines do not require it.)Imaging Buffer100µl PBS 10x (Phosphate buffered saline ; e.g. from Sigma: D1408)100µl MEA (Cysteamine Hydrochloride) stock concentration 1M (e.g. from Sigma M6500-25G) (toxic!!)Optional:Oxygen scavenger system (for cyanine dyes)500µl Glucose 20% (e.g. in solution from Sigma (49163-100ML))25µl Glucose Oxidase (e.g. 24 mg/ml GluOx from Aspergillus niger; Sigma G0543-50KU)5µl Catalase (e.g.: 12.6 mg/ml Catalase from Bovine liver; Sigma C3155-50 MG)Add H2O to final volume of 1mlVery important: adjust pH to 7.5-8.5 with 5 M NaOH or 4.5 M KOH(To test pH you can use pH paper in the indicated range)Note1 M MEA (Sigma M6500-25G): 0,113g in 1mlStore MEA (solid) at 4ºC. Prepare fresh as 1 M working stock solution in water. This stock can be kept at 4ºC and used within 1-2 weeks of preparation. Alternatively you can freeze small aliquots at -20ºC and keep them for several months.The MEA concentration depends highly on the fluorophore and the experimental condition. Therefore the best concentration has to be tested by trial and error for each sample (between 10mM and 100mM).If you use Cysteamine (not the Hydrochloride) you have to use 37% HCl to adjust the pH to 7.5-8.5As a general advice:• If blinking rates are too high: increase MEA concentration and/or increase pH• If blinking rates are too low: decrease MEA concentration and/or decrease pHFiducial Markers for Localization MicroscopyFiducial Markers (FM) are used to:1) correct for small drifts (tens of nanometer range) during the course of an experiment2) to align channels in multicolor experiments3) to correct for chromatic aberrations in multicolor experiments4) serve as a reference for the software autofocusFor PALM imaging we recommend fluorescent beads (e.g. Tetraspek beads from Invitrogen) or gold nanoparticles, dependant on the laser power, as fiducial markers. Choose the beads according to the fluorophores used in your experiment.For dSTORM imaging we recommend nanoparticles (gold colloids) of which the photoluminescence persists through the entire measurement and which should be immobilized on the coverslip.Ideally one has about 1-3 fiducial markers in the field of view. You can either:I) fix fiducial markers on coverslip before seeding of cells:• sonicate FM solution in an ultrasound bath for at least 5 min• add FM (100nm, BBI 1:1000 diluted) to dH2O solution• vortex FM solution• coat cover slips with Poly-L-lysine (PLL) for adhesion of FM• add FM Mix solution onto PLL coated cover slips• check density (1-3 FM in field of view)• prepare sample on the slideII) add fiducial markers to sample before experiment• sonicate FM solution• add 2-5µl FM solution into 2ml dH2O and vortex several minutes• apply onto fixed cells• wait ~1h for FM to settleNoteOnly dissolve FM in water (no salt solution), otherwise they will aggregate and not stick to the coverslip.Fiducial Marker Order Info• BB International [] & []Gold Colloid 80 nm (Cat. No. GM.GC 80) and 100 nm (Cat. No. GM.GC 100)• Microspheres-Nanospheres (/)40 nm (Cat. No. 790122-010) and 80 nm (Cat. No. 790120-010) nanospheres Au particles• Nanopartz []Nanorods 550 (Cat. No. 30-25-550) and 600 (Cat. No. 30-25-600)• Tetraspek Beads []Fiducial coverslips• Hestzig LLC ()• Fiducial coverslips 500 ± 50 nm spectral range at two densities (Cat. No. 550-30AuF & 550-100AuF);• Fiducial coverslips 600 ± 100 nm spectral range at two densities (Cat. No. 600-30AuF & 600-100AuF)Literature ReferencesReviews on Superresolution Microscopy1. Patterson G, Davidson M, Manley S, Lippincott-Schwartz J. (2010) Superresolution Imaging using Single-Molecule Localization.Annu Rev Phys Chem. 61:345-367.2. Huang B, Bates M, Zhuang X. (2009) Super resolution fluorescence microscopy. Annu Rev Biochem. 78:993-1016.3. Schermelleh L, Heintzmann R, Leonhardt H. (2010) A guide to super-resolution fluorescence microscopy. J Cell Bio.190(2):165-175.4. Toomre D, Bewersdorf J. (2010) A new wave of cellular imaging. Annu Rev Cell Dev Biol. 26:285-314.Superresolution Structured Illumination Microscopy (SR-SIM)1. Schermelleh L, Carlton PM, Haase S, et al. (2008) Subdiffraction Multicolor Imaging of the Nuclear Periphery with 3D StructuredIllumination Microscopy. Science 320(5881):1332-1336.2. Gustafsson MGL, Shao L, Carlton PM, et al. (2008) Three-Dimensional Resolution Doubling in Wide-Field Fluorescence Microscopyby Structured Illumination. Biophys J. 94(12):4957-4970.Sample Preparation and Fluorophore Properties for dSTORM1. Dempsey GT, Vaughan JC, Chen KH, Bates M, Zhuang X. (2011) Evaluation of fluorophores for optimal performance inlocalization-based super-resolution imaging. Nat Methods. 8(12):1027-1036.2. Van de Linde S, Löschberger A, Klein T, Heidbreder M, Wolter S, Heilemann M, Sauer M. (2011) Direct stochastic opticalreconstruction microscopy with standard fluorescent probes. Nat Protoc. 6(7):991-1009.Fluorescent Proteins for PALM1. Kremers GJ, Gilbert SG, Cranfill PJ, Davidson MW, Piston DW. (2011) Fluorescent proteins at a glance. J Cell Sci. 15;124:157–60.2.Patterson GH. (2011) Highlights of the optical highlighter fluorescent proteins. J Microsc. 243:1–7.3. Shroff H, White H, Betzig E. (2013) Photoactivated Localization Microscopy (PALM) of adhesion complexes.Curr Protoc Cell Biol. Chapter 4:Unit4.21.Further ReadingBiological NanoImaging (Prof. Rainer Heintzmann); http://www.nanoimaging.deBiozentrum Würzburg University (Prof. Markus Sauer); http://www.super-resolution.biozentrum.uni-wuerzburg.deZEISS Online Campus; /campusThis collection of protocols and information on superresolution sample preparation is just a general guideline.While all attempts are made to provide accurate, current and reliable information we cannot guarantee that theprotocols will work or that the information contained here will be error-free.Carl Zeiss Microscopy GmbH 07745 Jena, Germany BioSciences******************** /elyra E N _ 4 1 _ 0 0 1 _ 0 6 5 | C Z 1 2 -2 0 1 3 | D e s i g n , s c o p e o f d e l i v e r y a n d t e c h n i c a l p r o g r e s s s u b j e c t t o c h a n g e w i t h o u t n o t i c e . |©C a r l Z e i s s M i c r o s c o p y。
智能驾驶行业：智能驾驶计算芯片性能评测标准化白皮书
2023.9目录一、智能驾驶计算芯片产业现状 (3)1、国内外智能驾驶计算芯片发展情况 (3)2、智能驾驶计算芯片应用需求 (7)3、智能驾驶计算芯片标准需求 (8)二、智能驾驶计算芯片标准与评测 (9)1、智能驾驶计算芯片国内外政策和标准现状 (9)2、智能驾驶计算芯片性能评测标准 (11)3、智能驾驶计算芯片标准典型应用案例 (16)三、总结与展望 (28)四、参考文献 (29)一、智能驾驶计算芯片产业现状汽车产业正在被人工智能技术重构。
如同蒸汽机之于工业革命的意义，智能驾驶已经成为人类社会自发明汽车以来的一大颠覆性创新，持续推动汽车产品、整车市场格局和产业链变革，而数据和算力是驱动汽车智能化加速的两大动力。
关于智能驾驶发展的趋势，业内普遍认同的观点是：智能驾驶汽车将在2025年前后开始一轮爆发式增长。
智能驾驶汽车在传统驾驶的电子电气架构基础上，引入基于智能驾驶芯片的智能驾驶模块，搭载各类车载传感器、控制器、执行器等装置，并融合了现代通信、网络和计算技术，使得车辆具备复杂环境感知、智能决策、协同控制等功能，从而大大提升驾驶的自动化和智能化。
未来，汽车将从最常用的交通工具变成最大的智能终端，具备高度电动化、网联化、智能化、共享化的特征，传统汽车企业势必将重新定义和塑造自身的商业模式，传统汽车行业的市场也将向芯片厂商、互联网科技公司、造车新势力等逐步打开，生态格局不断走向开放。
1、国内外智能驾驶计算芯片发展情况——车载计算芯片成为行业竞争热点，国内外企业竞相发力随着智能驾驶技术的不断发展和汽车市场的逐渐开放，作为汽车智能化的核心，智能驾驶芯片的发展在全球范围内日益瞩目，市场也呈现出井喷式的增长态势。
除了传统的汽车制造商，科技公司也开始在智能驾驶芯片市场布局。
例如，英伟达的智能驾驶芯片“Drive”已经被包括奔驰、特斯拉和沃尔沃在内的多家汽车制造商采用。
此外，谷歌旗下的Waymo、苹果、百度和滴滴也都在智能驾驶芯片领域进行了大量尝试。
iMaster NCE-Campus VxLAN技术白皮书
iMaster NCE-Campus VxLAN技术白皮书文档版本01发布日期2020-07-08目录1 概述 (1)1.1 产生背景 (1)1.2 技术/方案特点 (2)1.3 关键术语解释 (2)2 关键技术介绍 (3)2.1 SDN网络架构 (3)2.1.1 SDN网络架构的三层模型 (3)2.1.2 SDN网络的优势 (4)2.2 OverLay网络技术 (4)2.3 VXLAN技术介绍 (5)2.3.1 VXLAN技术背景 (5)2.3.2 VXLAN技术原理 (7)2.4 VXLAN方案及其实现原理介绍 (10)2.4.1 分布式VXLAN方案实现原理 (11)3 华为iMaster NCE-Campus整体方案介绍 (13)3.1 整体方案关键部件 (13)3.2 iMaster NCE-Campus典型组网 (14)3.2.1 分布式VXLAN网络组网 (14)3.2.2 集中式VXLAN网络组网 (15)3.2.3 集中式VLAN组网 (16)3.2.4 用户网关在Fabric外VXLAN组网 (17)3.3 Fabric出口组网能力的多样性 (18)3.3.1 外部网络类型 (18)3.3.2 边界网关节点与外部设备的典型组网 (20)3.4 iMaster NCE-Campus典型业务流量 (24)3.4.1 访问DHCP等公共服务资源 (24)3.4.2 访问internet外部网关 (25)3.4.3 虚拟网络（VN）之间互访 (26)3.4.4 用户网关在外部网关设备 (27)3.5 用户接入典型组网 (27)3.5.1 有线用户认证 (27)3.5.2 无线用户认证 (29)3.6 iMaster NCE-Campus方案特点 (30)3.6.1 子网网段赋予业务属性 (30)3.6.2 网络按需实时交付 (32)3.6.3 有线无线深度统一 (33)1 概述1.1 产生背景1.2 技术/方案特点1.3 关键术语解释1.1 产生背景随着企业业务的快速发展和园区网络全面数字化转型的推进，企业园区网络的业务也越来越复杂。
NVIDIA A2 Tensor Core GPU 产品简介说明书
NVIDIA A2 Tensor Core GPU Product BriefDocument HistoryPB-10727-001_v01Version Date Authors Description of Change01 November 8, 2021 AV, SM Initial ReleaseTable of ContentsOverview (1)Specifications (2)Product Specifications (2)Environmental and Reliability Specifications (4)Airflow Direction Support (5)Product Features (6)PCI Express Interface Specifications (6)PCIe Support (6)Polarity Inversion and Lane Reversal Support (6)CEC Hardware Root of Trust (6)Form Factor (6)Hockey Stick Board Retention (8)Support Information (9)Certifications (9)Agencies (9)Languages (10)List of Figures Figure 1.NVIDIA A2 NVFF 5.0 HHL with Full Height Bracket (1)Figure 2.NVIDIA A2 Airflow Direction (5)Figure 3.NVIDIA A2 PCIe Card Dimensions with Full Height Bracket (7)Figure 4.NVIDIA A2 PCIe Card Dimensions with Low Profile Bracket (7)Figure 5.NVIDIA A2 Removable Hockey Stick Tab (8)Figure 6.NVIDIA A2 Removable Hockey Stick Tab Assembly (8)List of Tables Table 1.Product Specifications (2)Table 2.Memory Specifications (3)Table 3.Software Specifications (3)Table 4.Board Environmental and Reliability Specifications (4)Table nguages Supported (10)OverviewThe NVIDIA A2 Tensor Core GPU is a compact, lower power product, that delivers entry-level acceleration for Deep Learning, Graphics and Video processing in any server. It is a half-height (low profile), half-length, single slot card featuring 16 GB of GDDR6 memory and a 60 W maximum power limit. The A2 supports x8 PCIe Gen4 connectivity. It is a passively cooled card with a superior thermal design that requires system airflow to operate and handles challenging ambient environments with ease (NEBS-3 capable).The NVIDIA A2 is powered by the NVIDIA Ampere Architecture. It provides revolutionary multi-precision performance to accelerate deep learning and machine learning training, as well as inference, video transcoding, AI audio and video effects, rendering, data analytics, virtual workstation, virtual desktop, and many other workloads.As part of NVIDIA AI, the A2 supports all AI frameworks and network models, delivering dramatic performance and efficiency that maximizes the utility of at-scale deployments. Figure 1. NVIDIA A2 NVFF 5.0 HHL with Full Height BracketSpecificationsProduct SpecificationsTable 1 through Table 3 provide the product, memory, and software specifications for the NVIDIA A2 PCIe card.Table 1. Product SpecificationsSpecification NVIDIA A2Product SKU PG179 SKU 220NVPN: 699-2G179-0220-xxxTotal board power 60 W default60 W maximum40 W minimumThermal solution PassiveMechanical Form Factor HHHL-SS (half-height, half-length, single-slot)PCI Device IDs Device ID: 0x25B6Vendor ID: 0x10DESub-Vendor ID: 0x10DESub-System ID: 0x157EFour part ID (VID:DEVID:SVID:SSID)110DE:25B6:10DE:157EGPU clocks Base: 1440 MHzMaximum boost: 1770 MHzVBIOS EEPROM size: 16 MbitUEFI: SupportedPCI Express interface Physical x8 PCIe lanesPCIe Gen4 x8, x4; Gen3 x8Lane and polarity reversal supported Performance States P0, P8Secure Boot SupportedZero Power Not supportedNEBS readiness SupportedWeight Board: 260 Grams (excluding bracket)Bracket (Full height) with screws: 14 GramsBracket (Half height) with screws: 9 Grams Note:1The NVIDIA A2 is uniquely identified by its complete four part ID.Table 2. Memory SpecificationsSpecification DescriptionMemory clock 6251 MHzMemory type GDDR6Memory size 16 GBMemory bus width 128 bitsPeak memory bandwidth 200 GB/secTable 3. Software SpecificationsSpecification Description1SR-IOV support Supported: 16 VF (virtual functions)BAR address (physical function) BAR0: 16 MiB1BAR1: 16 GiB1BAR3: 32 MiB1BAR address (virtual function) BAR0: 4 MiB (256 KiB per VF)1BAR1: 32 GiB, 64-bit (2 GiB per VF)1BAR3: 512 MiB, 64-bit (32 MiB per VF)1Message signaled interrupts MSI-X: SupportedMSI: Not supportedARI Forwarding SupportedDriver support R470.82 (or later)CEC firmware v6.07 (or later)NVIDIA® CUDA® support CUDA 11.1 (or later)Virtual GPU software support Supports vGPU 14.0 (or later)NVIDIA-Certified Systems™ Program NVIDIA-Certified Systems v2.5 (or later)PCI class code 0x03 – Display ControllerPCI sub-class code 0x02 – 3D ControllerECC support Enabled (by default); can be disabled using softwareSMBus (8-bit address) 0x9E (write), 0x9F (read)SMBus direct access SupportedSMBPBI (SMBus Post-Box Interface) SupportedNote:1The KiB, MiB, and GiB notation emphasizes the “power of two” nature of the values. Thus,•256 KiB = 256 x 1024•16 MiB = 16 x 10242•64 GiB = 64 x 10243Environmental and Reliability Specifications Table 4 provides the environment conditions specifications for the A2 PCIe card.Table 4. Board Environmental and Reliability SpecificationsSpecification DescriptionAmbient operating temperature 0 °C to 50 °CAmbient operating temperature (short term)3, 4-5 °C to 55 °CStorage temperature -40 °C to 75 °COperating humidity (short term)35% to 93% relative humidityOperating humidity 5% to 85% relative humidityStorage humidity 5% to 95% relative humidityMean time between failures (MTBF) Uncontrolled environment:1 TBD hours at 35 °CControlled environment:2 TBD hours at 35 °C Notes:1Some environmental stress with limited maintenance (GF35).2No environmental stress with optimum operation and maintenance (GB35).3A period not more than 96 hours consecutive, not to exceed 15 days per year.4Short-term operating conditions include 55 °C at 6000 feet, per NEB’s objective. Cooling guidance approximates for sea-level non-pressure-controlled testing.Airflow Direction SupportThe NVIDIA A2 PCIe card employs a bidirectional heat sink, which accepts airflow either left-to-right or right-to-left directions.Figure 2. NVIDIA A2 Airflow DirectionProduct FeaturesPCI Express Interface SpecificationsThe following subsections describe the PCIe interface specifications for the A2 PCIe card. PCIe SupportThe A2 card supports PCIe Gen4. Gen4 x8 interface should be used when connecting to the A2 PCIe card.Polarity Inversion and Lane Reversal SupportLane Polarity Inversion, as defined in the PCIe specification, is supported on the A2 PCIe card. Lane Reversal, as defined in the PCIe specification, is supported on the A2 PCIe card. When reversing the order of the PCIe lanes, the order of both the Rx lanes and the Tx lanes must be reversed.CEC Hardware Root of TrustThe NVIDIA A2 provides secure boot capability via CEC. Implementing code authentication, rollback protection and key revocation, the CEC device authenticates the contents of the GPU firmware ROM before permitting the GPU to boot from its ROM.It also provides out-of-band (OOB) secure firmware update, secure application processor recovery, and remote attestation.Form FactorIn this product brief, nominal dimensions are shown.Figure 3. NVIDIA A2 PCIe Card Dimensions with Full Height BracketFigure 4. NVIDIA A2 PCIe Card Dimensions with Low Profile BracketHockey Stick Board RetentionThe NVDIA A2 enables south edge board retention using a “hockey stick” tab located to the east of the PCIe card fingers, as shown in Figure 5. The A2 hockey stick tab is optional. A2 hockey stick tab assembly is depicted in Figure 6.Figure 5. NVIDIA A2 Removable Hockey Stick TabFigure 6. NVIDIA A2 Removable Hockey Stick Tab AssemblySupport InformationCertifications④Windows Hardware Quality Lab (WHQL):•Certified Windows 7, Windows 8.1, Windows 10•Certified Windows Server 2008 R2, Windows Server 2012 R2④Ergonomic requirements for office work W/VDTs (ISO 9241)④EU Reduction of Hazardous Substances (EU RoHS)④Joint Industry guide (J-STD) / Registration, Evaluation, Authorization, and Restriction ofChemical Substance (EU) – (JIG / REACH)④Halogen Free (HF)④EU Waste Electrical and Electronic Equipment (WEEE)Agencies④Australian Communications and Media Authority and New Zealand Radio SpectrumManagement (RCM)④Bureau of Standards, Metrology, and Inspection (BSMI)④Conformité Européenne (CE)④Federal Communications Commission (FCC)④Industry Canada - Interference-Causing Equipment Standard (ICES)④Korean Communications Commission (KCC)④Underwriters Laboratories (cUL, UL)④Voluntary Control Council for Interference (VCCI)Support Information LanguagesTable 5. Languages SupportedLanguages Windows1LinuxEnglish (US) Yes YesEnglish (UK) Yes YesArabic YesChinese, Simplified YesChinese, Traditional YesCzech YesDanish YesDutch YesFinnish YesFrench (European) YesGerman YesGreek YesHebrew YesHungarian YesItalian YesJapanese YesKorean YesNorwegian YesPolish YesPortuguese (Brazil) YesPortuguese (European/Iberian) YesRussian YesSlovak YesSlovenian YesSpanish (European) YesSpanish (Latin America) YesSwedish YesThai YesTurkish YesNote:1Microsoft Windows 7, Windows 8, Windows 8.1, Windows 10, Windows Server 2008 R2,Windows Server 2012 R2, and Windows 2016 are supported.NoticeThis document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of aproduct. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of theinformation contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This documentis not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any timewithout notice.Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. 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智能穿戴 白皮书
智能穿戴白皮书报告导读2015年之后智能穿戴设备市场遭遇了几年的冷遇期，用户的高期待和产品的实用价值之间产生了明显落差，市场增速也从300%、200%突然掉落至不到30%。
2015年，曾经风光一时的Google Glass项目被叫停，2017年转型至行业应用2015年9月，Apple Watch Series2上市，而当时还未找到产品方向的苹果智能手表的一项噱头是提供配套的爱马仕表带2016年，曾经的行业宠儿Jawbone宣布停产智能手环，行业进入寒冬。
明星项目Pebble则被过去的竞争对手Fitbit收购，而Fitbit自己也是股价一路下坡。
2015年之后智能穿戴设备市场遭遇了几年的冷遇期，用户的高期待和产品的实用价值之间产生了明显落差，市场增速也从300%、200%突然掉落至不到30%。
2015年，曾经风光一时的Google Glass项目被叫停，2017年转型至行业应用2015年9月，Apple Watch Series2上市，而当时还未找到产品方向的苹果智能手表的一项噱头是提供配套的爱马仕表带2016年，曾经的行业宠儿Jawbone宣布停产智能手环，行业进入寒冬。
明星项目Pebble则被过去的竞争对手Fitbit收购，而Fitbit自己也是股价一路下坡。
关于我们全行业报告圈旗下全行业报告库是一家专注于分享国内外各类行业研究报告/专题热点行业报告/白皮书/蓝皮书/年度报告等各类优质研究报告分享平台。
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台湾小学奈米科技应用于环境议题之漫画自学教材研究
台湾小学奈米科技应用于环境议题之漫画自学教材研究台湾小学奈米科技应用于环境议题之漫画自学教材研究1.台北市立教育大学应用物理暨化学系 2.台北市立五常小学吴月娥1黄如骏2[摘要]环境问题与新兴的奈米科技，是目前世界各地两大议题。
奈米科技教育的往下扎根与环境教育法的重视，让我们联想到能否利用奈米科技的发展，以解决部分的环境问题。
藉由对奈米科技基础概念的深入了解，选定相关的环境议题，发展台湾小学教师奈米科技问卷，并做问卷分析整理。
由此，我们试着以漫画方式，发展一套完整的自学教材，希望能够藉由奈米科技概念的介绍，延伸至利用奈米科技应用于环境议题，甚至可藉以减缓环境日益恶化的现象。
[关键词]奈米科技环境议题气候变迁0前言经济起飞固然是好事，然而经济发展大量依赖化石产物的结果，不但加速化石资源的耗竭，也产生大量的温室气体，使得全球暖化效应快速恶化。
近年来更因为自然资源的大量损耗与污染，导致气候变迁与环境恶化；全球各地陆续传出气候极端异常现象，使得我们必须面对环境问题日益严重的课题。
因此，如何因应能源危机，减缓环境的变迁、促使环境的永续发展，成为目前各国政府积极思考的方向。
奈米科技也是现代科技努力的新方向，且其所涵盖的范围甚广。
近十年来各国积极发展奈米科技，也能展现出亮眼的成绩；更由于其性质之特殊，需要各领域跨界合作，才能使其成果更臻完美，故奈米科技教育的往下扎根实属需要。
由于物质奈米化而产生的特殊效应，若能应用在环境议题上，或许可以减缓环境的污染，减少资源的浪费。
漫画是一种男女老少皆宜的创作品，以图像为主，文字为辅，利用图片与文字的搭配传达知识，长久以来便深受国小学童的喜爱。
我们将奈米科技应用于环境议题的想法，以漫画方式呈现。
漫画教材的内容，主要由回收问卷的分析整理，蒐集教师对奈米科技教育的概念与教学需求，发展一套奈米科技漫画辅助教材。
1奈米科技融入环境议题1.1台湾的奈米科技教育台湾的奈米科技相关政策始于2000年，2002年由有关行政与科研部门组成奈米技计画工作小组，并成立奈米科技计画办公室。
jetson nano认证标准
文章标题：探索Jetson Nano认证标准的深度和广度Jetson Nano作为一款人工智能计算机，具有广泛的应用前景和巨大的市场需求。
为了确保它的质量和可靠性，在市场上它需要通过一系列的认证标准来验证其性能和稳定性。
本文将从多个角度探讨Jetson Nano认证标准的深度和广度，以便读者能更好地理解其重要性和影响。
一、 Jetson Nano认证标准的概述Jetson Nano认证标准是一系列涵盖性能、稳定性、兼容性、安全性等方面的标准，用于对Jetson Nano进行严格的评估和验证。
这些标准涉及到硬件和软件两个方面，包括处理器性能、系统稳定性、接口兼容性、供电需求、系统安全等多个方面的要求。
二、 Jetson Nano认证标准的重要性Jetson Nano认证标准的制定和执行，对于确保Jetson Nano的质量和可靠性至关重要。
它不仅可以帮助客户更好地选择适合他们需求的产品，也可以促进市场竞争力和行业规范化。
符合认证标准的产品还可以获得更多的市场推广和认可。
三、 Jetson Nano认证标准的具体内容1.性能要求：Jetson Nano在处理复杂计算任务和图形处理时，需要具备一定的性能水平，以满足用户对高性能计算的需求。
2.稳定性测试：Jetson Nano在长时间运行和高负载条件下需要保持稳定，不出现崩溃或性能下降的情况。
3.接口兼容性：Jetson Nano对于外部设备和接口的兼容性也是认证标准的一部分，包括HDMI、USB、以太网等接口的正常工作和稳定性。
4.供电需求：认证标准也会对Jetson Nano的供电需求进行测试和验证，确保其在正常使用情况下的稳定供电。
5.系统安全：考虑到人工智能应用的安全需求，认证标准还会对Jetson Nano的系统安全性进行测试和验证，以保护用户数据和隐私。
四、对Jetson Nano认证标准的个人观点和理解作为一名人工智能计算机的爱好者和研究者，我认为Jetson Nano认证标准的制定和执行非常必要。
PC特工的自我修养 HP Spectre x2二合一可拆分本
PC特工的自我修养 HP Spectre x2二合一可拆分本
SwaT+Yu
【期刊名称】《微型计算机》
【年(卷),期】2016(0)5
【摘要】在“六代目”Skylake的支持下．PC超根本们的性能参数无可厚非地愈发强悍,然而除了性能的提升,超极本的外观、使用方式也在不断被尝试改进并精心
打磨。
在近期上市的二合一本之中,就有一款邦德范儿十足的新品。
“极客不打码”视频组特地奉上视频评测,让我们来一探究竟,这款HP Spectre x2到底素质如何吧!【总页数】2页(P36-37)
【关键词】二合一;PC;HP;修养;拆分;性能参数;视频
【作者】SwaT+Yu
【作者单位】
【正文语种】中文
【中图分类】TP36
【相关文献】
1.拆分的真谛：HP Envy x2可拆分式笔记本电脑 [J],
2.可拆分笔记本推荐PC平板二合一 [J],
3.酷睿M7与双Type-C惠普Spectre x2 [J], 贺鹏
4.“超极”之外HP Pavilion TouchSmart 11、HP Split X2 [J], 王欣
5.通过英特尔Evo平台认证 11代酷睿全新加持的HP Spectre x360 [J], 王健鹏
因版权原因，仅展示原文概要，查看原文内容请购买。
惠普品牌 介质测试 白皮书
惠普非常清楚，在不同磁带机之间交换介质以及一些外部环境会严重影响磁带的误码率，而误码率升高会对备 份乃至企业的业务连续性造成威胁。为了保护客户不会深受高误码率的困扰，惠普对每批介质都进行了严格的测试。
1
图 1.
HP Branded Media Continuous Quality 惠普品牌介质持续质量测试(CQT)
超越行业标准
当前的行业标准格式规范，如线性磁带开放(LTO)技术提供商（TPC—惠普、IBM 和昆腾）采用的格式规范，只 能够确保在不同磁带机之间无缝地进行介质交换。这些规范旨在确保由符合格式规范的提供商出售的数据磁带可在 客户的磁带机上正常运行，而且写入磁带的数据可在不同类型的硬件之间进行传输。
然而，行业标识测试一般不能够衡量产品的质量或可靠性，因为这需要行业制定真正的“开放式”标准，使客 户除了兼容性因素之外，还能够从成本、价值和性能等其它方面进行考虑，获得更为广泛的产品选择。因此，许多 用户并不知道，厂商若想满足 Ultrium 或 DAT 格式规范，并不需要执行任何深入的环境或动态测试来衡量磁带系统 的质量性能和可靠性。
全卷“绿色磁带”测试 .......................................................................................................................... 6 全卷“寿命”测试 ................................................................................................................................. 6 严格的环境适应性测试 ..................................................................................................................... 6 严格的跌落测试 ................................................................................................................................ 7 广泛的“加载”/“卸载”操作测试 ......................................................................................................... 7 查找/倒带/添加测试 .......................................................................................................................... 8 大规模运输与存储 ............................................................................................................................ 8 摩擦测试，实现高负载循环自动化 ................................................................................................... 8 可靠的互换测试 ................................................................................................................................ 8 测试可以确保出色的归档稳定性 .............................................................................................................. 8 总结.......................................................................................................................................................... 9 更多信息 ................................................................................................................................................ 10
白皮书：高性能集成电路设计的技术趋势和挑战说明书
WHITE PAPER Introduction Applications such as deep-learning, autonomous driving vehicles, and mobility on 5G networks fuel the need for continuous advancements in IC integration. Growing design complexity,pressure on design cycle time, process advancements and increasing verification requirementsare driving the need for faster, more efficient physical verification flows. The current state-of-the-art FinFET processes at 7nm and 5nm are complex feats of engineering. As has been the ‘law’for some time, IC manufacturers can fit more and more transistors into the lithography reticlelimit. For example, at 16nm, a typical 80 mm2 die has approximately 2 billion transistors, while at5nm the same size die has over 12 billion transistors. Foundries utilize complicated front-end-of-line layer stacks and deploy multi-patterning lithography on many masks. This means more andmore masks are required for advanced processes.100,00010,0001,000100180190906555Technology 45281675DRC RulesDRC OperationsFigure 1: Increasing DRC complexityThe increase in density, plus the added number and complexity of process layers means that asdesigners migrate from older nodes to 7nm and 5nm physical verification has the potential to bemore and more of a bottleneck for tapeout. Expect complaints such as:• “Physical verification runs take too long for us to do overnight runs.”• “Physical verification takes too many resources.”• “DRC runs (particularly on early/dirty designs) are difficult to debug.”AuthorsRon DuncanSr. Manager ApplicationsEngineering, Synopsys Accelerating Physical Verification Productivity for Advanced Node Designs with IC ValidatorProductivity Improvements for Physical VerificationHere are three approaches to improve physical verification productivity:• Run early and “clean as you go” during the IP and block-level design• Run full chip verification efficiently to rapidly converge on a clean design that is ready for tapeout• Run on more CPU resources to shorten signoff runsSeamless verification during IP and block level design“Cleaning as you go” is a concept that can streamline many of life’s processes (think: washing dishes as you cook). Running DRCat each stage as you build your design is a simple, yet obvious way to avoid last-minute surprises that might impact your abilityto hit the tapeout date. IC Validator’s Fusion Technology TM integrates advanced flows, such as design rule checking (DRC), layout versus schematic checking (LVS), timing-aware fill, programmable electrical rule checking (PERC) with automatic place and route and custom design.After analog and custom layout creation, designers typically find that complicated layouts and DRC rules make it difficult to converge on a DRC-clean design. IC Validator provides the ability to do a fast DRC check that covers all signoff checks in a small design, or a window of a larger design. Designers can:• Run with all qualified foundry runsets and all technology nodes• Run only the foundry rules that are of interest. For example: metal spacing violations• Run on only the area within the view windowNo longer does the design-then-check-then-fix loop take several minutes or several hours. Now, the layout tool sends the data within the view window to a streamlined IC Validator Live DRC engine. The signoff DRC checks are executed in a few seconds and any resulting violations can be viewed in an error viewer window within the layout editor tool and fixed immediately. Designers create the layout, run DRC checks, view violations and, fix them - all in one environment, and in just a few seconds.IC Validator supports full interoperability for job execution, layout error shape probing, schematic cross probing within Synopsys Custom Compiler TM and Cadence® Virtuoso®.Figure 2: Live DRC checking for custom design flowsEarly and efficient verification on the full design to rapidly converge on a clean design that is ready for tapeoutIC Validator Explorer DRC is designed to rapidly assess the status of a full chip design and give useful and actionable feedback to fix problems. Today’s large chips consist of hundreds of blocks, such as place and route blocks, analog cells, memory, third party IP, and I/O cells. While each may have been verified independently as they were designed, when complied into the full chip, there are often high-level problems, such as missing blockages, block placement errors, pad ring misalignment, or block revision control issues that must be identified and fixed. A handful of high-level issues are often exhibited as an unworkable number of low-level DRC violations. It’s common for a top-level designer run to encounter billions of DRC errors the first time the chip is compiled and run. This first “dirty” run may take multiple days on hundreds of cores to complete in a traditional DRC tool, as it brute-forces it’s way through detailed over-analysis. Obviously, this could cause the tapeout team to waste weeks of compute time at the very end of the tapeout cycle.IC Validator Explorer DRC automatically runs fundamental rules from the foundry runset and additional methodology to rapidly asses the health of the design. If the design is relatively clean, IC Validator continues progressively towards completing all required DRC signoff checks.Explorer DRC brings a dramatic change in compute efficiency on dirty designs versus the traditional flow: five times faster runtime with five times few cores used.In practice, this means that typical full-chip 7nm designs can be run using Explorer DRC in several hours with 16 or 32 cores, even when dirty. The Explorer DRC enables tapeout engineers to do an overnight run to detect fundamental design problems and begin fixing them immediately. IC Validator includes an error heatmap for rapid and intuitive visual topological assessment of your design. Within minutes, designers can often identify the macro-problems to fix (such as overlaps), instead of getting stuck in the weeds of billions of errors. The heatmap shows hot areas (where there are many violations) in red, progressing to cool areas (with relatively few violation) in blue. Often, as in the example below, designers immediately recognize where errors correspond to problems with specific blocks, such as overlaps or incorrect fill.Macro OverlapFILL-Signal Alignment IssueFigure 3: DRC heatmap highlighting error locations, density, and severityRun verification on more resources to shorten signoff runsAs tapeout deadlines near, tapeout engineers often need to reserve a large number of cores to ensure that final verification jobs have enough compute power to complete as quickly as they can. The compute requirements to tape out the largest FinFET chips are straining the IT infrastructure of many companies.The key to IC Validator’s ability to efficiently distribute a job across thousands of cores is its unparalleled scheduler that initializes and controls the process. The scheduler queues commands that will run on each core to optimize file locality with the check sequence. During the run it intelligently estimates and balances the memory needs across cores while minimizing peak disk usage. It dynamically monitors the load on each core and adjusts the system to improve core and memory utilization.That’s great when everything is working well, but what about when a job is run using a thousand cores on a heterogenous collection of hosts and disks connected by a network with real-world latency? IC Validator has fault-tolerant abilities to detect and recover from unexpected host reboots, network and socket failures, machine crashes or disk space limitations.Beyond the automation of the scheduler, IC Validator enables the user to manually change job resources during the run. Elastic CPU Management allows the user to start a job with a few cores, then add cores on-the-fly to accelerate the job execution. On a typical compute farm, a job requesting a few hundred cores might have to wait indefinitely to start until they all become available. Instead, a tapeout engineer can start a 200 or 300 core DRC run with, say 16 available cores and automatically add the rest as they become available.With such an efficient and scalable physical verification system you can get nearly any runtime you want. Want a faster run? Simply distribute across more cores. But what if your company’s farm just doesn’t have 1000 cores available for you to accelerate your job as you would like? Look to the cloud.IC Validator is proven “cloud-ready” physical signoff solution and has been deployed on the cloud for production tapeouts. The chart below shows how the runtime of a production 7nm design can be scaled down to less than a day by deploying more cores on the Amazon Web Services cloud.Figure 4: DRC runtime on AWS Cloud exampleIC Validator for Tapeout ProductivityDesign engineers will always want more performance from their tools to be able to meet tapeout schedules. IC Validator delivers productivity to accelerate physical verification time during IP and block-level design phases through to full-chip runs.To learn more about physical verification using IC Validator go to: /icvalidator©2019 Synopsys, Inc. All rights reserved. Synopsys is a trademark of Synopsys, Inc. in the United States and other countries. A list of Synopsys trademarks is availableat /copyright.html . All other names mentioned herein are trademarks or registered trademarks of their respective owners.。
白皮书 全新EMC VNX系列简介 VNX5200 VNX5400 VNX5600 VNX5800 VNX7600 VNX8000详细介绍 20131031
全新 EMC VNX 系列简介
VNX5200、VNX5400、VNX5600、VNX5800、VNX7600 和 VNX8000
详细介绍
摘要 本白皮书介绍了 EMC® VNX™ 系列平台。文中讨论了不同的 型号、新功能和改进的功能，以及主要优点。 2013 年 11 月
版权所有 © 2013 EMC Corporation。保留所有权利。 EMC 确信本出版物在发布之日内容准确无误。本出版物中的 信息可随时更改而不另行通知。 本出版物的内容按“原样”提供。EMC Corporation 对本出 版物的内容不提供任何形式的陈述或担保，明确拒绝对有特 定目的的适销性或适用性进行默示担保。 使用、复制或分发本出版物所描述的任何 EMC 软件都要有相 应的软件许可证。 有关 EMC 产品名称的最新清单，请参见 上的 EMC Corporation 商标。 VMware 是 VMware, Inc. 的注册商标。此处使用的所有其他 商标均为其各自所有者的资产。 部件号 H12145.1
I/O 模块 ................................................................................................................ 42
存储处理器的 I/O 模块 .................................................................................................... 42 Data Mover 的 I/O 模块.................................................................................................... 46
DSO Nano v2数字存储波形显示器用户手册说明书
↓ marker to higher level marker, the signal syncs. S means vice versa.
Press button A to freeze current display (status HOLD) and press again to resume refreshing (status RUN). When status is hold, you can move cursors to T0 and press up/down button to pan back and forth. Press OK button to display or hide X position marker (a yellow dotted vertical line)
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TRIGGERING MODE:
AUTO: Always refresh display, synchronize when triggered.
NORM (al): Display synchronized waveform when triggered, blank if not triggering.
DSO NANO V2 MANUAL V0.9B
8/25/2010 Original Design: Chai Xiaoguang Documentation: Eric Pan
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INTRO
DSO Nano v2 is a Digital Storage Oscilloscope designed for basic electronic engineering tasks. Within its smart shell, the device runs on ARM Cortex™-M3 32 bit platform, provides basic waveform monitoring with extensive functions. It is equipped with 320*240 color LCD, micro SD card storage, portable probes, LiPo Battery, USB connection and signal generator. Due to palm size and handy performance, it fits in-field diagnosis, quick measurement, hobbyist projects and wherever convenience matters. Scheme and source files are also open for re-innovating.
白皮书 全新EMC VNX系列简介 VNX5200 VNX5400 VNX5600 VNX5800 VNX7600 VNX8000详细介绍 20131031
I/O 模块 ................................................................................................................ 42
存储处理器的 I/O 模块 .................................................................................................... 42 Data Mover 的 I/O 模块.................................................................................................... 46
白皮书
全新 EMC VNX 系列简介
VNX5200、VNX5400、VNX5600、VNX5800、VNX7600 和 VNX8000
详细介绍
摘论了不同的 型号、新功能和改进的功能，以及主要优点。 2013 年 11 月
版权所有 © 2013 EMC Corporation。保留所有权利。 EMC 确信本出版物在发布之日内容准确无误。本出版物中的 信息可随时更改而不另行通知。 本出版物的内容按“原样”提供。EMC Corporation 对本出 版物的内容不提供任何形式的陈述或担保，明确拒绝对有特 定目的的适销性或适用性进行默示担保。 使用、复制或分发本出版物所描述的任何 EMC 软件都要有相 应的软件许可证。 有关 EMC 产品名称的最新清单，请参见 上的 EMC Corporation 商标。 VMware 是 VMware, Inc. 的注册商标。此处使用的所有其他 商标均为其各自所有者的资产。 部件号 H12145.1
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VIA Nano X2 (1.2+)
GCC
Intel Atom D525 (1.8)
GCC
VIA Nano X2 (1.2+)
ICC
Intel Atom D525 (1.8)
ICC
696
500
891
725
799
582
955
725
SPECfp2000
SPECint2000
GNU (GCC)Compiler
Intel (ICC) Compiler
图1：SPEC CPU2000单线程性能测试（GCC 编译器及英特尔编译器的峰值速度比较）
2 Threads Int 2 Threads FP
3 Threads Int 3 Threads FP
4 Threads Int 4 Threads FP
12.6
10.8
14.4
12.7
16.7
15
17.114.516.7
14.416.5
14.317.9
14.9
23.6
19.3
30.1
24.1
2 Threads
3 Threads
4 Threads
图2：SPEC CPU2000多线程性能测试（GCC 编译器的峰值速率比较）
英特尔® Atom TM处理器结构图
编译器通过统计对指令进行配对，避免资源冲突，但是顺序执行的需求删除了跳跃处理指令流的能力，这样就无法充分利用可用的计算资源。
此外，顺序执行流水线不具备乱序架构的寄存器重命名功能，很难通过每周期内增加更多执行资源或获取
：威盛Nano TM处理器结构图
Nano处理器的其他高性能特性
计算机设计师的主要目标是平衡存储带宽和“运算带宽”，Nano架构拥有一系列存储和总线的高性能特性——大容量多通道缓存、先进的分支预测、数据预先获取等。
本白皮书对Nano架构的很多关键特性并无涉及（虚拟化、自适应电源管理、加密等）。
如需了解更多详情的读者，可登录下方网站Nano架构（Isaiah）首次发布时，。