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6G Typical Scenarios and Key Capabilities1. Introduction6G, the next generation of mobilemunication technology, is expected to bring about revolutionary changes in various aspects of our lives. In this article, we will explore the typical scenarios and key capabilities of 6G, and discuss how they will impact the future ofmunication and technology.2. Enhanced ConnectivityOne of the key capabilities of 6G is enhanced connectivity. With the advent of 6G, we can expect to see a significant improvement in network speeds and coverage. This will enable seamless connectivity in urban areas, remote regions, and even in outer space, paving the way for new applications and services that were previously unimaginable.3. Ultra-Reliable CommunicationAnother important aspect of 6G is ultra-reliablemunication. This implies that the network will be able to provide extremely reliable and low-latencymunication, making it suitable for critical applications such as remote surgery, autonomous vehicles, and industrial automation. The ultra-reliable nature of6G will redefine what is possible in terms of mission-criticalmunication.4. Massive Internet of Things6G is also expected to support a massive Internet of Things (IoT) ecosystem, which will epass billions of devices and sensors. The network will be able to efficiently handle the massive amount of data generated by these devices, and provide the necessary connectivity and security for various IoT applications, ranging from smart cities to precision agriculture.5. Holographic CommunicationOne of the more futuristic capabilities of 6G is holographicmunication. This technology will enable users to interact with each other in a more immersive and lifelike manner, blurring the lines between physical and virtualmunication. Holographicmunication has the potential to revolutionize the way we collaborate, learn, and entertain ourselves.6. Personalized Artificial Intelligence6G will also pave the way for personalized artificial intelligence (本人) experiences. The network will be able to supportadvanced 本人 applications that are tailored to individual users, providing them with personalized rmendations, insights, and assistance. This will bring 本人 closer to users and enable a more seamless integration of 本人 into our daily lives.7. ConclusionIn conclusion, the typical scenarios and key capabilities of 6G promise to bring about a new era of connectivity,munication, and technological innovation. The enhanced connectivity, ultra-reliablemunication, massive Internet of Things, holographicmunication, and personalized 本人 experiences will revolutionize various industries and reshape the way we interact with technology. As we look forward to the deployment of 6G, it is important to consider the possibilities and implications that this new generation of technology will bring.。
超科幻未来房子英语作文Title: The Ultimate Abode: A Glimpse into the Super-Sci-Fi Future Home。
In the not-so-distant future, humanity's relationship with technology will transcend current boundaries, paving the way for a futuristic living experience that blurs the lines between imagination and reality. Enter the super-sci-fi future home, a marvel of innovation and comfort unlike anything seen before.At the heart of this futuristic dwelling lies an intricate network of artificial intelligence (AI) and advanced robotics, seamlessly integrated into every aspect of daily life. Imagine waking up to the gentle hum of your personal AI assistant, who not only greets you with your customized morning routine but also anticipates your needs before you even realize them yourself.Step into the living area, where the walls themselvescome to life with immersive holographic displays, allowing you to transform your surroundings at will. Whether you prefer a serene forest retreat or a bustling cityscape, the possibilities are endless, limited only by your imagination.But the true magic of the super-sci-fi future home lies in its adaptive architecture. Gone are the days of rigid floor plans and static living spaces. Instead, nanotechnology-infused materials enable the home to shape-shift according to your preferences, creating dynamic environments tailored to your mood and activities.Need a cozy nook for reading or a spacious area for entertaining guests? With a simple voice command or gesture, the furniture seamlessly reconfigures itself to suit your needs, blurring the lines between form and function.Of course, no futuristic home would be complete without cutting-edge sustainability features. Advanced solar panels and energy-efficient systems ensure minimal environmental impact, while integrated recycling and waste management systems work tirelessly behind the scenes to keep the homerunning smoothly.But perhaps the most awe-inspiring feature of thesuper-sci-fi future home is its ability to transcendphysical limitations. Through the power of virtual reality and augmented reality technologies, you can explore distant worlds, attend live events, or even meet up with friendsand family from the comfort of your own home.In this hyper-connected future, distance becomes obsolete, and the boundaries between the virtual and thereal begin to blur. Yet, amidst all the technological marvels, the super-sci-fi future home remains a sanctuary,a place where comfort, convenience, and innovation converge to create an unparalleled living experience.As we peer into the future, one thing becomes clear:the possibilities are as limitless as the imaginationitself. The super-sci-fi future home is not just a dwelling; it's a glimpse into a world where the boundaries between fantasy and reality fade away, leaving only endless possibilities in their wake.。
《视觉语法视域下竖屏微电影广告的多模态隐喻构建——以华为广告《悟空》为例》篇一一、引言随着移动互联网的快速发展，竖屏微电影广告作为新兴的传播方式，逐渐受到广告业界的青睐。
这些广告往往利用多模态隐喻构建的方式，通过图像、文字、色彩、声音等元素的综合运用，来达到与受众之间的情感共鸣与信息传递。
本文以华为广告《悟空》为例，从视觉语法的视域出发，深入探讨竖屏微电影广告中多模态隐喻的构建及其传播效果。
二、视觉语法与多模态隐喻理论视觉语法是指通过图像、色彩、光影等视觉元素来传达意义的一种符号系统。
而多模态隐喻则是通过多种符号模态（如语言、图像、音乐等）共同构建的隐喻，能够更全面、深入地传达信息，引起受众的共鸣。
在竖屏微电影广告中，这两种理论相互融合，共同构成了广告信息的传递机制。
三、《悟空》广告的多模态隐喻构建华为广告《悟空》以其独特的竖屏形式和丰富的多模态元素，成功构建了一系列多模态隐喻。
广告中，通过对“悟空”这一经典文化符号的重新解读与诠释，结合现代科技元素，形成了一种跨越时空的隐喻。
这种隐喻不仅在视觉上呈现出强烈的冲击力，更在情感上引起了受众的共鸣。
四、视觉元素的多模态隐喻构建在《悟空》广告中，视觉元素发挥了至关重要的作用。
画面中的色彩运用巧妙，如悟空的金箍棒与华为产品的色彩相呼应，形成了视觉上的统一与和谐。
同时，通过高超的摄影技巧和剪辑手法，将传统与现代、东方与西方的元素巧妙地融合在一起，形成了一种独特的视觉冲击力。
这些视觉元素共同构建了多模态隐喻，使得广告信息得以更加有效地传递。
五、听觉元素的多模态隐喻构建除了视觉元素外，《悟空》广告中的听觉元素也发挥了重要作用。
广告中的背景音乐既符合了广告的节奏，又为观众营造了一种强烈的情感氛围。
这种听觉元素与视觉元素的完美结合，使得多模态隐喻的构建更加完善，使得观众能够在感官上得到全面的体验。
六、结论通过对华为广告《悟空》的分析，可以看出竖屏微电影广告中多模态隐喻构建的重要性。
Arnold featuresMemory-efficient, scalable raytracer rendering software helps artists render complex scenes quickly and easily.◆ See what's new (video: 2:31 min.)◆ Get feature details in the Arnold for Maya, Houdini, Cinema 4D, 3ds Max, or Katana user guidesSubsurface scatterHigh-performance ray-traced subsurface scattering eliminates the need to tune point clouds. Hair and furMemory-efficient ray-traced curve primitives help you create complex fur and hair renders.Motion blur3D motion blur interacts with shadows, volumes, indirect lighting, reflection, or refraction. Deformation motion blur and rotational motion are also supported. VolumesThe volumetric rendering system in Arnold can render effects such as smoke, clouds, fog, pyroclastic flow, and fire.InstancesArnold can more efficiently ray trace instances of many scene objects with transformation and material overrides. Subdivision and displacementArnold supports Catmull-Clark subdivision surfaces.OSL supportArnold now features support for Open Shading Language (OSL), an advanced shading language for Global Illumination renderers. Light Path ExpressionsLPEs give you power and flexibility to create Arbitrary Output Variables to help meet the needs of production.NEW|Adaptive samplingAdaptive sampling gives users another means of tuning images, allowing them to reduce render times without jeopardizing final image quality. NEW|Toon shaderAn advanced Toon shader is part of a non-photorealistic solution provided in combination with the Contour Filter.NEW|DenoisingTwo denoising solutions in Arnold offer flexibility by allowing users to use much lower-quality sampling settings. NEW|Material assignments and overrides Operators make it possible to override any part of a scene at render time and enable support for open standard framework such as MaterialX.NEW|Alembic proceduralA native Alembic procedural allows users to render Alembic files directly without any translation.NEW|Profiling API and structured statistics An extensive set of tools allow users to more easily identify performance issues and optimize rendering processes.Standard Surface shaderThis energy-saving, physically based uber shader helps produce a wide range of materials and looks. Standard Hair shaderThis physically based shader is built to render hair and fur, based on the d'Eon and Zinke models for specular and diffuse shading.Flexible and extensible APIIntegrate Arnold in external applications and create custom shaders, cameras, light filters, and output drivers. Stand-alone command-line rendererArnold has a native scene description format stored in human-readable text files. Easily edit, read, and write these files via the C/Python API.◆ See Arnold 5.1 release notesIntegrate Arnold into your pipeline•Free plug-ins provide a bridge to the Arnold renderer from within many popular 3D applications.•Arnold has supported plug-ins available for Maya, Houdini, Cinema 4D, 3ds Max, and Katana.•Arnold is fully customizable, with a powerful API to create custom rendering solutions.◆ See Arnold plug-ins。
《基于机器学习算法的超材料快速自动设计研究》篇一一、引言超材料作为一种具有独特物理特性的新型材料，近年来在众多领域中得到了广泛的应用。
然而，超材料的设计往往需要复杂的物理模型和精细的实验验证，导致设计过程既耗时又昂贵。
因此，寻求一种高效、自动化的超材料设计方法成为了科研领域的迫切需求。
本文提出了一种基于机器学习算法的超材料快速自动设计方法，旨在通过算法的智能优化来提高超材料设计的效率和准确性。
二、研究背景及意义随着人工智能和机器学习技术的快速发展，其在材料科学领域的应用也越来越广泛。
通过机器学习算法，我们可以从大量的材料数据中学习并发现材料性质与结构之间的内在规律，为材料的快速设计和优化提供有力支持。
将机器学习算法应用于超材料设计，不仅可以提高设计效率，还可以降低实验成本，推动超材料在各个领域的应用。
三、研究方法本研究采用了一种基于深度学习的机器学习算法，通过构建超材料设计的智能优化模型，实现超材料的快速自动设计。
具体步骤如下：1. 数据准备：收集超材料的相关数据，包括材料的组成、结构、性质等，建立超材料数据库。
2. 特征提取：从超材料数据库中提取对设计有用的特征，如材料的组成比例、结构参数等。
3. 构建模型：利用深度学习算法，构建超材料设计的智能优化模型。
该模型可以根据提取的特征，预测材料的性质和性能。
4. 模型训练与验证：使用部分数据对模型进行训练和验证，确保模型的准确性和可靠性。
5. 自动设计：将模型应用于超材料的自动设计过程中，根据设计要求，自动寻找满足条件的材料组成和结构。
四、实验结果与分析通过实验，我们验证了基于机器学习算法的超材料快速自动设计方法的有效性和准确性。
实验结果如下：1. 设计效率：与传统的超材料设计方法相比，基于机器学习算法的设计方法可以大大提高设计效率。
在同样的时间内，可以设计出更多的超材料方案。
2. 设计准确性：机器学习模型可以准确预测超材料的性质和性能，降低实验验证的次数和成本。
1 VGA/DVI/HDMI support via adapter/connector/bracket |2 Windows 7, 8, 8.1 and Linux |3 Product is based on a published Khronos Specification, and is expected to pass the Khronos Conformance Testing Process when available. Current conformance status can be found at /conformance |4 GPU supports DX 12.0 API, Hardware Feature Level 12_1© 2021 NVIDIA Corporation and PNY. All rights reserved. NVIDIA, the NVIDIA logo, Quadro, nView, CUDA, and NVIDIA Pascal are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. The PNY logotype is a registered trademark of PNY Technologies. OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc. All other trademarks and copyrights are the property of their respective owners. J U N21Full Professional Performance and Features in a Small Form Factor . The Quadro P1000 combines a 640 CUDA core Pascal GPU, 4 GB GDDR5 on-board memory and advanced display technologies in a low-profile form factor to deliver amazing graphics performance for demanding professional applications. Support for four 4K displays (4096x2160 @ 60Hz) with HDR color gives you an expansive visual workspace to see your ideas come to life in stunning detail.Quadro cards are certified with a broad range of sophisticated professional applications, tested by leading workstation manufacturers, and backed by a global team of support specialists. This gives you the peace of mind to focus on doing your best work. Whether you’re developing revolutionary products or telling spectacularly vivid visual stories, Quadro gives you the performance to do it brilliantly. THE PNY ADVANTAGE PNY provides unsurpassed service and commitment to its professional graphics customers. In addition, PNY delivers a complete solution that includes the appropriate adapters, cables, brackets, driver software installation disc, and documentation to ensure a quick and successful install.FEATURES >Four mini DisplayPort 1.4 Connectors 1 >DisplayPort with Audio >NVIDIA nView ® Desktop Management Software >HDCP 2.2 Support >NVIDIA Mosaic 2 >NVIDIA Iray and MentalRay Support PACKAGE CONTENTS >NVIDIA ® Quadro ® P1000 Professional Graphics Board >Aattached full-height (ATX) bracket >Unattached low-profile (SFF) bracket >Four mDP to DP adapters >Printed Quick Start Guide >Printed Support Guide WARRANTY AND SUPPORT >3-Year Warranty >Pre- and Post-Sales Technical Support >Dedicated Field Application Engineers >Direct Tech Support Hot Lines PNY PART NUMBER VCQP1000V2-PB SPECIFICATIONS GPU Memory 4 GB GDDR5Memory Interface 128-bit Memory Bandwidth Up to 80 GB/s NVIDIA CUDA ® Cores 640 System Interface PCI Express 3.0 x16Max Power Consumption 47 W Thermal Solution Active Form Factor2.713” H x 6.061” L, Single Slot, Low Profile Display Connectors4x mDP 1.4 Max Simultaneous Displays4 direct, 4 DP 1.4 Multi-Stream Display Resolution4x 4096x2160 @ 60Hz 4x 5120x2880 @ 60Hz Graphics APIs Shader Model 5.1, OpenGL 4.53, DirectX 12.04, Vulkan 1.03Compute APIsCUDA, DirectCompute, OpenCL ™ UNMATCHED POWER.UNMATCHED CREATIVE FREEDOM. NVIDIA ® QUADRO ®P1000。
DS-D5C75RB/B75-inch Interactive Flat PanelThe Hikvision Interactive Flat Panel is a kind of newly developed intelligent teaching assistance product which integrates with the latest technologies of touch control, HD display, video processing, network communication, interactive control, sound control. It can be widely applied to the classroom, online teaching, training center, etc.⏹UHD 4K display with resolution of 3840 × 2160.⏹Anti-glare and anti-blocking design to ensure smooth interaction.⏹Dual systems of Android and Windows（Optional）saving cost and easy to use, video conference software arerecommonded to run under Windows system.⏹Quick response, at most 45-points touch control and smooth writing experience.⏹Built-in network switch module saves a network switch.⏹All-in-one design with tempered glass and intelligent temperature control, making it environmental-friendly andsafety-guaranteed, support blue light protection.SpecificationModel DS-D5C75RB/BProduct Model ProductModelDS-D5C75RB/BPanel GlassThickness3.2 mmGlass BondingMethodZero LaminationDisplay Screen Size 75 inchBacklight DLEDPixel Pitch 0.14325(H) × 0.42975(V) mm Resolution 3840 × 2160 @60 Hz Brightness 400 cd/m² with Brightness sensor Color Depth 10 bitContrast Ratio 4000 : 1ResponseTime8 msColor Gamut 72% NTSCRefresh Rate 60 HzViewing Angle 178°(H)/178°(V)Optical Aspect Ratio 16 : 9Built-in System OperationSystemAndroid 12.0Processor Quad-core Cortex-A76 × 4（2.4 GHz）and quad-core Cortex-A55 × 4（1.8 GHz）Memory 8 GBBuilt-inStorage128 GBPanel Type VAHDCP Version 2.0TouchResolution3840 × 2160Touch Type Infrared Touch Screen Touch Point 45 point multi-touchTouchResponseTime< 8 msTouchPrecision± 1 mm (≥ 90% touching area)ProcessingPerformanceDisplay Color 1.07GInternal Function Loudspeaker Built-in 2 ×15 W+25 W loudspeaker;Bluetooth Built-in BLE (Bluetooth Low Energy) module supports Bluetooth 5.1ImageProcessingSupport 3D digital noise reduction, face detection, automatic white balance, automatic purpleedge removal, automatic defogging；support shadow correction and distortion correction forTechnology lens.Audio Processing Technology Support dereverberation, adaptive noise reduction, adaptive non-stationary noise reduction, adaptive echo cancellation, automatic gain control, automatic electrical level control, sound source localization, speech dynamic detection, digital beam forming, speech detection.Interface Video ＆Audio InputHDMI In 2.0 × 3, VGA In × 1, DP In 1.2 × 1, Audio In × 1Video ＆audio outputHDMI Out 2.0 × 1, Audio Out × 1, SPDIF Out × 1ControlinterfaceTOUCH-USB 3.0 × 3, RS232 × 1NetworkInterfaceLAN (1000 Mbps) × 2WIFI AP&Station，2.4 G/5 GIEEE 802.11 a/b/g/n/ac 2 × 2 MIMO (2.4 GHz and 5 GHz) authentication protocols WEP, WPA,WPA2, PSK and 802.1X EAPDataTransmissionInterfaceUSB 3.0 × 4, USB 2.0 × 1, Type-C × 2 (Type-C supports USB 2.0, DP 1.2, and chargingfunctions; side charging 15 W, front charging 65 W)Power Standby Consumption≤ 0.5 W Max.Consumption< 250WWorking Environment WorkingTemperature0 °C to 40 °C (32 °F to 104°F) WorkingHumidity10%~90% RHGeneral VESA 800 mm × 400 mm （4 × M8-25 mm）Power Supply 100 to 240 VAC, 50/60 HzProductdimensions1713.2 × 1034.5 × 82.5AccessoryWall mount bracket, hook, user manual, touch writing pen, packing list, 3M touch USB cable,3M HDMI cable (2.0), 1.5m power cable (European standard), Bluetooth remote control. PackageDimensions(W × H × D)1937 × 264 × 1222 mmNet Weight 61.86Gross Weight 73.36NFC Support screen mirroring by Android phones.Camera Pixel 8 MPCameraFunctionSupport intelligent switching between Android system, OPS, and PC. Field Angle 120°(diagonal), 110°(horizontal), 75°(vertical)Distortion ≤2.5%CameraResolutionRatioUp to 4KMicrophone Specification Omni-directional 8-array layoutMic Function Echo reduction and smart noise-cancellation VoiceDistance12 mSampling Rate 32 KMic SamplingRate16 bit⏹Physical InterfaceInterface Description Interface Description1.SPDIF SPDIF interface2.Line OUT Audio output interface3.Line IN Audio input interface4.VGA IN Video devices connectioninterface5.TYPE-C Type-C interface6.DP IN Display input interface7.TOUCH-USB 2Peripheral touch interface8.HDMI IN 2HDMI 2 input interface9.TOUCH-USB Peripheral touch interface10.HDMI IN 1HDMI 1 input interface B USB interface12.MULTI-USB 2Multi-functional USBinterface13.MULTI-USB 1Multi-functional USBinterface14.HDMI OUT HDMI output interface⏹InstallationAvailable Model DS-D5C75RB/BDimension。
Key FeaturesA new frame of mind.No other full frame, interchangeable-lens camera is this light or this portable. 24.3 MP of rich detail. A true-to-life 2.4 million dot OLED viewfinder. Wi-Fi sharing and an expandable shoe system. It’s all the full-frame performance you ever wanted in a compact size that will change your perspective entirely.World’s smallest lightest interchangeable lens full-frame cameraSony’s Exmor image sensor takes full advantage of the Full-frame format, but in a camera body less than half the size and weight of a full-frame DSLR.Full Frame 24.3 MP resolution with 14-bit RAW outputA whole new world of high-quality images are realized through the 24.3 MP effective 35 mm full-frame sensor, a normal sensor range of ISO 100 – 25600, and a sophisticated balance of high resolving power, gradation and low noise. The BIONZ® X image processor enables up to 5 fps high-speed continuous shooting and 14-bit RAW image data recording.Fast Hybrid AF w/ phase-detection for DSLR-like focusing speedEnhanced Fast Hybrid auto focus combines speedy phase-detection AF with highly accurate contrast-detection AF , which has been accelerated through a new Spatial Object Detection algorithm, to achieve among the fastest autofocusing performance of any full-frame camera. First, phase-detection AF with 117 densely placed phase-detection AF points swiftly and efficiently moves the lens to bring the subject nearly into focus. Then contrast-detection AF with wide AF coverage fine-tunes the focusing in the blink of an eye.Fast Intelligent AF for responsive, accurate, and greater operability with full frame sensorThe high-speed image processing engine and improved algorithms combine with optimized image sensor read-out speed to achieve ultra high-speed AF despite the use of a full-frame sensor.New Eye AF controlEven when capturing a subject partially turned away from the camera with a shallow depth of field, the face will be sharply focused thanks to extremely accurate eye detection that can prioritize a single pupil. A green frame appears over the prioritized eye when focus has been achieved for easy confirmation. Eye AF can be used when the function is assigned to a customizable button, allowing users to instantly activate it depending on the scene.Fully compatible with Sony’s E-mount lens system and new full-frame lensesTo take advantage of the lightweight on-the-go body, the α7 is fully compatible with Sony’s E-mount lens system and expanded line of E-mount compact and lightweight full-frame lenses from Carl Zeiss and Sony’s premier G-series.Direct access interface for fast, intuitive shooting controlQuick Navi Pro displays all major shooting options on the LCD screen so you can rapidly confirm settings and make adjustments as desired without searching through dedicated menus. When fleeting shooting opportunities arise, you’ll be able to respond swiftly with just the right settings.High contrast 2.4M dot OLED EVF for eye-level framingView every scene in rich detail with the XGA OLED Tru-Finder, which features OLED improvements and the same 3-lens optical system used in the flagship α99. The viewfinder faithfully displays what will appear in your recording, including the effects of your camera settings, so you can accurately monitor the results. You’ll enjoy rich tonal gradations and 3 times the contrast of the α99. High-end features like 100% frame coverage and a wide viewing angle are also provided.3.0" 1.23M dot LCD tilts for high and low angle framingILCE-7K/Ba7 (Alpha 7) Interchangeable Lens CameraNo other full frame, interchangeable-lens camera is this light or this portable. 24.3 MP of rich detail. A true-to-life 2.4 million dot OLED viewfinder. Wi-Fi ® sharing and an expandable shoe system. It’s all the full-frame performance you ever wanted in a compact size that will change your perspective entirely.The tiltable 3.0” (1,229k dots) Xtra Fine™ LCD Display makes it easy to photograph over crowds or low to capture pets eye to eye by swinging up approx. 84° and down approx. 45°. Easily scroll through menus and preview life thanks to WhiteMagic™ technology that dramatically increases visibility in bright daylight. The large display delivers brilliant-quality still images and movies while enabling easy focusing operation.Simple connectivity to smartphones via Wi-Fi® or NFCConnectivity with smartphones for One-touch sharing/One-touch remote has been simplified with Wi-Fi®/NFC control. In addition to Wi-Fi support for connecting to smartphones, the α7 also supports NFC (near field communication) providing “one touch connection” convenience when transferring images to Android™ smartphones and tablets. Users need only touch devices to connect; no complex set-up is required. Moreover, when using Smart Remote Control — a feature that allows shutter release to be controlled by a smartphone — connection to the smartphone can be established by simply touching compatible devices.New BIONZ X image processing engineSony proudly introduces the new BIONZ X image processing engine, which faithfully reproduces textures and details in real time, as seen by the naked eye, via extra high-speed processing capabilities. Together with front-end LSI (large scale integration) that accelerates processing in the earliest stages, it enables more natural details, more realistic images, richer tonal gradations and lower noise whether you shoot still images or movies.Full HD movie at 24p/60i/60p w/uncompressed HDMI outputCapture Full 1920 x 1080 HD uncompressed clean-screen video files to external recording devices via an HDMI® connection in 60p and 60i frame-rates. Selectable in-camera A VCHD™ codec frames rates include super-smooth 60p, standard 60i or cinematic 24p. MP4 codec is also available for smaller files for easier upload to the web.Up to 5 fps shooting to capture the decisive momentWhen your subject is moving fast, you can capture the decisive moment with clarity and precision by shooting at speeds up to 5 frames per second. New faster, more accurate AF tracking, made possible by Fast Hybrid AF, uses powerful predictive algorithms and subject recognition technology to track every move with greater speed and precision. PlayMemories™ Camera Apps allows feature upgradesPersonalize your camera by adding new features of your choice with PlayMemories Camera Apps. Find apps to fit your shooting style from portraits, detailed close-ups, sports, time lapse, motion shot and much more. Use apps that shoot, share and save photos using Wi-Fi that make it easy to control and view your camera from smartphone, and post photos directly to Facebook or backup images to the cloud without connecting to a computer.114K Still image output by HDMI8 or Wi-Fi for viewing on 4K TVsEnjoy Ultra High Definition slide shows directly from the camera to a compatible 4K television. The α7 converts images for optimized 4K image size playback (8MP). Enjoy expressive rich colors and amazing detail like never before. Images can be viewed via an optional HDMI or WiFi.Vertical Grip CapableEnjoy long hours of comfortable operation in the vertical orientation with this sure vertical grip, which can hold two batteries for longer shooting and features dust and moisture protection.Mount AdaptorsBoth of these 35mm full-frame compatible adaptors let you mount the α7R with any A-mount lens. The LA-EA4 additionally features a built-in AF motor, aperture-drive mechanism and Translucent Mirror Technology to enable continuous phase-detection AF. Both adaptors also feature a tripod hole that allows mounting of a tripod to support large A-mount lenses.Specifications1. Among interchangeable-lens cameras with an full frame sensor as of October 20132. Records in up to 29 minute segments.3. 99 points when an APS-C lens compatible with Fast Hybrid AF is mounted.7. Actual performance varies based on settings, environmental conditions, and usage. Battery capacity decreases over time and use.8. Requires compatible BRA VIA HDTV and cable sold separately.9. Auto Focus function available with Sony E-Mount lenses and Sony A-mount SSM and SAM series lenses when using LA-EA2/EA4 lens adaptor.。
虚拟现实实现身临其境的关键技术虚拟现实（Virtual Reality，简称VR）是当今科技领域的热门话题之一。
通过虚拟现实技术，人们可以沉浸在一个虚拟的环境中，享受身临其境的感觉。
然而，实现真正身临其境的体验并非易事，需要依赖一些关键技术。
本文将探讨虚拟现实实现身临其境的关键技术。
一、仿真图像技术在虚拟现实中，图像是最直观、最重要的信息传达方式之一。
因此，实现身临其境的关键是提供逼真的、真实感十足的图像。
在仿真图像技术方面，主要包括以下几个方面的内容。
1.1 分辨率和清晰度虚拟现实头显设备的分辨率和清晰度对图像质量有着重要的影响。
高分辨率的显示设备可以提供更清晰的图像，帮助用户更好地融入虚拟环境中。
因此，提高虚拟现实设备的分辨率和清晰度是实现身临其境的首要任务。
1.2 实时渲染技术实时渲染技术是虚拟现实中不可或缺的一部分。
它能够在虚拟环境中实时生成逼真的图像，使用户可以与环境进行互动。
实时渲染技术的不断发展和进步，为虚拟现实带来了更加真实、逼真的体验。
1.3 光线追踪技术光线追踪技术是一种高级的渲染技术，它可以模拟光线在真实世界中的传播情况，从而生成更加真实的图像。
光线追踪技术在虚拟现实中的应用，可以提供更加细腻的光照效果，增强虚拟环境的真实感。
二、空间声音技术虚拟现实不仅需要逼真的图像，还需要逼真的声音来增强用户体验。
空间声音技术是实现身临其境的另一个关键技术。
2.1 3D音频技术3D音频技术可以模拟声音在真实世界中的传播方式，并使用户能够感知声音的方向和距离。
通过3D音频技术，虚拟现实可以为用户提供真实的声音体验，使用户感觉自己置身于一个真实的环境中。
2.2 头部定位技术头部定位技术是虚拟现实中实现空间声音的重要手段。
通过感知用户头部的运动和位置，系统可以实时调整声音的方向和强度，以确保用户听到来自正确方向的声音。
头部定位技术的发展，使得虚拟现实的声音体验更加真实。
三、交互技术虚拟现实的真实感不仅仅体现在视觉和声音上，还需要通过交互来增强用户体验。
《虚拟现实增强技术综述》篇一一、引言随着科技的飞速发展，虚拟现实（Virtual Reality，简称VR）和增强现实（Augmented Reality，简称AR）技术已经逐渐成为当今科技领域的热点话题。
虚拟现实增强技术，即通过技术手段将虚拟的信息、内容与真实的环境相结合，为人们带来全新的沉浸式体验。
本文将对虚拟现实增强技术的定义、特点、应用领域及发展前景进行综述。
二、虚拟现实增强技术的定义与特点虚拟现实增强技术是一种将虚拟信息和真实环境进行融合的技术，它通过先进的计算机图形技术、传感器技术和人机交互技术等手段，将虚拟的信息内容嵌入到真实的环境中，使用户在真实环境中体验到虚拟信息带来的感觉和体验。
该技术的特点主要表现在以下几个方面：1. 沉浸性：用户可以完全沉浸在虚拟与现实的融合环境中，获得真实的体验感。
2. 交互性：用户可以通过各种设备与虚拟信息进行实时交互，如手势识别、语音识别等。
3. 实时性：虚拟信息能够实时地与真实环境进行融合，为用户带来实时的交互体验。
三、虚拟现实增强技术的应用领域虚拟现实增强技术的应用领域非常广泛，主要表现在以下几个方面：1. 娱乐领域：游戏、电影、音乐等领域是虚拟现实增强技术的主要应用领域。
通过该技术，用户可以获得更加真实的游戏体验和电影观赏体验。
2. 教育领域：虚拟现实增强技术可以为学生提供更加生动、形象的教学内容，帮助学生更好地理解和掌握知识。
3. 医疗领域：在医疗领域，虚拟现实增强技术可以用于手术模拟、康复训练、医学教育等方面，提高医疗水平和效率。
4. 商业领域：在商业领域，虚拟现实增强技术可以用于产品展示、广告宣传、购物体验等方面，提高用户体验和购买意愿。
四、虚拟现实增强技术的发展现状与前景目前，虚拟现实增强技术已经取得了长足的发展，各大科技公司都在积极投入研发该技术。
随着技术的不断进步和应用领域的不断拓展，虚拟现实增强技术的应用前景非常广阔。
未来，该技术将更加普及和成熟，为人们带来更加丰富、真实的体验感。
Digital cameras have become an integral part of modern life,offering a myriad of benefits and features that have revolutionized the way we capture and share memories.In this essay,we will explore the various aspects of digital cameras,from their technological advancements to their impact on society.Introduction to Digital CamerasDigital cameras,also known as digicams,are electronic devices that capture photographs in digital form.Unlike traditional film cameras,digital cameras store images on memory cards,allowing for easy transfer and manipulation of the images.The advent of digital cameras has made photography more accessible to the general public,as they are relatively inexpensive and userfriendly.Technological AdvancementsOver the years,digital cameras have seen significant improvements in technology.The development of higher resolution sensors,such as those found in DSLRs Digital SingleLens Reflex and mirrorless cameras,has enabled photographers to capture images with incredible detail and clarity.Additionally,advancements in image processing software have allowed for better color reproduction and noise reduction,particularly in lowlight conditions.Features of Digital CamerasModern digital cameras come equipped with a variety of features that enhance the photography experience.These include:1.Autofocus:Most digital cameras have an autofocus system that quickly and accurately focuses on the subject,ensuring sharp images.2.Image Stabilization:This feature helps to reduce blur caused by camera shake, especially useful in lowlight situations or when using longer lenses.3.HighSpeed Continuous Shooting:Some cameras can take multiple shots in quick succession,ideal for capturing fastmoving subjects.4.Builtin WiFi and Bluetooth:These connectivity options allow for easy transfer of images to smartphones or tablets,facilitating instant sharing on social media platforms.5.Touchscreen Interface:Many cameras now feature a touchscreen,making it easier to navigate menus and select focus points.Impact on SocietyThe widespread use of digital cameras has had a profound impact on society.Here are a few notable effects:1.Democratization of Photography:The affordability and ease of use of digital cameras have allowed more people to explore photography as a hobby or profession.2.Instant Gratification:The ability to instantly review and share photos has changed the way we document and share our experiences.3.Photography as a Social Activity:The rise of social media has turned photography intoa social activity,with platforms like Instagram dedicated to sharing images.4.Impact on Professional Photography:Professional photographers have had to adapt to the digital age,embracing new technologies and techniques to stay relevant.ConclusionDigital cameras have transformed the way we capture and appreciate visual moments. Their technological advancements have made photography more accessible and versatile, while their societal impact has reshaped how we document and share our lives.As technology continues to evolve,it is exciting to consider the future possibilities that digital cameras will offer to both amateur and professional photographers alike.。
一种基于DQN的全景视频边缘缓存优化方案摘要：随着全景视频技术的迅猛进步，对于全景视频的边缘缓存优化已成为一个迫切需要解决的问题。
本文提出了一种基于深度强化进修（DQN）的全景视频边缘缓存优化方案。
通过分析全景视频边缘缓存的特点，建立了一个适应于边缘缓存优化的DQN模型，并设计了一种新型的嘉奖函数。
试验结果表明，该方案在提高全景视频边缘缓存的命中率和缓存利用率方面具有明显的优势。
关键词：全景视频；边缘缓存；深度强化进修；DQN；命中率；缓存利用率1. 引言全景视频技术自问世以来，已经在虚拟现实、游戏、体育等领域获得了广泛的应用。
然而，全景视频的大规模传输和实时性要求给网络带宽和传输延迟带来了巨大的挑战。
为了解决全景视频传输中的带宽和延迟问题，边缘缓存被广泛应用于全景视频传输中，从而提高传输效率和用户体验。
然而，现有的边缘缓存方案在全景视频传输的优化上还存在着一些问题，如缓存命中率低、缓存利用率不高等。
2. 相关工作2.1 全景视频边缘缓存目前，全景视频边缘缓存技术主要包括预取缓存和动态适应缓存两种。
预取缓存通过提前从源节点将全景视频片段缓存到边缘节点，以缩减用户访问时的传输延迟。
动态适应缓存依据用户的请求模式自适应地调整缓存内容，以提高缓存命中率。
2.2 深度强化进修（DQN）深度强化进修是人工智能领域的一个热门探究方向，通过强化进修和深度神经网络相结合，可以实现智能体在复杂环境中进修和决策。
DQN是深度强化进修的经典算法之一，通过将Q值函数用卷积神经网络靠近，可以有效地解决状态空间较大的强化进修问题。
3. 基于DQN的全景视频边缘缓存优化方案3.1 全景视频边缘缓存建模在全景视频边缘缓存优化中，我们通过建立一个基于DQN的缓存模型来进修边缘节点的缓存策略。
详尽来说，我们将整个全景视频画面分割成多个小块，并使用一个二维矩阵表示缓存状态，其中每个元素表示对应小块的缓存状况（是否被缓存）。
通过监控用户的缓存访问模式，将用户的访问行为转化为一系列的状态-动作-嘉奖序列，作为DQN模型的训练数据。
《视觉语法视域下竖屏微电影广告的多模态隐喻构建——以华为广告《悟空》为例》篇一一、引言随着数字化媒体的飞速发展，竖屏微电影广告作为新兴的传播形式，逐渐在广告市场中占据一席之地。
这种新型的广告形式不仅在视觉上给予观众强烈的冲击，更在多模态隐喻的构建上展现出独特的魅力。
华为广告《悟空》便是一个典型的例子，它巧妙地运用视觉语法，通过竖屏的呈现方式，构建出丰富的多模态隐喻，传达品牌的核心价值和情感。
本文将从视觉语法的角度出发，深入分析《悟空》广告中多模态隐喻的构建。
二、视觉语法与多模态隐喻视觉语法是研究视觉图像如何传递信息、表达意义的一种理论。
在广告中，视觉语法通过图像、文字、色彩等元素的组合与互动，构建出各种隐喻，以实现广告的传播效果。
多模态隐喻则是运用多种模态，如图像、文字、声音等，共同构建的隐喻。
这种隐喻方式能够更全面、深刻地传达广告的信息和情感。
三、华为广告《悟空》的多模态隐喻构建1. 图像隐喻《悟空》广告中，通过竖屏呈现的画面，将孙悟空的形象与华为的产品相结合，构建出一种图像隐喻。
孙悟空作为中国传统文化中的英雄形象，具有智慧、勇敢、无畏等特质。
这些特质被巧妙地转移到华为产品上，传达出华为产品的高科技、创新、可靠的品牌形象。
2. 色彩隐喻在色彩运用上，《悟空》广告也展现了高超的技巧。
广告中大量运用了金色、红色等鲜艳的色彩，这些色彩不仅与中华文化的审美相契合，更传达出一种高贵、尊贵的品牌氛围。
同时，这些色彩也被用作构建隐喻的元素，如金色的科技界面象征着华为产品的先进性。
3. 文字隐喻除了图像和色彩，《悟空》广告中的文字也承担着构建隐喻的任务。
广告中的文字不仅传递了产品的基本信息，更通过双关、隐喻等修辞手法，传达出品牌的情感和价值观。
例如，“一触即发”这样的文字既表达了产品的操作简单，又隐喻了产品的强大性能。
四、《悟空》广告多模态隐喻的效果通过上述多模态隐喻的构建，《悟空》广告成功地传达了华为品牌的形象和价值观。
PROJECTION SYSTEM CONVENIENCE FEATURES CONT.Projection Type DLP Picture Engine Ceiling Mode (Image Flip)●Native Resolution WXGA (1280 x 800)Automatic Standby ●Lightsource RGB LEDContrast Ratio DLP High Contrast RatioCONNECTIONS Brightness 800 Lumen REAR LED Light Source 30,000 hours Antenna ● (1)Operation Noise26dB - 34dBHDMI● (1)VGA/Component Input ● (Shared)BROADCASTING SYSTEM A/V (Composite/Audio) Input● (1)Tuner MPEG-2/4 DVB-T2USB 2.0 Input● (1)HbbTV -Headphone (3.5mm) Output ● (1)Closed Caption CompatibleKensington Lock Compatible Tripod Mount Hole CompatiblePROJECTION IMAGE Native Aspect Ratio 16:10SUPPLIED ACCESSORIES Throw Ratio1.5Remote Control● (1)1.3m Distance = 40"Power Supply Adaptor (100V - 240V)● (1)2.6m Distance = 80"AV Composite Video/Audio Adaptor ● (1)3.25m Distance = 100"Instruction Manual Booklet, CD ZoomFixedCarry Bag ● (1)Aspect Ratio Modes 4:3/Just/Auto/16:9/Cinema Zoom/Full)Battery (AAA)● (2)Picture ModesVivid/Standard/Eco/Cinema/Game/ExpertDigital Keystone Adjustment ●DIMENSIONS Manual Focus Adjustment ●W x H x D 140mm x 140mm x 50mm Net Weight620gINPUT SIGNAL COMPATIBILITY HDMI 1080p/1080i/720p/576p/576i/480p WARRANTYVGAup to WSXGA+ (1680x1050@60Hz) 3 Year Warranty - Parts and Labour Component Video 1080p/1080i/720p/576p/576i/480p/480iComposite Video 576i/480i EANHD Tuner 1080i/720p/576i88060872670133D FURTHER INFORMATION3D Type DLP Link Compatible 3D Glasses 1Sold SeparatelyAUDIOAudio Decoder DTS/Dolby Digital/AAC/PCMSpeaker StereoAudio Output 1W+1W Total Audio OutputSound ModesStandard/Music/Cinema/Sport/Game/UserCONVENIENCE FEATURES Quick Power On10 secondsFile Office ViewerXLS, DOC, PPT, XLSX, PPTX, DOCX, PDFUSB Video Playback FormatsDivX (XViD, H.264/MPEG-4 AVC)/WMV/AVI/M4V/MOV/MPG/MPEG/M2TS/MKV/TS/TPUSB Music Playback Formats MP3/AC3/MPEG/AAC/CDDA/LPCMUSB Photo Playback Formats JPEG MHL 2●Intel's Wireless Display (WiDi ®)3●Automatic Keystone (Vertical)●Miracast 4●PW800 (MiniBeam)High Definition LED DLP Projector with HDTV Tuner^ Recommended Retail Price (RRP). Prices subject to change and does not include retailer charges.1 Glasses available for purchase separately from DLP Link suppliers.2 MHL enabled Android Smartphone or Tablet & MHL cable required (sold separately).3 Intel WiDi® enabled PC must be in range of the projector4 Miracast compatible Android™ device required for this feature.All product specifications correct at time of printing. Specifications may change without prior notice. Customers are advised to check with your retailer before purchase.High Definition LED DLP Projector with HDTV TunerDesignPicture QualityConvenience FeaturesAudioLaunch DetailsCompact Design White Colour Joystick ControlSky Blue Colour CoverWXGA (1280x800)DLP Picture Engine 800 LumenDLP High Contrast RatioHDTV TunerUSB Media Playback30,000 hour LED Light Source Intel's Wireless Display (WiDi®)3D Compatible2W Total Audio Out Headphone OutputLong Lasting LED Light SourceIf you're concerned with bulb-life, don't be. Unlike conventional lamp-based projectors, LED light sources can last up to 30,000 hours. Thatequates to 4 hours viewing per day for 20 years.PW800 (MiniBeam)Experience TV on the big screen!Excellent ConnectivityThe MiniBeam range has excellent connectivity. Watch videos directly from a USB stick on your MiniBeam without the need of a PC. The MiniBeam supports a wide range of file formats, including: DivX HD, MP3, Photo and Microsoft® Office documents. MiniBeam also supports high definition sources such as Blu-ray, game consoles, HDMI and Cable TV. With built-in Miracast and Intel WiDi technology users can wirelessly share content from compatible phones, tablets and computers.Versatility meets portability with the PW800, featuring 800 Lumen brightness, High Definition native resolution and high contrast ratio.Watch the big game and other broadcast TV shows up to100", directly from the built-in HDTV tuner.A wide range of connectivity options ensures users can display their presentation -whether that be from phone, tablet or a document directly off a USB drive.The long lasting LED light source runs cooler than a conventional lamp -based projector and can last up to 30,000 hours.HDTV TunerThe PW800 is able to receive HDTV signals via the antenna input. Experience the big game or your favourite TV show -on a screen up to 100" from 3.25m.Available:May 2015RRP^: $1099。
NVIDIA L40SUnparalleled AI and graphics performance for the data center.Generative AI is fueling transformative change, unlocking a new frontier ofopportunities for enterprises across every industry. To transform with AI, enterprises need more compute resources, greater scale, and a broad set of capabilities to meet the demands of an ever-increasing set of diverse and complex workloads.The NVIDIA L40S GPU is the most powerful universal GPU for the data center, delivering end-to-end acceleration for the next generation of AI-enabledapplications—from generative AI and model training and inference to 3D graphics, rendering, and video applications.Powered by the NVIDIA Ada Lovelace ArchitectureFourth-Generation Tensor CoresHardware support for structural sparsity and optimized TF32 format provides out-of-the-box performance gains for faster AI and data science model training. Accelerate AI-enhanced graphics capabilities with DLSS to upscale resolution with better performance in select applications.DatasheetAccelerate Next-Generation Workloads >Generative AI >Large language model (LLM) training and inference >NVIDIA Omniverse ™ Enterprise>Rendering and 3D graphics>Streaming and video content AI Training2x1.5x 1.0x 0.5x0xNVIDIA A100NVIDIA L40S T i m e t o T r a i n (860M T o k e n s )GPT-408 LoRA (Relative Performance)Generative AI 1.5x 1.0x 0.5x 0x NVIDIA A100NVIDIA L40SI m a g e P e r S e c o n d Stable Diffusion, 512x512 (Relative Performance) Fine-tuning LoRA (GPT-40B, GPT-175B): global train batch size: 128 (sequences), seq-length: 256 (tokens). NVIDIA HGX™ A100 (8x A100) vs. Two systems with 4x L40S GPUs. Performance based on prerelease build, subject to change.Stable Diffusion v2.1. Relative speedup for 512 x 512 resolution image generation. NVIDIA HGX A100 (8x A100) vs. Two systems with 4x L40S GPUs. Performance based on prerelease build, subject to change.Third-Generation RT CoresEnhanced throughput and concurrent ray-tracing and shading capabilities improve ray-tracing performance, accelerating renders for product design and architecture, engineering, and construction workflows. See lifelike designs in action with hardware-accelerated motion blur and stunning real-time animations. Transformer EngineTransformer Engine dramatically accelerates AI performance and improves memory utilization for both training and inference. Harnessing the power of the Ada Lovelace fourth-generation Tensor Cores, Transformer Engine intelligently scans the layers of transformer architecture neural networks and automaticallyrecasts between FP8 and FP16 precisions to deliver faster AI performance and accelerate training and inference.Data Center ReadyThe L40S GPU is optimized for 24/7 enterprise data center operations and designed, built, tested, and supported by NVIDIA to ensure maximum performance, durability, and uptime. The L40S GPU meets the latest data center standards, is Network Equipment-Building System (NEBS) Level 3 ready, and features secure boot with root of trust technology, providing an additional layer of security for data centers.GPU Architecture NVIDIA Ada Lovelace ArchitectureGPU Memory48GB GDDR6 with ECCMemory Bandwidth864GB/sInterconnect Interface PCIe Gen4 x16: 64GB/s bidirectional NVIDIA Ada Lovelace Architecture-18,176Based CUDA® CoresNVIDIA Third-Generation RT Cores 142NVIDIA Fourth-Generation Tensor568CoresRT Core Performance TFLOPS209 FP32 TFLOPS91.6TF32 Tensor Core TFLOPS183 I 366*BFLOAT16 Tensor Core TFLOPS362.05 I 733*FP16 Tensor Core362.05 I 733*FP8 Tensor Core733 I 1,466*Peak INT8 Tensor TOPS733 I 1,466*Peak INT4 Tensor TOPS733 I 1,466*Form Factor 4.4" (H) x 10.5" (L), dual slotDisplay Ports4x DisplayPort 1.4aMax Power Consumption350WPower Connector16-pinReady to get started?To learn more about the NVIDIA L40S, visit /l40s© 2023 NVIDIA Corporation and affiliates. All rights reserved. NVIDIA, the NVIDIA logo, CUDA, HGX, NVLink, and Omniverse are trademarks and/or registered trademarks of NVIDIA Corporation and affiliates in the U.S. and other countries. Other company and product names may be trademarks of the respective owners with which they are associated. 2841316. AUG23Thermal PassiveVirtual GPU (vGPU) Software Support YesvGPU Profiles Supported See the virtual GPU licensing guideNVENC I NVDEC 3x l 3x (includes AV1 encode and decode)Secure Boot With Root of Trust YesNEBS Ready Level 3MIG Support NoNVIDIA® NVLink® Support No* With sparsityPNY Technologies Europe Rue Joseph Cugnot BP 4018133708 Mérignac Cedex | France T +33 (0)5 56 13 75 75 | F +33 (0)5 56 13 75 77For more information visit: www.pny.eu。
低光照增强lime算法
低光照增强是指对于光线较暗的图像进行处理，提高图像的亮度和清晰度。
LIME（Low Light Image Enhancement）算法是
一种常见的低光照增强算法之一。
LIME算法的基本原理是通过对图像进行自适应直方图均衡和
双边滤波来增强图像的亮度和对比度。
具体步骤如下：
1. 自适应直方图均衡化：首先对图像的亮度进行增强，通过调整图像的像素值分布来拉伸图像的动态范围。
这样可以使图像中的细节更加明显。
2. 双边滤波：直方图均衡化会增强图像的噪声，为了减少噪声的影响并保持图像的细节，使用双边滤波来平滑图像。
双边滤波在保持图像边缘的同时，对图像进行平滑。
3. 融合图像：将原始图像与经过直方图均衡化和双边滤波处理后的图像进行融合，以保持图像的细节和结构，并增强图像的亮度和对比度。
LIME算法可以有效地增强低光照图像，并提高图像的可视性。
但是在处理过程中，可能会产生一些副作用，如图像的噪点和伪影。
因此，在实际应用中，需要根据具体情况进行参数调整和后期处理，以达到最佳效果。
英语作文手写模板范文英文回答：Topic: The Role of Technology in Education: Empoweringor Enervating?Introduction:Technology has become an indispensable part of our lives, influencing every aspect of our existence. Education, in particular, has witnessed a profound transformation with the advent of digital tools and resources. However, the integration of technology into education has sparked a debate: Is it empowering or enervating?Empowering Effects:Enhanced Accessibility: Technology bridges physicaland temporal barriers, making education accessible to a wider range of learners, including those in remote areas orwith disabilities.Personalized Learning: Digital platforms enable educators to tailor instruction to individual student needs, creating personalized learning experiences that foster academic growth.Interactive Content: Technology provides interactive content, such as simulations, videos, and games, which engage students and make learning more enjoyable.Collaboration and Communication: Technologyfacilitates collaboration among students and teachers, fostering a sense of community and promoting effective communication.Enervating Effects:Digital Distraction: The constant availability of technology can be distracting, leading students to lose focus on academic tasks.Equity Concerns: Access to and proficiency withdigital tools vary widely, creating an opportunity gap between students with and without access to technology.Reduced Social Interaction: Excessive use of technology can reduce face-to-face interaction, which is essential for social and emotional development.Erosion of Critical Thinking Skills: Reliance on technology for information retrieval may diminish students' ability to think critically and develop independent problem-solving skills.Balancing Empowerment and Enervation:To harness the benefits of technology while mitigating its potential drawbacks, a balanced approach is crucial:Integrate Technology Meaningfully: Use technology to enhance student learning, rather than merely as a distraction.Foster Digital Literacy: Provide students with instruction and support in digital literacy to bridge the equity gap.Encourage Human Interaction: Create opportunities for students to interact with teachers and peers face-to-face, fostering social and emotional development.Cultivate Critical Thinking: Encourage students to critically evaluate information obtained through digital sources and develop problem-solving skills.Conclusion:The integration of technology into education has the potential to both empower and enervate students. By understanding the empowering and enervating effects of technology, educators and policymakers can strike a balance that maximizes its benefits while minimizing its negative consequences. Ultimately, technology should be used as a tool to enhance student learning, rather than a replacement for traditional educational practices.中文回答：科技在教育中的作用，赋能还是萎靡？引言：科技已成为我们生活中不可或缺的一部分，影响着我们存在的方方面面。
《视觉语法视域下竖屏微电影广告的多模态隐喻构建——以华为广告《悟空》为例》篇一一、引言随着数字媒体的快速发展，竖屏微电影广告逐渐成为品牌传播的重要手段。
这种新型的广告形式以其独特的视觉冲击力和多模态表达方式，在极短的时间内传达品牌价值和文化内涵。
本文以华为广告《悟空》为例，从视觉语法的角度出发，探讨竖屏微电影广告中多模态隐喻的构建及其所体现的广告传播效果。
二、竖屏微电影广告的特点竖屏微电影广告，作为一种新兴的广告形式，具有其独特的特点。
它以竖屏模式呈现，适应移动设备的观看习惯，同时通过故事化的叙述方式，增强观众的代入感和情感共鸣。
在有限的时间内，通过图像、文字、音乐等多模态元素的综合运用，传达品牌信息，实现品牌价值的传递。
三、视觉语法与多模态隐喻视觉语法是分析视觉图像的一种理论框架，它关注图像的构成元素及其之间的关系，以及这些元素如何通过特定的排列和组合来传达意义。
多模态隐喻则是在视觉语法的基础上，通过多种模态的协同作用，构建出具有隐喻性质的广告信息。
这种隐喻不仅体现在图像的视觉表达上，还体现在声音、文字等元素的综合运用上。
四、《悟空》广告的多模态隐喻构建华为广告《悟空》以其独特的叙事方式和视觉冲击力，成功构建了多模态隐喻。
广告中，通过孙悟空这一经典形象，隐喻性地表达了华为品牌的创新精神和科技实力。
同时，通过画面、音乐、文字等元素的巧妙组合，构建了丰富的视觉和听觉空间，使观众在观看过程中产生共鸣和情感投射。
五、《悟空》广告中的视觉语法分析从视觉语法的角度来看，《悟空》广告中的图像构图、色彩运用、视觉焦点等元素都经过了精心设计。
画面中孙悟空的形象与现代科技元素的结合，既体现了品牌的传统精神，又展现了品牌的科技创新。
色彩的运用上，通过鲜明的色彩对比，强化了画面的视觉冲击力。
而视觉焦点的安排，则有效地引导了观众的注意力，突出了广告中的关键信息。
六、多模态隐喻的传播效果《悟空》广告通过多模态隐喻的构建，成功地实现了品牌价值的传递。
Computer Science DepartmentTechnical ReportNWU-CS-04-47Oct 28, 2004Temporally Adaptive Frameless RenderingAbhinav Dayal, Cliff Woolley, David Luebke, Benjamin WatsonAbstractRecent advances in computational power and algorithmic sophistication have made ray tracing an increasingly viable and attractive algorithm for interactive rendering. Assuming that these trends will continue, we are investigating novel rendering strategies that exploit the unique capabilities of interactive ray tracing. Specifically, we propose a system that adapts rendering effort spatially, sampling some image regions more densely than others, and temporally, sampling some regions more often than others. Our system revisits and extends Bishop et al.'s frameless rendering with new approaches to sampling and reconstruction. We make sampling both spatially and temporally adaptive, using closed loop feedback from the current state of the image to continuously guide sampling toward regions of the image with significant change over space or time. We then send these frameless samples in a continuous stream to a temporally deep buffer, which stores all the samples created over a short time interval. The image to be displayed is reconstructed from this deep buffer. Reconstruction is also temporally adaptive, responding both to sampling density and color gradient. Where the displayed scene is static, spatial color change dominates and older samples are given significant weight in reconstruction, resulting in sharper images. Where the scene is dynamic, more recent samples are emphasized, resulting in a possibly blurry but up-to-date image. We describe a CPU-based implementation that runs at near-interactive rates on current hardware, and analyze simulations of the real-time performance we expect from future hardware-accelerated implementations. Our analysis accounts for temporal as well as spatial error by comparing displayed imagery across time to a hypothetical ideal renderer capable of instantaneously generating optimal frames. From these results we argue that the temporally adaptive approach is not only more accurate than frameless rendering, but also more accurate than traditional framed rendering at a given sampling rate.Keywords:[Computer Graphics]: Display algorithms, Raytracing, Virtual reality, frameless rendering, adaptive rendering, reconstruction, sampling, global illumination.Technical Report(NWU-CS-04-47)Northwestern University(Evanston,IL)Temporally Adaptive Frameless RenderingAbhinav Dayal†,Cliff Woolley‡,David Luebke‡,and Benjamin Watson††Department of Computer Science,Northwestern University‡Department of Computer Science,University of VirginiaAbstractRecent advances in computational power and algorithmic sophistication have made ray tracing an increasingly viable and attractive algorithm for interactive rendering.Assuming that these trends will continue,we are inves-tigating novel rendering strategies that exploit the unique capabilities of interactive ray tracing.Specifically,we propose a system that adapts rendering effort spatially,sampling some image regions more densely than others, and temporally,sampling some regions more often than others.Our system revisits and extends Bishop et al.’s frameless rendering with new approaches to sampling and reconstruction.We make sampling both spatially and temporally adaptive,using closed loop feedback from the current state of the image to continuously guide sampling toward regions of the image with significant change over space or time.We then send these frameless samples ina continuous stream to a temporally deep buffer,which stores all the samples created over a short time interval.The image to be displayed is reconstructed from this deep buffer.Reconstruction is also temporally adaptive,re-sponding both to sampling density and color gradient.Where the displayed scene is static,spatial color change dominates and older samples are given significant weight in reconstruction,resulting in sharper images.Where the scene is dynamic,more recent samples are emphasized,resulting in a possibly blurry but up-to-date image.We describe a CPU-based implementation that runs at near-interactive rates on current hardware,and analyze sim-ulations of the real-time performance we expect from future hardware-accelerated implementations.Our analysis accounts for temporal as well as spatial error by comparing displayed imagery across time to a hypothetical ideal renderer capable of instantaneously generating optimal frames.From these results we argue that the temporally adaptive approach is not only more accurate than frameless rendering,but also more accurate than traditional framed rendering at a given sampling rate.Categories and Subject Descriptors(according to ACM CCS):I.3.3[Computer Graphics]:Picture/Image Generation1.Improving interactive renderingIn recent years a number of traditionally offline rendering al-gorithms have become feasible in the interactive realm.The sudden appearance of programmable high-precision graph-ics processors(GPUs)has drastically expanded the range of algorithms that can be employed in real-time graphics; meanwhile,the steady progress of Moore’s Law has made techniques such as ray tracing,long considered a slow algo-rithm suited only for offline realistic rendering,feasible in real-time rendering settings[23]:.These trends are related; indeed,some of the most promising research on interactive global illumination performs algorithms such as ray tracing and photon mapping directly on the GPU[18,19].Future hardware should provide even better support for these algo-rithms,quickening the day when ray-based algorithms are an accepted and powerful component of every production ren-dering system.What makes interactive ray tracing attractive? Researchers in the area have commented on the ray tracing’s ability to model physically correct global illumination phe-nomena,its easy applicability to different shaders and primi-tive types,and its output-sensitive running time,only weakly dependent on scene complexity[25].We focus on another unique capability available in a ray-based renderer but not a depth-buffered rasterizer.We believe that the ability of in-teractive ray tracing to selectively sample the image plane enables a new approach to rendering that is more interactive, more accurate,and more portable.To achieve these goals, we argue that the advent of real-time ray tracing demandsc Northwestern University NWU-CS-04-47.1Figure1:Adaptive frameless sampling of a moving car in a static scene.Left,the newest samples in each pixel region.Middle, the tiles in the spatial hierarchy.Note small tiles over car and high contrast edges.Right,the per-tile derivative term,which very effectively focuses on the car.a rethinking of the fundamental sampling strategies used in computer graphics.The topic of sampling in ray tracing,and related ap-proaches such as path tracing,may seem nearly exhausted, but almost all previous work has focused on spatial sam-pling,or where to sample in the image plane.In an interac-tive setting,the question of temporal sampling,or when to sample with respect to user input,becomes equally impor-tant.Temporal sampling in traditional graphics is bound to the frame:an image is begun in the back buffer incorporat-ing the latest user input,but by the time the frame is swapped to the front buffer for display,the image reflects stale input. To mitigate this,interactive rendering systems increase the frame rate by reducing the complexity of the scene,trad-ing offfidelity for performance.We consider this tradeoff in terms of spatial error and temporal error.Spatial error is caused by rendering coarse approximations for speed,and includes such factors as resolution of the rendered image and geometric complexity of the rendered models.Tempo-ral error is caused by the delay imposed by rendering,and includes such factors as how often the image is generated (frame rate)and how long the image takes to render and dis-play(latency).In this paper we investigate novel sampling schemes for managing thefidelity-performance tradeoff.Our approach has two important implications.First,we advocate adaptive temporal sampling,analogous to the adaptive spatial sam-pling that takes place in progressive ray tracing[16,2,14]. Just as spatially adaptive renderers display detail where it is most important,adaptive temporal sampling displays de-tail when it is most important.Second,we advocate frame-less rendering[3],in which samples are not collected into coherent frames for double-buffered display,but instead are incorporated immediately into the image.Frameless render-ing,which requires a per-sample rendering algorithm such as real-time ray tracing,decouples spatial and temporal updates and thus enables veryflexible adaptive spatial and temporal sampling.Our prototype adaptive frameless render is broken into three primary sub-systems.An adaptive sampler directs ren-dering to image regions undergoing significant change(in space and/or time).The sampler produces a stream of sam-ples scattered across space-time;recent samples are col-lected and stored in a temporally deep buffer.An adap-tive reconstructor repeatedly reconstructs the samples in the deep buffer into an image for display,adapting the recon-structionfilter to local sampling density and color gradients. Where the displayed scene is static,spatial color change dominates and older samples are given significant weight in reconstruction,resulting in sharper images.Where the scene is dynamic,only more recent samples are emphasized,re-sulting in a possibly blurry but up-to-date image.We describe the design of an interactive system built on these principles,and show in simulation that this system achieves superior rendering accuracy and responsiveness. We evaluate our system with a“gold standard”analysis that compares displayed imagery to the ideal image that would be displayed by a hypothetical ideal renderer,evaluating the im-age difference at using mean RMS error and and show that it outperforms not only the pseudorandom frameless sam-pling of Bishop et al.[3],but also traditional framed sam-pling strategies with the same overall sampling rate.Since our approach is self-monitoring,we also argue that it can achieve a new level of portability and adaptivity to changes in platform and load.2.Related work2.1.Interactive ray tracingRecent years have seen interactive ray tracing go from an oxymoron to a reality.Interactive ray tracers have been demonstrated on supercomputers[17],PC clusters[26],onc Northwestern University NWU-CS-04-47.2the SIMD instruction sets of modern CPUs[24],and on graphics hardware[18][4].Wald et al.provide a good sum-mary of the state of the art[25].To build an interactive ray tracer,while hardly simple,is becoming a matter of engi-neering.Looking forward,hardware and software systems are being developed[21][10]that help harness the render-ing power of PC clusters,and consumer graphics hardware is growing more powerful and moreflexible at an astonishing rate.Real-time ray tracing is currently feasible;we believe it will soon become commonplace even on commodity desk-top hardware.2.2.Interruptible renderingRecent work on temporally adaptive sampling includes a new approach tofidelity control called interruptible render-ing[30]that adaptively controls frame rate to minimize the sum of spatial and temporal error.They propose a progres-sive rendering framework that renders a coarse image into the back buffer and continuously refines it,while tracking the error introduced by subsequent input(such as changes in viewpoint).When this temporal error exceeds the spatial er-ror caused by coarse rendering,there is no longer any reason to refine further,since any improvement to the appearance of objects in the image will be overwhelmed by their incor-rect position and/or size.In other words,further refinement becomes pointless when the error due to the image being late is greater than the error due to the image being coarse. The front and back buffers are then swapped and rendering begins again into the back buffer for the most recent view-point.The resulting system produces coarse,high frame-rate display when input is changing rapidly,andfinely detailed, low frame rate display when input is static.2.3.Frameless renderingInterruptible rendering retains a basic underlying assump-tion of interactive computer graphics:all pixels in a given image represent a single moment in time(or possibly afixed duration surrounding that moment,e.g.the“shutter speed”used for motion blur).When the system swaps buffers,all pixels in the image are simultaneously replaced with pix-els representing a different moment in time.In interactive settings,this coherent temporal sampling strategy has sev-eral unfortunate perceptual consequences:temporal alias-ing,delay,and temporal discontinuity.Temporal aliasing re-sults when the sampling rate is inadequate to capture high speed motion.Motion blur techniques can compensate for this aliasing,but are generally so expensive that in interac-tive settings they actually worsen the problem.Delay is a byproduct of double buffering,which avoids tearing(simul-taneous display of two partial frames)at the cost of ensuring that each displayed scene is at least two frames old before it is swapped out.Even at a60Hz frame rate,this introduces 33ms of delay–a level that human factors researchers have consistently shown can harm task performance[29][20].Fi-nally,when frame rates fall below60Hz,the perceptual sen-sation of image continuity is broken,resulting in display of choppy or“jerky”looking motion.Interruptible rendering performs adaptive temporal sam-pling to achieve higher accuracy,but that sampling is still coherent:all pixels(or,more generally,spatial samples) still represent the same moment in time.We have since fo-cused our research on the unique opportunities for tempo-rally adaptive rendering presented by Bishop et al.’s frame-less rendering[3].This novel rendering strategy replaces the coherent,simultaneous,double-buffered update of all pixels with stochastically distributed spatial samples,each repre-senting the most current input when the sample was taken. Frameless rendering thus decouples spatial and temporal sampling,so that the pixels in a frameless image represent many moments in time.We build on and improve the original frameless rendering approach.Our rendering system samples rapidly changing regions of an image coarsely but frequently to reduce tempo-ral error,while refining static portions of the image to reduce spatial error.We improve the displayed image by perform-ing a reconstruction step,filtering samples in space and time so that older samples are weighted less than recent samples; reconstruction adapts to the local sampling density and color gradient to optimize thefilter width for different parts of the scene.In the following sections we describe our temporally adaptive sampling and reconstruction strategies,and discuss their implementation in a simulated prototype system.We next describe the“gold standard”evaluation technique and analyze our prototype against traditional rendering as well as an“oracle”system that knows which samples are valid and which contain stale information.We close by discussing fu-ture directions and argue that frameless,temporally adaptive systems will ultimately provide more interactive,accurate, portable rendering.3.Temporally adaptive,closed-loop samplingWhile traditional frameless sampling is unbiased,we make our frameless renderer adaptive to improve rendering qual-ity.Sampling is both spatially adaptive,focusing on regions where color changes across space;and temporally adaptive, focusing on regions where color changes over time(Fig-ure1).As in previous spatially adaptive rendering methods [16][2][14][9],adaptive bias is added to sampling with the use of a spatial hierarchy of tiles superimposed over the view.However,while previous methods operated in the static context of a single frame,we operate in a dynamic frameless context.This is has several implications.First, rather than operating on a frame buffer,we send samples to a temporally deep buffer that collects samples scattered across space-time.Our tiles therefore partition a space-time vol-ume using planes parallel to the temporal axis.As in framedc Northwestern University NWU-CS-04-47.3schemes,color variation within each tile guides rendering bias,but variation represents change over not just space but also time.Moreover,variation is not monotonically decreas-ing as the renderer increases the number of tiles,but con-stantly changing in response to user interaction and anima-tion.Therefore the hierarchy is also constantly changing, with tiles continuously merged and split in response to dy-namic changes in the contents of the deep buffer.We implement our dynamic spatial hierarchy using a K-D tree.Given a target number of tiles,the tree is managed to en-sure that the amount of color variation per unit space-time in each tile is roughly equal:the tile with the most color varia-tion is split and the two tiles with the least summed variation are merged,until all tiles have roughly equal variation.As a result,small tiles are located over buffer regions with signif-icant change orfine spatial detail,while large tiles emerge over static or coarsely detailed regions(Figure1). Sampling then becomes a biased,probabilistic process. Since time is notfixed as in framed renderers,we cannot simply iteratively sample the tile with the most variation per unit space-time–in doing so,we would overlook newly emerging motion and detail.At the same time,we cannot leave rendering unbiased and unimproved.Our solution is to sample each tile with equal probability,and select the sampled location within the tile using a uniform distribu-tion.Because tiles vary in size,sampling is biased towards those regions of the image which exhibit high spatial and/or temporal variance.Because all tiles are sampled,we remain sensitive to newly emerging motion and detail.This sampler is in fact a closed loop control system[7], capable of adapting to user input with greatflexibility.In control theory,the plant is the process being directed by the compensator,which must adapt to external disturbance. Output from the plant becomes input for the compensator, closing the feedback loop.In a classic adaptive framed sam-pler,the compensator chooses the rendered location,the ray tracer is the plant that must be controlled,and disturbance is Figure2:Adaptive frameless sampling as closed loop con-trol.Output sample from the ray tracer(plant)is sent to an error tracker,which adjusts the spatial tiling or error map. As long as the error map is not zero everywhere,the adap-tive sampler(compensator)selects one tile to render,and one location in the tile.Constantly changing user input(dis-turbance)makes it very difficult to limiterror.Figure3:A snapshot of color gradients in the car scene. Green and red are spatial gradients,blue is the temporal gradient.Here the spatial gradients dominate,and the num-ber of tiles is fairly high.provided by the scene as viewed at the time being rendered. Our frameless sampler(Figure2)faces a more difficult chal-lenge:view and scene state may change after each sample. Unfortunately,a ray tracer is extremely nonlinear and highly multidimensional,and therefore very difficult to analyze us-ing control theoretic techniques.Nevertheless,more pragmatic control engineering tech-niques may be applied.One such technique is the use of PID controllers,in which control may respond in proportion to error itself(P),to its integral(I),and to its derivative(D). In our sampler,error is color variation–if it were small enough,we could assume that rendering was complete.In biasing sampling toward variation,we are already respond-ing in proportion to error.However,we have also found it useful to respond to error’s derivative.By biasing sampling toward regions in which variation is changing,we compen-sate for delay in our control system and direct sampling to-ward changing regions of the deep buffer,such as the edges of the car in Figure1.We accomplish this by tracking vari-ation change d,and adding it to variation itself p to form a new summed control error:e=kp+(1−k)d,where k in the range[0,1]is the weight applied to the proportional term.The right image in Figure1visualizes d for each tile by mapping high d values to high gray levels.Our prototype adaptive sampler will be less effective when the rendered scene is more dynamic.In control the-ory,one way of compensating for varying rates of change in the target signal is to adjust gain,thereby damping or am-c Northwestern University NWU-CS-04-47.4plifying the impact of control on the plant.A similar sort of compensation is possible in our sampling control system by restricting or increasing the ability of the sampler to adapt to deep buffer content.We implement this approach by ad-justing the number of tiles in the K-D tree according to the ratio of color change over time to color change across space. We achieve this by ensuring that dC/dsS=dC/dtT,where dC/ds and dC/dt are color change over space and time(Fig-ure3),S is the average width of the tiles,and T the average age of the samples in each tile.By solving for S we can de-rive the current number of tiles that would be appropriate.4.Space-time reconstruction for interactive rendering Frameless sampling strategies demand a rethinking of the traditional computer graphics concept of an“image”,since at any given moment the samples in an image plane repre-sent many different moments in time.The original frame-less work[3]simply displayed the most recent sample at every pixel,a strategy we refer to as traditional reconstruc-tion of the frameless sample stream.The result is a noisy, pixelated image which appears to sparkle or scintillate as the underlying scene changes(see Figure4).Instead we store a temporally deep buffer of recent frameless samples, and continuously reconstruct images for display by convolv-ing the samples with a space-timefilter.This is similar to the classic computer graphics problem of reconstruction of an image from non-uniform samples[14],but with a tem-poral element:since older samples may represent“stale”data,they are treated with less confidence and contribute less to nearby pixels than more recent samples.The resulting images greatly improve over traditional reconstruction(see again Figure4).The key question is what shape and sizefilter to use.A temporally narrow,spatially broadfilter(i.e.afilter which falls off rapidly in time but gradually in space)will give very little weight to relatively old samples;such afilter em-phasizes the newest samples and leads to a blurry but very current image.Such afilter provides low-latency response to changes and should be used when the underlying image is changing rapidly(Figure4,right member of leftmost pair).A temporally broad,spatially narrowfilter will give nearly as much weight to relatively old samples as to recent sam-ples;such afilter accumulates the results of many samples and leads to afinely detailed,antialiased image when the underlying scene is changing slowly(Figure4,right mem-ber of rightmost pair).However,often different regions of an image change at different rates;for example,in a sta-tionary view in which an object is moving across a static background.A scene such as this demands spatially adap-tive reconstruction,in which thefilter width varies across the image.What should guide this process?We use local sampling density and space-time gradient information to guidefilter size.The sampler provides an es-timate of sampling density for an image region,based on the overall sampling rate and on the tiling used to guide sampling.We size ourfilter–which can be interpreted as a space-time volume–as if we were reconstructing a regular sampling with this local sampling density,and while pre-serving the total volume of thefilter perturb thefilter widths according to local gradient information.We reason that a large spatial gradient implies an edge,which should be re-solved with a narrowfilter to preserve the underlying high frequencies.Similarly,a large temporal gradient implies a “temporal edge”such as an occlusion event,which should be resolved with a narrowfilter to avoid including stale sam-ples from before the event.What function to use for thefilter kernel remains an open question.Signal theory tells us that for a regularly sampled bandlimited function,ideal reconstruction should use a sinc function,but our deep buffer is far from regularly sampled and the underlying signal(an image of a three-dimensional scene)contains high-frequency discontinuities such as oc-clusion boundaries.We currently use an inverse exponential filter so that the relative contribution of two samples does not change as both grow older;however,the bandpass properties of thisfilter are less than ideal.We would like to investigate multistage approaches inspired by the classic Mitchellfilter [14].Our implementation of a deep buffer stores the last n sam-ples within each pixel;typical values of n range from1to8. As samples arrive they are bucketed into pixels and added to the deep buffer,displacing the oldest sample in that pixel; average gradient information is also updated incrementally as samples arrive.At display time a reconstruction process adjusts thefilter size and widths at each pixel as described (using gradient and local sample density)and gathers sam-ples“outwards”in space and time until the maximum possi-ble incremental contribution of additional samples would be less than some thresholdε(ε=1%in our case).Thefinal color at that pixel is computed as the normalized weighted average of sample colors.This process is expensive–our simulation requires reconstruction times of a few hundred ms for small(256×256)image sizes–so we are investi-gating several techniques to accelerate reconstruction.One currentlykey technique will be to implement the reconstruc-tion process directly on the graphics hardware,and we have a prototype implementation of a GPU-based reconstructor in which the deep buffer is represented as a texture with sam-ples interleaved in columns;samples are added to the buffer by rendering points with a special pixel shader enabled.At display time the system reconstructs an image by drawing a single screen-sized quad with a(quite elaborate)pixel shader that reads andfilters samples from the deep buffer texture. Though not yet fully adaptive,our initial GPU implementa-tion provides a promising speedup(more than an order of magnitude)over the CPU version.We plan to revisit this implementation,which is far from optimized,and hope for another order of magnitude to allow the system to achieve interactive frame rates on realistic image resolutions.c Northwestern University NWU-CS-04-47.5Figure4:Adaptive reconstructions.The left pair shows a dynamic scene,with the traditional frameless reconstruction on the left and the adaptive reconstruction on the right.The right pair shows a static scene,with the traditional reconstruction once more on the left and the adaptive reconstruction on the right.5.Gold standard evaluationUsing the gold standard validation described in[30],wefind that our adaptive frameless renderer consistently out-performs other renders that have the same sampling rates.Gold standard validation uses as its standard an ideal ren-derer I capable of rendering imagery in zero time.To per-form comparisons to this standard,we create n ideal imagesI j(j in[1,n])at60Hz for a certain animation A using a sim-ulated ideal renderer.We then create n more images R j foranimation A using an actual interactive renderer R.We nextcompare each image pair(I j,R j)using an image comparisonmetric comp,and average the resulting image differences:1/nΣn1comp(I j,R j).Note that if this comparison metric isroot mean squared(RMS)error,this result is very closely re-lated to the peak signal-to-noise ratio(PSNR),a commonlyused measure of video quality.We report the results of our gold standard evaluation us-ing PSNR in Figure5below.In thefigure,we compare sev-eral rendering methods.Two framed renderings either max-imize Hz at the cost of spatial resolution(lo-res),or spatialresolution at the cost of Hz(hi-res).The traditional frame-less rendering uses a pseudorandom non-adaptive samplerand simply displays the newest sample at a given pixel.Theadaptive frameless renderings come in two groups:one thatuses afixed number of tiles(256),and one that uses a vari-able number of tiles,as determined by balance between spa-tial and temporal change in the sample stream.In both thefixed and variable groups,there are three biases in responseto sampling:biased toward color change itself(k=.8)(P),to-ward the derivative of color change(k=.2)(D),or balanced.Rendering methods were tested in3different animations:thepublicly available BART testbed[12];a closeup of a toy carin the same testbed,and a dynamic interactive recording.Allof these animations were rendered at sampling rates of100Kpixels per second,two at1M pixels per second.Adaptive frameless rendering is the clear winner,withhigh PSNRs throughout.In the largely static Toycar stream,it made almost no difference whether adaptive frameless ren-dering used afixed or variable number of tiles.But in theother more dynamic streams,the variable number of tilesconsistently had a small edge.Responding to the deriva-tive of color change was slightly more effective when thescene was static,but the pattern here was less clear.In allcases however,adaptive frameless rendering was better thanframed or traditional frameless rendering.A quick glance at Figure6confirms this impression.These graphs show frame by frame comparisons using RMSbetween many of these rendering techniques and the idealrendering.Adaptive frameless rendering is the consistentwinner with traditional frameless and framed renderings al-most always with more error.The lone exception is in theBART100K stream,when the animation begins with a verydynamic content and uses sampling rate20x too slow to sup-port60Hz at full resolution.In addition to evaluating the ability of our entire adap-tive renderer to approximate a hypothetical ideal renderer,we can evaluate the ability of our reconstruction sub-systemto approximate a hypothetical ideal reconstructor.This sam-ple oracle evaluation generates crucial and detailed feedbackfor the development of both the reconstruction sub-systemand by extension,the entire rendering system.Sample ora-cle evaluation is identical to gold standard evaluation in allrespects save one:rather than comparing the reconstructedinteractive image R j to a corresponding ideal image I j,itRender Method Toycar 100K Toycar 1M BART 100K BART 1M Dynamic 100KFramed: lo-res11.2615.8210.1612.6910.35Framed: hi-res9.0115.38 4.587.517.54Traditional frameless16.7118.07 6.319.9710.54Adaptive: fixed (k=0.2)18.111910.3515.0813.95Adaptive: fixed (k=0.5)18.0118.9910.415.3914.17Adaptive: fixed (k=0.8)17.8618.9310.2115.5814.13Adaptive: var (k=0.2)18.1518.9710.8615.0714.04Adaptive: var (k=0.5)18.0118.961115.4214.31Adaptive: var (k=0.8)17.8518.9210.9515.714.36Animation / Sampling RateFigure5:A comparison of several rendering techniques toan ideal rendering using peak signal to noise ratio(PSNR),across three animations and two sampling rates.c Northwestern University NWU-CS-04-47. 6。
