英文文献 科技类 原文及译文33
科技英语 全文翻译
机器人走进千家万户(1)比尔﹒盖茨设想一下：在一个新的产业诞生之际, 你目睹见证了这一切！这个产业是在前所未有的新技术基础上发展起来的, 其中包括一些实力雄厚企业销售的高度专业化商务设备, 还有越来越多的新兴公司生产的新奇玩具、为玩具藏家青睐的机巧装置以及其他一些奇特有趣的特殊产品。
但同时, 这还是一个缺乏行业标准和平台的产业，且尚不成规模。
项目复杂, 进步缓慢, 实际应用更是少之有少。
事实上, 尽管对这个产业的未来充满热情和希望,但是没有人能明确地说出什么时间-- 或究竟是否有可能--它能取得关键性的规模发展。
但是,若真能实现发展, 那么,它很可能改变整个世界。
当然, 上述描述可算是上世纪70 年代中期计算机产业的写照, 也就在那时, 保罗·艾伦和我成立了微软公司。
当时,部分大企业、政府部门和其他一些机构都在使用笨重昂贵的主计算机进行后台运算。
知名大学和大型工业实验室的研究人员正试图建造出最基本的构件, 以使信息化时代的到来成为可能。
当时因特尔公司刚刚推出他们的8080 微处理器,安他利公司正在销售一款流行电子游戏Pong 。
而在一些自发组成的计算机俱乐部里,热忠于此的人们急切地努力探索这种新技术带来的好处究竟是什么。
但当时我脑海中所萦绕的则是更具前瞻性的问题:机器人产业即将作为一项新兴的产业而崛起,其当时的发展同30 年前计算机的发展如出一辙。
想想看, 目前汽车组装线上使用的制造型机器人已替代了昔日的主计算机。
这个产业其他的典型产品包括可进行外科手术的机器手, 在伊拉克和阿富汗用于路边及地面排雷的侦察机器人, 以及可以进行地板吸尘的家用机器人。
电子产品公司还推出了可模仿人类、狗、恐龙等的机器人玩具, 而玩具收藏者们正迫不及待地想要猎取一套乐高公司生产的最新机器人系列玩具。
与此同时, 世界尖端科技人员正试图解决机器人技术中最棘手的难题, 诸如视觉识别、远程操控、以及学习型机器等问题, 而且他们正在不断获得成功。
科技文献中英文摘要范文
科技文献中英文摘要范文English:Nowadays, with the rapid advancement of technology, there has been increasing interest in applying artificial intelligence (AI) to various fields such as healthcare, finance, transportation, and more. AI has the potential to revolutionize these industries by improving efficiency, accuracy, and decision-making processes. In healthcare, AI tools can assist doctors in diagnosing diseases, predicting patient outcomes, and even personalizing treatment plans. In finance, AI algorithms can analyze market trends, predict stock prices, and detect fraudulent activities. In transportation, AI can optimize routes, reduce traffic congestion, and improve safety measures. Despite the great benefits AI can bring, there are also ethical and privacy concerns that need to be addressed. It is essential for policymakers, researchers, and industry professionals to work together to ensure responsible and ethical AI development.中文翻译:如今，随着科技的快速发展，人们越来越热衷于将人工智能（AI）应用于医疗保健、金融、交通等各个领域。
英文文献 科技类 原文及译文33
Multi-texture-model for Water Extraction Based on Remote Sensing ImageHua WANG, Li PAN, Hong ZHENGSchool of Remote Sensing and Information & Engineering, Wuhan University 129 Luoyu Road,Wuhan 430079,P.R.ChinaSchool of Electronic Information, Wuhan University 129 Luoyu Road, Wuhan 430079,P.R.ChinaAbstract:In this paper, a multi-texture-model for water extraction based on remote sensing imagery is proposed. The model is applied to extract inland water (including wide river, lake and reservoir)from high-resolution panchromatic images. Firstly directional variance is used to find river regions, and then grain table is adopted to avoid noise including objects that have similar directional variance characteristic as water surfaces. The experiment result shows that the proposed method provides an effective way for water extraction.1. IntroductionThe recognition of water from remote sensing image has drawn considerable attention in recent yeas. A large number of publications about water extraction appeared and various approaches for water extraction have been proposed. Zhou developed a descriptive model for automatic extraction of water based on spectral characteristics[1]. Barton applied channel 4 for NOAA/AVHRR to extract water[2]. Du proposed a approach for water extraction from SPOT-5 based on decision tree algorithm[3]. Li recognized and monitored clear water from MODIS[4]. Wu extracted water from Quick Bird image and used active contour model to obtain accurate position of river bank[5]; In order to extract water from high-spatial remote sensing images, He used wavelet technique to expend the information and cleaned main noise of the images, and then presented multi-window linearity reserve technique to conserve linear water[6].Recently, most research work on water extraction was forced on automatic recognition of water from remote sensing images based on spectral characteristics. However, there are some disadvantages of these methods: (1) The resolution of image used for water extraction is low. The minimum size of recognizable object is depended on the spatial resolution of sensor. Therefore it is difficult to obtain accurate position of water boundary. (2) Due to the characteristic of water itself and the sensor applied, in certain channels the spectral features of different objects are equilibrated. The equilibration leads to the phenomena of “different objects same image” or“different images same object”, which results in noise objects included in extraction result.In this paper, a multi-texture-model for water extraction based on remote sensing is proposed. The model is applied to extract inland water (including wide river, lake and reservoir) fromhigh-resolution panchromatic image. Firstly directional variance is applied to find river regions, and then grain table is adopted to avoid noise including objects that have similar directional variance characteristic as water surfaces. The experiment result shows that the proposed method provides an effective way for water extraction.This paper is organized as follows. In Section 2, the directional variance model adopted is introduced. Then, fusion of proposed grain table model with directional variance model is discussed in Section 3.The experimental results of the proposed multi-texture-model and comparative studies with single models are given in Section 4. We conclude this paper in Section 5.2. Directional Variance ModelThe aim of our research is to extract water larger than 100m2from panchromatic images. As shown in Figure 2(a), the research objects can be divided into three classes: wide river, lake and reservoir, which all represent as region in high-resolution imageries. The objects of background can be divided into two classes: building and cropland, which also represent as region.In panchromatic imagery, wide river has a similar gray level to building and cropland, though the mean grayof lake and reservoir is much lower than the background objects. Conventional methods for water extraction based on spectral characteristics are not effective in the situation. In the meantime, water body defines homogeneous areas whereas building and cropland correspond to heterogeneous regions. Therefore, we take into account the homogeneity of the image to separate wide river, lake and reservoir from background instead. To characterize the difference of homogeneity between water body and the other types of areas, we use a textual operator: the directional variance.2.1. The Directional Variance OperatorThe operator is derived from those defined by Guerin & Maitre and Airault & Jamet[10]. As shown in Figure1, the directional variance consists in computing, for each pixel M of the image, the variance of the gray levels of the image on several direction of a circle whose center is M and radius is R. Then, the direction with the highest variance value is kept. Its direction defines the direction for which image is the most heterogeneous, locally. Its variance value is the directional variance value of the pixel M.2.2. Extraction of water based on directional varianceAccording to the definition of the operator, the minimum acreage of recognizable water body is depended on the length of radius R. We have chosen a length of 10 pixels for 1m resolution. The directional variances of the five typical training samples (wide river, lake, reservoir, building and cropland) have been computed and the statistical comparison is summarized in Table1. The overall average of water directional variance is lower than the objects of background.Nevertheless, the directional variance of cropland is similar to wide river with overlapping potion over 90%.Inhigh-resolution panchromatic imagery, details inside wide river, such as boat, wave, etc, are represented clearly which result in the heterogeneous of water. In the meantime, the textures of parts of building (for example, roof ) and cropland are rather fine. In a small window, these potions define homogeneous areas with similar directional variance as wide river. The result is improved if we chosen a length of 100 pixels. The statistical comparison is shown in Table2. If the length of radius is large enough, directional variance of building is higher than other objects obviously with no overlapping portion; the difference between cropland and wide river is increased while the overlapping potion is decreased. However, increasing the radius leads to two problems which are outlined as follow:1) The size of recognizable water body increases;therefore water which has small acreage (for example narrow river) can not be detected.2) The position of water bank is not accurate although the spatial resolution of imagery is rather high.Hence, in this paper, a multi-texture-model is presented and two texture models are fused to extract water from panchromatic images. Firstly, we chose a radius of 10 pixels to extract water based on directional variance; and then, grain table is adopted to avoid noise including parts of building and cropland that have similar directional variance characteristic as water surface.3. Multi-texture-modelIn high-resolution imagery, cropland and building represents structural characteristic. According to this characteristic, grain analysis is adopted for further research on the original extraction based on directional variance. The grain table histogram is able to represent structural characteristic of the research object, which can be applied to recognize many kinds of different objects [12].3.1. Extraction of water fused by grain tableThe grain table histograms of the five typical training samples (wide river, lake, reservoir, building and cropland) are computed and correlation coefficients between them are summarized in Table3. Correlation coefficients between water classes are over 85%, however, correlation coefficients between water classes and background classes are lower than 65%.Hence, we compare the correlation coefficients of regions in extraction image base on directional variance with three water samples and two background samples respectively. If the region has a higher correlation coefficient with background classes, it will be marked background and wiped off[13].4.Experimental ResultsWe run the algorithm on several high-resolution panchromatic images. In Figure2.(a), we have been considering an aerial photograph(6126×4800) of a region in Wuhan, China, the resolution of which is 1m,including building, cropland, wide river( Changjiang river), lake, reservoir and cropland. The results of extraction based on directional variance with radius of 10 pixels is displayed in Figure2.(b), and clearly, water has been detected completely, whereas parts of building and cropland are included as noise objects in the result. Water extraction using directional variance with radius of 100 pixels is displayed in Figure2.(c)with correctness over 95%, however, small lakes are missed and the position of bank is not as accurate as Figure2.(b). Finally, in Figure2.(d), the result of Figure2.(b) is fused by grain table analysis, so that the correctness and completeness of extraction are both over 90%.5. ConclusionsBased on textural analysis of water in high-resolution panchromatic imagery, a multi-texture-model is presented for water extraction.The experimental results proved that the approach is efficient for inland water (including wide river, lake and reservoir) extraction. As the complexity and diversity of water, the rate of recognition of our algorithm fluctuates. Furthermore, the method is supervised which needs a lot of human interference to obtain training samples. Therefore, there are problems to be solved in future:1) Our further work should be extensible to multispectral remote sensing images.2) To decrease human interference, old vector will be applied to obtain training samples instead. 6. AcknowledgmentsThe work was supported by the National Key Technology R&D Program of China under grant No.2006BAB10B01.根据遥感图象的多纹理模型相关的水抽取Hua WANG, Li PAN, Hong ZHENGSchool of Remote Sensing and Information & Engineering, Wuhan University 129 Luoyu Road,Wuhan 430079,P.R.ChinaSchool of Electronic Information, Wuhan University 129 Luoyu Road, Wuhan 430079,P.R.China文摘：在本文中，提议了一个多纹理模型为根据遥感成像的水提取。
科技文献中英文对照翻译
Sensing Human Activity:GPS Tracking感应人类活动:GPS跟踪Stefan van der Spek1,*,Jeroen van Schaick1,Peter de Bois1,2and Remco de Haan1Abstract:The enhancement of GPS technology enables the use of GPS devices not only as navigation and orientation tools,but also as instruments used to capture travelled routes:assensors that measure activity on a city scale or the regional scale.TU Delft developed aprocess and database architecture for collecting data on pedestrian movement in threeEuropean city centres,Norwich,Rouen and Koblenz,and in another experiment forcollecting activity data of13families in Almere(The Netherlands)for one week.Thequestion posed in this paper is:what is the value of GPS as‘sensor technology’measuringactivities of people?The conclusion is that GPS offers a widely useable instrument tocollect invaluable spatial-temporal data on different scales and in different settings addingnew layers of knowledge to urban studies,but the use of GPS-technology and deploymentof GPS-devices still offers significant challenges for future research.摘要：增强GPS技术支持使用GPS设备不仅作为导航和定位工具,但也为仪器用来捕捉旅行路线:作为传感器,测量活动在一个城市或区域范围内规模。
英文文献科技类原文及翻译（电子电气自动化通信…）50
目录1译文 (1)2原文 (7)1参考文献译文绿色创想建筑商计划提供了节能解决方案与行业认可的新住房平均相比，绿色畅想建筑商计划旨在降低家用能源和水的想好，减少排放。
该项目创新性地结合了建筑科学和高品质的产品，在帮助建筑商和开发商建造舒适型住房的同时，降低房屋对环境的影响。
随着生活费用的不断上涨，悦来愈多的人开始考虑将环保技术纳入新住房当中。
与行业认可的新住房陪你冠军水瓶相比，依照GE绿色创想建筑商计划所建造的房屋每年客减少20%的能耗与室内用水量，并且使生活用气排放量减少20%。
对于一套面积为2500平方英尺的住房而言，该计划每年可使购房者减少600至1500美元的电费和水费。
自该计划于2007年5月启动以来，整个美国与加拿大的建筑商与开发商纷纷申请建造绿色创想式房屋，其中包括德州西斯顿峡谷们的社区开发商。
按照绿色畅想计划正在开发的首个峡谷么社区被称为Discovery Companies，预计将于2008年夏季开盘。
加拿大的Fi的零售税环保想象住房计划推出在2007年9月，GE加拿大与波尔多发展组织签订计划，决定在位于加拿大阿尔伯他省卡尔加里西部的社区Rocky View实施加拿大首个绿色创想建筑商计划。
这块地区60多年来，一直有当地的一个牧民家庭所有，长期以来除了放养家畜之外始终难以用于其他用途。
迫于地区发展的强大压力，这个家庭决定对这块土地进行开发。
当这家人了解到如何最邮箱的进行地产开发之后，开始认真考虑如何处理这篇土地。
其中，家庭价值、对环境的保护意识以及社区精神都称为了需要考虑的关键问题。
实施证明，将GE的绿色创想建筑商计划与波尔多发展组织的环境可持续发展战略相结合是非常成功的。
规划中的面积为1750英亩的混用型绿色创想建筑商和谐开发项目见那个进行客持续开发，其中包括关于有效实用土地的创新性环保计划。
竣工使，此开放项目将建筑起3500所住房和衣架保健中心、一个27洞国际高尔夫球场、一所学校和一篇商业用地。
外文参考文献译文及原文
目录1介绍 (1)在这一章对NS2的引入提供。
尤其是，关于NS2的安装信息是在第2章。
第3章介绍了NS2的目录和公约。
第4章介绍了在NS2仿真的主要步骤。
一个简单的仿真例子在第5章。
最后，在第.8章作总结。
2安装 (1)该组件的想法是明智的做法，以获取上述件和安装他们的个人。
此选项保存downloadingtime和大量内存空间。
但是，它可能是麻烦的初学者，因此只对有经验的用户推荐。
(2)安装一套ns2的all-in-one在unix-based系统 (2)安装一套ns2的all-in-one在Windows系统 (3)3目录和公约 (4)目录 (4)4运行ns2模拟 (6)ns2程序调用 (6)ns2模拟的主要步骤 (6)5一个仿真例子 (8)6总结 (12)1 Introduction (13)2 Installation (15)Installing an All-In-One NS2 Suite on Unix-Based Systems (15)Installing an All-In-One NS2 Suite on Windows-Based Systems (16)3 Directories and Convention (17)Directories and Convention (17)Convention (17)4 Running NS2 Simulation (20)NS2 Program Invocation (20)Main NS2 Simulation Steps (20)5 A Simulation Example (22)6 Summary (27)1介绍网络模拟器(一般叫作NS2)的版本，是证明了有用在学习通讯网络的动态本质的一个事件驱动的模仿工具。
模仿架线并且无线网络作用和协议(即寻址算法，TCP，UDP)使用NS2，可以完成。
一般来说，NS2提供用户以指定这样网络协议和模仿他们对应的行为方式。
科技外文文献原文
AMBULANT:A Fast,Multi-Platform Open Source SML Player Dick C.A. Bulterman, Jack Jansen, Kleanthis Kleanthous, Kees Blom and Daniel Benden CWI: Centrum voor Wiskunde en InformaticaKruislaan 4131098 SJ Amsterdam, The Netherlands +31 20 592 43 00 Dick.Bulterman@cwi.nl ABSTRACTThis paper provides an overview of the Ambulant Open SMIL player. Unlike other SMIL implementations, the Ambulant Player is a reconfigureable SMIL engine that can be customized for use as an experimental media player core.The Ambulant Player is a reference SMIL engine that can be integrated in a wide variety of media player projects. This paper starts with an overview of our motivations for creating a new SMIL engine then discusses the architecture of the Ambulant Core (including the scalability and custom integration features of the player).We close with a discussion of our implementation experiences with Ambulant instances for Windows,Mac and Linux versions for desktop and PDA devices.Categories and Subject Descriptors H.5.1 Multimedia Information Systems [Evaluation]H.5.4 Hypertext/Hypermedia [Navigation]. General TermsExperimentation, Performance, V erification KeywordsSMIL, Player, Open-Source, Demos1.MOTIV ATIONThe Ambulant Open SMIL Player is an open-source, full featured SMIL 2.0 player. It is intended to be used within the researcher community (in and outside our institute) in projects that need source code access to a production-quality SMIL player environment. It may also be used as a stand-alone SMIL player for applications that do not need proprietary mediaformats.The player supports a range of SMIL 2.0 profiles ( including desktop and mobile configurations) and is available in distributions for Linux, Macintosh, and Windows systems ranging from desktop devices to PDA and handheld computers. While several SMIL player implementationsexist,including the RealPlayer [4], InternetExplorer [5], PocketSMIL [7],GRiNS [6],X-SMILES [8] and various proprietary implementations for mobile devices, we developed Ambulant for three reasons:Permission to make digital or hard copiesof all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish,to post on servers or to redistribute tolists,requires prior specific permissionand/or a fee.'MM' 04, October 10-16, 2004, New Y ork, New Y ork, USA.Copyright 2004 ACM 1-58113-893-8/04/0010...$5.00.•N one of the existi ng SMIL players provides a complete and correct SMIL 2.0 implementation. The Ambulant player implements all of SMIL, based on the SMIL 2.0 Language profile plus extensions to support advanced animation and the needs of the mobile variant used by the 3GPP/PSS-6 SMIL specification [9]. •A ll commercial SMIL players are geared to the presentation of proprietary media. The Ambulant player uses open-source media codecs and open-source network transfer protocols, so that the player can be easily customized foruse in a wide range of researchprojects.• Our goal is to build a platform that will encourage the development of comparable multimedia research output.By providing what we expect will be a standard baseline player, other researchers and developmentorganizations can concentrate on integratingextensions to the basic player (either in terms of new media codecs or new network control algorithms). These extensions can then be shared by others.In contrast to the Helix client architecture [10], which also moved to a GPL core in mid-2004, the Ambulant player supports a wider range of SMIL target application architectures,it provides a more complete and correct implementation of the SMIL language,it provides much better performance on low-resource devices and it provides a more extensible media player architecture. It also provides an implementation that includes all of the media codecs as part of the open client infrastructure.The Ambulant target community is not viewers of media content, but developers of multimedia infrastructures, protocols and networks. Our goal has been to augument the existing partial SMIL implementations produced by many groups with a complete implementation that supports even the exotic features of the SMIL language. The following sections provide an introduction to the architecture of the player and describe the state of the various Ambulant implementations. We then discuss how the Ambulant Core can be re-purposed in other projects. We start with a discussion of Ambulant 's functional support for SMIL.2.FUNCTIONAL SUPPORT FOR SMIL 2.0The SMIL 2.0 recommendation [1] defines 10 functional groups that are used to structure the standard '5s0+ modules. These modules define the approximately 30 XML elements and 150 attributes that make up the SMIL 2.0 language. In addition to defining modules, the SMIL 2.0 specification also defines a number of SMIL profiles: collection of elements, attributes and attribute values that are targeted to meet the needs of a particular implementation community. Common profiles include the full SMIL 2.0 Language, SMIL Basic, 3GPP SMIL,XHTML+SMIL and SMIL 1.0 profiles.A review of these profiles is beyond the scope of this paper(see [2]), but a key concern of Ambulant ' sdevelopment has been to provide a player core that can be used to support a wide range of SMIL target profiles with custom player components.This has resulted in an architecture that allows nearly all aspects of the player to be plug-replaceable via open interfaces. In this way, tailored layout, scheduling, media processing and interaction modules can be configured to meet the needs of individual profile requirements. The Ambulant player is the only player that supports this architecture.The Ambulant player provides a direct implementation of the SMIL 2.0 Language profile, plus extensions that provide enhanced support for animation and timing control. Compared with other commercial and non-commercial players, the Ambulant player implements not only a core scheduling engine, it also provides complete support for SMIL layout,interaction, content control and networking facilities.Ambulant provides the most complete implementation of the SMIL language available to date.3.AMBULANT ARCHITECTUREThis section provides an overview of the architecture of the Ambulant core. While this discussion is high-level, it will provide sufficient detail to demonstrate the applicability of Ambulant to a wide range of projects. The sections below consider thehigh-level interface structure, the common services layer and the player com mon core architecture.3.1The High-Level Interface StructureFigure 1 shows the highest level player abstract ion. The player core support top-level con trol exter nal entry points (in clud ing play/stop/pause) and in turn man ages a collection of external factories that provide in terfaces to data sources (both for sta ndard and pseudo-media), GUI and window system interfaces and in terfaces to ren derers. Unlike other players that treat SMIL as a datatype [4],[10], the Ambula nt en gi ne has acen tral role in in teractio n with the input/output/scree n/devices in terfaces.This architecture allows the types of entry points (and the moment of evaluation) to be customized and separated from the various data-sources and renderers. This is important forintegration with environments that may use non-SMIL layout or special device in terface process ing.Figuit 1 k Ambulaittliigk-ljtwLstruchm.3.2The Common Services LayerFigure 2 shows a set of com mon services that are supplied for the player to operate. These in clude operati ng systems in terfaces, draw ing systems in terfaces and support for baseli ne XML fun ctio ns.All of these services are provided by Ambulant; they may also be integrated into other player-related projects or they may be replaced by new service components that are optimized for particular devices or algorithms. Hsurt 2. Amldant Common [Services Liwr/3.3The Player Common CoreFigure 3 shows a slightly abstracted view ofthe Ambula nt com mon core architecture. The view is essentially that of a single instanceof the Ambula nt player. Although only oneclass object is shown for eachservice,multiple interchangeable implementations have been developed for all objects (except the DOM tree) during theplayer 'development. As an example,multiple schedulers have bee n developed to match the fun cti onalcapabilities of various SMIL profiles.Arrows in the figure denote that one abstract class depends on the services offered by the other abstract class. Stacked boxes denote that a si ngle in sta nce of the player will con tain in sta nces of multiple con crete classes impleme nting that abstract class: one for audio, one for images, etc. All of the stacked-box abstract classes come with a factory function to create the in sta nces of the required con crete class.The bulk of the player implementation is architected to be platform in depe ndent. As we will discuss, this platform in depe ndent component has already been reused for five separate player impleme ntati ons. The platform dependent portions of the player include support for actual ren deri ng, UI in teract ion and datasource processing and control. When the player is active, there is asingle instanee of the scheduler and layout manager, both of which depend on the DOM tree object. Multiple instances of data source and playable objects are created. These in teract with multiple abstract rendering surfaces. The playable abstract class is the scheduler in terface (play, stop) for a media no de, while the renderer abstract class is the drawing in terface (redraw). Note that not all playables are ren derers (audio, SMIL ani mati on). The architecture has bee n desig ned to have all comp onents be replaceable, both in terms of an alter native impleme ntati on of a give n set of functionality and in terms of a complete re-purposing of the player components. In this way, the Ambulant core can be migrated to being a special purpose SMIL engine or a non-SMIL engine (such as support for MPEG-4 or other sta ndards).The abstract in terfaces provided by the player do not require a “ SMIL on Top” model of docume nt process ing. The abstract in terface can be used with other high-level control 4.1 Implementation PlatformsSMIL profiles have been defined for a widerange of platforms and devices, ranging fromdesktop implementations to mobile devices. Inorder to support our research on distributedmodels (such as in an XHTML+SMIL implementation), or to control non-SMILlower-level rendering (such as timed text).Note that in order to improve readability of theillustrati on, all auxiliary classes (threadi ng, geometry and color han dli ng, etc.) and several classes that were not important for general un dersta nding (player driver engine, transitions, etc.) have been left out of the diagram.4. IMPLEMENTATION EXPERIENCESThis sectio nwill briefly review ourimpleme ntatio n experie nces with theAmbula nt player. We discuss the implementation platforms used during SMIL ' s development and describe a set of test documents that were created to test the fun cti on ality of the Ambula nt player core. We con clude with a discussi on on the performa nee of the Ambula nt player.SMIL document extensions and to provide a player that was useful for other research efforts, we decided to provide a wide range of SMIL implementations for the Ambulant project. The Ambulant core is available as a single C++ source distribution that provides support for the following platforms:•Linux: our source distributi on in elude makefiles that are used with the RH-8 distribution of Linux. We provide support for media using the FF-MPEG suite [11]. The player interface is built using the Qt toolkit [12]. •Macintosh:Ambulant supports Mac OS X 10.3. Media rendering support is available via the internal Quicktime API and via FF-MPEG . The player user interface uses standard Mac conventions and support (Coca). •Windows: Ambulant provides conventional Win32 support for current generation Windows platforms. It has been most extensivelytested with XP (Home,Professional and TabletPC) and Windows-2000. Media rendering include third-party and local support for imaging and continuous media. Networking and user interface support are provided using platform-embeddedlibraries.•PocketPC: Ambulant supports PocketPC-2000,PocketPC-2002andWindows Mobile 2003 systems. The PocketPC implementations provide support for basic imaging, audio and text facilities.•Linux PDA support:Ambulant provides support for the Zaurus Linux-PDA. Media support is provided via the FF-MPEG library and UI support is provide via Qt. Media support includes audio, images and simple text.In each of these implementations, our initial focus has been on providing support for SMIL scheduling and control functions.We have not optimized media renderer support in the Ambulant 1.0 releases, but expect to provide enhanced support in future versions. 4.2 Demos and Test SuitesIn order to validate the Ambulant player implementation beyond that available with the standard SMIL test suite [3], several demo and test documents have been distributed with the player core. The principal demos include: •Welcome: A short presentation that exercises basic timing,media rendering, transformations and animation.•NYC: a short slideshow in desktop and mobile configurations that exercises scheduling, transformation and media rendering.•News: a complex interactive news document that tests linking, event-based activation, advanced layout, timing and media integration. Like NYC, this demo support differentiated mobile and desktop configurations.•Links: a suite of linking and interaction test cases.•Flashlight: an interactive user'sguide that tests presentation customization using custom test attributes and linking/interaction support. These and other demos are distributed as part of the Ambulant player web site [13].4.3Performance EvaluationThe goal of the Ambulant implementation was to provide a complete and fast SMIL player. We used a C++ implementation core instead of Java or Python because our experience had shownthat on small devices (which we feel hold significant interest for future research), the efficiency of the implementation still plays a dominant role. Our goal was to be able to read, parse, model and schedule a 300-node news presentation in less than two seconds on desktop and mobile platforms. This goal was achieved for all of the target platforms used in the player project. By comparison, the same presentation on the Oratrix GRiNS PocketPC player took 28 seconds to read, parse and schedule. (The Real PocketPC SMIL player and the PocketSMIL players were not able to parseand schedule the document at all because of their limited SMIL language support.)In terms of SMIL language performance, our goal was to provide a complete implementation of the SMIL 2.0 Language profile[14]. Where other players have implemented subsets of this profile,Ambulant has managed to implement the entire SMIL 2.0 feature set with two exceptions: first, we currently do not support the prefetch elements of the content control modules; second, we provide only single top-level window support in the platform-dependent player interfaces. Prefetch was not supported because of the close association of an implementation with a given streaming architecture. The use of multiple top-level windows, while supported in our other SMIL implementation, was not included in version 1.0 of Ambulant because of pending working on multi-screen mobile devices. Both of these feature are expected to be supported in the next release of Ambulant.5.CURRENT STATUS AND AVAILABILITYT his paper describes version 1.0 of the Ambulant player, which was released on July 12, 2004. (This version is also known as the Ambulant/O release of the player.) Feature releases and platform tuning are expected to occur in the summer of 2004. The current release of Ambulant is always available via our SourceForge links [13], along with pointers to the most recent demonstrators and test suites.The W3C started its SMIL 2.1 standardization in May, 2004.At the same time, the W3C' s timed text working group is completing itsfirst public working draft. We will support both of these activities in upcoming Ambulant releases.6.CONCLUSIONSWhile SMIL support is becoming ubiquitous (in no small part due to its acceptance within the mobile community), the availability of open-source SMIL players has been limited. This has meant that any group wishing to investigate multimedia extensions or high-/low-level user or rendering support has had to make a considerable investment in developing a core SMIL engine.We expect that by providing a high-performance, high-quality and complete SMIL implementation in an open environment, both our own research and the research agendas of others can be served. By providing a flexible player framework, extensions from new user interfaces to new rendering engines or content control infrastructures can be easily supported.7.ACKNOWLEDGEMENTS This work was supported by the Stichting NLnet in Amsterdam.8.REFERENCES[1]W3C,SMIL Specification,/AudioVideo.[2]Bulterman,D.C.A and Rutledge, L.,SMIL 2.0:Interactive Multimedia for Weband Mobile Devices, Springer, 2004.[3]W3C,SMIL2.0 Standard Testsuite,/2001/SMIL20/testsuite/[4]RealNetworks,The RealPlayer 10,/[5]Microsoft,HTML+Time in InternetExplorer 6,/workshop/author/behaviors/time.asp[6]Oratrix, The GRiNS 2.0 SMIL Player./[7]INRIA,The PocketSMIL 2.0 Player,wam.inrialpes.fr/software/pocketsmil/. [8],X-SMILES: An Open XML-Browser for ExoticApplications./[9]3GPP Consortium,The Third-GenerationPartnership Project(3GPP)SMIL PSS-6Profile./ftp/Specs/archive/26_series/26.246/ 26246-003.zip[10]Helix Community,The Helix Player./.[11]FFMPEG ,FF-MPEG:A Complete Solution forRecording,Converting and Streaming Audioand Video./[12]Trolltech,Qtopia:The QT Palmtop/[13]Ambulant Project,The Ambulant 1.0 Open Source SMIL 2.0Player, /.[14]Bulterman,D.C.A.,A Linking andInteraction Evaluation Test Set for SMIL,Proc. ACM Hypertext 2004, SantaCruz,August, 2004.。
科技英语阅读与翻译全文
科技英语阅读与翻译全文Humanitarian Aid in SpaceSpace exploration technology will benefit developing countries in a variety of ways. Whether it's information about climate change or communication technologies that give remote areas access to the world outside, space science can come to the aid of vulnerable people in many countries.For the past two decades, Japan Aerospace Exploration Agency (JAXA) has been sending humanitarian aid dispatched from its space platform. This ambitious project has proved successful, and it’s been praised for its achievements in various aspects.The two strategic areas set forth for JAXA’s humanitarian aid effort are science and education. JAXA’s donations of books and puzzle sets are enabling elementary and junior high school kids in India to study science and math. There are plans to utilize remote sensing data to map out natural resources in Nepalese countryside and expand education related to environmental issues in Vietnam. In addition the agency is sending educational videos to the island nation of Palau tobetter understand their own local wildlife.JAXA is considered to be a pioneer in this area since the launch of their humanitarian aid initiative in 1997. The organization strives to make use of space applications for social welfare and reduce disparities in the world through a number of practical endeavors. The effort currently has a global reach, with projects taking place in seven continent, from Latin America to Africa.JAXA’s humanitarian aid programs will continue to grow with better technology and increased resources. The ongoing work reinforces the concept that space science and technology have the potential to contribute to enhancing the lives of people on Earth.人道主义援助在太空太空探索技术将在各个方面受益于发展中国家。
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Multi-texture-model for Water Extraction Based on Remote Sensing ImageHua WANG, Li PAN, Hong ZHENGSchool of Remote Sensing and Information & Engineering, Wuhan University 129 Luoyu Road,Wuhan 430079,P.R.ChinaSchool of Electronic Information, Wuhan University 129 Luoyu Road, Wuhan 430079,P.R.ChinaAbstract:In this paper, a multi-texture-model for water extraction based on remote sensing imagery is proposed. The model is applied to extract inland water (including wide river, lake and reservoir)from high-resolution panchromatic images. Firstly directional variance is used to find river regions, and then grain table is adopted to avoid noise including objects that have similar directional variance characteristic as water surfaces. The experiment result shows that the proposed method provides an effective way for water extraction.1. IntroductionThe recognition of water from remote sensing image has drawn considerable attention in recent yeas. A large number of publications about water extraction appeared and various approaches for water extraction have been proposed. Zhou developed a descriptive model for automatic extraction of water based on spectral characteristics[1]. Barton applied channel 4 for NOAA/AVHRR to extract water[2]. Du proposed a approach for water extraction from SPOT-5 based on decision tree algorithm[3]. Li recognized and monitored clear water from MODIS[4]. Wu extracted water from Quick Bird image and used active contour model to obtain accurate position of river bank[5]; In order to extract water from high-spatial remote sensing images, He used wavelet technique to expend the information and cleaned main noise of the images, and then presented multi-window linearity reserve technique to conserve linear water[6].Recently, most research work on water extraction was forced on automatic recognition of water from remote sensing images based on spectral characteristics. However, there are some disadvantages of these methods: (1) The resolution of image used for water extraction is low. The minimum size of recognizable object is depended on the spatial resolution of sensor. Therefore it is difficult to obtain accurate position of water boundary. (2) Due to the characteristic of water itself and the sensor applied, in certain channels the spectral features of different objects are equilibrated. The equilibration leads to the phenomena of “different objects same image” or“different images same object”, which results in noise objects included in extraction result.In this paper, a multi-texture-model for water extraction based on remote sensing is proposed. The model is applied to extract inland water (including wide river, lake and reservoir) fromhigh-resolution panchromatic image. Firstly directional variance is applied to find river regions, and then grain table is adopted to avoid noise including objects that have similar directional variance characteristic as water surfaces. The experiment result shows that the proposed method provides an effective way for water extraction.This paper is organized as follows. In Section 2, the directional variance model adopted is introduced. Then, fusion of proposed grain table model with directional variance model is discussed in Section 3.The experimental results of the proposed multi-texture-model and comparative studies with single models are given in Section 4. We conclude this paper in Section 5.2. Directional Variance ModelThe aim of our research is to extract water larger than 100m2from panchromatic images. As shown in Figure 2(a), the research objects can be divided into three classes: wide river, lake and reservoir, which all represent as region in high-resolution imageries. The objects of background can be divided into two classes: building and cropland, which also represent as region.In panchromatic imagery, wide river has a similar gray level to building and cropland, though the mean grayof lake and reservoir is much lower than the background objects. Conventional methods for water extraction based on spectral characteristics are not effective in the situation. In the meantime, water body defines homogeneous areas whereas building and cropland correspond to heterogeneous regions. Therefore, we take into account the homogeneity of the image to separate wide river, lake and reservoir from background instead. To characterize the difference of homogeneity between water body and the other types of areas, we use a textual operator: the directional variance.2.1. The Directional Variance OperatorThe operator is derived from those defined by Guerin & Maitre and Airault & Jamet[10]. As shown in Figure1, the directional variance consists in computing, for each pixel M of the image, the variance of the gray levels of the image on several direction of a circle whose center is M and radius is R. Then, the direction with the highest variance value is kept. Its direction defines the direction for which image is the most heterogeneous, locally. Its variance value is the directional variance value of the pixel M.2.2. Extraction of water based on directional varianceAccording to the definition of the operator, the minimum acreage of recognizable water body is depended on the length of radius R. We have chosen a length of 10 pixels for 1m resolution. The directional variances of the five typical training samples (wide river, lake, reservoir, building and cropland) have been computed and the statistical comparison is summarized in Table1. The overall average of water directional variance is lower than the objects of background.Nevertheless, the directional variance of cropland is similar to wide river with overlapping potion over 90%.Inhigh-resolution panchromatic imagery, details inside wide river, such as boat, wave, etc, are represented clearly which result in the heterogeneous of water. In the meantime, the textures of parts of building (for example, roof ) and cropland are rather fine. In a small window, these potions define homogeneous areas with similar directional variance as wide river. The result is improved if we chosen a length of 100 pixels. The statistical comparison is shown in Table2. If the length of radius is large enough, directional variance of building is higher than other objects obviously with no overlapping portion; the difference between cropland and wide river is increased while the overlapping potion is decreased. However, increasing the radius leads to two problems which are outlined as follow:1) The size of recognizable water body increases;therefore water which has small acreage (for example narrow river) can not be detected.2) The position of water bank is not accurate although the spatial resolution of imagery is rather high.Hence, in this paper, a multi-texture-model is presented and two texture models are fused to extract water from panchromatic images. Firstly, we chose a radius of 10 pixels to extract water based on directional variance; and then, grain table is adopted to avoid noise including parts of building and cropland that have similar directional variance characteristic as water surface.3. Multi-texture-modelIn high-resolution imagery, cropland and building represents structural characteristic. According to this characteristic, grain analysis is adopted for further research on the original extraction based on directional variance. The grain table histogram is able to represent structural characteristic of the research object, which can be applied to recognize many kinds of different objects [12].3.1. Extraction of water fused by grain tableThe grain table histograms of the five typical training samples (wide river, lake, reservoir, building and cropland) are computed and correlation coefficients between them are summarized in Table3. Correlation coefficients between water classes are over 85%, however, correlation coefficients between water classes and background classes are lower than 65%.Hence, we compare the correlation coefficients of regions in extraction image base on directional variance with three water samples and two background samples respectively. If the region has a higher correlation coefficient with background classes, it will be marked background and wiped off[13].4.Experimental ResultsWe run the algorithm on several high-resolution panchromatic images. In Figure2.(a), we have been considering an aerial photograph(6126×4800) of a region in Wuhan, China, the resolution of which is 1m,including building, cropland, wide river( Changjiang river), lake, reservoir and cropland. The results of extraction based on directional variance with radius of 10 pixels is displayed in Figure2.(b), and clearly, water has been detected completely, whereas parts of building and cropland are included as noise objects in the result. Water extraction using directional variance with radius of 100 pixels is displayed in Figure2.(c)with correctness over 95%, however, small lakes are missed and the position of bank is not as accurate as Figure2.(b). Finally, in Figure2.(d), the result of Figure2.(b) is fused by grain table analysis, so that the correctness and completeness of extraction are both over 90%.5. ConclusionsBased on textural analysis of water in high-resolution panchromatic imagery, a multi-texture-model is presented for water extraction.The experimental results proved that the approach is efficient for inland water (including wide river, lake and reservoir) extraction. As the complexity and diversity of water, the rate of recognition of our algorithm fluctuates. Furthermore, the method is supervised which needs a lot of human interference to obtain training samples. Therefore, there are problems to be solved in future:1) Our further work should be extensible to multispectral remote sensing images.2) To decrease human interference, old vector will be applied to obtain training samples instead. 6. AcknowledgmentsThe work was supported by the National Key Technology R&D Program of China under grant No.2006BAB10B01.根据遥感图象的多纹理模型相关的水抽取Hua WANG, Li PAN, Hong ZHENGSchool of Remote Sensing and Information & Engineering, Wuhan University 129 Luoyu Road,Wuhan 430079,P.R.ChinaSchool of Electronic Information, Wuhan University 129 Luoyu Road, Wuhan 430079,P.R.China文摘：在本文中，提议了一个多纹理模型为根据遥感成像的水提取。