Sun Microsystems Laboratories
sphinx4-作用
/sphinx4/#sou rce#sourceGeneral Information about Sphinx-4∙IntroductionSphinx-4 is a state-of-the-art speech recognition system written entirely in the Java TM programming language. It was created via a joint collaboration between the Sphinx group at Carnegie MellonUniversity, Sun Microsystems Laboratories, Mitsubishi ElectricResearch Labs (MERL), and Hewlett Packard (HP), with contributions from the University of California at Santa Cruz (UCSC) and theMassachusetts Institute of Technology (MIT).Sphinx-4 started out as a port of Sphinx-3 to the Java programming language, but evolved into a recognizer designed to be much more flexible than Sphinx-3, thus becoming an excellent platform forspeech research.∙CapabilitiesLive mode(实时模式) and batch mode(批处理模式) speech recognizers, capable of recognizing discrete(离散) and continuous(连续)speech.Generalized pluggable front end architecture. Includes pluggable implementations of preemphasis(预加重), Hamming window, FFT, Mel frequency filter bank(mel频率过滤), discrete cosine transform(离散cos变换), cepstral(同态谱/倒频谱) mean normalization(标准化), and feature extraction(特征提取) of cepstra, delta cepstra, double delta cepstra features.Generalized pluggable (语言学模型)language model architecture.Includes pluggable language model support for ASCII and binaryversions of unigram(一元模型), bigram(二元模型), trigram(三元模型), Java Speech API Grammar Format (JSGF), and ARPA-format FST grammars.Generalized (声学模型)acoustic model architecture. Includespluggable support for Sphinx-3 acoustic models.Generalized search management. Includes pluggable support for breadth first(宽度优先) and word pruning(单词修剪)searches.Utilities for post-processing(后处理) recognition results, including obtaining confidence scores(获取信心分), generating lattices(点阵) and embedding ECMAScript into JSGF tags.Standalone tools. Includes tools for displaying waveforms and spectrograms(声谱图) and generating features from audio.(NOTE: The links in this section point to local files created by javadoc. If they are broken, please follow the instructions on Create Javadocs to create these links.)PerformanceSphinx-4 is a very flexible system capable of performing many different types of recognition tasks. As such, it is difficult to characterize the performance and accuracy of Sphinx-4 with just a few simple numbers such as speed and accuracy. Instead, we regularly run regression (回归退化)tests on Sphinx-4 to determine how it performs under a variety of tasks. These tasks and their latest results are as follows (each task is progressively more difficult than the previous task):o Isolated Digits (TI46): Runs Sphinx-4 with pre-recorded test data to gather performance metrics for recognizing just oneword at a time. The vocabulary is merely the spoken digitsfrom 0 through 9, with a single utterance(表达,说话方式)containing just one digit.(TI46 refers to the "NIST(美国国家标准技术研究所) CD-ROM Versionof the Texas(德克萨斯州(美国州名)) Instruments-developed46-Word Speaker-Dependent(特定人的孤立词) Isolated WordSpeech Database".)o Connected Digits (TIDIGITS): Extends the Isolated Digits test to recognize more than one word at a time (i.e.,continuous speech). The vocabulary is merely the spokendigits from 0 through 9, with a single utterance containinga sequence of digits.(TIDIGITS refers to the "NIST CD-ROM Version of the TexasInstruments-developed Studio Quality Speaker-Independent(连续非特定人) Connected-Digit Corpus(语料库)".) o Small Vocabulary (AN4): Extends the vocabulary toapproximately(大约,近似地;) 100 words, with input data rangingfrom speaking words as well as spelling words out letter by letter.o Medium Vocabulary (RM1): Extends the vocabulary to approximately 1,000 words.o Medium Vocabulary (WSJ5K): Extends the vocabulary toapproximately 5,000 words.o Medium Vocabulary (WSJ20K): Extends the vocabulary toapproximately 20,000 words.o Large Vocabulary (HUB4): Extends the vocabulary toapproximately 64,000 words.∙ The following table compares the performance of Sphinx 3.3 with Sphinx-4. Test S3.3 WER S4 WER S3.3 RT S4 RT(1) S4 RT (2) Vocabulary SizeLanguage Model TI46 1.217 0.168 0.14 .03 .02 11 isolated digitsrecognitionTIDIGITS 0.661 0.549 0.16 0.07 0.05 11 continuous digitsAN4 1.300 1.192 0.38 0.25 0.20 79 trigramRM1 2.746 2.88 0.50 0.50 0.41 1,000 trigramWSJ5K 7.323 6.97 1.36 1.22 0.96 5,000 trigramHUB4 18.845 18.756 3.06 ~4.4 3.9560,000 trigram ∙ Note that performance work on the HUB4 test is not complete ∙ Key:o WER - Word error rate (%) (lower is better)错误率o RT - Real Time - Ratio of processing time to audio time -(lower is better)o S3.3 RT - Results for a single or dual (双核) CPUconfigurationo S4 RT(1) - Results on a single-CPU configurationo S4 RT(2) - Results for a dual-CPU configurationThis data was collected on a dual CPU UltraSPARC(R)-III running at 1015 MHz with 2G of memory.。
外籍人才个人英文简历
外籍⼈才个⼈英⽂简历外籍⼈才个⼈英⽂简历stanford university, stanford, cam.s. degree in engineering economic systems and operations research in june 2000.ph.d. degree in management science and engineering june 2004.dissertation title: "multi-agent learning and coordination algorithms for distributed dynamic resource allocation." dissertation advisor: nicholas bambosmassachusetts institute of technology, cambridge, mab.s. degree in mathematics in june 1997.m.s. degree in systems science and control engineering from the department of electrical engineering and computer science in june 1998. masters thesis topic: context-sensitive planning for autonomous vehicles operating in complex, uncertain, and nonstationary environments.experiencesun microsystems laboratories, menlo park, caapril 2003 – present:conceiving, developing and implementing self-managing and self-optimizing capabilities in computer systems, covering domains such as: cache-aware thread scheduling and cpu power management, dynamic sharing of cpu/memory/bandwidth, dynamic data migration in distributed storage systems, dynamic job scheduling and job pricing in cloud computing, dynamic user migration in distributed virtual environments, etc.principal investigator for the adaptive optimization project since 2006.multiple patent applications filed, conference/journal papers published, multiple successful adaptive learning systems designed and implemented. the publicly available case studies are in the “technical reports” section of/people/vengerov/publications.html.intelligent inference systems corp., sunnyvale, ca research scientistapril 2002 – april 2003: started a new research initiative in applying the acfrl algorithm and the previously developed multi-agent coordination algorithms to power control in wireless networks. published several conference papers on this topic. results demonstrate an improvement by more than a factor of 2 in comparison with the algorithms used in is-95 andcdma2000 standards.april 2002 – april 2003: wrote a phase i sttr proposal to the office of naval research and received funding for the topic of “perception-based co-evolutionary reinforcement learning for uav sensor allocation.” developed theoretical algorithms and designed a practical implementation strategy, which demonstrated excellent results in a high-fidelity robotic simulator. published a conference paper.october 1998 – april 2002: wrote a proposal to the nasa program in thinking systems and received multi-year funding for the topic of cooperation and coordination in multi-agent systems. developed, evaluated, and published new reinforcement learning algorithms for dynamic resource allocation among distributed agents operating jointly in complex, uncertain, and nonstationary environments.fall 2000: developed a new algorithm for single-agent learning in noisy dynamic environments with delayed rewards: actor-critic fuzzy reinforcement learning (acfrl). published a conference and a journal paper with a convergence proof for acfrl. us patent (number 6,917,925) was granted for the acfrl algorithm on july 12, 2005.chaincast inc., san jose, caaug 2000 – oct 2000: conducted a survey of techniques for dynamic updating of multicasting trees and suggested a novel approach based on using multi-agent learning.nasa ames research center, moffet field, ca summer 1998: designed a framework for multiple agents operating in a complex,uncertain, and nonstationary environment. agents learn to improve their policies using fuzzy reinforcement learning.sri international, artificial intelligence center, menlo park, casummer 1998: developed a methodology for representing a replanning problem in the space of plans as a reinforcement learning problem.bear, stearns & co., inc. - proprietory trading department, new york, nysummer 1996, 1997: conducted a comprehensive study of time series forecasting models with neural networks. recommended a hybrid model combining best features of the existing models and implemented it in c++.summer 1995: developed a stock forecasting system based on conventional econometric techniques and implemented it in sas language. gained exposure to various proprietary trading models.alphatech, inc., burlington, mafeb 1997 - may 1997: developed an algorithm for optimal control of macroeconomic systems described by simultaneous-time equations and implemented it in matlab.arthur andersen, inc., boston, mafeb 1996 - may 1996: developed an internal system dynamics cashflow model of startup businesses. gained experience in management level client interactions and in project presentation skills.summer 1996: independently designed a game theoretic bid forecasting system in procurement auctions for a large construction company. the project involved extensive on-site client interactions during model development as well as a final presentation to the top level management.property & portfolio research, inc., boston, mafeb 1994 - may 1995: designed a mortgage portfolio analysis model and implemented it in visual basic for excel. developed a methodology for grouping real estate time series using cluster and factor analyses in spss. designed an optimal investment strategy for a class of mortgage backed securities based on the efficient frontier characteristics. gained broad exposure to real estate markets and models.donaldson, lufkin & jenrette, inc. — pershing division, jersey city, njsummer 1994: developed a stock forecasting system based on technical analysis and economic indicators. developed a djia trading strategy based on s&p 500 futures and demonstrated its profitability.mit laboratory for information and decision systems, cambridge, maaug 1993 - may 1994: developed a trading strategy for us treasury bonds based on multi-resolution wavelet analysis. demonstrated its profitability as compared to the conventional moving average models.programmingc++, java, matlab; various packages for statistics, neural networks and system dynamics.publicationspublished 13 papers in refereed conferences, 8 journal papers, 1 book chapter. the complete list, including technical reports, is available at /people/vengerov/publications.html.patentsfour patents granted, 10 patent applications are currently under review at the us patent bureau.personalunited states citizen. fluent in russian and english. black belt and instructor in tae kwon do.last updated 5/26/2009david vengerov【外籍⼈才个⼈英⽂简历】相关⽂章:1.2.3.4.5.6. 7. 8. 9.。
C++初学者指南
《C++Beginner's Guide》C++初学者指南中英双语对照译本1.1---C++简史(下)C++Is BornC++的诞生C++was invented byBjarne Stroustrupin1979,at Bell Laboratories in Murray Hill,New Jersey.He initially called the new language"C with Classes".However in1983the name wan changed to C++. C++是1979年比亚尼·斯特鲁普在新泽西州的莫里山的贝尔实验室发明的。
最初他把这个新的语言命名为“C with Classes”。
到了1983年被重新命名为C++。
Stroustrup built C++on the foundation of C,including all of C’s features,attributes,and benefits. He also adhered to C’s underlying philosophy that the programmer,not the language,is in charge. At this point,it is critical to understand that Stroustrup did not create an entirely new programming language.Instead,he enhanced an already highly successful language.斯特鲁普在C语言的基础上创建了C++,包含全部C语言的特色、属性、和优点。
并且他追随着C语言的基础理念而不是追随C语言,这是很负责的。
在这点上,很明确的事情是,斯特鲁普没有创造出一个全新的编程语言,而是他增强了一个已经很成功了的编程语言。
NIS+ 和 DNS 设置和配置指南说明书
Sun, Sun Microsystems, le logo Sun, Solaris, Solstice, AdminTool, AdminSuite sont des marques deposées ou enregistrées par Sun Microsystems, Inc. aux Etats-Unis et dans certains autres pays. UNIX est une marque enregistrée aux Etats-Unis et dans
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史上最经典的slogan广告词
经典的slogan 广告词1.Good to the last drop.滴滴香浓,意犹未尽。
(麦斯威尔咖啡)2.Obey your thirst.服从你的渴望。
(雪碧)3.The new digital era.数码新时代。
(索尼影碟机)4.We lead. Others copy.我们领先,他人仿效。
(理光复印机)5.Impossible made possible.使不可能变为可能。
(佳能打印机)6.Take time to indulge.尽情享受吧!(雀巢冰激凌)7.The relentless pursuit of perfection.不懈追求完美。
(凌志轿车)8.Poetry in motion, dancing close to me.动态的诗,向我舞近。
(丰田汽车)9.Come to where the flavor is. Marlboro Country.光临风韵之境,万宝路世界。
(万宝路香烟)10.To me, the past is black and white, but the future is always color.对我而言,过去平淡无奇;而未来,却是绚烂缤纷。
(轩尼诗酒)11. Just do it. 只管去做。
(耐克运动鞋)12. Ask for more. 渴望无限。
(百事流行鞋)13. The taste is great. 味道好极了。
(雀巢咖啡)14. Feel the new space. 感受新境界。
(三星电子)15. Intelligence everywhere.智慧演绎,无处不在。
(摩托罗拉手机)16. The choice of a new generation.新一代的选择。
(百事可乐)17. We integrate, you communicate.我们集大成,您超越自我。
(三菱电工)18. Take TOSHIBA, take the world.拥有东芝,拥有世界。
世界500强企业名称中英文对照翻译
世界500强企业名称中英文对照翻译1 Exxon Mobil 埃克森美孚美国炼油301 Onex 的加拿大电子电气2 Wal-Mart Stores 沃尔玛商店美国零售302 Liberty Mutual Insurance Group 利保相互保险集团美国保险3 General Motors 通用汽车美国汽车303 Dentsu 电通日本广告4 Ford Motor 福特汽车美国汽车304 TransCanada Pipelines 的加拿大能源5 DaimlerChrysler 戴姆勒克莱斯勒德国汽车305 NKK 日本钢管日本金属6 Royal Dutch/Shell Group 皇家荷兰壳牌集团荷兰/英国炼油306 Diageo 迪阿吉奥英国饮料7 BP 英国石油英国炼油307 AMP 安宝澳大利亚保险8 General Electric 通用电气美国电子电气308 Sakura Bank 樱花银行日本银行9 Mitsubishi 三菱商事日本多样化309 Weyerhaeuser 惠好美国纸产品10 Toyota Motor 丰田汽车日本汽车310 Nippon Express 日本通运日本邮递运输11 Mitsui 三井物产日本多样化311 Delta Air Lines 德尔塔航空美国航空公司12 Citigroup 花旗集团美国金融312 Skandia Group 斯堪地亚集团瑞典保险13 Itochu 伊藤忠商事日本多样化313 Taisei 大成建设日本工程建筑14 Total Fina Elf 道达尔菲纳埃尔夫法国炼油314 Mitsubishi Chemical 三菱化学日本化学15 Nippon Telegraph & Telephone 日本电报电话日本电信315 Adecco 的瑞士的16 Enron 安然美国能源316 Washington Mutual 华盛顿相互美国银行17 AXA 安盛法国保险317 MYCAL 的日本零售18 Sumitomo 住友商事日本多样化318 Bayerische Landesbank 巴伐利亚银行德国银行19 Intl. Business Machines 国际商用机器美国计算机319 Sun Microsystems 太阳微系统美国计算机20 Marubeni 丸红商事日本多样化320 Dexia Group 的比利时/法国银行21 Volkswagen 大众德国汽车321 Faros 的法国的22 Hitachi 日立日本电子电气322 Canadian Imperial Bank of Commerce 加拿大帝国商业银行加拿大银行23 Siemens 西门子德国电子电气323 Emerson Electric 艾默生电气美国电子电气24 Ing Group 荷兰国际集团荷兰保险324 Tohoku Electric Power 东北电力日本电力25 Allianz 安联德国保险325 Shimizu 清水日本工程建筑26 Matsushita Electric Industrial 松下电器日本电子电气326 Coles Myer 科斯迈尔澳大利亚零售27 E. ON 的德国多样化327 Royal Bank of Canada 皇家加拿大银行加拿大银行28 Nippon Life Insurance 日本生命日本保险328 Japan Airlines 日本航空日本航空公司29 Deutsche Bank 德意志银行德国银行329 Best Buy 的美国零售30 Sony 索尼日本电子电气330 Halifax 哈里法克斯英国银行31 AT&T 美国电话电报美国电信331 Corus Group 的英国金属32 Verizon Communications 弗莱森电讯美国电信332 Rite Aid 来爱德美国零售33 U.S. Postal Service 美国邮政总局美国邮递包裹333 Norinchukin Bank 农林中央金库日本银行34 Philip Morris 菲利普莫里斯美国食品烟草334 Swiss Life Ins. & Pension 瑞士人寿与养老金瑞士保险35 CGNU 商联保险英国保险335 Centrica 的英国电力煤气36 J.P. Morgan Chase 摩根大通银行美国银行336 China Mobile 中国移动通信中国电信37 Carrefour 家乐福法国零售337 George Weston 乔治威斯顿加拿大零售38 Credit Suisse 瑞士信贷集团瑞士银行338 BHP 布鲁肯希尔澳大利亚采矿原油39 Nissho Iwai 日商岩井日本多样化339 BCE 贝尔加拿大电子加拿大电信40 Honda Motor 本田汽车日本汽车340 Groupama-Gan 安盟-甘集团法国保险41 Bank of America Corp. 美洲银行美国银行341 Anglo American 的英国采矿42 BNP Paribas 法国巴黎银行法国银行342 DG Bank Group 的德国银行43 Nissan Motor 日产汽车日本汽车343 La Poste 法国邮政法国邮递44 Toshiba 东芝日本电子电气344 Seagram 施格兰加拿大饮料45 PDVSA 委内瑞拉石油委内瑞拉炼油345 UniCredito Italiano 意大利联合信贷银行意大利银行46 Assicurazioni Generali 忠利保险意大利保险346 Nationwide Insurance Enterprise 的美国保险47 Fiat 菲亚特意大利汽车347 Coca-Cola Enterprises 可口可乐企业美国饮料48 Mizuho Holdings 瑞穗控股日本银行348 Hartford Financial Services 哈德福德金融服务美国保险49 SBC Communications 西南贝尔美国电信349 Valero Energy 的美国炼油50 Boeing 波音美国航空航天350 National Australia Bank 澳大利亚国家银行澳大利亚银行51 Texaco 德士古美国炼油351 BAE Systems 的英国航空航天52 Fujitsu 富士通日本计算机352 Man Group 曼德国汽车53 Duke Energy 杜克能源美国电力煤气353 Michelin 米其林法国轮胎橡胶54 Kroger 克罗格美国零售354 Publix Super Markets 的美国零售55 NEC 日本电气公司日本电子电气355 Occidental Petroleum 西方石油美国化学56 Hewlett-Packard 惠普美国计算机356 Usinor 法国北方钢铁联合公司法国金属57 HSBC Holdings 汇丰控股英国银行357 May Department Stores 五月百货美国零售58 Koninklijke Ahold 的荷兰零售358 Suzuki Motor 铃木汽车日本汽车59 Nestlé雀巢瑞士食品359 Fleming 佛莱明美国零售60 Chevron 雪佛龙美国炼油360 Goodyear Tire & Rubber 固特异轮胎橡胶美国轮胎橡胶61 State Farm Insurance Cos. 州立农业保险美国保险361 Lukoil 的俄罗斯采矿原油62 Tokyo Electric Power 东京电力日本电力煤气362 SK Global 鲜京全球韩国多样化63 UBS 瑞士联合银行瑞士银行363 Ultramar Diamond Shamrock 钻石三叶草美国炼油64 Dai-ichi Mutual Life Insurance 第一生命日本保险364 Deutsche Bahn 德国联邦铁路德国铁路运输65 American International Group 美国国际集团美国保险365 Endesa 的西班牙电力66 Home Depot 家庭百货美国零售366 McDonald's 麦当劳美国餐饮服务67 Morgan Stanley Dean Witter 摩根士丹利添惠美国证券经纪367 Isuzu Motors 五十铃汽车日本汽车68 Sinopec 中国石化中国石油化工368 Volvo 沃尔沃瑞典汽车69 ENI 埃尼意大利炼油369 Solectron 的美国电子电气70 Merrill Lynch 美林美国证券经纪370 Banco Bradesco 的巴西银行71 Fannie Mae 范妮梅美国金融371 News Corp. 新闻集团澳大利亚娱乐72 Unilever 联合利华荷兰/英国食品372 KarstadtQuelle 卡尔施泰特德国零售73 Fortis 福尔蒂荷兰/比利时保险373 Lear 里尔美国汽车零件74 ABN AMRO Holding 荷兰银行荷兰银行374 Lufthansa Group 汉莎航空德国航空公司75 Metro 麦德龙德国零售375 Eastman Kodak 伊斯曼柯达美国摄影器材76 Prudential 保诚保险英国保险376 Kimberly-Clark 金百利克拉克美国纸产品77 State Power Corporation 国家电力公司中国电力377 Ricoh 理光日本办公用品78 Rwe Group 莱茵集团德国电力煤气378 American Home Products 美国家庭用品美国制药79 Compaq Computer 康柏电脑美国计算机379 Abbott Laboratories 雅培美国制药80 Repsol YPF 莱普索尔西班牙炼油380 British Airways 英国航空英国航空公司81 Pemex 墨西哥石油墨西哥原油381 Winn-Dixie Stores 温迪克西百货美国零售82 McKesson HBOC 麦卡森美国零售382 American Electric Power 美国电力美国电力83 China Petroleum 中国石油天然气中国炼油383 Otto Versand 奥托邮购德国邮购84 Lucent Technologies 朗讯科技美国电子电气384 Gap 的美国零售85 Sears Roebuck 西尔斯罗巴克美国零售385 RAG 鲁尔德国采矿原油86 Peugeot 标致法国汽车386 Vinci 的法国的87 Munich Re Group 慕尼黑再保险德国保险387 Toronto-Dominion Bank 的加拿大银行88 Merck 默克美国制药388 Sumitomo Metal Industries 住友金属日本金属89 Procter & Gamble 宝洁美国家用化学品389 Sumitomo Electric Industries 住友电工日本金属90 WorldCom 世界电讯美国电信390 Halliburton 哈利佰顿美国工程建筑91 Vivendi Universal 威望迪环球法国娱乐391 Japan Telecom Co. Ltd. 日本电信日本电信92 Samsung Electronics 三星电子韩国电子电气392 Montedison 蒙特爱迪生意大利食品93 TIAA-CREF 美国教师退休基金会美国保险393 Groupe Danone 达能法国食品94 Deutsche Telekom 德国电信德国电信394 Deere 迪尔美国工农业设备95 Motorola 摩托罗拉美国电子电气395 Kyushu Electric Power 九州电力日本电力96 Sumitomo Life Insurance 住友生命日本保险396 Textron 达信美国航空航天97 Zurich Financial Services 苏黎士金融服务瑞士保险397 Carso Global Telecom 的墨西哥电信98 Mitsubishi Electric 三菱电机日本电子电气398 Electrolux 伊莱克斯瑞典家用电器99 Renault 雷诺法国汽车399 Fuji Photo Film 富士胶卷日本摄影器材100 Kmart 卡马特美国零售400 Arrow Electronics 的美国零售101 Target 塔吉特美国零售102 Albertson's 艾伯森美国零售103 Hyundai 现代韩国多样化104 Thyssen Krupp 蒂森克虏伯德国工农业设备世界500强企业名称中英对照(五)105 Samsung 三星韩国多样化401 Circuit City Stores, Inc. 巡回城市百货公司美国零售106 USX 美国钢铁马拉松美国炼油402 Akzo Nobel 阿克苏诺贝尔荷兰化学107 Royal Philips Electronics 皇家飞利浦电子荷兰电子电气403 Woolworths 沃尔沃斯澳大利亚零售108 Crédit Agricole 农业信贷银行法国银行404 Bank of Nova Scotia 丰业银行加拿大银行109 Berkshire Hathaway 伯克希尔哈撒韦美国保险405 Archer Daniels Midland 阿彻丹尼尔斯米德兰美国食品110 Intel 英特尔美国半导体406 Banco Do Brasil 巴西银行巴西银行111 BASF 巴斯夫德国化学407 Dana 达纳美国汽车零件112 Goldman Sachs Group 高盛集团美国证券经纪408 Sunoco 的美国炼油113 J.C. Penney 彭尼美国零售409 Taiyo Mutual Life Insurance 太阳生命日本保险114 BMW 宝马德国汽车410 Bank of Montreal 蒙特利尔银行加拿大银行115 Conoco 的美国炼油411 China Construction Bank 中国建设银行中国银行116 Costco Wholesale 价格成本美国零售412 Cosmo Oil 的日本炼油117 HypoVereinsbank 联合抵押银行德国银行413 Sekisui House 积水建房日本工程建筑118 Suez 苏伊士里昂水务法国水务414 COFCO 中粮集团中国多样化119 Safeway 西夫韦美国零售415 Waste Management 废物处理美国废物处理120 MetLife 都市人寿保险美国保险416 Telstra 澳洲电信澳大利亚电信121 Santander Central Hispano Group 桑坦德集团西班牙银行417 Kobe Steel 神户制钢日本金属122 Dell Computer 戴尔电脑美国计算机418 Amerada Hess 阿拉美达赫斯美国炼油123 SK 鲜京韩国炼油419 Anheuser-Busch 安海斯布希美国饮料124 Electricite De France 法国电力法国电力420 Farmland Industries 农场工业美国食品125 Deutsche Post 德国邮政德国邮递包裹421 Arbed 阿尔贝德钢铁卢森堡金属126 Tesco 特斯科英国零售422 Pohang Iron & Steel 埔项制铁韩国金属127 France Télécom 法国电信法国电信423 Yasuda Fire & Marine Insurance 安田海上火灾保险日本保险128 BT 英国电信英国电信424 Dai Nippon Printing 大日本印刷日本印刷出版129 Ingram Micro 英格雷姆麦克罗美国零售425 Flextronics International 伟创力新加坡电子电气130 Nortel Networks 北电网络加拿大电子电气426 Royal KPN 的荷兰电信131 Freddie Mac 弗雷德马克美国金融427 Central Japan Railway 中央日本铁路日本铁路运输132 Cardinal Health 卡地纳健康美国的428 Safeway 西夫韦英国零售133 L.M. Ericsson 爱立信瑞典电子电气429 Stora Enso 的芬兰纸产品134 Meiji Life Insurance 明治生命日本保险430 Consignia 的英国邮递包裹135 United Parcel Service 联合包裹运输服务美国邮递包裹431 Cable & Wireless 大东电报局英国电信136 Royal Bank of Scotland 皇家苏格兰银行英国银行432 Household International 的美国金融137 Mitsubishi Motors 三菱汽车日本汽车433 Lagardère Groupe 拉加代尔集团法国出版印刷138 Pfizer 辉瑞美国制药434 Marks & Spencer 马克思斯班塞英国零售139 Dynegy 的美国能源435 Kawasaki Steel 川崎制铁日本金属140 Reliant Energy 的美国电力煤气公用436 Obayashi 大林组日本工程建筑141 E.I. du Pont de Nemours 杜邦美国化学437 Union Pacific 联合太平洋美国铁路运输142 Delphi Automotive Systems 德尔福汽车系统美国汽车零件438 Texas Instruments 德州仪器美国半导体143 Johnson & Johnson 强生美国制药439 Asahi Glass 朝日玻璃日本建材玻璃144 Allstate 好事达保险美国保险440 Fuji Heavy Industries 富士重工日本汽车145 Robert Bosch 罗伯特博世德国电子电气441 Henkel 汉高德国化学146 Alcatel 阿尔卡特法国电子电气442 Skanska 的瑞典工程建筑147 UtiliCorp United 公用事业联合公司美国电力煤气公用443 Nomura Securities 野村证券日本证券经纪148 Tyco International 特科国际美国电子电气444 Imperial Chemical Industries 帝国化学英国化学149 Hyundai Motor 现代汽车日本汽车445 Edison International 爱迪生国际美国电力煤气150 Bayer 拜尔德国化学446 L'Oréal 欧莱雅法国肥皂化妆品151 Aegon 的荷兰保险447 Toppan Printing 凸版印刷日本印刷152 Ito-Yokado 伊藤洋华堂日本零售448 Agricultural Bank of China 中国农业银行中国银行153 International Paper 国际造纸美国纸产品449 Migros 的瑞士零售154 Nokia 诺基亚芬兰电子电气450 Invensys 的英国工农业设备155 Nippon Mitsubishi Oil 日本三菱石油日本炼油451 Kyocera 京都陶瓷日本电子电气156 Olivetti 好利获得意大利电信452 AmeriSource Health 的美国零售157 Wells Fargo 富国银行美国银行453 Xcel Energy 的美国能源158 Mitsubishi Heavy Industries 三菱重工日本工农业设备454 Kawasho 川铁商事日本多样化159 GlaxoSmithKline 葛兰素史克英国制药455 All Nippon Airways 全日空日本航空公司160 Petrobrás 巴西石油巴西炼油456 Office Depot 办公用品美国零售161 Aetna 安泰美国保险457 Daido Life Insurance 的日本保险162 Daiei 大荣日本零售458 Old Mutual 的南非保险163 Saint-Gobain 圣戈班法国玻璃459 Asahi Kasei 旭化成日本化学164 United Technologies 联合技术美国航空航天460 Williams 的美国能源165 Prudential Ins. Co. of America 美国宝德信人寿保险美国保险461 PacifiCare Health Systems 太平洋健康系统美国医疗健康166 Lehman Brothers Holdings 雷曼兄弟美国证券经纪462 Northwest Airlines 西北航空美国航空公司167 Bank of Tokyo-Mitsubishi 东京三菱银行日本银行463 Tenet Healthcare 的美国医疗健康168 Telefónica 西班牙电话西班牙电信464 Takenaka 竹中日本工程建筑169 PG&E Corp. 太平洋煤气电力美国电力煤气465 Suntory 三得利日本饮料170 BellSouth 贝尔南方美国电信466 Power Corp. of Canada 加拿大鲍尔公司加拿大保险171 Canon 佳能日本办公设备467 Showa Shell Sekiyu 的日本炼油172 Royal & Sun Alliance Insurance Group 皇家太阳保险集团英国保险468 Oji Paper 王子纸日本纸产品173 J. Sainsbury 桑斯博里英国零售469 Toys 'R' Us 玩具反斗店美国零售174 Walt Disney 沃特迪斯尼美国娱乐470 Lafarge 的法国建材175 ConAgra 康尼格拉美国食品471 Mass. Mutual Life Insurance 麻省人寿美国保险176 Lockheed Martin 洛克希德马丁美国航空航天472 Cepsa 的西班牙能源177 Bank One Corp. 第一银行美国银行473 Air France Group 法国航空法国航空公司178 Barclays 巴克莱银行英国银行474 Sun Life 太阳人寿加拿大保险179 Jusco 吉之岛日本零售475 American General 美国普通保险美国保险180 Honeywell International 霍尼韦尔国际美国航空航天476 Fluor 福陆美国工程建筑181 Nippon Steel 新日铁日本金属477 Matsushita Elec. Wks. 松下电工日本电子182 Sumitomo Bank 住友银行日本银行478 Christian Dior 克里斯叮迪奥法国服装183 Tosco 的美国炼油479 Takashimaya 高岛屋日本零售184 First Union Corp. 第一联合银行美国银行480 Eli Lilly 礼来大药厂美国制药185 Société Générale 兴业银行法国银行481 Manpower 的美国临时帮助186 Kansai Electric Power 关西电力日本电力482 Canadian Pacific 的加拿大的187 Dresdner Bank 德累斯顿银行德国银行483 West Japan Railway 西日本铁路日本铁路运输188 American Express 美国运通美国金融484 Mitsui Fudosan 三井不动产日本工程建筑189 Statoil 挪威石油挪威炼油485 Bank of Scotland 苏格兰银行英国银行190 Sprint 斯普林特美国电信486 Uny 的日本零售191 Westdeutsche Landesbank 西德意志银行德国银行487 Staples 斯特普尔斯美国零售192 Lloyds TSB Group 劳埃德集团英国银行488 Great Atl. & Pacific Tea 的美国零售193 LG International 乐喜金星国际韩国多样化489 Computer Sciences 计算机科学美国软件数据服务194 Southern 南方美国电力煤气490 Humana 胡马纳美国医疗健康195 Supervalu 超价商店美国零售491 Magna International 的加拿大电子电气196 Enel 国家电力意大利电力492 Kinki Nippon Railway 的日本铁路运输197 Alcoa 美国铝业美国金属493 Norddeutsche Landesb. 的德国银行198 East Japan Railway 东日本铁路日本铁路运输494 Jardine Matheson 怡和中国香港多样化199 Dow Chemical 道化学美国化学495 General Dynamics 通用动力美国航空航天200 ABB 阿西布朗勃法瑞瑞士电子电气496 Gaz de France 法国煤气法国能源497 Mitsubishi Materials 三菱材料日本金属498 Whirlpool 惠尔普美国家用电器499 Snow Brand Milk Products 雪印乳业日本食品世界500强企业名称中英对照(三)500 Sodexho Alliance 索迪斯联合美国餐饮服务网201 Microsoft 微软美国软件256 Cisco Systems 思科系统美国电子电气202 Groupe Pinault-Printemps 皮诺春天集团法国零售257 Lowe's 劳氏美国零售203 Tomen 东绵日本多样化258 Swiss Reinsurance 瑞士再保险瑞士保险204 FleetBoston 佛雷特波士顿银行美国银行259 Xerox 施乐美国办公设备205 CNP Assurances 法国国家人寿保险法国保险260 Bridgestone 普利斯通日本轮胎橡胶206 Intesabci 的意大利银行261 British American Tobacco 英美烟草英国烟草207 AutoNation 的美国汽车销售服务262 Foncière Euris 的法国零售208 Alstom 阿尔斯通法国电子电气263 Federated Department Stores 联合百货美国零售209 Indian Oil 印度石油印度炼油264 Standard Life Assurance 标准人寿保险英国保险210 Preussag 普罗伊萨格德国多样化265 SNCF 国家铁路法国铁路运输211 Georgia-Pacific 佐治亚太平洋美国纸产品266 Raytheon 雷神美国航空航天212 Vodafone 沃达丰英国电信267 Idemitsu Kosan 出光兴产石油日本炼油213 Industrial & Commercial Bank of China 中国工商银行中国银行268 FedEx 联邦快递美国邮递包裹214 Banco Bilbao Vizcaya Argentaria 毕尔巴鄂比斯开银行西班牙银行269 Kingfisher 翠丰集团英国零售215 TXU 德州公用美国电力煤气270 Mazda Motor 马自达汽车日本汽车216 El Paso Corp. 的美国能源271 Denso 电装日本汽车零件217 Nichimen 日绵日本多样化272 Sharp 夏普日本电子电气218 Groupe Auchan 的法国零售273 Pharmacia 法玛西亚美国制药219 New York Life Insurance 纽约人寿保险美国保险274 AstraZeneca 阿斯特拉捷利康英国制药220 Bristol-Myers Squibb 百时美施贵宝美国制药275 Japan Energy 日本能源日本炼油221 Phillips Petroleum 菲利普石油美国炼油276 Sinochem 中国化工进出口公司中国多样化222 Samsung Life Insurance 三星人寿保险韩国保险277 EADS 的法国航空航天223 Walgreen 沃尔格林美国零售278 Norsk Hydro 挪威水电挪威化学224 Novartis 诺华瑞士制药279 Tokio Marine & Fire Insurance 东京海上火灾保险日本保险225 UnitedHealth Group 联合健康集团美国医疗健康280 Gazprom 俄罗斯天然气俄罗斯能源226 Commerzbank 德国商业银行德国银行281 Bouygues 布伊格法国工程建筑227 Crédit Lyonnais 里昂信贷银行法国银行282 Franz Haniel 弗朗茨海涅尔德国零售228 China Telecommunications 中国电信中国电信283 Almanij 的比利时银行229 Loews 洛斯美国保险284 Kajima 鹿岛建设日本工程建筑230 Japan Tobacco 日本烟草日本烟草285 TRW 汤姆森拉莫伍尔德里奇美国汽车零件231 Aventis 安内特法国制药286 Sanwa Bank 三和银行日本银行232 KDDI 的日本电信287 Johnson Controls 约翰逊控制美国汽车零件233 Coca-Cola 可口可乐美国饮料288 Legal & General 的英国保险234 PepsiCo 百事公司美国饮料289 Roche Group 罗氏制药瑞士制药235 Tech Data 技术数据美国零售290 Northwestern Mutual Life Ins. 西北相互人寿保险美国保险236 Sara Lee 沙拉李美国食品291 IBP 艾奥瓦牛肉罐头美国食品237 Chubu Electric Power 中部电力日本电力292 Yasuda Mutual Life Insurance 安田生命日本保险238 Sanyo Electric 三洋电机日本电子电气293 Delhaize 'Le Lion' 的比利时零售239 AMR 美利坚公司美国航空公司294 Minnesota Mining & Mfg. 明尼苏达矿业制造美国多样化240 Caterpillar 卡特彼勒美国工农业设备295 HCA 的美国医疗健康241 Japan Postal Service 日本邮政日本邮递包裹296 Mitsui Mutual Life Insurance 三井生命日本保险242 Rabobank 拉博银行荷兰银行297 Qwest Communications 奎斯特电信美国电信243 CVS 的美国零售298 Landesbank Baden-Württemberg 的德国银行244 LG Electronics 乐喜金星电子韩国电子电气299 Bertelsmann 贝塔斯曼德国出版245 Viacom 维亚康姆美国娱乐300 Korea Electric Power 韩国电力韩国电力246 Cigna 信诺美国保险250 Toyota Tsusho 丰田通商日本多样化247 Abbey National 阿比国民银行英国银行251 Bank Of China 中国银行中国银行248 Asahi Mutual Life Insurance 朝日生命日本保险252 UAL 联合航空美国航空公司249 Bergen Brunswig 伯根布鲁斯威格美国零售253 Sysco 西斯科美国零售255 Electronic Data Systems 电子数据系统美国数据服务254 Petronas 马来西亚石油马来西亚炼油。
Curriculum Vitae 样板 (13)
Joshua S. Chao (趙士驊)1522 Henry Street #DBerkeley CA 94709Telephone: (408) 834-9702Fax: (510) 642-5814Email: joshuach@Web: /~joshuach/EDUCATIONMaster of Information Management & Systems May 2006 School of Information Management & Systems (SIMS) UC Berkeley /Management of Technology Certificate (MOT) May 2006 College of Engineering, Haas School of Business, and SIMS joint program UC Berkeley /Bachelor of Arts,Cognitive Science with a Minor in Computer Science December 2001 /ugis/cogsci/ UC Berkeley EXPERIENCEVertSearch Project, SIMS, UC Berkeley12/2005 - Present /groups/vertsearch/Taking the role of team lead and product manager for the VertSearch project, a SIMS Master’s Project. The project is an online personal profiler designed to facilitate the task of employment matching for both job seekers and employers. Features include profile customization, visualization of professional qualifications, document repository and indexing (search), and job filtering. Responsibilities include managing a technical development team and a non-technical research team to complete the product design, prototyping, needs assessment, and market research. We will present our findings and demo our prototype at the end-of-term project showcase.China Club, UC Berkeley12/2005 - Present /Elected as the president of the UC Berkeley China Club. The China Club brings together UC Berkeley graduate students from campus departments such as the schools of information, engineering, and business who share a common interest in understanding and meeting the challenges related to China's rise as a global cultural, economic, and intellectual power. Our mission is to promote Chinese awareness by fostering strong ties between UC Berkeley graduate students and members of industry, academia, and governmental bodies who share a common interest in China. In January 2006, we organized a trip to China where we met with student organizations and Chinese companies. The trip was a success and resulted in the creation of strong bilateral ties between our club and our Chinese counterparts.Center for Research on Chinese & American Strategic Cooperation, UC Berkeley 10/2005 - Present /Selected as a fellowship recipient and IT Manager for the CRC, a think-tank dedicated to strategy, education, and policy challenges regarding China’s rise as a global power. Responsibilities include IT strategy, event planning and logistics, and event promotion. Currently assisting in the organization of the China Venture Capital Forum in Shenzhen, China to be held in April 2006.California Digital Library, University of California08/2005 - 01/2006 /Consulted for CDL as a Graduate Student Researcher with the School of Information Management & Systems. The project involved an assessment of a usage statistic collection and reporting system for the online Melvyl library catalog. Responsibilities included evaluation of the system and research into additional methods and sources of information. Resulting from this project was a recommendation document outlining improvements to the system.China Digital Times, UC Berkeley08/2005 - 12/2005 /Worked as a member of the IT strategy team at the China Digital Times, an online collaborative news project focused on China issues spearheaded by the Graduate School of Journalism. Tasks included feature design and content contribution.Garage Cinema Research, UC Berkeley01/2005 - 08/2005 /Employed as a Graduate Student Researcher and system developer for the Mobile Media Metadata (MMM) group. The MMM project collected mobile phone camera usage data through a centralized photo-sharing application. Worked on both the server and client applications for the system.NTT Multimedia Communications Laboratories, Inc.06/2001 - 06/2005 /Employed as a software engineer on the Wireless team working on various projects related to enterprise wireless LANs. Some of my projects included the access system used in NTT Communication’s HotSpot WiFi service in Japan, a logging and analysis system on the AirBears WiFi network at UC Berkeley, and a WiFi VPN product. Since NTT MCL was a small company, I worked on many different aspects of each project such as web design, software design, database design, documentation and marketing materials for trade-shows.Department of Computer Science, UC Berkeley08/2000 - 12/2000 /Instructed as a teaching assistant for Structure and Interpretation of Computer Programs (CS61A) during the Fall semester of 2000 where I lead weekly discussion sections, office hours, review sessions, and graded assignments and tests.Sun Microsystems Laboratories05/2000 - 08/2000 /Employed as an engineering intern on the Phaser project, a massively parallel simulation engine, where I developed a Verilog HDL language compliance test-suite for the compiler group. Over the course of the internship, my team helped discover and catalog hundreds of bugs.HONORSFellowship, Institute of Management, Innovation & Organization, UC Berkeley, 02/2006, 10/2005 Fellowship, CRC China Intellectual Property Research Program, UC Berkeley, 12/2005 Fellowship, School of Information Management & Systems, UC Berkeley, 08/2004 - 05/2005 Undergraduate Academic Honors, UC Berkeley, 12/1997 - 05/1998LANGUAGESConversational Fluency, Mandarin Chinese。
世界超级计算机TOP500_201206
Rank Previous R First AppeaCountry YearFirst Rank Name Computer Site Manufactur 1173817Sequoia BlueGene/Q,DOE/NNSA/L IBM United States2011Fujitsu Japan2011RIKEN Advan21371K computer, SIBM United States2012 3393Mira BlueGene/Q,DOE/SC/ArgoIBM Germany2012 4394SuperMUCiDataPlex DX Leibniz RecheNational SupeNUDT China2010 52361Tianhe-1A NUDT YH MPDOE/SC/Oak Cray Inc.United States2009 63341Jaguar Cray XK6, Op7397Fermi BlueGene/Q,CINECA IBM Italy2012 8398JuQUEEN BlueGene/Q,ForschungszeIBM Germany2012Bull SA France2012 9399Curie thin nodBullx B510, X CEA/TGCC-GDawning China2010 104352Nebulae Dawning TC3National Supe117377Pleiades SGI Altix ICE NASA/Ames SGI United States2011 12283828Helios Bullx B510, X International Bull SA Japan2011 133913Blue Joule BlueGene/Q,Science and IBM United Kingdo2012GSIC Center,NEC/HP Japan2010 145364TSUBAME 2.HP ProLiant SDOE/NNSA/L Cray Inc.United States2011 156376Cielo Cray XE6, OpDOE/SC/LBN Cray Inc.United States2010 168365Hopper Cray XE6, Op179366Tera-100Bull bullx sup Commissaria Bull SA France2010Information T Fujitsu Japan2012 183918Oakleaf-FXPRIMEHPC F1910342Roadrunner BladeCenter DOE/NNSA/L IBM United States2009 203920DiRAC BlueGene/Q,University of IBM United Kingdo2012 21113711Kraken XT5Cray XT5-HE National Insti Cray Inc.United States2011T-Platforms Russia2011Moscow State22183713LomonosovT-Platforms TIBM United States2012 233923DARPA Trial Power 775, P IBM Developm24123812HERMIT Cray XE6, OpHWW/Univer Cray Inc.Germany2011IBM Germany2009 2513333JUGENE Blue Gene/P ForschungszeNational ReseChina2011 26143814Sunway Blue Sunway Blue National SupeLawrence Liv Appro Interna United States2011 27153815Zin Xtreme-X GreNUDT China2011National Supe28163816Tianhe-1A HuNUDT YH MP293929Zumbrota BlueGene/Q,EDF R&D IBM France2012 303930BlueGene/Q,IDRIS/GENC IBM France2012 313931Avoca BlueGene/Q,Victorian Life IBM Australia2012University of Cray Inc.United Kingdo2011 32193724HECToR Cray XE6, OpNOAA/Oak R Cray Inc.United States2011 33203820Gaea C2Cray XE6, Op3448338483Power 775, P ECMWF IBM United Kingdo2011 35553855Power 775, P ECMWF IBM United Kingdo2011IBM Japan2012 363936BlueGene/Q,High Energy AChina2011 37213519Mole-8.5Mole-8.5 ClusInstitute of Pr IPE, Nvidia, TIBM United States2007 3823314Intrepid Blue Gene/P DOE/SC/Argo39243510Red Sky Sun Blade x6Sandia NationSun Microsys United States2010Texas Advan Sun Microsys United States2008 4025327Ranger SunBlade x64Center for Co Appro Interna Japan2012 413941HA-PACS Xtream-X Gre4226339Dawn Blue Gene/P DOE/NNSA/L IBM United States2009IBM United Kingdo2011 43633863Power 775, P United KingdoSGI Norway2012 443944SGI Altix X, XNorwegian UnBullx B510, X Bull Bull SA France2011 45273827Bull BenchmaLawrence Liv Appro Interna United States2012 463946Cab Xtreme-X , XeAppro Interna United States2011 47453845Luna Xtreme-X GreLos Alamos N483948Vulcan BlueGene/Q,DOE/NNSA/L IBM United States2012 49293829BlueGene/Q,IBM - Roches IBM United States2011 50643864BlueGene/Q,IBM - Roches IBM United States2011IBM United Kingdo2011 51623862Power 775, P United KingdoAppro Interna United States2012 523952Pecos Xtreme-X , XeSandia NationCray Inc.United States2010 53303618Raptor Cray XE6 8-c Air Force Res54613861Chama Xtreme-X GreAppro Interna United States2011Sandia Nation55323619Haedam Cray XE6 12-Korea Meteor Cray Inc.Korea, South2010 56313620Haeon Cray XE6 12-Korea Meteor Cray Inc.Korea, South2010Swiss Scienti Cray Inc.Switzerland2011 57343421Monte Rosa Cray XE6, Op583958Cluster Platfo CSIR Centre Hewlett-Pack India2012Universitaet FClustervision/Germany2011Supermicro C59333722LOEWE-CSC60353621Cray XE6 12-Government Cray Inc.United States2010174743767.xlsSegmentTotal Core Accelerato Rmax Rpeak Effeciency Nmax Nhalf Power Mflops/Wa Research15728640163247512013265981.0912681215078902069.04 Research7050240105100001128038493.1711870208012659.89830.18 Research786432081623761006633081.090039452069.04 Academic14745602897000318505090.96520192003422.67846.42 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Infiniband QDAppro XtremeWestern EuroEurope Infiniband Infiniband QDSun-Bull-ParTSun Blade Sy Infiniband Infiniband Eastern Asia A siaNorth Americ Americas Infiniband Infiniband QDAppro XtremeIBM iDataPle Infiniband Infiniband North Americ AmericasNorth Americ Americas Dell PowerEd Infiniband Infiniband QDSouth Americ Americas Itautec ClusteInfiniband Infiniband QDWestern EuroEurope HP Cluster Pl Infiniband Infiniband QDCustom Eastern Asia A sia Hitachi SR16Custom IntercNorth Americ Americas Rackable Clu Infiniband Infiniband QDNOW - Intel G igabit Ether10G Ethernet North Americ Americas Cray XE Cray Intercon Cray Gemini Eastern Asia A siaNorth Americ Americas Appro XtremeInfiniband Infiniband QDEurope SGI Altix Infiniband Infiniband Western EuroCray XE Custom IntercEuropeCustom Northern EuroNorth Americ Americas Appro XtremeInfiniband Infiniband QDEurope Bull Bullx Infiniband Infiniband QDWestern EuroCray XE Cray Intercon Cray Gemini South Americ Americas NUDT MPP Proprietary N Proprietary Eastern Asia A siaEuropeWestern Euro Megware Clu Infiniband Infiniband QDCray XT Proprietary N Proprietary North Americ Americas Cray XT Custom IntercCustom North Americ AmericasEastern Asia A sia Fujitsu Cluste Infiniband Infiniband QDIBM BlueGen Proprietary N Proprietary Western Asia AsiaInfiniband Infiniband QDSouth-central AsiaZ24XX/SL390Europe IBM iDataPle Infiniband Infiniband FD Western EuroHP Cluster Pl Infiniband Infiniband FD North Americ Americas HP Cluster Pl Infiniband Infiniband QDEastern Euro EuropeCustom North Americ Americas IBM pSeries C ustom IntercCustom North Americ Americas IBM pSeries C ustom IntercIBM BlueGen Proprietary N Proprietary North Americ AmericasEurope Dell PowerEd Infiniband Infiniband FD Northern EuroEastern Asia A siaInfiniband Infiniband DDDawning ClusEurope HP Cluster Pl Infiniband Infinband DD Western Euro Megware Clu Infiniband Infiniband FD Northern EuroEuropeEurope Bull Bullx Infiniband Infiniband QDWestern EuroEastern Asia A siaInfiniband Infiniband QDAcer Group CCustom North Americ Americas IBM BlueGen Custom IntercCustom North Americ Americas IBM BlueGen Custom IntercIBM BlueGen Custom IntercCustom North Americ AmericasCustom North Americ Americas IBM BlueGen Custom IntercCustom North Americ Americas IBM BlueGen Custom IntercEurope IBM iDataPle Infiniband Infiniband Western EuroNorth Americ Americas Dell Xanadu Infiniband Infiniband QDTofu intercon Eastern Asia A sia Fujitsu Cluste Custom IntercCray XT Proprietary N XT4 Internal I North Americ AmericasNorthern EuroEurope IBM iDataPle Infiniband Infiniband QDNorth Americ Americas SGI Altix Infiniband Infiniband QDEastern Asia A siaSGI Altix Infiniband Infiniband QDSGI Altix Infiniband Infiniband DDNorth Americ AmericasEurope Cray XE Cray Intercon Cray Gemini Northern EuroCustom North Americ Americas IBM pSeries C ustom IntercEurope IBM iDataPle Infiniband Infiniband FD Northern EuroWestern EuroEurope Bull Bullx Infiniband Infiniband QDCustom North Americ Americas Cray XE Custom IntercEuropeWestern Euro Megware Clu Infiniband Infiniband QDEurope HP Cluster Pl Infiniband Infiniband FD Western EuroNorth Americ Americas Dell PowerEd Infiniband Infiniband QDDell/Sun/IBM M yrinet Myrinet 10G North Americ Americas IBM iDataPle Infiniband Infiniband FD Southern Eur Europe174743767.xls。
VSS公司介绍
公司简介分布式流量捕获是网络监测的基础,它是一个智能的监测架构,提高分析设备对网络的可视性,即使面向最复杂的大型网络,也可以捕获所有网络流量,同时并能增加用户生产效率、提高网络监测和安全工具的投资回报率。
在此之前,由于在整个网络中大量部署分析设备的成本很高,网络的全面可视性是很难实现的。
分布式流量捕获系统是设计为介于用户网络基础架构和监测系统之间的一个实体层,代表了网络监测的新方法,提供完全的、可随网络规模扩展的、集中的网络可视性。
VSS Monitoring公司是全球分布式流量捕获系统和网络Tap的革新领导者。
总部位于美国加州硅谷的VSS公司已经成长为业内最大的领导厂商,在美洲、亚洲和欧洲拥有许多资深的客户支持工程师和专业经销商。
公司名字“VSS”的缩写代表了可视性(Visibility)、隐蔽性(Stealth)和安全性(Security)。
创建于2003年10月。
2005年1月第一款产品交付用户使用。
美国加州硅谷 │ 中国北京 │ 日本东京Martin Breslin创始人及首席营运官 David Kucharczyk首席技术官 增加现有网络设施的投资回报率增加生产效率通过完全的网络可视性进行前瞻性管理根据Aberdeen Group 调研公司的报告,部署分布式流量捕获系统的企业, 相比于没有使用此系统的公司,可以:提高网络应用有效性达120%改善用户网络应用性能问题达85%降低网络管理人力成本达60% ( 平均每年每用户成本从253美元降为146美元)(引用自网络和应用可视性的价值:提升应用数据的可用性》,作者Bojan Simic,2008年9月)VSS Monitoring 公司高度智能的分布式流量捕获系统,是业界性能最好的产品。
VSS的分布式流量捕获系统是唯一融合可选择汇聚、过滤、负载均衡、网络集中管理、图形化用户界面(GUI)等功能的网络Tap,支持网络双向串接(Inline)和镜像(Span)端口捕获,并有最广的产品线提供多种不同端口密度的选择,满足用户需求。
费列罗的经典广告词(1篇)
费列罗的经典广告词(1篇)以下是网友分享的关于费列罗的经典广告词的资料1篇,希望对您有所帮助,就爱阅读感谢您的支持。
篇一经典的slogan 广告词1.Good to the last drop.滴滴香浓,意犹未尽。
(麦斯威尔咖啡)2.Obey your thirst.服从你的渴望。
(雪碧)3.The new digital era.数码新时代。
(索尼影碟机)4.We lead. Others copy.我们领先,他人仿效。
(理光复印机)5.Impossible made possible.使不可能变为可能。
(佳能打印机)6.T ake time to indulge.尽情享受吧!(雀巢冰激凌)7.The relentless pursuit of perfection.不懈追求完美。
(凌志轿车)8.Poetry in motion, dancing close to me.动态的诗,向我舞近。
(丰田汽车)9.Come to where the flavor is. Marlboro Country.光临风韵之境,万宝路世界。
(万宝路香烟)10.T o me, the past is black and white, but the future is always color.对我而言,过去平淡无奇;而未来,却是绚烂缤纷。
(轩尼诗酒)11. Just do it. 只管去做。
(耐克运动鞋)12. Ask for more. 渴望无限。
(百事流行鞋)13. The taste is great. 味道好极了。
(雀巢咖啡)14. Feel the new space. 感受新境界。
(三星电子)15. Intelligence everywhere.智慧演绎,无处不在。
(摩托罗拉手机)16. The choice of a new generation.新一代的选择。
(百事可乐)17. We integrate, you communicate.我们集大成,您超越自我。
甲骨文:全球商业软件市场的领军者
甲骨文:全球商业软件市场的领军者甲骨文公司(Oracle)是世界上最大的数据库软件公司,也是全球第二大软件公司,主要业务是开发、生产、营销、经销和维护数据库与中间件软件、应用软件和硬件系统。
公司创始人拉里·埃里森(Larry Ellison)以傲慢自大,挥金如土著称,被称为最有趣、最有争议的CEO。
甲骨文公司在2009年收购了曾经名噪一时的太阳计算机系统公司(Sun Microsystems)。
创立“关系数据库”公司1944年,拉里·埃里森(Larry Ellison)出生在曼哈顿,他的未婚妈妈只有19岁。
他从来不知道自己的生父是谁,就连自己的生母也只见过一次。
埃里森由舅舅一家抚养,在芝加哥犹太区中下阶层长大。
1962年埃里森高中毕业,进入伊利诺伊州大学就读,二年级时离开了学校。
过了一个夏天他进入芝加哥大学,同时还在美国西北大学学习。
虽然经历了三个大学,但他没有得到任何大学文凭。
1966年,埃里森来到加州的伯克利准备就读研究生,同时开始工作赚钱。
他自学了电脑编程,主要工作是给一些大公司开发应用程序。
就这样,从来没有上过一堂计算机课的埃里森成了程序员。
1973年,埃里森在日本富士通参股的Amdahl公司工作,也让他迷上了日本文化。
随后,他进入了硅谷一家生产影像设备的公司Ampex,在那里认识了他一生中最重要的两个人:鲍勃·迈纳(Bob Miner)和爱德华·奥德斯(Edward Oates)。
Ampex公司当时正为美国中央情报局(CIA)设计一套名叫Oracle的数据库,埃里森是程序员之一。
1976年,IBM的研究人员发表了一篇关于“关系数据库”的论文,埃里森被其内容震惊,敏锐意识到在这个研究基础上可以开发商用软件系统。
IBM当时有一个销售得还不错的层次数据库产品IMS,因此并没有开发关系数据库产品。
数据库在当时是静态的,无法交互工作。
于是,埃里森决定开发通用商用数据库系统Oracle,名字来源于之前给CIA做过的项目。
世界500强企业名称中英文对照翻译301-400
世界500强企业名称中英对照(四)公司名称中文名称总部所在地主要业务301 Onex 的加拿大电子电气302 Liberty Mutual Insurance Group 利保相互保险集团美国保险303 Dentsu 电通日本广告304 TransCanada Pipelines 的加拿大能源305 NKK 日本钢管日本金属306 Diageo 迪阿吉奥英国饮料307 AMP 安宝澳大利亚保险308 Sakura Bank 樱花银行日本银行309 Weyerhaeuser 惠好美国纸产品310 Nippon Express 日本通运日本邮递运输311 Delta Air Lines 德尔塔航空美国航空公司312 Skandia Group 斯堪地亚集团瑞典保险313 Taisei 大成建设日本工程建筑314 Mitsubishi Chem ical 三菱化学日本化学315 Adecco 的瑞士的316 Washington Mutual 华盛顿相互美国银行317 MYCAL 的日本零售318 Bayerische Landesbank 巴伐利亚银行德国银行319 Sun Microsystem s 太阳微系统美国计算机320 Dexia Group 的比利时/法国银行321 Faros 的法国的322 Canadian Im perial Bank of Comm erce 加拿大帝国商业银行加拿大银行323 Em erson Electric 艾默生电气美国电子电气324 Tohoku Electric Power 东北电力日本电力325 Shimizu 清水日本工程建筑326 Coles Myer 科斯迈尔澳大利亚零售327 Royal Bank of Canada 皇家加拿大银行加拿大银行328 Japan Airlines 日本航空日本航空公司329 Best Buy 的美国零售330 Halifax 哈里法克斯英国银行331 Corus Group 的英国金属332 Rite Aid 来爱德美国零售333 Norinchukin Bank 农林中央金库日本银行334 Swiss Life Ins. & Pension 瑞士人寿与养老金瑞士保险335 Centrica 的英国电力煤气336 China Mobile 中国移动通信中国电信337 George Weston 乔治威斯顿加拿大零售338 BHP 布鲁肯希尔澳大利亚采矿原油339 BCE 贝尔加拿大电子加拿大电信340 Groupam a-Gan 安盟-甘集团法国保险341 Anglo Am erican 的英国采矿342 DG Bank Group 的德国银行343 La Poste 法国邮政法国邮递344 Seagram施格兰加拿大饮料345 UniCredito Italiano 意大利联合信贷银行意大利银行346 Nationwide Insurance Enterprise 的美国保险347 Coca-Cola Enterprises 可口可乐企业美国饮料348 Hartford Financial Services 哈德福德金融服务美国保险349 Valero Energy 的美国炼油350 National Australia Bank 澳大利亚国家银行澳大利亚银行351 BAE System s 的英国航空航天352 Man Group 曼德国汽车353 Michelin 米其林法国轮胎橡胶354 Publix Super Markets 的美国零售355 Occidental Petroleum西方石油美国化学356 Usinor 法国北方钢铁联合公司法国金属357 May Departm ent Stores 五月百货美国零售358 Suzuki Motor 铃木汽车日本汽车359 Flem ing 佛莱明美国零售360 Goodyear Tire & Rubber 固特异轮胎橡胶美国轮胎橡胶361 Lukoil 的俄罗斯采矿原油362 SK Global 鲜京全球韩国多样化363 Ultram ar Diam ond Sham rock 钻石三叶草美国炼油364 Deutsche Bahn 德国联邦铁路德国铁路运输365 Endesa 的西班牙电力366 McDonald's 麦当劳美国餐饮服务367 Isuzu Motors 五十铃汽车日本汽车368 Volvo 沃尔沃瑞典汽车369 Solectron 的美国电子电气370 Banco Bradesco 的巴西银行371 News Corp. 新闻集团澳大利亚娱乐372 KarstadtQuelle 卡尔施泰特德国零售373 Lear 里尔美国汽车零件374 Lufthansa Group 汉莎航空德国航空公司375 Eastm an Kodak 伊斯曼柯达美国摄影器材376 Kim berly-Clark 金百利克拉克美国纸产品377 Ricoh 理光日本办公用品378 Am erican Hom e Products 美国家庭用品美国制药379 Abbott Laboratories 雅培美国制药380 British Airways 英国航空英国航空公司381 Winn-Dixie Stores 温迪克西百货美国零售382 Am erican Electric Power 美国电力美国电力383 Otto Versand 奥托邮购德国邮购384 Gap 的美国零售385 RAG 鲁尔德国采矿原油386 Vinci 的法国的387 Toronto-Dom inion Bank 的加拿大银行388 Sum itom o Metal Industries 住友金属日本金属389 Sum itom o Electric Industries 住友电工日本金属390 Halliburton 哈利佰顿美国工程建筑391 Japan Telecom Co. Ltd. 日本电信日本电信392 Montedison 蒙特爱迪生意大利食品393 Groupe Danone 达能法国食品394 Deere 迪尔美国工农业设备395 Kyushu Electric Power 九州电力日本电力396 Tex tron 达信美国航空航天397 Carso Global Telecom的墨西哥电信398 Electrolux 伊莱克斯瑞典家用电器399 Fuji Photo Film富士胶卷日本摄影器材400 Arrow Electronics 的美国零售。
oracle发展历程
oracle发展历程Oracle公司成立于1977年,是世界上最大的计算机科技企业之一,总部位于美国加州红木城。
Oracle的发展历程可以追溯到20世纪70年代中期,当时由拉里·埃里森(Larry Ellison)、鲍勃·明德尔(Bob Miner)和艾德·奥茨(Ed Oates)创立了一个名为Software Development Laboratories(SDL)的公司。
在创立初期,SDL公司致力于为大型计算机开发软件。
然而,随着个人电脑的兴起,SDL意识到市场的需求正在发生变化。
因此,他们决定进入关系数据库管理系统(Relational Database Management System, RDBMS)领域,并在1979年发布了他们的第一个商业化数据库产品Oracle V2。
Oracle V2的发布引起了广泛的关注和成功,吸引了一些大型企业的注意。
这一成功激励了SDL团队继续研究和开发新的数据库产品,以满足不断变化的市场需求。
1982年,SDL公司将其名字更改为Oracle Corporation,以适应新的业务方向。
此后,Oracle加快了在全球范围内的业务扩张和产品创新。
在1983年,他们推出了第一个跨平台RDBMS产品Oracle V3。
Oracle在1980年代和1990年代继续推出了一系列重要的产品和技术创新。
这些包括Oracle 6、Oracle 7和Oracle 8等版本的发布,以及应用程序开发工具Oracle Forms和Oracle Reports的推出。
此外,Oracle还推出了分布式数据库技术,使得用户可以将数据存储在多个服务器中,并通过网络进行访问。
到了21世纪初,Oracle成为了全球领先的关系数据库管理系统提供商。
然而,随着互联网和电子商务的快速发展,Oracle意识到他们需要更好地适应新的市场环境。
为了满足互联网时代的需求,Oracle在2001年推出了Oracle9i,该版本具有更强大的互联网功能和更高的可伸缩性。
SONY 蓝光家庭影音系统 BDV-E970W 说明书
4-178-245-41(1)蓝光家庭影音系统使用说明书BDV-E970W在操作本系统之前,请详细阅读本手册并妥善保管以备将来参考。
©2010 Sony Corporation切勿将本设备安装在狭窄的空间内,如书柜或壁橱。
为防止火灾,切勿用报纸、桌布、窗帘等遮住设备的通风口。
并且切勿将点燃的蜡烛等明火火源放在设备上。
为防止火灾或触电危险,切勿使本设备受到水滴或飞溅,且切勿将盛满液体的花瓶等物体放在设备上。
切勿将电池或装有电池的设备暴露在极端高温的环境下,如阳光、明火之类的环境。
为了防止人身伤害,必须根据安装说明将本设备牢牢固定到地板/墙壁上。
仅限室内使用。
注意本产品所使用的光学装置将增加对眼睛的伤害。
在本蓝光家庭影音系统中使用的激光束对眼睛有害,请勿试图拆卸机壳。
请仅联系合格人员进行维修。
本设备定级为CLASS 3RLASER产品。
激光保护罩打开时会发出可见和不可见激光辐射,因此切勿直接照射眼睛。
此标志位于机壳内的激光保护罩上。
LASER产品。
此标志位于背部外表面。
有触电危险严禁拆开内部无操作者可修理元件,维修请咨询具备资格人士。
为防止触电严禁拆开机壳,维修请咨询具备资格人士。
对于无线收发器(EZW-RT10)a不得擅自更改发射频率、加大发射功率(包括额外加装射频功率放大器),不得擅自外接天线或改用其它发射天线;b使用时不得对各种合法的无线电通信业务产生有害干扰;一旦发现有干扰现象时,应立即停止使用,并采取措施消除干扰后方可继续使用;c使用微功率无线电设备,必须忍受各种无线电业务的干扰或工业、科学及医疗应用设备的辐射干扰;d不得在飞机和机场附近使用。
警告2CS关于电源•只要本机的交流电源线还连接在交流电源插座上,即使本机的电源已经关闭,但本机仍然未与交流电源断开连接。
•由于主电源插头用于将本机与主电源断开连接,请将本机连接至易于插拔的交流电源插座上。
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SRL – A simple retargetable loader
SRL–A Simple Retargetable LoaderDavid Ung Cristina CifuentesCentre for Software MaintenanceSchool of Information TechnologyUniversity of Queenslanddavidu,cristina@.auAbstractA loader is a systems program used by an operatingsystem(OS)to load a binary executablefile onto memory toexecute it.The internal format of a binary executablefile iscalled the binary-file format(BFF);this format is dependenton the OS and the particular computer architecture it runson.Traditionally,when developing machine-code manipu-lation tools such as binary translators and disassemblers,developers need to write a decoder for each type of binaryexecutablefile they want to manipulate,i.e.for differentbinary executables,they need to write different loaders.With the advent of binary translation technology and theincreased number of machines and operating systems,a re-targetable loader(RL)would eliminate the effort requiredin creating different loaders;if only one such environmentexisted.SRL,a simple retargetable loader,is afirst attempt atdeveloping an RL framework by means of a simple BFFgrammar.Three different environments,(x86,DOS,EXE),(x86,Windows,NE)and(Sparc,Solaris,ELF),were used asthe basis for the development and testing of SRL.The threeenvironments give a good coverage of different BFFs cur-rently in use by OSs for RISC and CISC machines.1.IntroductionMachine-code manipulation tools such as binary trans-lators,disassemblers,decompilers,binary debuggers,andtracers or profilers,are systems programming tools that in-teract with the computer’s operating system(OS)to eithertranslate low-level machine instructions into a higher levelof abstraction(e.g.in the case of disassemblers,decompil-ers and debuggers),or to another low-level of abstraction1Different names are used in the literature to refer to executable pro-grams:binary programs,object code,binary executables,or executableprograms.All of these names are used as synonyms in this paper,and referto the end product of the compilation and linkage process.Figure1.Object code decoding abstractionBinary translators are of particular importance in this study as they interact with BFFs twice;once during the de-coding of the source eecutable program,and once during the encoding of the new target executable program.Exist-ing binary translators such as Digital’s VEST and mx[13], Freeport Express[5]and FX!32[15];Sun’s Wabi[14];Ap-ple’s MAE[4]and A T&T’s Flashport[1]support one or two different platforms only.We are developing a retargetable binary translation framework that supports a larger set of platforms.1.1.Retargetable loadingTraditionally,when developing a machine-code manipu-lation tool,developers need to write a decoder for every BFF they want to manipulate.For example,if we want to write a disassembler for an Intel x86machine running DOS and using the EXE binaryfile format[6,12],we write a loader for the EXE format and a decoder of machine instructions for(x86,DOS).If we then decide to write another disassem-bler for the Windows New Executable(NE)BFF[6,12],we need to write another loader for NE and a modified machine instruction decoder for(x86,Windows)as the interface to information on the BFF is different.So,if we have dif-ferent(M,OS,BFF)tuples,we will need to write different loaders.A systems programmer developing a machine-code ma-nipulation tool and wanting to test the new tool on A different machine architectures,usingB different operating systems andC different BFFs,will need to write AxBxC different loaders.However,the process carried out by all loaders is similar despite the fact that the(M,OS,BFF)tuples are dif-ferent.Ideally,developers would like to write as minimum code as possible in order to cater for different(M,OS,BFF) tuples.This approach is possible with the development of reusable components that are automatically generated from specifications;i.e.a retargetable loader(RL)framework.The input to the RL framework is a BFF specification and a binary executable.The BFF specification is an unam-biguous description of a binary-file format.The output of the RL is a high-level language interface for the loading of the program.The rest of this paper is structured in the following way. Section2discusses previous works related to the design of a retargetable loader.Section3describes the development of an RL using specifications;structure of binary-file formats (BFFs)are discussed in Section4.The BFF properties and the grammar used by the Simple Retargetable Loader(SRL) are the main topic of Section5.Section6describes how we tested the SRL and the results accomplished.A summary and conclusion follow the paper.2.Previous workA few methods and tools are currently available for cap-turing binary information stored in executable programs. Overall,there are3different approaches that can be used in developing a loader(or any other system tool)[16]:1.hand-craft the code,e library routines to assist in the writing of the code,ore specifications for automatic generation of the code.Thefirst approach is the easiest and quickest to imple-ment,although sometimes tedious to test.This simplicity advantage is only good for creating loaders that are lim-ited to the knowledge of one BFF.As described earlier,one would need to hand craft different loaders for differ-ent(M,OS,BFF)system tuples,and hence this approach is unsuitable for retargetability purposes.Approaches2and3can be time-consuming to implement atfirst(i.e.developing the library or the generator of code based on specifications),however,once this time investment has been made,the production of other loaders is quick and simple.An overhead in learning the tool at hand will always be needed though.Either approach provides support for the creation of an RL framework.A widely used example of the second approach is provided by the Binary-File Descriptor (BFD)library[2],which uses an extensive structure to rep-resent details within a binaryfile;routines for new BFFs are added to the library incrementally.An example of the third approach was attempted in the DWG(AutoCAD)work[7], which uses a specification language to describe the contents of thefile.Both these methods are reviewed in the coming subsections.With the recent trend towards Java and portable software, the use of machine-independent representations to store the final"executable"program is bound to grow.Java"exe-cutable"programs are binary representations of bytecodesfor an abstract stack machine.This machine is implemented by the Java Virtual Machine(VM)[11].Java"executa-bles"also contain other information required by the VM for execution of the program.The creation of a library interface for loading such byte-code programs,or the specification of such binary format, are possible.However,the loading of the program as such will be governed by rules in the VM rather than traditional V on Neumann machine rules(i.e.execute machine instruc-tions sequentially from the given entry point,following the flow of control of the program).In the work reported in this paper,we have concentrated on the traditional,in use,CISC and RISC machines.2.1.Binary-file descriptor libraryGNU’s Binary-File Descriptor(BFD)Library[2]is a package containing common routines that applications can use regardless of their underlying binary-file format.The BFD library divides each specified BFF into the front-end and the back-end.The front-end interfaces between the user and the BFD,while the back-end provides a set of calls which the BFD front-end can use to decode and manage the objectfile.To support a new BFF,the programmer needs to create a new BFD back-end and add it to the library.BFD has its own binary representation for internal pro-cessing known as the canonical objectfile format.When an objectfile(M,OS,BFF)is opened,the front-end BFD routines automatically determine the format of the input object-file.A descriptor is built in memory with informa-tion about which routines are to be used to access elements of the objectfile’s data structure.When the program wants information about the objectfiles,the BFD reads from dif-ferent sections of thefile and processes them.Each BFD back-end will have routines to convert section representa-tions of the objectfile to BFD’s internal canonical object-file format.The BFD library is a good example of using library rou-tines to develop an RL.Unfortunately,the library itself is very large;the number of functions offered in the front-end are exceptionally many.The BFD front-end was designed in mind to allow programmers to be able to retrieve all types of information about any BFF;at least the existing ones at the time.Due to its generality and bulkiness,it is difficult to use without spending a big overhead on learning how to use it.Perhaps because it is too general,it often contain more information than is needed for system applications.2.2.DWG-based grammarAn initial attempt at developing a BFF grammar was done by Faase using the AutoCAD’s DWG format[7].In this grammar,terminal symbols consist of a number of bytes and are the fundamental set of base types found in most programming languages:char,int,long,float and double.A binary objectfile is viewed as a stream of bytes.The grammar supports different byte ordering for integers,and different formats forfloating point numbers for various ma-chines.For example,the definition of a word representationin a little-endian machine is given by:type word:=byte:first,byte:secondreturn((word)first|((word)second<<8).In a little-endian machine,the lower order byte is stored before the higher order byte.New types are defined as a series of bytes following this rule:type_def_rule:="typedef"data_type basic_type_name":="("byte"":"byte_name)LIST"return"expr".".AutoCAD’sfile format contains a header,several sec-tions and blocks of information related to2D or3D draw-ings.This generalfile structure resembles that of a simple BFF.However,the information stored in the sections is very different as binary executables contain relocation informa-tion,symbol table information,dynamic linking informa-tion,and more.Also,since the DWG format is used as the basis for development,the resulting grammar is biased to-wards DWG.A complete specification for the DWG format can be found in[7].3.Developing a retargetable loader via specifi-cationsThe use of specifications in the development of software engineering tools is not new.Parser and compiler genera-tor tools based on specifications,such as lex[10],yacc[9] and Eli[8],have been around for a while and have provento be very useful.The input to these generator tools is often a specification,usually in the form of a context free grammar commonly found in specifying the syntax of pro-gramming languages.This concept can also be applied to binary objects where each of the BFFs can be specified ina grammar that can be understood by the RL.The resulting objectfile specification needs to contain information about the structure of the objectfile and how various sections canbe accessed.Each specification acts as a framework for all objectfiles in the group,i.e.a template for all objects be-longing to the environment(M,OS,BFF).In programming language terms,the BFF template resembles the variable types while each of the objectfiles is an instance of this type.Figure2.Developing an RL via specificationFigure2describes the RL approach;an objectfile (M1,OS1,BFF1)has a specification template according to the syntax of the BFF grammar.A retargetable loader would use the template information as the basis for processing the objectfile(M1,OS1,BFF1).4.Binary-file formatsThe general structure of a BFF can be seen to be made up by the following abstraction:A header containing general information about the pro-gram and information needed to access various parts of thefile.A number of sections holding code and data(raw data).Relocation table(s).Symbol table information.Most BFFs can be mapped to the general model in rmation regarding the location of sections,sym-bol tables,etc are usually identified within thefile header. Nevertheless,some BFFs do not distinguish between these structures;in the DOS EXE format,thefile header contains information about the relocation table,but there is no in-formation about where the symbol table is stored(if any), and where data is;there is only one section that embodies all code,data and symbol table information.In all cases though, the program’s header will contain enough information to de-termine the entry point(i.e.the start of the program’s code) in thefile.The current development domain for our tools is based on the DOS EXE format[6,12]Windows(16-bit)NE[6,12] and Solaris ELF[14]BFF formats.These formats vary in their degree of complexity and information stored:the DOS EXE is very simple and limited in structure,whereas the Solaris ELF format is the most complex,while the Win-dows NE is somewhere in between.For example,for a simple“Hello world”program,using a DOS EXEfile will contain afile header,relocation table and a single image for both code and data.The Windows NE versionwilFigure3.BFF abstractioncontain most DOS EXE information plus additional de-tails such as the resource table,entry table,etc.The ELF format contains even more information about thefile;sec-tions within the objectfile hold information used in dy-namic linking:code,data,relocation tables,symbol ta-bles,dynamic linking information,etc.Typical“Hello world”binaries for(x86,DOS,EXE),(x86,Windows,NE) and(Sparc,Solaris,ELF)are6432,16384and5280bytes long respetively.It can clearly be seen that although the latter twofiles are dynamically linked,their sizes are not necessarily smaller than the static(first)case.This is due to the small nature of the example program and the inclusion of the DOS EXE header information within the NE format.5.BFF grammarIn this section we describe the BFF grammar developed based on the EXE,NE and ELF formats.We start by describ-ing some of the properties of BFFs,followed by a description of the grammar itself.5.1.BFF propertiesIn a binaryfile,some parts within thefile are interrelated. Although the structure of the binaryfile does not change at run-time,it’sfile size,location of sections(or regions) and contents can vary significantly,sometimes having in-formation at the end of thefile refering to sections in the middle of thefile.Because of this behaviour,the ability of the grammar to reference previously defined information is very important.The resulting grammar not only needs to be general,it must also beflexible to assist thefinal RL in re-referencing previously parsed rmation that needs to be re-referenced is usually found in thefile header of the binary objectfile.As the specification for a particular BFF is parsed,any reference to previously read information needs to be handled appropriately.The ability for a programming language to re-use defined information later in the program can be quite restricted. Although user defined types can be referenced later whendeclaring instances,they do not have a value.Macros in languages can be used(referenced)throughout the rest of the program after its definition,but their values arefixed and cannot be changed.In contrast,each binaryfile of a given BFF has its own set of records that identifies itself.The BFF specification captures the structure of these records,but not its information.The general structure of the BFF is known through the specification,however each instance of this BFF can only be understood by an RL during its parsing process at run-time.The specification defines the items in thefile, but their run-time values give meaning to other definitions in the rest of the specification.The following example will clarify the idea of re-referencing in a BFF specification:let us assume we have a“Hello World”program stored in a Windows NE BFF. The segment table for the Windows NE BFF consists of a fixed number of segment table entries.The exact number of entries is listed as one of thefields in the program’sfile header–NumSegEnt.To create a copy of the segment table for the“Hello World”program in memory,the RL must allo-cate the number of entries according to NumSegEnt.In the Windows NE specification,the definition of thefile header and segment table could be as follows:FileHeader:STRUCTURE{..NumSegEnt:int;..}SegmentTable:ARRAY NumSegEnt OF SegTableEnt;In other words,the value of NumSegEnt is used to specify the size of the array SegmentTable.In traditional languages, the array size needs to be specified at compile time.How-ever,in this language,the size of the segment table is only known at run-time,hence the array is allocated at run-time when the information becomes available.This behaviour is what we refer to as re-referencing of information.Most re-referenced information is located in thefile header,but sometimes this is not the case.For example,to locate the segment table,the address where it can be located must be defined:SegmentTable:ARRAY NumSegEnt OF SegTableEnt;ADDRESS NewHoff+SegToff;The address is the addition of SegToff,found within the second header,and NewHoff,located in thefirst header.5.2.BFF grammar for simple retargetable loaderSRL,a simple retargetable loader,was constructed to de-velop a tool that would support the developed BFF grammar, and generate C code for the loading of a binaryfile stored in the specified binary-file format.SRL requires a generic BFF grammar such that it can be easily extended if later found insufficient to describe other BFFs.Our focus has been mainly on three BFFs:DOS EXE,Windows(16bit) NE and Solaris ELF formats.The difference in complexity between these BFF(DOS EXE–simple,Solaris ELF–very general and Windows NE–moderate)gives an indication of how well the grammar works.We present the abstract syn-tax of BFFG,SRL’s BFF grammar.The grammar syntax is in extended BNF(EBNF)format.EBNF has the following language symbols:Sequences are denoted by.E.g.rep specifies zero or more repetitions of rep.Optional is denoted by[].E.g.[opt]specifies zero or one occurrences of opt.Selection is denoted by.E.g.A B C specifies a choice between A,B and C.In the grammar,non-terminals appear in italics,termi-nals appear in normal fontface,“literal strings”appear with double quotes,and examples appear in courier.The start symbol for this grammar is BFFspec:BFFspec spec.spec format-def defin defin load-infoformat-def“DEFINITION”“FORMAT”ident ident“END”“FORMAT”defin“DEFINITION”ident“ADDRESS”expression scope-def“END”ident.load-info“FILEHEADER”ident“IMAGESIZE”expression“IMAGEADDRESS”expression scope-def ident type-exp ident type-exptype-exp“SIZE”expression“ARRAY”expression scope-def“END”identexpression“(”ident operator expression“)”ident operator expressionoperator“+”“-”“*”“/”“ˆ’’“%”ident“a”..“z”“A”..“Z”“a”..“z”“A”..“Z”“DEFINITION FORMATfile_headersectionEND FORMATIn this example,file_header and section will need to be defined later on in the grammar.If we want to specify the DOS EXEfile,then the relocation table would go between the file_header and section.However, if we are not concerned with its details,it can be omitted from the definition.The organisation of identifiers is not forced;it merely indicates the relative ordering of divisions. The above definition does not suggest that section starts at the end of file_header;in fact,section could be placed before file_header.The syntax of format-def does not place any ordering restrictions on it.But for clarity and ease of understanding,the user should arrange the defi-nitions in a well-formed manner so that it reflects the actual file’s structure.5.2.2definEach declared identifier ident in a format-def is defined using the following defin rule:defin“DEFINITION”ident“ADDRESS”expressionscope-def“END”identDefin declares a new structure(section or block of the file).The ident that follows the keyword DEFINITION must be previously declared in the grammar.The start location of this new structure(relative to the start of thefile)is specified by the expression after the keyword ADDRESS;e.g.the definition of the file_header might look like this: DEFINITION file_header ADDRESS0h_sigLo SIZE8h_sigHi SIZE8h_lastPageSize SIZE16..END file_headerThe above definition indicates that the file_header starts at the beginning of thefile(i.e.offset0).All declara-tions that follow the ADDRESS belong to this definition;in the above case the file_header.The entries h_sigLo, h_sigHi and h_lastPageSize all belong to the same scope level and have a parent named file_header.This concept is equivalent to the definition of a structural type in most programming languages.5.2.3Loading-infoload-info“FILEHEADER”ident“IMAGESIZE”expression“IMAGEADDRESS”expressionLoad-info holds the fundamental information about the objectfile for loading to occur.It is crucial for any BFF specification to provide its loading information.There is no order on the occurrence of the three constructs,as long as all three exist in the specification.The FILEHEADER construct identifies thefirst region of the object that must be loaded into memory.This region is often thefile header as it contain critical information about the locations of other regions and some house keeping information.The IMAGE-SIZE specifies the load image size;the size is often calcu-lated based on the information obtained in thefile header. The IMAGEADDRESS specifies the start address(relative to the beginning of thefile)where the image is stored.5.2.4scope-defscope-def ident type-exp ident type-expScope-def captures all information belonging to the same scope level within one structure.Its properties are analogous to the structural types in the language C.An ident name and type information defines eachfield within the scope.5.2.5type-exptype-exp“SIZE”expression“ARRAY”expression scope-def“END”identType-exp defines the type for the identifier ident in bits. An identifier can be either a single element of a particular size or a group that is specified by the ARRAY construct. The expression after the keyword ARRAY identifies the number of elements in the ARRAY.Declarations within the ARRAY definition are bounded to the same scope,with array identifier being their parent.For example,the definition of the segment table in the Windows NE format follows: DEFINITION seg_table ADDRESS(sh_segToff+sho_off) seg_table_ent ARRAY sh_segTentste_logSectoff SIZE16ste_size SIZE16ste_flag SIZE16ste_minsize SIZE16END seg_table_entEND seg_tableIn the Windows NE format,the segment table is defined to be an array of structures named seg_table_ent.The number of array elements is sh_segTent,which must have been parsed earlier in the specification and its value is used at run-time.ste_logSectoff,ste_size, ste_flag and ste_minsize are the components(or fields)of one element of seg_table_ent.6.ExperimentationSRL,the Simple Retargetable Loader,is an attempt to demonstrate the benefit of using an RL to build a machine-code manipulation tool.It is a scaled downed version of an RL and is implemented in C.The SRL is limited in a way by its simple grammar which contains a small number of con-structs.As described in the previous section,the BFFG for the SRL was constructed using three different base environ-ments:(x86,DOS,EXE),(x86,Windows,NE)and (Sparc,Solaris,ELF).The ELF (on a RISC architecture)being the most complex BFF of the three,the EXE (CISC)being the simplest,and the NE (CISC)somewhere in between.These three formats allowed the develoopment of a generic BFF grammar for the SRL.When implementing an RL,one must consider to what extent does this RL take part in the decoding of the binary file.Does it decode the whole file and rewrite it to another representation or does it simply load the whole file to mem-ory?How much detail is interpreted by the RL?In the case of SRL,the primary function was to produce a high-level language interface to represent the structures of the binary file (i.e.a header file in C),and the loading of the program’s image (i.e.a C file).The input to the SRL is the BFF specification:a binary description of the object file for an environment (M,OS,BFF)written in SRL’s syntax grammar.Figure 4is a description of what the SRL produces.The object structures are the type definitions for various regions of the binary executable file.The loading routine contains initialized information for the object structures and loading of the object image to memory.The object structure and loading routine are implemented as .H and .C files respectively using the Clanguage.Figure 4.The Simple Retargetable Loader (SRL)The specifications for (x86,DOS,EXE),(x86,Windows,NE)and (Sparc,Solaris,ELF)were used as inputs to the SRL and the set of corresponding .H and .C interface files were produced.The SRL output for the (x86,DOS,EXE)specification is listed in Figures 5and 6.The data structure for manipulating the binary is generated in the.H file while the .C file provides a function LoadImage()which ini-/*This file is generated by the BFF generator using the grammar in "dosexe.txt"*/#ifndef _LOAD_H_#define _LOAD_H_#ifdef __MSDOS__#define INT int#define LONG unsigned long #else#define INT short int#define LONG unsigned int #endif __MSDOS__typedef unsigned char byte;typedef short int16;#define LH(p)((int16)((byte*)(p))[0]+((int16)((byte*)(p))[1]<<8))typedef struct {byte h_sigLo;byte h_sigHi;INT h_lastPageSize;INT h_numPages;INT h_numReloc;INT h_numParaHeader;INT h_minAlloc;INT h_maxAlloc;INT h_initSS;INT h_initSP;INT h_checkSum;INT h_initIP;INT h_initCS;INT h_relcTabOffset;INT h_overlayNum;}headerT;typedef struct {headerT *header;byte*section;char*filename;LONG imagesize;byte*image;}BFF;extern BFF*aBFF;LoadImage(char*filename);#endif _LOAD_H_Figure 5.loader.H file generated by SRLtializes the aBFF structure andfinds the entry point to the program.Surprisingly,the specification for the Windows NE man-aged to be larger than that for the ELF,although the ELF is presumed to be the most complex format of the three.Per-haps if the grammar had more constructs,thenfiner details could be captured.In that case,we would see more of the ELF structure.But is that part of the loader?Or does it de-pend on the needs of the manipulation tool?Does the loader need to examine and be able to identify and understand all the different regions of the objectfile?How much dispo-sure should the loader know?Due to all these questions, a complete interface is still left as discussion and work in progress.To examine the usability of the SRL’s output,thus demon-strating the generality and usefulness of an RL;the generated loadingfiles(.H and.C)produced from the(x86,DOS,EXE) specification were integrated with the DCC decompiler[3]. The hand-coded loading module for the Intel286DCC de-compiler was replaced with the corresponding SRL outputs. With a few minor changes(calls to function names and vari-ables used were changed),DCC was reconstructed using the generated loadingfiles.The behaviour for the two versions of DCC was the same,hence demonstrating the correctness of the SRL output.6.1.InterfaceThe interface routines produced by the SRL are very simple.The.Cfile merely provides a loading module for setting up an image in memory(see Figure6).This isfine for a DOS EXE format as it is extremely simple,but for other types of BFFs,one would like to provide interface functions to access different regions of the binaryfile.For example, the Windows NE BFF has a number of tables–imported-name table,segment table,module reference table,etc.The structure of the segment table in the specification is: DEFINITION seg_table ADDRESS(sh_segToff+sho_off) seg_table_ent ARRAY sh_segTentste_logSectoff SIZE16ste_size SIZE16ste_flag SIZE16ste_minsize SIZE16END seg_table_entEND seg_tableThe SRL creates the structure for the segment table in the .Hfile and sets up a pointer that points to the beginning of the table in the image.There are no routines generated from the SRL for accessing this structure.If the programmer wants to access a particular entry in the table,then he/she must directly manipulate this structure by hand crafting that piece of code.A desirable feature for an RL would be to automatically generate a set of interface routines,thus eliminating the need for the programmer to hand code routines to manipulate the structures directly.6.2.Limitation of SRL’s BFFGThe SRL’s BFFG was designed to be as simple as possi-ble.It merely provides a most basic framework model for creation of elementary loading routines.Most of the SRL functions deal with information about thefile header and apply its definitions to the rest of the objectfile.There are a number of areas that the SRL grammar does not include:Relocation information is not capture by the SRL but can be included easily by adding new constructs to the BFF grammar.System architectures such as big and little endian ma-chine types are undefined.Such details need to be added to the grammar as well.The techniques used in describing the DWG format can be used to identify the byte ordering of the processor:type word:=byte:first,byte:second,return((word)first|((word)second<<8).Alternatively,a much simpler way would be just re-serving the words big-endian or little-endian and let the SRL handle the byte ordering for these machines.7.Summary and conclusionsThere are essentially three basic approaches to provide loading of a binary object:handcraft the code,use library routines or use specifications.A retargetable loader can be built using library routines or specifiing library routines is simpler but can be difficult;attempts to use tools such as the BFD library are uninviting due to their com-plexity.Specifications are easily understood and are trouble free once they have been developed.It is an ideal method to develop a retargetable loader(RL)based on a binary-file format(BFF)grammar.There are a few differences between grammars used in programming languages and the grammar used for describ-ing BFFs.The most significant difference is the ability of the BFF grammar to re-reference information that was pre-viously defined.Previously defined information is critical in binaryfile processing:addresses and segment sizes are usually controlled by definitions found in thefile header and their values are determined only at run-time.。
SunMicrosystemsLaboratories,Inc.
UNANNOTATED TEXT
INITIAL STATE
ANNOES
Transformation-Based Error-Driven Learning
Transformation-based error-driven learning is a simple learning algorithm that has been applied to a number of natural language problems, including part of speech tagging and syntactic parsing (Bri92, Bri93a, Bri93b, Bri94). Figure 1 illustrates the learning process. First, unannotated text is passed through the initial-state annotator. The initial-state annotator can range in complexity from quite trivial (e.g. assigning random structure) to quite sophisticated (e.g. assigning the output of a knowledge-based annotator that was created by hand). Once text has been passed through the initial-state annotator, it is then compared to the truth, as indicated in a manually annotated corpus, and transformations are learned that can be applied to the output of the initial state annotator to make it better resemble the truth. So far, only a greedy search approach has been used: at each iteration of learning, the transformation is found whose application results in the greatest improvement; that transformation is then added to the ordered transformation list and the corpus is updated by applying the learned transformation. (See (RM94) for a detailed discussion of this algorithm in the context of machine learning issues.) Once an ordered list of transformations is learned, new text can be annotated by rst applying the initial state annotator to it and then applying each of the transformations, in order.
Ad-hoc on-demand distance vector routing
本科教学工作水平评估情况汇报.ppt
– Boeing 777
• 1280 CPUs onboard • 7 million lines of code
– Autonomous Vehicles
• networked embedded systems • safety- and mission-critical
Feb 21–22, 2006, Science Park, Shatin, Hong Kong Jointly organized by Hong Kong Science and Technology Parks Corporation (HKSTP), Department of Automation and Computer-Aided Engineering, CUHK With support of Chess - UC Berkeley, ISIS - Vanderbilt University ESCHER, ESI - HKUST, Department of Computer Science - HKUST
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– National Science Foundation Faculty Early CAREER Development Program – National Science Foundation Information Technology Research Center Program – Department of Electrical Engineering and Computer Science, Vanderbilt University – DARPA Information Technology Office Software Enabled Control (SEC) Program – DARPA Infromation Technology Office Model Based Integration of Embedded Software (MoBIES) Program
眼科主要设备及品牌
耗材:手术刀,包括穿刺刀、巩膜隧道刀、超乳手术劈核刀、角膜切开刀、巩膜切开刀、碎核刀、晶体植入刀,主要生产厂家夏普特、日本马尼眼科缝线眼贴膜泪点塞义眼台义眼片青光眼引流阀泪道引流管眼用粘弹剂医用透明质酸钠凝胶眼科用重水眼科手术用硅油晶体囊袋张力环一、手术显微镜(Surgical Microscope):国外:1. Leica Microsystems (Schweiz) AG(瑞士品牌)徕卡显微系统(瑞士)公司2. 株式会社拓普康,株式会社トプコン/ TOPCON CORPORATION(日本品牌)日本拓普康有限公司3. NIDEK eye and health care CO., LTD. 尼德克医疗器械有限公司(日本品牌)4. Carl Zeiss Meditec AG(德国品牌)德国卡尔蔡司光学仪器有限公司5. MOELLER-WEDEL GmbH(德国品牌)德国Moller-Wedel 光学仪器公司6. Scan Optics Pty Ltd(澳大利亚品牌)澳大利亚视强光学器械有限公司7.Alcon Laboratories, Inc. Ophthalmic Surgical Mircoscopes (美国爱尔康公司)爱尔康(中国)眼科产品有限公司国内: 1. 苏州六六视觉科技股份有限公司2.上海轶德医疗设备有限公司3. 成都科奥达光电技术有限公司4. 杭州永新光电仪器有限公司医疗仪器厂5. 成都三信医疗电子有限责任公司 3S Medical electronic co.,Ltd6. 苏州医疗器械总厂二、超声仪(Echograph):国外:1. Quantel Medical(法国品牌)法国光大医疗公司2. Sonomed, Inc. 美国所罗门公司3. Accutome,Inc. 美国豪迈健康光学旗下子公司4. 日本尼德克株式会社(NIDEK CO., LTD)国内:1. 成都三信医疗电子有限责任公司 3S Medical electronic co.,Ltd2. 武汉思创电子有限公司3. 天津迈达医学科技有限公司MEDA Co., Ltd. Tianjin, China4. 重庆康华瑞明科技有限公司Chongqing Kang Huarui Ming technology co., LTD5. 天津市索维电子技术有限公司6. 上海同舸医疗器械有限公司7. 南昌吾方医疗器械有限公司8.无锡市康宁医疗电子设备开发公司9.上海瑞影医疗科技有限公司三、眼科超声生物显微镜(Ophthalmic Imaging System)国外:1. IScience Interventional国内:1. 天津迈达医学科技有限公司MEDA Co., Ltd. Tianjin, China2. 深圳市博视达光学仪器有限公司四、白内障超声乳化机国外:1. Abbot Medical Optics, Inc. (AMO) 美国AMO眼力健公司2. Alcon Laboratories, Inc. (美国爱尔康公司)3. Bausch & Lomb Incorporated.(美国博士伦有限公司)五、超声眼科乳化玻切治疗仪国外:1. Geuder AG Medical Supplies Companies(德国歌德医疗器械公司)2. Carl Zeiss Meditec AG德国卡尔蔡司光学仪器有限公司3. 美国AMO PHACO公司(美国AMO眼力健公司)六、光学相干断层扫描仪(OCT)国外:1. OPTOPOL Technology S.A. (OPTOPOL科技股份有限公司(波兰))2.Carl Zeiss Meditec Incorporated(美国卡尔蔡司有限公司) 3.Optos Inc.(美国Optos有限公司)4.株式会社拓普康(日本拓普康有限公司)5. Optovue, Inc.(美国Optovue有限公司)国内:1. 仪和仪美(北京)科技有限公司2 . 深圳市斯尔顿科技有限公司七、眼科Nd:YAG激光治疗仪国外:1. Ellex Medical Pty. Ltd (澳大利亚Ellex 医疗公司)2. Quantel Medical法国光太医疗公司3.Lumenis Inc.(美国Lumenis公司)国内:1.台湾台北承贤科技股份有限公司2.天津迈达医学科技股份有限公司3. 保定市万得光电科技开发有限公八、角膜内皮细胞计数仪1.株式会社拓普康, TOPCON CORPORATION(日本品牌)日本拓普康有限公司2. NIDEK eye and health care CO., LTD. 尼德克医疗器械有限公司(日本品牌)九、视网膜冷冻仪 1. 康捷医疗器械公司2. 北京精诚创业医疗器械有限公司十、眼科电生理仪 1. America kaif-lynk co., ltd 美国凯福林克有限公司2.Mednet international limited (加拿大麦特瑞医疗技术有限公司)。
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Collision Avoidance and Resolution Multiple Access for Multichannel Wireless NetworksR ODRIGO G ARC´E S rgarces@Metricom Inc.980University AvenueLos Gatos,CA95032J.J.G ARCIA-L UNA-A CEVES Computer Engineering Department University of CaliforniaSanta Cruz,California95064 Networking and Security Center Sun Microsystems LaboratoriesPalo Alto,California94303Abstract—We introduce and analyze CARMA-MC(for Collision Avoid-ance and Resolution Multiple Access MultiChannel),a new stable chan-nel access protocol for multihop wireless networks with multiple channels. CARMA-MC relies on the assignment of a unique channel and a unique identifier to each node to support correct deterministic collision resolution in the presence of hidden terminals.CARMA-MC dynamically divides the channel of each node into cycles of variable length;each cycle consists of one or more receiving periods and a transmission period.During the re-ceiving period,stations with one or more packets to send compete for the right to acquire thefloor of a particular receiver’s channel using a deter-ministic tree-splitting algorithm.Each receiving period consists of collision resolution steps.A single round of collision resolution(i.e.,a success,and idle or a collision of control packets)is allowed in each contention step.The receiving period is initiated by the receiver and takes place in the channel assigned to the receiver station.The channel utilization and packet delays are studied analytically and by simulation.I.I NTRODUCTIONCollisions in a packet-radio network can be cause by direct or by secondary interference.Direct interference occurs when two neighboring nodes transmit to each other at the same time. Secondary interference occurs when two or more stations un-aware of each other’s existence transmit to the same receiver at the same time or when a station is transmitting to its neighbor and a third stations transmission to some other station causes an interference.This problem wasfirst introduced by Tobagi and Kleinrock[15]and is known in the literature as the hidden terminal problem.Several approaches have been proposed in the past to resolve the hidden terminal problem,and collision-avoidance protocols have recently received considerable atten-tion(e.g.,[13],[6]).In a collision-avoidance protocol,sender and receiver collaborate trying to avoid data packets from col-liding with other packets at the receiver.However,as traffic load increases in the network,the collision of collision-avoidance control packets increases and throughput in the system drops.A way to stabilize the operation of contention-based protocols is by means of collision resolution mechanisms.Several stable MAC protocols have been proposed in the past based on tree-splitting algorithms for collision resolution (e.g.,[4],[7],[20]).Those protocols in which data packets are used to resolve collisions achieve throughput below[22]for a single channel and fully connected networks.Several MAC The work at UCSC was supported in part by the Defense Advanced Research Projects Agency(DARPA)under grant DAAB07-95-C-D157protocols have been proposed that implement collision reso-lution using either control packets that are much smaller than data packets,or are based on the ability of the transmitter to abort transmission rapidly after detecting collision(e.g.,[2], [8],[14]).Among those stable MAC protocols that achieve high throughput,some build a separate queue for the transmission of data packets,in addition to the stack or queue of the con-trol packets used for collision resolution.However,the stable collision resolution approaches reported to date operate in fully-connected networks or networks based on central base stations. On the other hand,several reservation based protocols have been proposed(e.g.,[1],[10],[12],[14])which provide stabil-ity at high load levels,and efficient service at low load levels. Resource auction protocols,i.e.,[1],[14],require a significant amount of overhead for each auction period and are difficult to implement.On the other hand,PRMA[10]is relatively easy to implement but uses afixed frame length which can lead to star-vation if the number of active stations is large.These protocols all require a base station,and do not operate in a network with hidden terminals.The limitation of these schemes is that most of them require the use of a base station which is a single point of failure.This paper presents an approach to utilizing collision reso-lution in multihop wireless networks by taking advantage of unique channel(or code)assignments to network nodes.In the past,multichannel networks have been constructed using mul-tiple transceivers operating on separatefixed channels[21]. Such devices were expensive to construct.However,current transceiver technology(e.g.,Metricom Inc.new generation net-work devices),enables radio devices with as many as chan-nels to be controlled by a single DSP,enabling radios to switch from one channel to the other within1sec.This allows multi-channel networks to be constructed inexpensively using a sin-gle device at each station.In addition,using multiple chan-nels renders better delay characteristics than single-channel net-works[16],[17],[19]and have better fault tolerance against fading and noise[5],[17].Early work in protocol design for multichannel networks used CSMA or ALOHA protocols in slotted multiple channels[18].A reservation protocol over multiple channels is investigated in[16]for satellite commu-nication systems.A sequential multichannel system which usesCSMA/CA on each channel to dynamically assign stations to channels is presented in[3].Analysis of multi-hop multichan-nel networks using CDMA in sparse networks with receiver-based,transmitter-based,pairwise-based,and common channel assignment is presented in[11].In this paper we introduce a new stable receiver-initiated mul-tiple access protocol with collision resolution called C ollision A voidance and R esolution M ultiple A ccess M ulti C hannel (CARMA-MC)protocol.CARMA-MC operates in a multi-channel network in which hidden terminals may exist.It as-sumes that each network node is assigned a unique identifier and a unique channel,at least within the two-hop neighborhood of any network node.CARMA-MC is a receiver initiated protocol that dynamically divides the channel assigned to each receiver into receiving and transmitting periods.The transmission pe-riod has a maximum-length duration and each receiving period consists of collision resolution steps,i.e.,of success,idle or col-lision of control packets.The protocol maintains a stack for the transmission of control packets used in collision resolution. During the receiving period CARMA-MC uses a determinis-tic tree-splitting algorithm and an RTR/RTS/CTS exchange.A receiving period is initiated by the receiver sending a ready-to-receive(RTR)signal and takes place in the channel assigned to the receiver station.During contention intervals a station at-tempts to acquire thefloor by sending an RTS to the intended receiver who,in turn,sends a CTS if the received RTS is error-free.RTSs are sent according to a deterministic tree-splitting algorithm that resolves all the requests that arrive during the same receiving interval.A packet is transmitted from the sta-tion that has acquired thefloor by successfully completing a collision-resolution round.The control packets used in each contention step are much smaller than a single data packet. Because CARMA-MC uses a deterministic collision-resolution mechanism,average delays incurred in the network are bounded and are a function of the number of one-hop neighbors of a re-ceiver.In stark contrast to prior approaches to collision resolu-tion,CARMA-MC operates correctly in multihop networks with hidden terminals.The rest of this paper is organized as follows.Section II de-scribes CARMA-MC in detail.In Section III we present the worst case packet delay and channel utilization.Section IV compares the analytical results with the simulation.The analyt-ical results are very close to the results obtained by the simula-tion,and this validates the approximations made in the analysis. Our results confirm that CARMA-MC is stable at any load level, and that it provides high throughput and bounded delays byfirst avoiding collisions of data packets and then efficiently resolving collisions of control packets.II.CARMA-MCA.Definitions and AssumptionsIn CARMA-MC,the channel access time is divided into re-ceiving and transmitting periods.Each station has a unique re-ceiving channel that is used by its one-hop neighbors to trans-mit packets.A station is allowed to transmit packets in any of the unique receiving channels assigned to its one-hop neighbors. Therefore,stations switch from their default receiving frequency to any of the frequencies assigned to its one-hop neighbors. We assume that there exists a mechanism that ensures that nostation is assigned the same receiving frequency as any of its two-hop neighbors.Each station has knowledge of the assigned frequencies for all its one-hop neighbors.The assignment of frequencies can be achieved by applying the distributed assign-ment of codes algorithm proposed in[9].This algorithm assignsa different frequency to each station within a two-hops neigh-borhood,provided that the number of frequencies available for assignment is at least,where is the maximum number of one-hop neighbors that any station can have.This mechanism eliminates co-channel interference andavoids the hidden terminal problem.Besides the assignment of a unique channel,each station is also assigned a unique identifier(ID),that is known by all its one-hop neighbors.This can be achieved by exchanging the ID information at the same time that the receiving frequencies are assigned,i.e.,applying the distributed assignment algorithm. Stations in CARMA-MC are half duplex;they can be senders or receivers.A station in the sender state participates in a collision-resolution interval(CRI)based on the deterministic tree-splitting algorithm introduced in[8].The determinis-tic tree-splitting algorithm resolve collisions among competing senders.The CRI evolves in terms of collision-resolution steps, where the size of a CRI is bounded and is a function of the num-ber of senders(see[8]for more details).We assume a ternary feedback model,i.e.,there are three types of collision-resolution steps:collision,success,and idle.Collision-resolution steps follows a handshake procedure meant to eliminate collisions among data packets.This procedure is known in the literature as“floor acquisition”[6].In a single-channel network,floor ac-quisition entails allowing one and only one station at a time to send data packets without collisions.To achieve this,a station that wishes to send one or multiple data packets must send a request-to-send packet(RTS)to an intended destination and re-ceive a clear-to-send packet(CTS)from it,before it is allowed to transmit any data.RTSs are required to last a minimum amount of time that is a function of the channel propagation time.In CARMA-MC a station in the sender state is allowed to participate in the CRI in the unique receiving frequency assigned to its one-hop intended destination.It is the responsibility of the receiver to initiate and gear the collision-resolution interval(CRI).The receiver can be vi-sualize as the master of its one-hop neighbors.The receiver uses the unique ID to resolve contentions among transmitters. Like previous efficient MAC protocols based on tree-splitting algorithms,the receiver maintains a stack and two variables, and.and are respectively the lowest and highest ID numbers of the stations that are initially allowed to transmit.Together,they define the allowed ID inter-val,.The allowed ID interval is broadcasted by the receiver to all its one-hop neighbors in a ready-to-receive packet(RTR)at the beginning of every collision resolution step. While a station is a receiver,it transmits an RTR at the be-ginning of each collision-resolution step and transmits CTSs in response to RTSs.It receives RTSs and data packets from its one-hop neighbors.All packet exchanges take place in the re-ceivers assigned channel.3On the other hand,a station in the sender state,waits on the intended receiver’s channel for RTRs and CTSs and transmits RTSs and data packets on the the same channel.rmation Maintained and ExchangedInformation is maintained,exchanged,and broadcasted by the receiver station.Each station is assigned a unique identifier and a unique receiving channel within the two-hop neighborhood of any network node.The receiver station maintains a stack and two variables and.Recall that is the lowest ID number that is allowed to send an RTS and it is initially set to1.On the other hand,is the highest ID number that is allowed to send an RTS and it is initially set to the largest number of one-hop stations allowed in the net-work.and define the allowed ID-number inter-val,(,that can send RTSs.If the ID of a station is not within the allowed ID interval,then the station is not al-lowed to send its RTS.The RTSs and CTSs specify the IDs of the sender and of the intended receiver.Finally,the stack is the storage mechanism for ID intervals that are waiting for permis-sion to send an RTS.Throughout the paper we assume that each station knows the maximum number of one-hop stations allowed in the network and the maximum propagation delay.C.Basic OperationIf a station does not have a data packet to send,it returns to its receiving channel broadcasting an RTR initiating a new CRI.The allowed ID interval is broadcasted by the receiver to all its one-hop neighbors in a ready-to-receive packet(RTR).An RTR is also transmitted by the receiver at the beginning of every contention step,allowing new stations to know the state of the CRI.The allowed ID interval as well the unique receiver ID are embedded within the RTR.Once a station initiates a new CRI it remains as a receiver until the end of the current CRI.If at the end of the current CRI the station has a data packet to send then it switches to the channel of its intended receiver and becomes a sender,participating in the CRI of its intended receiver.On the other hand,if at the end of the CRI the station does not have a packet to send,then it remains as a receiver initiating a new CRI.Any station engaged in a CRI as a sender must remain in the receiver’s channel for at least the duration of a maximum CRI.As shown in Fig.1,the channel access time in CARMA-MC is divided into cycles,consisting of receiving periods and trans-mission periods.The CRI in receiving period is a sequence of collision-resolution steps,each initiated by an RTR.The one-hop stations participating in the CRI are assumed to constantly monitor the state of the channel while they are not transmitting. It is assumed that all one-hop neighbors are at most seconds apart from each other.The duration of a contention step varies according to the type of the collision-resolution step.It is possi-ble for a channel to have only receiving periods.This is the case if the station owner of the channel does not have data packets to send.Such a station remains in the receiving state initiate new CRI,as is the case for the station in channel2(as shown in Fig.1).Transmission Periods Receiving PeriodsFig.1.Each channel is composed of receiving periods and transmission periods.If a station has no local packet pending,then the station can initiate the receiving period by transmitting an RTR on the chan-nel assigned to it.The station becomes a receiver station for its one-hop neighbors by transmitting an RTR at the beginning of each contention-resolution step,i.e.,the station makes a transi-tion to the receiver state.Each RTR can be visualize as a small packet indicating to other stations that the station transmitting the RTR is ready to receive a request for thefloor.The RTR also contains information regarding the allowed ID interval of sta-tions that compete to acquire thefloor.Since only the receiver station sends an RTR in its corresponding channel,collisions of RTR can never occur.The receiver state is the default state for all active stations.A station in the receiver state with a local packet pending makes a transition to the sender state,if the current CRI is com-pleted,i.e.,the station must resolve all the contentions for the current CRI in its channel.Only then,it scans the destination channel for at most the maximum duration of a CRI.If an RTR is heard during this interval and the stations ID is within the al-lowed ID interval,then the station competes to acquire thefloor extending its duration in the intended receivers channel until it acquires thefloor.A station acquires thefloor by sending an RTS.The station follows a non-persistent CSMA strategy for the transmission of RTSs.More precisely,the RTS is directed to the receiver in the channel where the RTR was sensed.The sender of an RTS waits and listens for one maximum round-trip time,plus the time needed for the destination’s CTS to arrive.If the CTS is not corrupted and is received within the time limit,then the station acquires thefloor and transmits its data packet or train of data packets.If the CTS is not received,all stations monitoring the channel detect a collision.Because the handshake occurs on the receiver’s channel,a lost CTS on one of the contending stations due to errors or fading,leads to a loss of feedback but not inconsistent feedback.Notice that a station wishing to ac-quire thefloor spends at most the equivalent of two maximum CRI durations.The maximum CRI duration is determined by the maximum number of one-hop neighbors allowed.The rule4is that if within thefirst maximum CRI duration the station does not hear a valid RTR,the station transitions back to the receiver state and remains in this state until the end of a CRI. Although each station transmits an RTS only after the RTR is received and only if the station is within the allowed ID interval, collisions of RTSs may still occur due to propagation delays. RTSs are vulnerable to collisions for time periods equal to the propagation delays between the senders of RTSs.CARMA-MC uses a deterministic tree-splitting algorithm[8]to resolve colli-sions of RTSs,which are detected by the absence of a CTS.Each step of the CRI is initiated by an RTR.There are three types of steps,idle,collision,and success.An idle steps has a duration of,a collision step has a duration of,and a success step has a duration of,where is the size of an RTR,is the size of an RTS or a CTS,is the size of a data packet,and is the maximum channel delay.D.Collision Resolution IntervalCARMA-MC uses a deterministic tree-splitting algorithm to resolve collisions.This algorithm was presented and analyzed in detail in[8].Whenever a station has a local packet to send and is not cur-rently the receiver in a CRI,the following operations are exe-cuted.First,the station scans the channel of its intended receiver for an RTR.RTRs inform contenders that a resolution step can take place,allowing multiple contenders to respond.If the RTR is detected,the station sends an RTS and waits for one maxi-mum round-trip time plus the time needed for the destination to send a CTS.If the CTS is received within the allocated time, then the sender acquires thefloor and can start sending collision-free packets.On the other hand,if the CTS is not received within the allocated time,then the station sender of the RTS and all the other stations competing for thefloor detect a col-lision.In this case,the collision-resolution algorithm is started with thefirst round of RTS collisions.As soon as a collision is detected,the receiver station divides the allowed ID intervalinto two ID intervals.Thefirst ID interval, called the backoff interval,is.The receiver updates its stack by executing a PUSH stack command,where the key being pushed is the backoff interval.After this is done,the station up-dates and with the values from the new allowed ID interval broadcasting this information on the next RTR.This marks the beginning of the next collision-resolution step.This operation is repeated each time there is a collision.Recall that each step of the collision-resolution algorithm,and consequently each contention step,can be either:Case1–Idle:There is no station in the RTS state whose ID is within the allowed ID interval.The channel remains idle for the duration of one propagation delay.The stack and the variables and are updated.The receiver station executes a POP command in the stack updating the allowed ID interval with the new values.Case2–Success:There is a single station with an RTS to send whose ID lies within the allowed ID interval.In this case,a single station is able to complete an RTS/CTS handshake suc-cessfully,acquiring thefloor and transmitting its data packet.Case3–Collision:There are two or more stations with RTSs to send whose IDs are within the allowed ID interval.In this case,each of these stations sends an RTS creating a collision. The stations in the allowed ID interval are again split into two new ID intervals and the stack and the variables for the receiver station are updated.Fig.2shows two stations contending for thefloor.The re-ceiver initiates the CRI.Each CRI is composed of a sequence of collision-resolution steps,each initiated by an RTR.III.C HANNEL U TILIZATION AND P ACKET D ELAYIn this section we derive a lower bound on the average uti-lization of the channel as well as an upper bound on the average delay that a station experiences in transmitting a data packet. Recall that,the channel utilization is defined as the amount of time that the channel is used to transmit data packets divided by the total time.In order to simplify our analysis,we assume that each station has at most one-hop neighbors and that each station has a different receiving frequency or channel than its two-hop neigh-bors.Furthermore,we assume that all the channels have similar behaviors and this enable us to study the utilization of a generic channel.Observe that,if then the number of distinct receiving channels is.Let denote the receiving channel,the frequency associ-ated with channel and the receiving station.Then,station is the intended receiver for at most one-hop neighbors. At the same time,any of the one-hop neighbors is a poten-tial receiver for station.We also let denote the intended receiver of regardless of which of the one-hop neigh-bors station chooses to send its data packet.Thus,is the transmitting frequency of station and is the channel as-sociated with frequency.Finally,we define as the average size of the maxi-mum duration of a CRI for a receiver with at most one-hop neighbors and unique channels within the two-hop neighbor-hood of the network node and at the same time the maximum number of nodes in the deterministic tree.In order to derive ,we make use of the analytical results obtained in [8]for the number of collision-resolution steps.For all,where is the maximum number of stations and is the maximum number of stations participat-ing in the CRI,the average number of collision steps required to resolve all collisions is(1)while the average number of idle steps required to resolve all collisions is5Fig.2.Receiving mode:The intended receiver sends an RTR to initiate the CRI.Notice that each collision-resolution step is initiated by an RTR packet.6 idle steps(i.e.,non of its neighbors will request thefloor)andreceive a data packet in its transmission queue when found inthe receiver state.This will force the receiver to switch to thesender state and then to remain in this state for as long as possi-ble.Now,since the duration of an idle step is where isthe size of the RTR packet and is the maximum channels de-lay,and the duration of the longest possible transmission period is,each time a sender targets the worst possible receiver its probability of success is(5)In general we can approximate this event by assuming an in-dependent Bernoulli trial with probability.Each time the sta-tion tries to send a data packet,the packet will be successful with probability.This can be approximated by a geometric distri-bution function whose expected value is(8) which is equivalent to the expected number of failures before a success.Thus,the average time spend by station transmitting a data packet can be written as(9) Now observe that the data packet had to be originated in the last CRI in which the station was in the receiver state.If this was not the case the station would have remained in the receiver state and not switched to the sender state.Therefore,we need to add to the expected duration of the last CRI(i.e.,)before the station switched from the receiver state to the sender state.The thesis then follows from the fact that for the worst possible receiver we have and.(10)where is the number of CRI in which station remains in the receiver state before it leaves its channel to transmit a data packet of its own on another channel;is the number of attempts that station must make before transmitting its data packet;is the expected number of data packets received by station per CRI;is the average duration of one CRI as a receiver;is the maximum duration of a CRI and is the size of one data packet.Proof:The frequency with which station tries to send a data packet is determined by its packet data rate.Once again we can approximate this procedure with a geometric distribution func-tion.At the end of each CRI a coin with probability is tossed to decide whether or not during the previous CRI station had a packet to send and thus must switch from the receiver state to the transmitting state.Hence,the expected number of CRIs occurring before station has a packet to send is7 and be the time needed to resolve all collisions.It isnot difficult to see that the expected duration of a CRI can beupper bounded by(12)and that the expected number of packets received in a CRI is(13)Since each station has at most one-hop neighbors,we havethat.Observe that the total duration in a chan-nel is composed of receiving periods and transmitting periodsthat appear as idle periods in the channel.Furthermore,since thenumber of receiving periods before station has a data packetto transmit on another channel is determined by the expectedvalue for a geometric random variable,i.e.,,it followsthat the expected arrival time for a packet is.Oncethe packet has arrived,stationfinishes as receiver the currentCRI adding one more period to the total duration.Then itswitches to the sender state.Now,station makes several trials before it is able to deliverits data packet.As explained in Theorem1,the trials are alsogoverned by a geometric distribution.Each failure has a costof plus,and the number of failures before thedata packet can be delivered successfully is.Once stationhears an RTR in the channel of the intended receiver,it has atmost to content successfully and deliver its packet.Therefore,the total period in the channel can be written as(14)where is the number of CRI in between two transmissions,is the expected duration of each CRI and is the ex-pected number of data packets transmitted in channel perCRI.On average the amount of time that channel is beingused to transmit data packets is(15)The thesis then 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