Modeling ad-hoc medical imaging workflows with BPEL4WS
基于SUMO的路由协议仿真研究
基于SUMO的路由协议仿真研究苗晓锋;罗志辉;洪亮【摘要】This paper refers TIGER database, builds a realistic road map as simulation scene. It evaluates the applicability of three routing protocols including AODV, DSR and DSDV in Vehicular Ad-hoc NETwork(VANET) by the joint simulation of Simulation of Urban MObility(SUMO) traffic simulator and NS2 network simulation platform. Experimental results show that existing routing protocols exist the shortages of low packet transmission success, high normalized routing load and big average end-to-end delay under urban VANET environment, it can not meet the needs of existing VANET communications in urban scenarios and needs developing new routing protocol.%利用TIGER数据库,构建一个实际道路地图作为仿真场景,借助SUMO交通仿真器和NS2网络仿真平台,评估ADOV、DSR、DSDR 3种路由协议在城市场景车载自组网(VANET)中的适用性.实验结果表明,上述3种协议在城市VANET环境下,存在分组传输成功率低、归一化路由负载高、平均端到端延时大的缺点,难以满足现有城市VANET的通信需求,需要开发新的路由协议.【期刊名称】《计算机工程》【年(卷),期】2011(037)001【总页数】3页(P107-109)【关键词】车载自组网;移动自组网路由协议;SUMO交通仿真器;TIGER数据库【作者】苗晓锋;罗志辉;洪亮【作者单位】西北工业大学自动化学院,西安710072;哥伦比亚大学工程与应用科学学院,纽约10025;延安大学西安创新学院,西安710100;哥伦比亚大学工程与应用科学学院,纽约10025;西北工业大学自动化学院,西安710072【正文语种】中文【中图分类】TP3931 概述车载自组网(Vehicular Ad-hoc NETwork,VANET)是一种以行驶车辆为节点,车辆间可以进行多跳无线通信的移动自组网(Mobile Ad-hoc NETwork,MANET),它是移动Ad Hoc技术在交通领域的新应用,具有较好的前景。
Multimodal Medical Image Fusion
Yosuke Uchimura, Atsushi Kobayashi
Graduate School of Engineering Kyushu Institute of Technology, KIT Kitakyushu, Japan ** serikawa@elcs.kyutech.ac.jp
Miroslaw Trzupek‡
Department of Automatics, Lab. of Biocybernetics AGH University of Science and Technology Krakow, Poland ‡ mtrzupek@.pl and MR images are better in present the normal and pathological soft tissue. Therefore, only one type of image with a single sensor cannot provide a complete view of the scene in many applications. The fused images, if suitably obtained from a set of multimodal sensor images, can provide a better view than that provided by any of the individual source images. In recent decades, growing interest has focused on the multimodal medical image fusion. Multimodal medical image fusion is the process of extracting significant information from multiple images and synthesizing them in an image. In literature, it is well established that the multi-resolution analysis (MRA) is the best approach that suits image fusion. Some MRA based fusion multimodal medical methods [5], such as wavelets [6], Laplacian pyramids [7], wedgelets [8], bandelets [9], curvelets [10], shearlets [36], contourlets [11], have been recognized as one of the most methods to obtain fine fusion images at different resolutions [29]. The pyramid method for multimodal image fusion method firstly constructs the input image pyramid, and then takes some feature selection approach to form the fusion value pyramid. By the inverter of the pyramid, the pyramid of image can be reconstructed, to produce fusion images. This method is relatively simple, but it has some drawbacks [6]. So, discrete wavelet transform (DWT) method is proposed to improve the multi-resolution problem [7]. Discrete wavelet transform (DWT) can be decomposed into a series of subband images with different resolution, frequency and direction characteristics. The spectral characteristics and spatial characteristics of image are completely separation. And then the different resolution image fusion is performed. But because of limited directional of wavelets, it cannot express line- or curve-singularities in two- or higher dimensional signal. So, other excellent MRA
IEEE SENSORS JOURNAL
Ieee Sensors JournalIeee Transactions On Circuits And Systems Ii-Express Briefs International Journal Of Innovative Computing Information And Control Ieice Transactions On Information And SystemsAd Hoc NetworksAnalog Integrated Circuits And Signal ProcessingSignal Image And Video ProcessingJournal Of Nanoelectronics And OptoelectronicsJournal Of Multiple-Valued Logic And Soft ComputingElectronics LettersIet Intelligent Transport SystemsJournal Of Zhejiang University-Science C-Computers & Electronics SensorsIeee Transactions On Electron DevicesIeee Transactions On Circuits And Systems For Video Technology Ieee Transactions On Image ProcessingIeee Transactions On MagneticsIeee Communications LettersIet CommunicationsTelecommunication SystemsMicroelectronics JournalInformation SciencesMultimedia Tools And ApplicationsBiomedical Signal Processing And ControlBioinformaticsInternational Journal Of Communication SystemsKsii Transactions On Internet And Information SystemsIeee Aerospace And Electronic Systems MagazinePattern Recognition LettersIet Signal ProcessingInformation Systems FrontiersIeee Transactions On Wireless CommunicationsSignal Processing-Image CommunicationApplied Surface ScienceIeee Signal Processing MagazineIet Radar Sonar And NavigationIeee Journal Of Selected Topics In Signal ProcessingIeee Transactions On Audio Speech And Language Processing Eurasip Journal On Wireless Communications And Networking Materials LettersIeee Transactions On NanotechnologyIeee Transactions On Antennas And PropagationIeee Transactions On Microwave Theory And Techniques Ieice Transactions On CommunicationsScience China-Information SciencesIeee Microwave And Wireless Components LettersJournal Of Communications And NetworksChinese Journal Of ElectronicsJournal Of ElectrostaticsOptical EngineeringChina CommunicationsEurasip Journal On Advances In Signal ProcessingIeee Transactions On Vehicular TechnologySoft Computing偏重的研究方向信息科学(5) 自动化(3) 人工智能与知识工程(3) 数理科学(2) 数学(2) 计算机软件(1) 计算机科学(1) 计算数学与科学工程计算(1) 数理逻辑和与计算机相关的数(1) 信息理论与信息系统(1) 电子学与信息系统(1)投稿录用比例40%审稿速度平均4.28571个月的审稿周期RadioengineeringComputer Vision And Image UnderstandingProgress In Electromagnetics Research-PierIeee Transactions On Geoscience And Remote SensingAeu-International Journal Of Electronics And Communications Digital Signal ProcessingIet Image ProcessingSignal ProcessingIeee Transactions On Ultrasonics Ferroelectrics And Frequency Control 电子学与信息系统计算机科学(75)IEEE transactions on electron devicesJournal of guidance control and dynamicsJournal of grey systemInternational journal of communication systemsIEEE transactions on semiconductor manufacturingIEEE transactions on circuits and systems for video technologyIEEE communications lettersJournal of systems engineering and electronicsNano lettersPattern recognition lettersPhysical review lettersJournal of information scienceElectronics lettersImage and vision computingExpert systems with applicationsInformation sciencesIeee transactions on automatic controlIntelligent automation and soft computingJournal of electronic imagingBiomedical signal processing and controlOptical reviewOrganic electronicsBioinformaticsPhysical review eJournal of modern opticsInternational journal of advanced manufacturing technology International journal of software engineering and knowledge engineering Ieee electron device lettersElectromagneticsMicroelectronics journalJournal of computer science and technologyIet communicationsVisual computerJournal of network and computer applicationsPattern recognitionComputer journalWorld wide web-internet and web information systemsInformation processing lettersActa astronauticaInternational journal of wavelets multiresolution and information processing Ieee aerospace and electronic systems magazineMultimedia systemsIet signal processingIeee transactions on aerospace and electronic systemsJournal of physics a-mathematical and theoreticalDigital signal processingJournal of micromechanics and microengineeringJournal of applied mechanics-transactions of the asmeInformation systems frontiersIndustrial & engineering chemistry research信息科学(207)IEEE journal of selected topics in signal processing International journal on artificial intelligence toolsIEEE transactions on wireless communicationsIet control theory and applicationsEurasip journal on wireless communications and networking Telecommunication systemsOptics communicationsKsii transactions on internet and information systems Engineering applications of artificial intelligenceIEEE transactions on control systems technologyIEEE signal processing magazineIet radar sonar and navigationIEEE transactions on audio speech and language processing Neural processing lettersScience china-information sciencesAcm transactions on the webThin solid filmsIntegration-the vlsi journalAcm transactions on graphicsIEEE transactions on nanotechnologyFuzzy sets and systemsJournal of zhejiang university-science c-computers & electronics SensorsJournal of communications and networksInternational journal of controlIEEE transactions on visualization and computer graphics Journal of physics-condensed matterAerospace science and technologyFundamenta informaticaeIEEE transactions on information forensics and securityIEEE transactions on antennas and propagationIEEE transactions on microwave theory and techniques MeasurementMultimedia tools and applicationsIEICE transactions on communicationsApplied opticsKnowledge and information systemsChaosComputing and informaticsJournal of electrostaticsIeee transactions on mobile computingComputers & security信息科学(1) 计算机科学(1) 信息安全(1) ;平均3个月的审稿周期;/wps/find ... ription#descriptionIeee-acm transactions on computational biology and bioinformaticsACTA physico-chimica sinicaJournal of systems and software国外计算机、控制与信息技术重要期刊/journal-of-systems-and-software/Soft matterAd hoc & sensor wireless networksIEEE-asme transactions on mechatronicsIEEE microwave and wireless components lettersWireless communications & mobile computingArtificial intelligenceJournal of management information systemsComputer communicationsMaterials lettersSoft computing信息科学(5) 自动化(3) 人工智能与知识工程(3) 数理科学(2) 数学(2) 计算机软件(1) 计算机科学(1) 计算数学与科学工程计算(1) 数理逻辑和与计算机相关的数(1) 信息理论与信息系统(1) 电子学与信息系统(1) 投稿录用比例40%审稿速度平均4.28571个月的审稿周期China communicationsSensors and materialsIeee transactions on vehicular technologyApplied surface scienceJournal of vacuum science & technology bJournal of testing and evaluationJapanese journal of applied physicsIeee transactions on magneticsOptical engineering月刊,最快的投稿到见刊就两个月,当然也有慢的。
CS专业研究方向详解(1)
美国大学CS专业十三大研究方向美国大学CS专业的研究分支也超级多,不同分支对学生的要求也会不同,因此,学生们要依照自己的条件选择适合自己的研究方向。
一、体系结构、编译器和并行计算 Architecture, Compilers and Parallel Computing 体系结构和编译器的研究要紧集中在硬件设计,编程语言和下一代编译器。
并行计算研究的包括范围很广,包括并行计算的计算模型,并行算法,并行编译器设计等。
二、系统与网络 Systems and Networking可细分为:(1)网络与散布式系统(Networking and distributed systems):移动通信系统,无线网络协议(wireless protocols),Ad-hoc网络,效劳质量治理(Quality of Service management,QoS),多媒体网络,运算机对等联网(peer-to-peer networking, P2P),路由,网络模拟,主动队列治理(active queue management, AQM)和传感器网络(sensor networks)。
(2)操作系统(Operating system):散布式资源治理,普适计算(ubiquitous computing/pervasive computing)环境治理,反射中间件(reflective middleware),中间件元级操作系统(middleware “meta-operating systems”),面向对象操作系统设计,许诺单个用户与多运算机、对等操作系统效劳交互的用户设计,上下文灵敏的散布式文件系统,数据中心的电源治理,文件/存储系统,自主计算(autonomic computing),软件健壮性的系统支持和数据库的系统支持。
(3)平安(Security): 隐私,普适计算,无线传感器(wireless sensors),移动式和嵌入式运算机,标准,认证,验证策略,QoS保证和拒绝效劳爱惜,下一代通信,操作系统虚拟化和认证,关键基础设施系统,例如SCADA操纵系统和医疗,消息系统,平安网关,可用性平安。
信息检索关键词部分
信息检索关键词部分Key word第1章信息检索(Information Retrieval, IR)数据检索(data retrieval)相关性(relevance)推送(Push)超空间(hyperspace)拉出(pulling)⽂献逻辑表⽰(视图)(logical view of the document)检索任务(retrieval task 检索(retrieval )过滤(filtering)全⽂本(full text)词⼲提取(stemming)⽂本操作(text operation)标引词(indexing term)信息检索策略(retrieval strategy)光学字符识别(Optical Character Recognition, OCR)跨语⾔(cross-language)倒排⽂档(inverted file)检出⽂献(retrieved document)相关度(likelihood)信息检索的⼈机交互界⾯(human-computer interaction, HCI)检索模型与评价(Retrieval Model & Evaluation)⽂本图像(textual images)界⾯与可视化(Interface & Visualization)书⽬系统(bibliographic system)多媒体建模与检索(Multimedia Modeling & Searching)数字图书馆(Digital Library)检索评价(retrieval evaluation)标准通⽤标记语⾔(Standard Generalized Markup Language, SGML)标引和检索(indexing and searching)导航(Navigation)并⾏和分布式信息检索(parallel and distribution IR)模型与查询语⾔(model and query language)导航(Navigation)有效标引与检索(efficient indexing and searching)第2章特别检索(ad hoc retrieval)过滤(filtering)集合论(set theoretic)代数(algebraic)概率(probabilistic 路由选择(routing)⽤户需求档(user profile)阙值(threshold)权值(weight)语词加权(term-weighting)相似度(similarity)相异度(dissimilarity)域建模(domain modeling)叙词表(thesaurus)扁平(flat)⼴义向量空间模型(generalized vector space model)神经元(neuron)潜语义标引模型(latent semantic indexing model)邻近结点(proximal node)贝叶斯信任度⽹络(Bayesian belief network)结构导向(structure guided)结构化⽂本检索(structured text retrieval, STR)推理⽹络(inference network)扩展布尔模型(extended Boolean model)⾮重叠链表(non-overlapping list)第3章检索性能评价(retrieval performance evaluation)会话(interactive session)查全率(R, Recall Ratio) 信息性(Informativeness)查准率(P, Precision Ratio) ⾯向⽤户(user-oriented)漏检率(O, Omission Ratio) 新颖率(novelty ratio)误检率(M, Miss Ratio) ⽤户负担(user effort)相对查全率(relative recall)覆盖率(coverage ratio)参考测试集(reference test collection)优劣程度(goodness)查全率负担(recall effort)主观性(subjectiveness)信息性测度(informativeness measure)第4章检索单元(retrieval unit)字母表(alphabet)分隔符(separator)复合性(compositional)模糊布尔(fuzzy Boolean)模式(pattern)SQL(Structured Query Language, 结构化查询语⾔) 布尔查询(Boolean query)参照(reference)半结合(semijoin)标签(tag)有序包含(ordered inclusion)⽆序包含(unordered inclusion)CCL(Common Command Language, 通⽤命令语⾔) 树包含(tree inclusion)布尔运算符(Boolean operator) searching allowing errors容错查询Structured Full-text relevance feedback 相关反馈Query Language (SFQL) (结构化全⽂查询语⾔) extended patterns扩展模式CD-RDx Compact Disk Read only Data exchange (CD-RDx)(只读磁盘数据交换)WAIS (⼴域信息服务系统Wide Area Information Service)visual query languages. 查询语⾔的可视化查询语法树(query syntax tree)第5章query reformulation 查询重构 query expansion 查询扩展 term reweighting 语词重新加权相似性叙词表(similarity thesaurus)User Relevance Feedback⽤户相关反馈 the graphical interfaces 图形化界⾯簇(cluster)检索同义词(searchonym) local context analysis局部上下⽂分析第6章⽂献(document)样式(style)元数据(metadata)Descriptive Metadata 描述性元数据 Semantic Metadata 语义元数据intellectual property rights 知识产权 content rating 内容等级digital signatures数字签名 privacy levels 权限electronic commerce电⼦商务都柏林核⼼元数据集(Dublin Core Metadata Element Set)通⽤标记语⾔(SGML,standard general markup language)机读⽬录记录(Machine Readable Cataloging Record, MARC)资源描述框架(Resource Document Framework, RDF) XML(eXtensible Markup Language, 可扩展标记语⾔) HTML(HyperText Markup Language, 超⽂本标记语⾔)Tagged Image File Format (TIFF标签图像⽂件格式)Joint Photographic Experts Group (JPEG) Portable Network Graphics (PNG新型位图图像格式)第7章分隔符(separator)连字符(hyphen)排除表(list of stopwords)词⼲提取(stemming)波特(porter)词库(treasury of words)受控词汇表(controlled vocabulary)索引单元(indexing component)⽂本压缩text compression 压缩算法compression algorithm注释(explanation)统计⽅法(statistical method)赫夫曼(Huffman)压缩⽐(compression ratio)数据加密Encryption 半静态的(semi-static)词汇分析lexical analysis 排除停⽤词elimination of stopwords第8章半静态(semi-static)191 词汇表(vocabulary)192事件表(occurrence)192 inverted files倒排⽂档suffix arrays后缀数组 signature files签名档块寻址(block addressing)193 索引点(index point)199起始位置(beginning)199 Vocabulary search词汇表检索Retrieval of occurrences 事件表检索 Manipulation of occurrences事件表操作散列变换(hashing)205 误检(false drop)205查询语法树(query syntax tree)207 布鲁特-福斯算法简称BF(Brute-Force)故障(failure)210 移位-或(shift-or)位并⾏处理(bit-parallelism)212顺序检索(sequential search)220 原位(in-place)227第9章并⾏计算(parallel computing) SISD (单指令流单数据流)SIMD (单指令流多数据流) MISD (多指令流单数据流)MIMD (多指令流多数据流)分布计算(distributed computing)颗粒度(granularity)231 多任务(multitasking)I/O(input/output)233 标引器(indexer)映射(map)233 命中列表(hit-list)全局语词统计值(global term statistics)线程(thread)算术逻辑单元(arithmetic logic unit, ALU 中介器(broker)虚拟处理器(virtual processor)240分布式信息检索(distributed information retrieval)249⽂献收集器(gatherer)主中介器(central broker)254第10章信息可视化(information visualization)图标(icon)260颜⾊凸出显⽰(color highlighting)焦点+背景(focus-plus-context)画笔和链接(brushing and linking)魔术透镜(magic lenses)移动镜头和调焦(panning and zooming)弹性窗⼝(elastic window)概述及细节信息(overview plus details)⾼亮⾊显⽰(highlight)信息存取任务(information access tasks)⽂献替代(document surrogate)常见问题(FAQ, Frequently Asked Question) 群体性推荐(social recommendation)上下⽂关键词(keyword-in-context, KWIC)伪相关反馈(pseudo-relevance feedback)重叠式窗⼝(overlapping window)⼯作集(working set)第11/12章多媒体信息检索(Multimedia Information Retrieval, MIR)超类(superclass)半结构化数据(semi-structured data)数据⽚(data blade)可扩充型系统(extensible type system)相交(intersect)动态服务器(dynamic server)叠加(overlaps)档案库服务器(archive server)聚集(center)逻辑结构(logical structure)词包含(contain word)例⼦中的查询(query by example)路径名(path-name)通过图像内容查询(Query by Image Content, QBIC)图像标题(image header)主要成分分析(Principal Component Analysis, PCA)精确匹配(exact match)潜语义标引(Latent Semantic Indexing, LSI)基于内容(content-based)范围查寻(Range Query)第13章exponential growth指数增长 Distributed data 数据的分布性volatile data 不稳定数据 redundant data 冗余数据Heterogeneous data异构数据分界点(cut point)373Centralized Architecture集中式结构收集器-标引器(crawler-indexer)373 Wanderers 漫步者 Walkers 步⾏者 Knowbots 知识机器⼈Distributed Architecture分布式结构 gatherers 收集器brokers 中介器 the query interface 查询界⾯the answer interface响应界⾯ PageRank ⽹页级别Crawling the Web漫游Web breadth-first ⼴度优先depth-first fashion 深度优先 Indices(index pl.)索引Web Directories ⽹络⽬录 Metasearchers元搜索引擎Teaching the User⽤户培训颗粒度(granularity)384超⽂本推导主题检索(Hypertext Included Topic Search, HITS)380 Specific queries专指性查询 Broad queries 泛指性查询Vague queries模糊查询 Searching using Hyperlinks使⽤超链接搜索Web Query Languages查询语⾔ Dynamic Search 动态搜索Software Agents 软件代理鱼式搜索(fish search)鲨鱼搜索(shark search)拉出/推送(pull/push)393门户(portal)395 Duplicated data 重复数据第14章联机公共检索⽬录(online public access catalog, OPAC)397化学⽂摘(Chemical Abstract, CA)399 ⽣物学⽂摘(Biological Abstract, BA)⼯程索引(Engineering Index,EI)国会图书馆分类法(Library of Congress Classification)408杜威⼗进分类法(Dewey Decimal Classification)408联机计算机图书馆中⼼(Online Computer Library Center, OCLC)409机读⽬录记录(Machine Readable Cataloging Record, MARC)409第15章NSF (National Science Foundation, 美国国家科学基⾦会)NSNA(National Aeronautics and Space Administration,美国航空航天局)数字图书馆创新项⽬(Digital Libraries Initiative, DLI)4155S(stream,信息流structure,结构space, 空间scenario, 场景society社会)416基于数字化对象标识符(Digital Object Identifier, DOI)420都柏林核⼼(Dublin Core, DC)430 数字图书馆(Digital Library, DL)资源描述框架(Resource Document Framework, RDF)431text encoding initiative (TEI) (⽂本编码创新项⽬)431v。
经导管主动脉瓣置换术中护理干预
医学影像学杂志2021年第31卷第3期J Med Imaging Vol.31Nc32021经导管主动脉瓣置换术中护理干预宋蕾1,黄杰21.山东第一医科大学附属省立医院介入手术室山东济南2500212山东大学附属山东省医学影像学研究所山东济南250021$摘要】目的探讨主动脉瓣置换术(TAVR)的术中护理干预及规范治疗。
方法根据TAVR不同手术路径制定护理配合计划,实施有针对性的护理干预,术前全面评估,术中对各项生命体征进行严密的观察和准确记录,对临时起搏、动脉置管、静脉用药管道、瓣膜安装、末梢循环、呼吸等进行系统护理干预。
结果经过医护密切配合,手术顺利,23例患者均康复出院。
出院后1~3个月随访,23例患者术后超声心动图(UCG)评价心功能I或"级,瓣膜启闭良好,无明显反流,无瓣周漏(结论术中护理干预为TAVR手术的顺利进行创造有利条件,减少并发症的发生。
$关键词】经导管;主动脉瓣置换;护理干预中图分类号:R815&R541文献标识码:A文章编号:1006—011(2021)03—510—3Nursing intervention in transcatheter aortic valve replacementSONG Lei1,HUANG J—1.Interventional Operation Room,,Provincial Hospital Aff—el to Shandong First Medical University,Jinan250021,P.R.China2.Shandoog Medical Imaging Institute A fi Oated a Shandong University,Jinan250021,P.R.IChna)Abstract]Objective To summarize the key points of intraoperative nursing intervention in23cases of aortic valve repEcc-ment(TAVR),shorten the surgical learning curve,and provide reference for nursing cooperation in the future.Methodc Before operation,besides targeted nursing interventions,preoperalve patient visits preparation of equipment and items,and psycho-.ogoca.caee,nuesongcoopeeaioon p.answeeeeoemu.aied accoedongioTAVRdo e e eenisuegoca.paihways.Dueongiheopeeaioon,ihe palents'vital s igns were strictly observed.Results After close cooperation with medical staX,the operation went smoothly,and 23paioeniseecoeeeed and weeedoschaeged.Dueongonemonih and3monihsoeeo.ow-up aeieedoschaege,and ihecaedoaceuncioon was grade I or-I.Valve opening and closing wel l,no obvious reVux,no leakage around the valve.Conclusion Intraoperative nuesongonieeeenioon ceeaieseaeoeab.econdoioonseoeihesmooih opeeaioon dueongihepeoce s oeTAVR opeeaioon and he.pseeduce iheoccu e nceoecomp.ocaioons.)Key words]Transcatheter;Aortic valve replacement;Nursing intervention经导管主动脉瓣置换术(TAVR)是治疗严重主动脉瓣狭窄(AS)患者的新方法。
CCF推荐的国际学术会议和期刊目录修订版发布
CCF推荐的国际学术会议和期刊目录修订版发布CCF(China Computer Federation中国计算机学会)于2010年8月发布了第一版推荐的国际学术会议和期刊目录,一年来,经过业内专家的反馈和修订,于日前推出了修订版,现将修订版予以发布。
本次修订对上一版内容进行了充实,一些会议和期刊的分类排行进行了调整,目录包括:计算机科学理论、计算机体系结构与高性能计算、计算机图形学与多媒体、计算机网络、交叉学科、人工智能与模式识别、软件工程/系统软件/程序设计语言、数据库/数据挖掘/内容检索、网络与信息安全、综合刊物等方向的国际学术会议及期刊目录,供国内高校和科研单位作为学术评价的参考依据。
目录中,刊物和会议分为A、B、C三档。
A类表示国际上极少数的顶级刊物和会议,鼓励我国学者去突破;B类是指国际上著名和非常重要的会议、刊物,代表该领域的较高水平,鼓励国内同行投稿;C类指国际上重要、为国际学术界所认可的会议和刊物。
这些分类目录每年将学术界的反馈和意见,进行修订,并逐步增加研究方向。
中国计算机学会推荐国际学术刊物(网络/信息安全)一、 A类序号刊物简称刊物全称出版社网址1. TIFS IEEE Transactions on Information Forensics andSecurity IEEE /organizations/society/sp/tifs.html2. TDSC IEEE Transactions on Dependable and Secure ComputingIEEE /tdsc/3. TISSEC ACM Transactions on Information and SystemSecurity ACM /二、 B类序号刊物简称刊物全称出版社网址1. Journal of Cryptology Springer /jofc/jofc.html2. Journal of Computer SecurityIOS Press /jcs/3. IEEE Security & Privacy IEEE/security/4. Computers &Security Elsevier http://www.elsevier.nl/inca/publications/store/4/0/5/8/7/7/5. JISecJournal of Internet Security NahumGoldmann. /JiSec/index.asp6. Designs, Codes andCryptography Springer /east/home/math/numbers?SGWID=5 -10048-70-35730330-07. IET Information Security IET /IET-IFS8. EURASIP Journal on InformationSecurity Hindawi /journals/is三、C类序号刊物简称刊物全称出版社网址1. CISDA Computational Intelligence for Security and DefenseApplications IEEE /2. CLSR Computer Law and SecurityReports Elsevier /science/journal/026736493. Information Management & Computer Security MCB UniversityPress /info/journals/imcs/imcs.jsp4. Information Security TechnicalReport Elsevier /locate/istr中国计算机学会推荐国际学术会议(网络/信息安全方向)一、A类序号会议简称会议全称出版社网址1. S&PIEEE Symposium on Security and Privacy IEEE /TC/SP-Index.html2. CCSACM Conference on Computer and Communications Security ACM /sigs/sigsac/ccs/3. CRYPTO International Cryptology Conference Springer-Verlag /conferences/二、B类序号会议简称会议全称出版社网址1. SecurityUSENIX Security Symposium USENIX /events/2. NDSSISOC Network and Distributed System Security Symposium Internet Society /isoc/conferences/ndss/3. EurocryptAnnual International Conference on the Theory and Applications of Cryptographic Techniques Springer /conferences/eurocrypt2009/4. IH Workshop on Information Hiding Springer-Verlag /~rja14/ihws.html5. ESORICSEuropean Symposium on Research in Computer Security Springer-Verlag as.fr/%7Eesorics/6. RAIDInternational Symposium on Recent Advances in Intrusion Detection Springer-Verlag /7. ACSACAnnual Computer Security Applications ConferenceIEEE /8. DSNThe International Conference on Dependable Systems and Networks IEEE/IFIP /9. CSFWIEEE Computer Security Foundations Workshop /CSFWweb/10. TCC Theory of Cryptography Conference Springer-Verlag /~tcc08/11. ASIACRYPT Annual International Conference on the Theory and Application of Cryptology and Information Security Springer-Verlag /conferences/ 12. PKC International Workshop on Practice and Theory in Public Key Cryptography Springer-Verlag /workshops/pkc2008/三、 C类序号会议简称会议全称出版社网址1. SecureCommInternational Conference on Security and Privacy in Communication Networks ACM /2. ASIACCSACM Symposium on Information, Computer and Communications Security ACM .tw/asiaccs/3. ACNSApplied Cryptography and Network Security Springer-Verlag /acns_home/4. NSPWNew Security Paradigms Workshop ACM /current/5. FC Financial Cryptography Springer-Verlag http://fc08.ifca.ai/6. SACACM Symposium on Applied Computing ACM /conferences/sac/ 7. ICICS International Conference on Information and Communications Security Springer /ICICS06/8. ISC Information Security Conference Springer /9. ICISCInternational Conference on Information Security and Cryptology Springer /10. FSE Fast Software Encryption Springer http://fse2008.epfl.ch/11. WiSe ACM Workshop on Wireless Security ACM /~adrian/wise2004/12. SASN ACM Workshop on Security of Ad-Hoc and Sensor Networks ACM /~szhu/SASN2006/13. WORM ACM Workshop on Rapid Malcode ACM /~farnam/worm2006.html14. DRM ACM Workshop on Digital Rights Management ACM /~drm2007/15. SEC IFIP International Information Security Conference Springer http://sec2008.dti.unimi.it/16. IWIAIEEE International Information Assurance Workshop IEEE /17. IAWIEEE SMC Information Assurance Workshop IEEE /workshop18. SACMATACM Symposium on Access Control Models and Technologies ACM /19. CHESWorkshop on Cryptographic Hardware and Embedded Systems Springer /20. CT-RSA RSA Conference, Cryptographers' Track Springer /21. DIMVA SIG SIDAR Conference on Detection of Intrusions and Malware and Vulnerability Assessment IEEE /dimva200622. SRUTI Steps to Reducing Unwanted Traffic on the Internet USENIX /events/23. HotSecUSENIX Workshop on Hot Topics in Security USENIX /events/ 24. HotBots USENIX Workshop on Hot Topics in Understanding Botnets USENIX /event/hotbots07/tech/25. ACM MM&SEC ACM Multimedia and Security Workshop ACM。
Journal Papers
166-A, St: 9, Chaklala Scheme IIIRawalpindi, PakistanEmail: khayam@.pkSyed Ali KhayamA SSISTANT P ROFESSORNUST Institute of Information Technology (NIIT)National University of Sciences & Technology (NUST)Rawalpindi, PakistanFebruary 2007 to dateR ESEARCH I NTERESTSProtocol and application design for sensor, mobile ad hoc, and infrastructure wireless networks, performance evaluation of network architectures and protocols, modeling and simulation of complex networking phenomena, detection and spread prevention of self-propagating malicious codesE DUCATIONPh.D. Michigan State University (MSU) – Electrical EngineeringGraduation: December 2006Academic Advisor: Professor Hayder RadhaResearch Lab: Wireless and Video Communications (WAVES) Lab, MSUM.S. MSU – Electrical EngineeringGraduation: May 2003Academic Advisor: Professor Hayder RadhaResearch Lab: Wireless and Video Communications (WAVES) Lab, MSUB. E. National University of Sciences & Technology (NUST), Pakistan – Computer SystemsEngineeringGraduation: November 1999R ESEARCH/A CADEMIC P UBLICATIONS A ND P ATENTSBook Chapter(s)Syed Ali Khayam and Hayder Radha, “MAC Layer Wireless Channel Modeling,” Multimedia over Wireless and IP Networks, Elsevier Science, March 2007.Journal PapersSyed Ali Khayam and Hayder Radha, “Modeling Worm Propagation over Vehicular Ad Hoc Networks,”to appear in SAE Transactions − Special Issue on Selected Papers from SAE World Congress 2006.Syed Ali Khayam,Hayder Radha, Selin Aviyente, and John R. Deller, Jr., “Markov and Multifractal Wavelet Models for Wireless MAC-to-MAC Channels,” to appear in Elsevier Performance Evaluation Journal, vol. 64, no. 4, pp. 298−314, May 2007.Syed Ali Khayam,Shirish Karande, Muhammad U. Ilyas, and Hayder Radha, “Header Detection to Improve Multimedia Quality over Wireless Networks,” IEEE Transactions on Multimedia, vol. 9, no. 2, pp.377−385, February 2007.Syed Ali Khayam and Hayder Radha, “Using Signal Processing Techniques to Model Worm Propagation over Wireless Sensor Networks,” IEEE Signal Processing, vol. 23, no. 2, pp. 164−169, March 2006.Syed Ali Khayam and Hayder Radha, “Linear-Complexity Models for Wireless MAC-to-MAC Channels,”ACM Wireless Networks (WINET) – Special Issue on Selected Papers from MSWiM’03, vol. 11, no. 5, pp.543−555, September 2005.Syed Ali Khayam, Shirish Karande, Hayder Radha, and Dmitri Loguinov, “Performance Analysis and Modeling of Errors and Losses over 802.11b LANs for High-Bitrate Real-time Multimedia,” Elsevier/EURASIP Signal Processing: Image Communication, vol. 18, no. 47, pp. 575—595, August 2003.Conference/Workshop/Magazine PapersSyed Ali Khayam and Hayder Radha, “On the Impact of Ignoring Markovian Channel Memory on Analysis of Wireless Systems,” to appear in IEEE International Conference on Communications (ICC), June 2007.Syed Ali Khayam and Hayder Radha, “Using Session-Keystroke Mutual Information to Detect Self-Propagating Malicious Codes,” to appear in IEEE International Conference on Communications (ICC), June 2007.Shirish Karande, Syed Ali Khayam, Yongju Cho, Kiran Misra, Hayder Radha, Jaegon Kim, and Jin-Woo Hong, “On Channel State Inference and Prediction using Observable Variables in 802.11b Networks,”to appear in IEEE International Conference on Communications (ICC), June 2007.Yongju Cho, Syed Ali Khayam, Shirish Karande, Hayder Radha, Jaegon Kim, and Jin-Woo Hong, “A Multi-tier Model for BER Prediction over Residual Wireless Channels,” International Conference on Information Sciences & Systems (CISS), March 2007.Syed Ali Khayam, Shirish Karande, Muhammad U. Ilyas, and Hayder Radha, “Improving Wireless Multimedia Quality using Header Detection with Priors,” IEEE International Conference on Communications (ICC), June 2006.Syed Ali Khayam and Hayder Radha, “Constant-Complexity Models for Wireless Channels,” IEEE Infocom, April 2006.Syed Ali Khayam and Hayder Radha, “Modeling Worm Propagation over Vehicular Ad Hoc Networks,”SAE World Congress, April 2006.Aparna Gurijala and Syed Ali Khayam, “Encryption Effects on Compression Efficiency of Still Images,”SPIE Newsroom Magazine, February 2006.Aparna Gurijala, Syed Ali Khayam, Hayder Radha, and John R. Deller, Jr., “On Encryption-Compression Trade-off of Pre/Post-Filtered Images,” SPIE Conference on Mathematics of Data/Image Coding, Compression, and Encryption (MDICCE), August 2005.Syed Ali Khayam and Hayder Radha, “A Topologically-Aware Worm Propagation Model for Wireless Sensor Networks,” IEEE ICDCS International Workshop on Security in Distributed Computing Systems (SDCS), June 2005.Syed Ali Khayam, Muhammad U. Ilyas, Klaus Pörsch, Shirish Karande,and Hayder Radha, “A Statistical Receiver-based Approach for Improved Throughput of Multimedia Communications over Wireless LANs,” IEEE International Conference on Communications (ICC), May 2005.Syed Ali Khayam, Selin Aviyente, and Hayder Radha, “On Long-Range Dependence in High-Bitrate Wireless Residual Channels,” International Conference on Information Sciences and Systems(CISS), March 2005.Syed Ali Khayam and Hayder Radha, “Analyzing the Spread of Active Worms over VANET,” ACM Mobicom International Workshop on Vehicular Ad Hoc Networks (VANET), October 2004.Syed Ali Khayam and Hayder Radha, “Markov-based Modeling of Wireless Local Area Networks,” ACM Mobicom International Workshop on Modeling, Simulation and Analysis of Wireless and Mobile Systems (MSWiM), September 2003.Syed Ali Khayam, Shirish Karande, Michael Krappel, and Hayder Radha, “Cross-Layer Protocol Design for Real-time Multimedia Applications over 802.11b Networks,” IEEE International Conference on Multimedia and Expo (ICME), July 2003.Shirish Karande, Syed Ali Khayam, Michael Krappel, and Hayder Radha, “Analysis and Modeling of Errors at the 802.11b Link-Layer,” IEEE International Conference on Multimedia and Expo(ICME), July 2003.M. Usman Ilyas, Syed Ali Khayam, Omer Suleman, and Shahid Masud, “A Configurable Platform for Simulating Multiprocessor-based System-on-Chip,” International Symposium on Wireless Systems and Networks (ISWSN), May 2003.Syed Ali Khayam, Shoab A. Khan, and Sohail Sadiq, “A Generic Integer Programming Approach to Hardware/Software Codesign,” IEEE International Multi-topic Conference (INMIC), December 2001.Syed Ali Khayam and Mudassar Farooq, "Voice and Video over IP: Challenges and the Existing Infrastructure," International Workshop on Distributed Computing, Communications and Applications (IWDCCA), January 2000.Ph.D. DissertationSyed Ali Khayam, “Wireless Channel Modeling and Malware Detection using Statistical and Information-Theoretic Tools,” December 2006.M.S. ThesisSyed Ali Khayam, “Analysis and Modeling of Errors and Losses over 802.11b LANs,” May 2003.Patents PendingSyed Ali Khayam and Hayder Radha, “Mobile Network System for Malware Monitoring,” filed to the United States Patents and Trademarks Office, application # PCT/US2006/021501, June 2005.Syed Ali Khayam and Hayder Radha, “Self-Propagating Malware Detection using Session-Keystroke Correlation,” submitted to the MSU Office of Intellectual Property Rights, January 2007.Shirish Karande, Syed Ali Khayam, Yongju Cho, Kiran Misra, Hayder Radha, Jaegon Kim, and Jin-Woo Hong, “Channel State Inference and Prediction using Observable Variables in 802.11b Networks,”submitted to the MSU Office of Intellectual Property Rights, September 2006.Syed Ali Khayam and Hayder Radha, “Worm Detection at Network Endpoints using Information-Theoretic Traffic Perturbations,” submitted to the MSU Office of Intellectual Property Rights, February 2006.Syed Ali Khayam, Shirish Karande, and Hayder Radha, “Header Estimation to Improve Multimedia Quality over Wireless Networks,” submitted to the MSU Office of Intellectual Property Rights, February 2006.Technical Report(s)Syed Ali Khayam, “The Discrete Cosine Transform: Theory and Application,” WAVES lab technical report, May 2004.P ROFESSIONAL A FFILIATIONSSession ChairIEEE International Conference on Communications (ICC) 2007− Computer and Communications Network Security SymposiumReviewerIEEE/ACM Transactions on Networking, IEEE Transactions on Wireless Communications, IEEE Transactions on Multimedia, IEEE Journal on Selected Topics in Signal Processing, IEEE Signal Processing Letters, EURASIP Journal on Wireless Communications & Networking (JWCN), Elsevier Performance Evaluation Journal, Journal of Communications and Networks (JCN) IEEE International Conference on Communications (ICC) 2007, IEEE Consumer Communications & Networking Conference (CCNC) 2007, IEEE Wireless Communications & Networking Conference (WCNC) 2006MemberIEEE, ACM,Pakistan Engineering Council (PEC)H ONORS/A WARDSInvited to chair a technical paper session at the IEEE International Conference on Communications (ICC) 2007, Computer and Communications Network Security SymposiumSelected to serve on the review panels of many prestigious journals and conferencesResearch paper on Markov models for wireless channels was selected in the top 9% papers of ACM MSWiM 2003 and was invited to be submitted as a journal paper to ACM WINET journalResearch paper on worm propagation modeling over vehicular networks in SAE World Congress 2006 was invited to appear in SAE TransactionsResearch paper on encryption-compression tradeoffs of image coding systems in SPIE MDICCE 2005 was invited to appear in SPIE Newsroom MagazineReceived a rating of 4.0 from 38 out of the total 40 students in the Electronic Design andInstrumentation Lab Course taught at MSUJournal paper on linear-complexity wireless channel models was one of ACM WINET’s five most downloaded articles in February/March 2006Opinion paper on “Faculty Hiring program of Pakistan Higher Education Commission” was selected as the finalist of the Virtual Think Tank of Pakistan − National Policy Dialog CompetitionRecipient MSU Research Enhancement Award to attend IEEE Infocom 2006Recipient of Pakistan HEC Partial Ph.D. Support Scholarship to complete Ph.D. studies at MSURecipient of Pakistan HEC Split Ph.D. Scholarship to pursue Ph.D. studies at MSURecipient of NUST Split M.S. Scholarship to pursue Masters studies at MSUMade key technical contributions to research proposals that were submitted to NSF CyberTrust Program in 2004 and 2007: For the first proposal, after peer-review the NSF funded the 3 year project in August 2004; the second proposal is still under reviewMade key technical contributions to a research proposal submitted to MSU CyberSecurity Initiative: MSU funded the 2 year project in August 2003Selected to serve as Graduate Representative on the MSU Engineering College Advisory Committee in 2004/2005E XPERIENCE I N A CADEMIASummer 2002 to present MSU – Research Assistant, Wireless and VideoCommunications (WAVES) Lab, MSUSpring 2002, Summer 2003, Spring 2004 MSU – Teaching AssistantE XPERIENCE I N I NDUSTRYOctober 2000 to AugustCommunications Enabling Technologies – Design Engineer 2001 Lead a team which designed layer controller of a VoIP chipLead a team which developed an IPSec security framework April 1999 to July 1999 Communications Enabling Technologies – InternR EFERENCESHayder Radha, Professor, MSU (radha@)John R. Deller, Jr., Professor, MSU (deller@)Dmitri Loguinov, Assistant Professor, Texas A&M University (TAMU) (dmitri@)。
医学成像设备研发流程
医学成像设备研发流程## Medical Imaging Equipment Development Process.### Overview.The development of medical imaging equipment is a complex and multidisciplinary process that involves a wide range of expertise, from engineering and physics to medicine and healthcare. The process typically involves several stages, including:Concept development: The initial stage of the process involves identifying a need for a new or improved medical imaging device. This may be driven by clinical needs, advances in technology, or changes in healthcare regulations.Feasibility study: Once a concept has been identified, a feasibility study is conducted to assess the technical and economic viability of the device. This study typicallyinvolves market research, engineering analysis, and financial modeling.Prototype development: If the feasibility study is positive, a prototype of the device is developed. This prototype is typically a working model that is used to demonstrate the basic functionality of the device and to identify any potential issues.Preclinical testing: Once the prototype has been developed, it is tested in a preclinical setting. This testing typically involves animal studies to evaluate the safety and efficacy of the device.Clinical trials: If the preclinical testing is successful, the device is then tested in clinical trials. These trials typically involve human subjects and are designed to evaluate the safety and efficacy of the device in a clinical setting.Regulatory approval: Once the clinical trials have been completed, the device must be approved by regulatoryauthorities before it can be marketed. This process typically involves submitting a comprehensive application to the regulatory authority, which includes data from the clinical trials.Commercialization: If the device is approved by the regulatory authorities, it can then be commercialized. This involves marketing the device to healthcare providers and distributors, and providing training and support to users.### Key Considerations.The development of medical imaging equipment is a challenging and complex process, but it is also a rewarding one. By following a rigorous and systematic approach, it is possible to develop innovative and life-saving technologies that can improve the lives of patients around the world.## 医学成像设备研发流程。
HR专业词典(中文版)
English 25 %ile 360° feedback 50 %ile 75 %ile 90 %ile a holistic approach to all workforce programs a piecemeal approach Abilities Absenteeism Accelerating Premium Accident Frequency Accident Insurance Accident Investigation Accident Loss Accident Prevention Accident Proneness Accident Severity Accident Severity Rate Accident Work Injury Accountability Accountant Accounting Department Accounting Manager Accounting Supervisor/Senior Accountant Achievement Need Achievement Test Action Learning Action Research Active Practice Actual Total Cash Ad hoc seminar Adjourning Administration Administration Department Administration Manager Administration Officer Administrative Director Administrative Level Administrative Line Administrator ADR-Alternative Dispute Resolution Adventure learning Adverse Impact Advertisement Recruiting Affective Commitment Affiliation Need
中英文对照适用社会学
专业英语Sociological Terminologies 中英文术语对照第一部分 Part OneI-欧洲古典社会学家Auguste Comte 奥古斯特孔德Karl Marx 卡尔马克思Herbert Spencer 赫伯特斯宾塞Vilfredo Pareto 维尔弗雷多帕累托Ferdinand Toennies 费迪南德滕尼斯Emile Durkheim 埃米尔涂尔干Georg Simmel 格奥尔格齐美尔Gaetano Mosca 加耶塔诺莫斯卡Max Weber 马克斯韦伯Leonard T. Hobhouse 莱奥纳多。
T。
霍布豪斯Robert Michels 罗伯特米歇尔斯II-北美古典社会学家William Graham Sumner 威廉姆格拉汉姆萨姆纳Lester Ward 莱斯特沃德Albion Small 阿比奥斯莫尔Franklin Giddings 弗兰克林吉丁斯Thorstein Veblen 索斯坦凡勃伦George Herbert Mead 乔治赫伯特米德W. I. Thomas W。
I。
托马斯Charles Horton Cooley 查尔斯霍顿库利Robert E. Park 罗伯特。
E。
帕克E. A. Ross E。
A。
罗斯III-现代早期Pitirim Sorokin 皮特里姆索罗金Elton Mayo 埃尔顿梅约Georg Lukacs 格奥尔格卢卡奇William F. Ogburn 威廉姆。
F。
奥格本Karl Mannheim 卡尔曼海姆Alfred Schutz 阿尔弗雷德舒茨Herbert Blumer 赫伯特布鲁默Paul Lazarsfeld 保罗拉扎斯菲尔德George Gallup 乔治盖洛普Tarcott Parsons 塔尔科特帕森斯George Homans 乔治霍曼斯IV-现代晚期David Riesman 大卫里斯曼Robert King Merton 罗伯特金默顿Barrington Moore 巴林顿摩尔Lewis Coser 刘易斯科塞Reinhard Bendix 莱因哈特本尼迪克斯C. W. Mills C。
西门子PLM软件Camstar Semiconductor Suite全球MES说明书
Siemens PLM SoftwareCamstar Semiconductor Suite Global MES to keep pace with demanding change in frontend and backend operationsBenefits• Rapidly implement a manufacturing execution system platform for maximum ROI• Increase process yields by building quality into processes• Quickly adapt to changing processes and product designsapplication can grow with and conform totheir business needs.Camstar Semiconductor Suite is designedfor frontend and backend manufacturingoperations, offering a high level of out-of-the-box industry functionality, the highestlevel of configurability and completeinteroperability with other business sys-tems. It provides instant intelligence; fromtest results and yields to statistical qualitycontrol that enable you to improve qualityand productivity.SummaryCamstar™ Semiconductor Suite effectivelyreplaces legacy and siloed manufacturingsystems that haven’t kept pace with thedemanding and ever-changing require-ments of semiconductor manufacturing.With Camstar Semiconductor Suite, manu-facturers are no longer hindered by islandsof automation and disjointed systems: nowthey can innovate, adapt and succeed.Manufacturers that choose CamstarSemiconductor Suite are up and runningquickly, and are assured that the/mom/camstarGetting moreReplace the basic work-in-progress (WIP) tracking of your legacy manufacturing exe-cution system (MES) with CamstarSemiconductor Suite and also get dispatch-ing, statistical process control (SPC), nonconformance management, dash-boards, maintenance management,paperless manufacturing and much more.Standardizing on a single systemDeploy one configurable enterprise MES across your global frontend and backend operations, including Fab, Probe, Assembly Test and Subcontractors. Standardization enables consistency in global reporting and simplification of application support.Benefits continued• Quickly and accurately deploy manufacturing process changes• Easily integrate withbusiness systems and shop floor equipment • Standardize on a single solution for frontend and backend plants • Eliminate the cost and risk of aging systemsCamstar Semiconductor SuiteEnhance efficiencyReplace your cumbersome systems with a modern and robust MES that is powerful enough to handle high transaction vol-umes, and is flexible enough to meet each site’s specific needs. Camstar MES platform enables you to efficiently innovate, adapt and change.One platform for semiconductor manufacturingComplete traceabilityCamstar Semiconductor Suite provides the complete history of all manufactured lots, wafers and serialized units, spanning pro-duction in multiple plants. Some of the information captured as part of the search-able, electronic audit trail includes materials consumed, processes utilized, parametric data collected, splits and com-bines, bins, shipments and receipts as well as dates and times.Visibility and control of WIPMultilevel work-in-process tracking provides unprecedented visibility and control over production processes. Data can be collected by lot, wafer, serial number, etc., as well as in combinations of these categories. Operators are presented with instructions for each product and process. Movement and processing can be controlled by myriad business logic functions, such as time lim-its, future holds and test results. Maintenance managementIntegrated equipment maintenance man-agement supports proactive problem resolution and optimal equipment schedul-ing for both primary and sub tools. It automatically tracks and schedules mainte-nance based on time or usage, which can include enforcement of predefined job pro-cess flows. It also tracks equipment, tool and carrier states, and ensures that only qualified and calibrated resources are used for processing. Downtime registration pro-vides grouped reason codes and operator logging functionality.Statistical process controlCamstar Statistical Process Control (SPC) applies statistical process control to quality and defect data that is collected during the manufacturing process, allowing manufac-turers to identify, analyze and solve potential problems while production continues before equipment is shut down, material is scrapped and production time is lost. Engineers select the statistical rules that the SPC engine will apply to the chart. Violation of a rule causes an alarm, and can also trig-ger actions such as generating an alert or email notification, changing the status of a machine, or placing material on hold. With ad hoc access to control charts, engineers can use Camstar SPC to monitor current con-ditions and to perform historical analysis. Graphical resource layoutDrawing on real-time data from Camstar MES, resource layout graphically displays the status of all your manufacturing resources so you can maximize throughput, pinpoint potential capacity issues and prior-itize maintenance.Manufacturing process change managementSuperior manufacturing process change management capabilities enable the swift deployment of new or updated products and processes across your global manufac-turing operations. It is a game changer for manufacturers of complex products who must quickly accommodate high volumes of manufacturing changes.Features• Visibility and control of work-in-progress• Automatically enforced dispatching• Integrated equipment maintenance management • Statistical process control and nonconformance management• Manufacturing process change management and quality enforcement• Comprehensive workflow management• Operator certification and trainingManufacturing quality enforcement Camstar Semiconductor Suite facilitates a self-auditing manufacturing process to con-trol production and collect detailed manufacturing quality data in real time. Electronically managed specifications and procedures significantly reduce the possibil-ity of human error, and direct integration with equipment and tools allows for maxi-mum data acquisition. Automatic detection and control of parametric data results com-bined with structured data and reporting and analysis tools make it possible to solve problems quickly, easily preventing recur-rences. Visualization of key manufacturing and quality performance indicators, root cause analysis of issues and controlled exe-cution of changes all facilitate continuous product and process prehensive workflow management Intuitive workflow modeling employs drag-and-drop tools, making it easy to set up dynamic routings, add new steps, vary pro-duction requirements and make customer order changes, all with revision control and an audit trail. Camstar Semiconductor Suite enables you to manage complex workflows with hundreds of Fab operations, frequent rework paths and parametric data collec-tion. Multiple products on a wafer, multi-die parts, stacked die assembly, wafer sort, bumping, back grind, assembly, test and binning are all part of the application, elimi-nating the need to modify the system.Achievements• Reduced costs while increasing throughput and quality• Reduced operating costs from between $2.6 and $3.3 million over three years• Lowered rollout costs for new sites by 75 percent • Met 90 percent of business requirements with an out-of-the-box solution• Implemented MES in new plant environment in 60 days • Implemented MES in three operating plants in 11monthsManufacturing business intelligenceA wide range of monitoring, reporting, ana-lytical and notification capabilities enable better and faster business decisions based on real-time, relevant manufacturing and qual-ity data across multiple manufacturing sites. Camstar Intelligence software provides state-of-the-art dashboard visualization of key manufacturing performance indicators with drill-down analysis, as well as the ability to close the loop on identified problems by managing root cause analysis and enforcing changes that prevent issue recurrence. Enterprise business process interoperabilityCamstar Semiconductor Suite creates an enterprise manufacturing and quality hub that aggregates real-time production and quality data for collaboration within the business and with suppliers and customers. It interacts easily with enterprise resource planning (ERP), applied power systems (APS), quality management system (QMS), data warehouse (DWH) and product lifecy-cle management (PLM) applications for synchronizing products and bills of material (BOM); downloading orders and providing timely and accurate work-in-process infor-mation for improving designs, quality, inventory, processes, planning and financial analysis. Camstar Semiconductor Suite delivers best practices interoperability with leading ERP systems such as SAP® software, Oracle® software and Microsoft Dynamics® software.Equipment tool trackingDetailed resource tracking supports the Semiconductor Equipment Materials Initiative (SEMI E10) and other state models and allows overall equipment effectiveness (OEE) key performance indicator (KPI) cal-culations to determine bottlenecks and inefficiencies on the shop floor. It also sup-ports tracking tools, tool life, tool usage and job models such as cleaning and refur-bishment. The flexibility of Camstar Semiconductor Suite supports processing multiple lots within different chambers of equipment and auto lines consisting of con-nected equipment.Nonconformance management Nonconformance management is used to automatically recognize and react to excep-tions or failures with parametric data specification limits, percent defect allow-ances and yield limits, material issues, binning and retest requirements. It enforces structured failure analysis, root cause iden-tification, quarantine and final disposition (release, rework, scrap, etc.), and prevents product shipment or processing beyond a prescribed step until all issues are resolved. Event managementEvent management enables the identifica-tion and documentation of quality events from any production or nonproduction source across the enterprise, and applies standard risk criteria to triage and route events appropriately. It monitors the enter-prise and identifies quality incidents, enables the necessary investigation and enforces quality processes.BEFORE… replace obsolete and cumbersome islands of automation and disjointed systems.Equipment type 1Equipment type 2Equipment type 3+1 314 264 8499 +852 2230 3308 Label printingLabel printing automatically prints product labels from actual specification and manu-facturing data, ensuring that labels are accurate, produced in a timely manner and are attached to the proper lot, wafer or unit.Operator certification and trainingOperator certification and training enables you to establish manufacturing roles, define training for the various roles, define process certification requirements, maintain train-ing records and establish certificationexpiration. Automatic certification verifica-tion ensures that only qualified employees perform prescribed shop floor functions. Fast, easy operator interactionOperators use simple forms to view instruc-tions and record data and events. Browser- based user interfaces can be configured to effectively guide and respond to the way people work. In addition, any data can be collected directly from production systems and equipment, ensuring maximum speed.Customization without programmingConfigurable business logic, rather than hard- coded logic, allows Camstar Semiconductor Suite to be tailored to meet unique factory requirements without changing program code. Server-side logic makes it easy to integrate the application with existing sys-tems, make new functionality available without disrupting operations and com-pletely support thin-client workstations. Process automation controlCamstar for Process Automation Control software integrates multiple pieces ofequipment within the factory infrastructure into the MES, providing fully automated control, status monitoring, material track-ing and data collection. The bi-directional communication allows the MES to verify that correct lots, products, tools, recipes and parameters are being used. Camstar for Process Automation Control supports multi-ple protocols, including Semiconductor Equipment Communication Standard/SEMI connectivity standard E30 (SECS/GEM), Extensible Markup Language (XML), SEMI PV2, OLE for process control (OPC),Structured Query Language (SQL) and sev-eral others, enabling rapid, reliable and cost effective integration.。
基于人工智能的冠状动脉易损斑块腔内影像学研究进展
基于人工智能的冠状动脉易损斑块腔内影像学研究进展陈远兴综述韩韦钰,赵然尊审校遵义医科大学附属医院心血管内科,贵州遵义563000【摘要】斑块的不稳定导致冠状动脉的血栓性闭塞是大多数急性冠脉综合征(ACS)的原因。
尽管罪犯血管得以及时开通,但非罪犯血管的易损斑块对患者远期预后仍存在较大威胁。
因此,动态评估易损斑块的变化,对冠心病患者格外重要。
冠状动脉血管腔内成像技术,如血管内超声(IVUS)、光学相干断层扫描(OCT)、近红外光谱(NIRS)以及其多模态融合技术等,因其可视化、准确度高,可以揭示易损斑块的不同特征,常用于检测易损斑块。
而IVUS 、OCT 等图像解释需有经验的心血管临床医生逐帧判断,需要大量的时间成本,且图像的解读存在的观察者内及观察者间的差异,这推动了人工智能(AI)在冠状动脉血管腔内影像学应用的发展。
由于电子医疗系统的广泛应用、临床大数据的日益暴增,AI 已在医疗行业获得了极大的进展。
人工智能结合腔内影像学在斑块的识别、干预、预后等诸多方面广泛应用,未来将不断优化诊疗系统,提高精准医疗水平,实现对易损斑块的早期诊断及合理干预。
【关键词】动脉粥样硬化;急性冠脉综合征;腔内成像;易损斑块;人工智能【中图分类号】R541.4【文献标识码】A【文章编号】1003—6350(2023)03—0445—05Research progress of intravascular imaging of vulnerable coronary plaque based on artificial intelligence.CHEN Yuan-xing,HAN Wei-yu,ZHAO Ran-zun.Department of Cardiovascular Medicine,Affiliated Hospital of Zunyi Medical University,Zunyi 563000,Guizhou,CHINA【Abstract 】Plaque vulnerability leading to thrombotic occlusion of coronary arteries is the main cause of majori-ty of acute coronary syndrome (ACS).Despite the criminal vessels can be opened in time,the vulnerable plaques of non-criminal vessels still cause a great threat to the long-term prognosis of patients.Thus,dynamic assessment of vulner-able plaque changes is particularly important for patients with coronary heart disease.Intravascular imaging techniques in coronary arteries,such as intravascular ultrasound (IVUS),Optical Coherence Tomography (OCT),Near Infrared Spectrum Instrument (NIRS),and its multi-mode fusion technology,are often used to detect vulnerable plaques due to their high visualization and accuracy,which can reveal different characteristics of vulnerable plaques.However,IVUS,OCT and other image interpretation requires experienced cardiovascular clinicians to judge frame by frame,which re-quires a large amount of time cost,and there are intra-observer and inter-observer differences in image interpretation,which all promotes the development of AI in the application of intravascular coronary imaging.Artificial intelligence (AI)has made great progress in the medical field due to the wide application of electronic medical information system and the increasing explosion of clinical big data.Artificial intelligence combined with intravascular imaging has been widely ap- ·综述·doi:10.3969/j.issn.1003-6350.2023.03.035第一作者:陈远兴(1995—),男,住院医师,主要研究方向为冠状动脉粥样硬化性心脏病腔内影像学图像分析。
移动Ad hoc网络安全路由协议综述
这样就可防止攻击者伪造报文。该协议的优点在于 采用双签名机制解决了中间节点回答路由请求的问 题。缺点是要求每个节点都知道其他所有节点的公 钥,以便用于签名认证;因为所有中间节点都需要验 证数字签名,计算开销比较大。
3.5
值密钥加以递推验证。通过验证则说明节点身份是 合法可信任的。 因为散列函数具有单向性,故恶意节点无法篡 改认证值(也就不能增加路由包的序列号),同样不 可减小度量值,最终抵制了路由信息被破坏和伪造
4结束语
本文介绍了针对移动Ad hoc网络的各种攻击,
并详细介绍了目前比较典型的几种安全路由协议。 随着计算机网络和移动通信的发展,移动Ad hoc网 络的安全路由协议仍然是研究热点。
参考文献
【111付芳,杨维,张恩东.移动Ad hoc网络路由协议的安全性分析与对策.中国安全科学学报,2005,15(12):75—78
额,就可以认定其为攻击者,对它发出的报文不再处 理而直接抛弃,这样就可将攻击者滥发的报文限制
分如跳数的认证。源节点进行路由申请时,发出带有 数字签名和散列值的路由中请消息,中间节点收到 路由申请报文时,首先验证数字签名和散列值,如验 证通过才处理该报文,否则抛弃该报文。路由申请消
息到达目的节点时,同样验证数字签名和散列值,验
【2】Papadimitrators P,Haas Z.Secure Routing for Mobile Ad hoe networks.Proeedings of the SCS Communication Networks and Dis-
tributed System
Modelingand Simulation Conference.2002:27-31
routing)协议“在基本路由协议DSDV的基础上设计 而出,继承了距离向量路由协议的思想,沿用其中的
MEDICAL IMAGE MODELING SYSTEM AND MEDICAL IMAGE MO
专利名称:MEDICAL IMAGE MODELING SYSTEM ANDMEDICAL IMAGE MODELING METHOD发明人:Kai-Szu Lo,Sheng-Hong Yang,Bo-WeiPan,Tsung-Chih Yu申请号:US15485238申请日:20170412公开号:US20180150992A1公开日:20180531专利内容由知识产权出版社提供专利附图:摘要:A medical image modeling system including a processing device, a displaydevice, and an input device is provided. The processing device is configured to execute animage processing module to generate three-dimensional bone model data based on medical image data of a biological bone tissue. The display device is configured to simultaneously display a medical image and a three-dimensional bone model in the same operation interface according to the medical image data and the three-dimensional bone model data. The input device is configured to receive a parameter instruction, so that the processing device edits the three-dimensional bone model according to the parameter instruction by the image processing module. A medical image modeling method is also provided.申请人:Metal Industries Research & Development Centre地址:Kaohsiung TW国籍:TW更多信息请下载全文后查看。
模拟心电门控技术用于3_岁以下儿童心脏CT_检查的可行性
Feasibility of simulated electrocardiogram-gated technologyapplicated in cardiac CT scanning inchildren under 3 years oldZHU Chen, XUN Chong, GUO Bin, YANG Ming, LI Shu*(Department of Radiology, the Affiliated Children's Hospital of NanjingMedical University, Nanjing 210008, China)[Abstract]Objective To investigate the feasibility of simulated electrocardiogram (ECG)-gated technology applicated in cardiac CT scanning in children under 3 years old.Methods Totally 100 children under 3 years old with congenital cardiac diseases who received cardiac CT examinations (50 underwent real ECG gating [real ECG group]and 50 underwent simulated ECG gating [simulated ECG group])were retrospectively analyzed.The subjective scores of imaging quality,including anatomical structure display score,beam-hardening artifact and overall image quality score were evaluated and compared between groups.Results The imaging quality of both groups met the requirements of clinical diagnosis.The anatomical structure display score was 2 (2, 3), the beam hardening artifact score was 3 (2, 3) and the overall image quality score was 4 (3,5)in real ECG group,while those of simulated ECG group was 2 (2,2),2 (2,3)and 4 (4,5),respectively. No significant difference of the above scores was found between groups (Z=0.259, 1.424, 0.373,P=0.796,0.154, 0.709).Conclusion Simulated ECG-gated technology could be used in cardiac CT of children under 3 years old.[Keywords]child; heart; tomography, X-ray computed; image quality; electrocardiogram-gated technologyDOI:10.13929/j.issn.1672-8475.2023.11.012模拟心电门控技术用于3岁以下儿童心脏CT检查的可行性竺陈,荀冲,郭斌,杨明,李姝*(南京医科大学附属儿童医院放射科,江苏南京 210008)[摘要]目的 评估模拟心电门控技术用于3岁以下儿童心脏CT检查的的可行性。
商业智能
Business Intelligent(BI)商业智能简介随着经济的发展,企业所面临的竞争日益激烈。
同时,信息技术的发展也使企业获取信息的手段和渠道也在不断增加,企业所面对的信息浩如烟海。
而任何好的决策都需要事实和真实的数据。
企业决策的正确程度也取决于所使用的事实和数字的准确程度。
另一方面,随着竞争的增加,决策需要在较短的时间内做出。
因此,在特定的时间段内,能够尽可能多地获得相关信息就变得越来越关键。
而为了使决策具有较好的正确度,却又需要更长的时间。
因此,企业需要高效数据分析工具,以减少高速、精确分析大量数据所需时间。
商业智能技术正是一种能够帮助企业迅速地完成信息采集、分析的先进技术。
它包含了决策过程中所有的查询和报告、在线分析处理(OLAP)和信息采集应用程序及工具。
商业智能解决方案在企业经营中的作用主要表现在三个领域:客户关系管理(CRM):通过有效的交流和良好的服务维持客户对企业来讲是至关重要的。
商业智能通过帮助企业完成客户划分、客户获得、赢回客户、交叉销售、客户保留等工作,使企业的目标、人员、商务处理流程和基础设施集中到根据客户的需要来定制产品、服务以及"面对面"的客户交流方面。
可赢利性分析:商业智能解决方案可以帮助企业分析利润的来源、各类产品对利润总额的贡献程度、广告费用是否与销售成正比等等。
减少成本:商业智能技术能够协助企业确定在哪些对业务影响最小的领域减少成本。
而降低成本的决策可基于详细的目标数据。
商业智能中所包含的数据分析技术主要可分为以下三个阶段:查询报告为了有效地进行营销管理,企业往往需要将各地的数据汇总到总部,并建立一个庞大的数据仓库。
这种数据仓库不但能够保存历史数据、阶段性数据,并从时间上进行分析,而且能够装载外部数据,接受大量的外部查询。
建立数据仓库的过程一般包括清洗、抽取数据操作,统一数据格式,设定自动程序以定时抽取操作数据并自动更新数据仓库,预先执行合计计算等步骤。
Healthcare Analytics Adoption Model医疗分析采用模型
•
Data governance expands 数据治理扩张
Deformable Medical Image Registration
Deformable Medical Image Registration:A Survey Aristeidis Sotiras*,Member,IEEE,Christos Davatzikos,Senior Member,IEEE,and Nikos Paragios,Fellow,IEEE(Invited Paper)Abstract—Deformable image registration is a fundamental task in medical image processing.Among its most important applica-tions,one may cite:1)multi-modality fusion,where information acquired by different imaging devices or protocols is fused to fa-cilitate diagnosis and treatment planning;2)longitudinal studies, where temporal structural or anatomical changes are investigated; and3)population modeling and statistical atlases used to study normal anatomical variability.In this paper,we attempt to give an overview of deformable registration methods,putting emphasis on the most recent advances in the domain.Additional emphasis has been given to techniques applied to medical images.In order to study image registration methods in depth,their main compo-nents are identified and studied independently.The most recent techniques are presented in a systematic fashion.The contribution of this paper is to provide an extensive account of registration tech-niques in a systematic manner.Index Terms—Bibliographical review,deformable registration, medical image analysis.I.I NTRODUCTIOND EFORMABLE registration[1]–[10]has been,alongwith organ segmentation,one of the main challenges in modern medical image analysis.The process consists of establishing spatial correspondences between different image acquisitions.The term deformable(as opposed to linear or global)is used to denote the fact that the observed signals are associated through a nonlinear dense transformation,or a spatially varying deformation model.In general,registration can be performed on two or more im-ages.In this paper,we focus on registration methods that involve two images.One is usually referred to as the source or moving image,while the other is referred to as the target orfixed image. In this paper,the source image is denoted by,while the targetManuscript received March02,2013;revised May17,2013;accepted May 21,2013.Date of publication May31,2013;date of current version June26, 2013.Asterisk indicates corresponding author.*A.Sotiras is with the Section of Biomedical Image Analysis,Center for Biomedical Image Computing and Analytics,Department of Radi-ology,University of Pennsylvania,Philadelphia,PA19104USA(e-mail: aristieidis.sotiras@).C.Davatzikos is with the Section of Biomedical Image Analysis,Center for Biomedical Image Computing and Analytics,Department of Radi-ology,University of Pennsylvania,Philadelphia,PA19104USA(e-mail: christos.davatzikos@).N.Paragios is with the Center for Visual Computing,Department of Applied Mathematics,Ecole Centrale de Paris,92295Chatenay-Malabry,France,and with the Equipe Galen,INRIA Saclay-Ile-de-France,91893Orsay,France,and also with the Universite Paris-Est,LIGM(UMR CNRS),Center for Visual Com-puting,Ecole des Ponts ParisTech,77455Champs-sur-Marne,France. Digital Object Identifier10.1109/TMI.2013.2265603image is denoted by.The two images are defined in the image domain and are related by a transformation.The goal of registration is to estimate the optimal transforma-tion that optimizes an energy of the form(1) The previous objective function(1)comprises two terms.The first term,,quantifies the level of alignment between a target image and a source image.Throughout this paper,we in-terchangeably refer to this term as matching criterion,(dis)sim-ilarity criterion or distance measure.The optimization problem consists of either maximizing or minimizing the objective func-tion depending on how the matching term is chosen.The images get aligned under the influence of transformation .The transformation is a mapping function of the domain to itself,that maps point locations to other locations.In gen-eral,the transformation is assumed to map homologous loca-tions from the target physiology to the source physiology.The transformation at every position is given as the addition of an identity transformation with the displacementfield,or.The second term,,regularizes the trans-formation aiming to favor any specific properties in the solution that the user requires,and seeks to tackle the difficulty associ-ated with the ill-posedness of the problem.Regularization and deformation models are closely related. Two main aspects of this relation may be distinguished.First, in the case that the transformation is parametrized by a small number of variables and is inherently smooth,regularization may serve to introduce prior knowledge regarding the solution that we seek by imposing task-specific constraints on the trans-formation.Second,in the case that we seek the displacement of every image element(i.e.,nonparametric deformation model), regularization dictates the nature of the transformation. Thus,an image registration algorithm involves three main components:1)a deformation model,2)an objective function, and3)an optimization method.The result of the registration algorithm naturally depends on the deformation model and the objective function.The dependency of the registration result on the optimization strategy follows from the fact that image regis-tration is inherently ill-posed.Devising each component so that the requirements of the registration algorithm are met is a de-manding process.Depending on the deformation model and the input data,the problem may be ill-posed according to Hadamard’s definition of well-posed problems[11].In probably all realistic scenarios, registration is ill-posed.To further elaborate,let us consider some specific cases.In a deformable registration scenario,one0278-0062/$31.00©2013IEEEseeks to estimate a vector for every position given,in general, scalar information conveyed by image intensity.In this case,the number of unknowns is greater than the number of constraints. In a rigid setting,let us consider a consider a scenario where two images of a disk(white background,gray foreground)are registered.Despite the fact that the number of parameters is only 6,the problem is ill-posed.The problem has no unique solution since a translation that aligns the centers of the disks followed by any rotation results in a meaningful solution.Given nonlinear and nonconvex objective functions,in gen-eral,no closed-form solutions exist to estimate the registration parameters.In this setting,the search methods reach only a local minimum in the parameter space.Moreover,the problem itself has an enormous number of different facets.The approach that one should take depends on the anatomical properties of the organ(for example,the heart and liver do not adhere to the same degree of deformation),the nature of observations to be regis-tered(same modality versus multi-modal fusion),the clinical setting in which registration is to be used(e.g.,offline interpre-tation versus computer assisted surgery).An enormous amount of research has been dedicated to de-formable registration towards tackling these challenges due to its potential clinical impact.During the past few decades,many innovative ideas regarding the three main algorithmic registra-tion aspects have been proposed.General reviews of thefield may be found in[1]–[7],[9].However due to the rapid progress of thefield such reviews are to a certain extent outdated.The aim of this paper is to provide a thorough overview of the advances of the past decade in deformable registration.Never-theless,some classic papers that have greatly advanced the ideas in thefield are mentioned.Even though our primary interest is deformable registration,for the completeness of the presenta-tion,references to linear methods are included as many prob-lems have been treated in this low-degree-of-freedom setting before being extended to the deformable case.The main scope of this paper is focused on applications that seek to establish spatial correspondences between medical im-ages.Nonetheless,we have extended the scope to cover appli-cations where the interest is to recover the apparent motion of objects between sequences of successive images(opticalflow estimation)[12],[13].Deformable registration and opticalflow estimation are closely related problems.Both problems aim to establish correspondences between images.In the deformable registration case,spatial correspondences are sought,while in the opticalflow case,spatial correspondences,that are associ-ated with different time points,are looked for.Given data with a good temporal resolution,one may assume that the magnitude of the motion is limited and that image intensity is preserved in time,opticalflow estimation can be regarded as a small defor-mation mono-modal deformable registration problem.The remainder of the paper is organized by loosely following the structural separation of registration algorithms to three com-ponents:1)deformation model,2)matching criteria,and3)op-timization method.In Section II,different approaches regarding the deformation model are presented.Moreover,we also chose to cover in this section the second term of the objective function, the regularization term.This choice was motivated by the close relation between the two parts.In Section III,thefirst term of the objective function,the matching term,is discussed.The opti-mization methods are presented in Section IV.In every section, particular emphasis was put on further deepening the taxonomy of registration method by grouping the presented methods in a systematic manner.Section V concludes the paper.II.D EFORMATION M ODELSThe choice of deformation model is of great importance for the registration process as it entails an important compromise between computational efficiency and richness of description. It also reflects the class of transformations that are desirable or acceptable,and therefore limits the solution to a large ex-tent.The parameters that registration estimates through the op-timization strategy correspond to the degrees of freedom of the deformation model1.Their number varies greatly,from six in the case of global rigid transformations,to millions when non-parametric dense transformations are considered.Increasing the dimensionality of the state space results in enriching the de-scriptive power of the model.This model enrichment may be accompanied by an increase in the model’s complexity which, in turns,results in a more challenging and computationally de-manding inference.Furthermore,the choice of the deformation model implies an assumption regarding the nature of the defor-mation to be recovered.Before continuing,let us clarify an important,from imple-mentation point of view,aspect related to the transformation mapping and the deformation of the source image.In the in-troduction,we stated that the transformation is assumed to map homologous locations from the target physiology to the source physiology(backward mapping).While from a theoretical point of view,the mapping from the source physiology to the target physiology is possible(forward mapping),from an implemen-tation point of view,this mapping is less advantageous.In order to better understand the previous statement,let us consider how the direction of the mapping influences the esti-mation of the deformed image.In both cases,the source image is warped to the target domain through interpolation resulting to a deformed image.When the forward mapping is estimated, every voxel of the source image is pushed forward to its esti-mated position in the deformed image.On the other hand,when the backward mapping is estimated,the pixel value of a voxel in the deformed image is pulled from the source image.The difference between the two schemes is in the difficulty of the interpolation problem that has to be solved.In thefirst case,a scattered data interpolation problem needs to be solved because the voxel locations of the source image are usually mapped to nonvoxel locations,and the intensity values of the voxels of the deformed image have to be calculated.In the second case,when voxel locations of the deformed image are mapped to nonvoxel locations in the source image,their intensities can be easily cal-culated by interpolating the intensity values of the neighboring voxels.The rest of the section is organized by following coarsely and extending the classification of deformation models given 1Variational approaches in general attempt to determine a function,not just a set of parameters.SOTIRAS et al.:DEFORMABLE MEDICAL IMAGE REGISTRATION:A SURVEY1155Fig.1.Classi fication of deformation models.Models that satisfy task-speci fic constraints are not shown as a branch of the tree because they are,in general,used in conjunction with physics-based and interpolation-based models.by Holden [14].More emphasis is put on aspects that were not covered by that review.Geometric transformations can be classi fied into three main categories (see Fig.1):1)those that are inspired by physical models,2)those inspired by interpolation and ap-proximation theory,3)knowledge-based deformation models that opt to introduce speci fic prior information regarding the sought deformation,and 4)models that satisfy a task-speci fic constraint.Of great importance for biomedical applications are the con-straints that may be applied to the transformation such that it exhibits special properties.Such properties include,but are not limited to,inverse consistency,symmetry,topology preserva-tion,diffeomorphism.The value of these properties was made apparent to the research community and were gradually intro-duced as extra constraints.Despite common intuition,the majority of the existing regis-tration algorithms are asymmetric.As a consequence,when in-terchanging the order of input images,the registration algorithm does not estimate the inverse transformation.As a consequence,the statistical analysis that follows registration is biased on the choice of the target domain.Inverse Consistency:Inverse consistent methods aim to tackle this shortcoming by simultaneously estimating both the forward and the backward transformation.The data matching term quanti fies how well the images are aligned when one image is deformed by the forward transformation,and the other image by the backward transformation.Additionally,inverse consistent algorithms constrain the forward and backward transformations to be inverse mappings of one another.This is achieved by introducing terms that penalize the difference between the forward and backward transformations from the respective inverse mappings.Inverse consistent methods can preserve topology but are only asymptotically symmetric.Inverse-consistency can be violated if another term of the objective function is weighted more importantly.Symmetry:Symmetric algorithms also aim to cope with asymmetry.These methods do not explicitly penalize asym-metry,but instead employ one of the following two strategies.In the first case,they employ objective functions that are by construction symmetric to estimate the transformation from one image to another.In the second case,two transformation functions are estimated by optimizing a standard objective function.Each transformation function map an image to a common domain.The final mapping from one image to another is calculated by inverting one transformation function and composing it with the other.Topology Preservation:The transformation that is estimated by registration algorithms is not always one-to-one and cross-ings may appear in the deformation field.Topology preserving/homeomorphic algorithms produce a mapping that is contin-uous,onto,and locally one-to-one and has a continuous inverse.The Jacobian determinant contains information regarding the injectivity of the mapping and is greater than zero for topology preserving mappings.The differentiability of the transformation needs to be ensured in order to calculate the Jacobian determi-nant.Let us note that Jacobian determinant and Jacobian are in-terchangeably used in this paper and should not be confounded with the Jacobian matrix.Diffeomorphism:Diffeomoprhic transformations also pre-serve topology.A transformation function is a diffeomorphism,if it is invertible and both the function and its inverse are differ-entiable.A diffeomorphism maps a differentiable manifold to another.1156IEEE TRANSACTIONS ON MEDICAL IMAGING,VOL.32,NO.7,JULY2013In the following four subsections,the most important methods of the four classes are presented with emphasis on the approaches that endow the model under consideration with the above desirable properties.A.Geometric Transformations Derived From Physical Models Following[5],currently employed physical models can be further separated infive categories(see Fig.1):1)elastic body models,2)viscousfluidflow models,3)diffusion models,4) curvature registration,and5)flows of diffeomorphisms.1)Elastic Body Models:a)Linear Models:In this case,the image under deforma-tion is modeled as an elastic body.The Navier-Cauchy Partial Differential Equation(PDE)describes the deformation,or(2) where is the forcefield that drives the registration based on an image matching criterion,refers to the rigidity that quanti-fies the stiffness of the material and is Lamésfirst coefficient. Broit[15]first proposed to model an image grid as an elastic membrane that is deformed under the influence of two forces that compete until equilibrium is reached.An external force tries to deform the image such that matching is achieved while an internal one enforces the elastic properties of the material. Bajcsy and Kovacic[16]extended this approach in a hierar-chical fashion where the solution of the coarsest scale is up-sam-pled and used to initialize thefiner one.Linear registration was used at the lowest resolution.Gee and Bajscy[17]formulated the elastostatic problem in a variational setting.The problem was solved under the Bayesian paradigm allowing for the computation of the uncertainty of the solution as well as for confidence intervals.Thefinite element method(FEM)was used to infer the displacements for the ele-ment nodes,while an interpolation strategy was employed to es-timate displacements elsewhere.The order of the interpolating or shape functions,determines the smoothness of the obtained result.Linear elastic models have also been used when registering brain images based on sparse correspondences.Davatzikos[18]first used geometric characteristics to establish a mapping be-tween the cortical surfaces.Then,a global transformation was estimated by modeling the images as inhomogeneous elastic ob-jects.Spatially-varying elasticity parameters were used to com-pensate for the fact that certain structures tend to deform more than others.In addition,a nonzero initial strain was considered so that some structures expand or contract naturally.In general,an important drawback of registration is that when source and target volumes are interchanged,the obtained trans-formation is not the inverse of the previous solution.In order to tackle this shortcoming,Christensen and Johnson[19]pro-posed to simultaneously estimate both forward and backward transformations,while penalizing inconsistent transformations by adding a constraint to the objective function.Linear elasticity was used as regularization constraint and Fourier series were used to parametrize the transformation.Leow et al.[20]took a different approach to tackle the incon-sistency problem.Instead of adding a constraint that penalizes the inconsistency error,they proposed a unidirectional approach that couples the forward and backward transformation and pro-vides inverse consistent transformations by construction.The coupling was performed by modeling the backward transforma-tion as the inverse of the forward.This fact was also exploited during the optimization of the symmetric energy by only fol-lowing the gradient direction of the forward mapping.He and Christensen[21]proposed to tackle large deforma-tions in an inverse consistent framework by considering a se-quence of small deformation transformations,each modeled by a linear elastic model.The problem was symmetrized by consid-ering a periodic sequence of images where thefirst(or last)and middle image are the source and target respectively.The sym-metric objective function thus comprised terms that quantify the difference between any two successive pairs of images.The in-ferred incremental transformation maps were concatenated to map one input image to another.b)Nonlinear Models:An important limitation of linear elastic models lies in their inability to cope with large defor-mations.In order to account for large deformations,nonlinear elastic models have been proposed.These models also guar-antee the preservation of topology.Rabbitt et al.[22]modeled the deformable image based on hyperelastic material properties.The solution of the nonlinear equations was achieved by local linearization and the use of the Finite Element method.Pennec et al.[23]dropped the linearity assumption by mod-eling the deformation process through the St Venant-Kirchoff elasticity energy that extends the linear elastic model to the non-linear regime.Moreover,the use of log-Euclidean metrics in-stead of Euclidean ones resulted in a Riemannian elasticity en-ergy which is inverse consistent.Yanovsky et al.[24]proposed a symmetric registration framework based on the St Venant-Kir-choff elasticity.An auxiliary variable was added to decouple the regularization and the matching term.Symmetry was im-posed by assuming that the Jacobian determinants of the defor-mation follow a zero mean,after log-transformation,log-normal distribution[25].Droske and Rumpf[26]used an hyperelastic,polyconvex regularization term that takes into account the length,area and volume deformations.Le Guyader and Vese[27]presented an approach that combines segmentation and registration that is based on nonlinear elasticity.The authors used a polyconvex regularization energy based on the modeling of the images under deformation as Ciarlet-Geymonat materials[28].Burger et al.[29]also used a polyconvex regularization term.The au-thors focused on the numerical implementation of the registra-tion framework.They employed a discretize-then-optimize ap-proach[9]that involved the partitioning voxels to24tetrahedra.2)Viscous Fluid Flow Models:In this case,the image under deformation is modeled as a viscousfluid.The transformation is governed by the Navier-Stokes equation that is simplified by assuming a very low Reynold’s numberflow(3) These models do not assume small deformations,and thus are able to recover large deformations[30].Thefirst term of theSOTIRAS et al.:DEFORMABLE MEDICAL IMAGE REGISTRATION:A SURVEY1157Navier-Stokes equation(3),constrains neighboring points to de-form similarly by spatially smoothing the velocityfield.The velocityfield is related to the displacementfield as.The velocityfield is integrated in order to estimate the displacementfield.The second term al-lows structures to change in mass while and are the vis-cosity coefficients.Christensen et al.[30]modeled the image under deformation as a viscousfluid allowing for large magnitude nonlinear defor-mations.The PDE was solved for small time intervals and the complete solution was given by an integration over time.For each time interval a successive over-relaxation(SOR)scheme was used.To guarantee the preservation of topology,the Jaco-bian was monitored and each time its value fell under0.5,the deformed image was regridded and a new one was generated to estimate a transformation.Thefinal solution was the con-catenation of all successive transformations occurring for each regridding step.In a subsequent work,Christensen et al.[31] presented a hierarchical way to recover the transformations for brain anatomy.Initially,global affine transformation was per-formed followed by a landmark transformation model.The re-sult was refined byfluid transformation preceded by an elastic registration step.An important drawback of the earliest implementations of the viscousfluid models,that employed SOR to solve the equa-tions,was computational inefficiency.To circumvent this short-coming,Christensen et al.employed a massive parallel com-puter implementation in[30].Bro-Nielsen and Gramkow[32] proposed a technique based on a convolutionfilter in scale-space.Thefilter was designed as the impulse response of the linear operator defined in its eigen-function basis.Crun et al.[33]proposed a multi-grid approach towards handling anisotropic data along with a multi-resolution scheme opting forfirst recovering coarse velocity es-timations and refining them in a subsequent step.Cahill et al.[34]showed how to use Fourier methods to efficiently solve the linear PDE system that arises from(3)for any boundary condi-tion.Furthermore,Cahill et al.extended their analysis to show how these methods can be applied in the case of other regu-larizers(diffusion,curvature and elastic)under Dirichlet,Neu-mann,or periodic boundary conditions.Wang and Staib[35]usedfluid deformation models in an atlas-enhanced registration setting while D’Agostino et al. tackled multi-modal registration with the use of such models in[36].More recently,Chiang et al.[37]proposed an inverse consistent variant offluid registration to register Diffusion Tensor images.Symmetrized Kullback-Leibler(KL)diver-gence was used as the matching criterion.Inverse consistency was achieved by evaluating the matching and regularization criteria towards both directions.3)Diffusion Models:In this case,the deformation is mod-eled by the diffusion equation(4) Let us note that most of the algorithms,based on this transforma-tion model and described in this section,do not explicitly state the(4)in their objective function.Nonetheless,they exploit the fact that the Gaussian kernel is the Green’s function of the diffu-sion equation(4)(under appropriate initial and boundary condi-tions)to provide an efficient regularization step.Regularization is efficiently performed through convolutions with a Gaussian kernel.Thirion,inspired by Maxwell’s Demons,proposed to perform image matching as a diffusion process[38].The proposed algo-rithm iterated between two steps:1)estimation of the demon forces for every demon(more precisely,the result of the appli-cation of a force during one iteration step,that is a displace-ment),and2)update of the transformation based on the cal-culated forces.Depending on the way the demon positions are selected,the way the space of deformations is defined,the in-terpolation method that is used,and the way the demon forces are calculated,different variants can be obtained.The most suit-able version for medical image analysis involved1)selecting all image elements as demons,2)calculating demon forces by considering the opticalflow constraint,3)assuming a nonpara-metric deformation model that was regularized by applying a Gaussianfilter after each iteration,and4)a trilinear interpo-lation scheme.The Gaussianfilter can be applied either to the displacementfield estimated at an iteration or the updated total displacementfield.The bijectivity of the transformation was en-sured by calculating for every point the difference between its initial position and the one that is reached after composing the forward with the backward deformationfield,and redistributing the difference to eachfield.The bijectivity of the transformation can also be enforced by limiting the maximum length of the up-date displacement to half the voxel size and using composition to update the transformation.Variants for the contour-based reg-istration and the registration between segmented images were also described in[38].Most of the algorithms described in this section were inspired by the work of Thirion[38]and thus could alternatively be clas-sified as“Demons approaches.”These methods share the iter-ative approach that was presented in[38]that is,iterating be-tween estimating the displacements and regularizing to obtain the transformation.This iterative approach results in increased computational efficiency.As it will be discussed later in this section,this feature led researchers to explore such strategies for different PDEs.The use of Demons,as initially introduced,was an efficient algorithm able to provide dense correspondences but lacked a sound theoretical justification.Due to the success of the algo-rithm,a number of papers tried to give theoretical insight into its workings.Fischer and Modersitzki[39]provided a fast algo-rithm for image registration.The result was given as the solution of linear system that results from the linearization of the diffu-sion PDE.An efficient scheme for its solution was proposed while a connection to the Thirion’s Demons algorithm[38]was drawn.Pennec et al.[40]studied image registration as an energy minimization problem and drew the connection of the Demons algorithm with gradient descent schemes.Thirion’s image force based on opticalflow was shown to be equivalent with a second order gradient descent on the Sum of Square Differences(SSD) matching criterion.As for the regularization,it was shown that the convolution of the global transformation with a Gaussian。
MEDICAL IMAGE DATA-BASED METHOD FOR ESTABLISHING S
专利名称:MEDICAL IMAGE DATA-BASED METHODFOR ESTABLISHING SMOOTH GEOMETRICMODEL发明人:LIU, Yuan-Hao,HSIAO, Ming-Chen申请号:EP17891766.2申请日:20170713公开号:EP3566747A1公开日:20191113专利内容由知识产权出版社提供专利附图:摘要:A method for establishing a smooth geometric model based on data of amedical image includes: inputting or reading the data of the medical image; establishing athree-dimensional medical image voxel model based on the data of the medical image, smoothing the three-dimensional medical image voxel model, and establishing a three-dimensional voxel phantom tissue model based on the smoothed three-dimensional medical image voxel model; or establishing the three-dimensional voxel phantom tissue model based on the data of the medical image, and smoothing the three-dimensional voxel phantom tissue model. The method for establishing a smooth geometric model based on the data of the medical image smoothes the three-dimensional medical image voxel model or the three-dimensional voxel phantom tissue model to make it closer to the real situation of the human organ, thereby improving the reliability of the dose calculation to improve the quality of the treatment.申请人:Neuboron Medtech Ltd.地址:3rd Floor Block 12 No. 568 Longmian Ave. Jiangning District Nanjing, Jiangsu 211112 CN国籍:CN代理机构:Sun, Yiming更多信息请下载全文后查看。
- 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。
2 Medical Imaging
In this section a brief introduction to the medical imaging domain is provided. A more detailed discussion can be found in [8], [9], [10], [11] and [12]. Next, two sample workflows are introduced to be inspected further. 2.1 HIS, RIS and PACS Three systems, the HIS (Hospital Information System) [13], the RIS (Radiology Information System) [12] and the PACS (Picture Achieving and Communication system) [10] are the backbone of current information systems in the hospital and medical imaging environment, comparable to ERP (Enterprise Resource Planning) and SCM (Supply Chain Management) systems. The HIS is an enterprise-wide system mostly used for administrative tasks like patient and visit management, bed reservations, patient referrals, operation planning, other scheduling tasks and billing management. The RIS is a patient- management system required for all organizational tasks of a medical imaging facility (whether in- or outside a hospital) like examination scheduling, patient registration, worklist generation, examination control, report generation (using digital dictation equipment and speech recognition) report transcription and
Modeling ad-hoc medical imaging workflows with BPEL4WS
Rainer Anzböck1, Schahram Dustdar2, and Masoud Gholami3
1, 3 D.A.T.A. Corporation, Invalidenstrasse 5-7/10, 1030 Wien, Austria {ar|gm}@data.at 2 Distributed Systems Group, Vienna University of Technology Argentinierstrasse 8/184-1, 1040 Wien, Austria dustdar@infosth recent work in the field of ad-hoc workflows it is possible to define more flexible business models than in traditional workflows based on the Workflow reference model (WFMC) [1]. With the standardization of the Business Process Execution Language (BPEL4WS, short BPEL) [2] a new implementation method for Web service based scenarios is available. The medical imaging domain is currently in a dynamic evolution to digitally connected networks. Standardization in the field of medical imaging has been covered by the HL7 [3] and DICOM [4] standards. A related standardization process for health informatics is enforced by the European Union with the CEN/TC 251 work program [5] which is out of the scope of this paper but is recognized as an important research area. The goal of this paper is to analyze a highly relevant ad-hoc workflow [6] in the medical imaging domain: the second opinion workflow. Since this workflow, by its nature, involves other (increasingly ubiquitous) information systems (e.g. imaging
equipment), it may serve as a test case for the applicability and suitability of BPEL in the medical imaging domain. The workflow model covers implementation relevant aspects of the DICOM and HL7 protocol while maintaining an abstract domain model. The contribution of this paper is as follows: First it introduces current research issues in loosely coupled ad-hoc workflow models and defines a formal basis for simple workflow pattern. Secondly, it provides a separation of a basic workflow and the extended ad-hoc (second opinion) workflow. In the next step it abstracts the communication protocols by implementing sub-workflows for the service interfaces. Furthermore we analyze mapping problems between the workflow and the communication layer and point out the relationships to ad-hoc workflow specifications for the medical imaging domain. Finally we provide a WSDL [7] and BPEL specification of the second opinion workflow. To summarize, our paper (i) analyzes the second opinion ad-hoc workflow in the medical imaging domain and analyzes the requirements for modeling such workflows, (ii) suggests flexible communication semantics not covered in recent work of workflow management and Web services technology and (iii) investigates whether the current BPEL definition is applicable and suitable for the medical imaging domain. The paper is structured as follows. Section 2 introduces ad-hoc workflows and provides definitions of workflow patterns. Section 3 provides an introduction to. Section 4 introduces the ad-hoc workflow and its ad-hoc mechanism required for modeling. Section 5 discusses the relationship between the workflow model and the implementation protocols and suggests a mapping terminology. It extends the discussion to current standardization processes. Section 6 concludes the results and provides information about related work.