Iterative Volumetric ECC Schemes for Holographic Data Storage

国际自动化与计算杂志.英文版.1.Improved Exponential Stability Criteria for Uncertain Neutral System with Nonlinear Parameter PerturbationsFang Qiu，Ban-Tong Cui2.Robust Active Suspension Design Subject to Vehicle Inertial Parameter VariationsHai-Ping Du，Nong Zhang3.Delay-dependent Non-fragile H∞ Filtering for Uncertain Fuzzy Systems Based on Switching Fuzzy Model and Piecewise Lyapunov FunctionZhi-Le Xia，Jun-Min Li，Jiang-Rong Li4.Observer-based Adaptive Iterative Learning Control for Nonlinear Systems with Time-varying DelaysWei-Sheng Chen，Rui-Hong Li，Jing Li5.H∞ Output Feedback Control for Stochastic Systems with Mode-dependent Time-varying Delays and Markovian Jump ParametersXu-Dong Zhao，Qing-Shuang Zeng6.Delay and Its Time-derivative Dependent Robust Stability of Uncertain Neutral Systems with Saturating ActuatorsFatima El Haoussi，El Houssaine Tissir7.Parallel Fuzzy P+Fuzzy I+Fuzzy D Controller:Design and Performance EvaluationVineet Kumar，A.P.Mittal8.Observers for Descriptor Systems with Slope-restricted NonlinearitiesLin-Na Zhou，Chun-Yu Yang，Qing-Ling Zhang9.Parameterized Solution to a Class of Sylvester MatrixEquationsYu-Peng Qiao，Hong-Sheng Qi，Dai-Zhan Cheng10.Indirect Adaptive Fuzzy and Impulsive Control of Nonlinear SystemsHai-Bo Jiang11.Robust Fuzzy Tracking Control for Nonlinear Networked Control Systems with Integral Quadratic ConstraintsZhi-Sheng Chen，Yong He，Min Wu12.A Power-and Coverage-aware Clustering Scheme for Wireless Sensor NetworksLiang Xue，Xin-Ping Guan，Zhi-Xin Liu，Qing-Chao Zheng13.Guaranteed Cost Active Fault-tolerant Control of Networked Control System with Packet Dropout and Transmission DelayXiao-Yuan Luo，Mei-Jie Shang，Cai-Lian Chen，Xin-Ping Guanparison of Two Novel MRAS Based Strategies for Identifying Parameters in Permanent Magnet Synchronous MotorsKan Liu，Qiao Zhang，Zi-Qiang Zhu，Jing Zhang，An-Wen Shen，Paul Stewart15.Modeling and Analysis of Scheduling for Distributed Real-time Embedded SystemsHai-Tao Zhang，Gui-Fang Wu16.Passive Steganalysis Based on Higher Order Image Statistics of Curvelet TransformS.Geetha，Siva S.Sivatha Sindhu，N.Kamaraj17.Movement Invariants-based Algorithm for Medical Image Tilt CorrectionMei-Sen Pan，Jing-Tian Tang，Xiao-Li Yang18.Target Tracking and Obstacle Avoidance for Multi-agent SystemsJing Yan，Xin-Ping Guan，Fu-Xiao Tan19.Automatic Generation of Optimally Rigid Formations Using Decentralized MethodsRui Ren，Yu-Yan Zhang，Xiao-Yuan Luo，Shao-Bao Li20.Semi-blind Adaptive Beamforming for High-throughput Quadrature Amplitude Modulation SystemsSheng Chen，Wang Yao，Lajos Hanzo21.Throughput Analysis of IEEE 802.11 Multirate WLANs with Collision Aware Rate Adaptation AlgorithmDhanasekaran Senthilkumar，A. Krishnan22.Innovative Product Design Based on Customer Requirement Weight Calculation ModelChen-Guang Guo，Yong-Xian Liu，Shou-Ming Hou，Wei Wang23.A Service Composition Approach Based on Sequence Mining for Migrating E-learning Legacy System to SOAZhuo Zhang，Dong-Dai Zhou，Hong-Ji Yang，Shao-Chun Zhong24.Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling SystemZhong-Qi Sheng，Chang-Ping Tang，Ci-Xing Lv25.Estimation of Reliability and Cost Relationship for Architecture-based SoftwareHui Guan，Wei-Ru Chen，Ning Huang，Hong-Ji Yang1.A Computer-aided Design System for Framed-mould in Autoclave ProcessingTian-Guo Jin，Feng-Yang Bi2.Wear State Recognition of Drills Based on K-means Cluster and Radial Basis Function Neural NetworkXu Yang3.The Knee Joint Design and Control of Above-knee Intelligent Bionic Leg Based on Magneto-rheological DamperHua-Long Xie，Ze-Zhong Liang，Fei Li，Li-Xin Guo4.Modeling of Pneumatic Muscle with Shape Memory Alloy and Braided SleeveBin-Rui Wang，Ying-Lian Jin，Dong Wei5.Extended Object Model for Product Configuration DesignZhi-Wei Xu，Ze-Zhong Liang，Zhong-Qi Sheng6.Analysis of Sheet Metal Extrusion Process Using Finite Element MethodXin-Cun Zhuang，Hua Xiang，Zhen Zhao7.Implementation of Enterprises' Interoperation Based on OntologyXiao-Feng Di，Yu-Shun Fan8.Path Planning Approach in Unknown EnvironmentTing-Kai Wang，Quan Dang，Pei-Yuan Pan9.Sliding Mode Variable Structure Control for Visual Servoing SystemFei Li，Hua-Long Xie10.Correlation of Direct Piezoelectric Effect on EAPap under Ambient FactorsLi-Jie Zhao，Chang-Ping Tang，Peng Gong11.XML-based Data Processing in Network Supported Collaborative DesignQi Wang，Zhong-Wei Ren，Zhong-Feng Guo12.Production Management Modelling Based on MASLi He，Zheng-Hao Wang，Ke-Long Zhang13.Experimental Tests of Autonomous Ground Vehicles with PreviewCunjia Liu，Wen-Hua Chen，John Andrews14.Modelling and Remote Control of an ExcavatorYang Liu，Mohammad Shahidul Hasan，Hong-Nian Yu15.TOPSIS with Belief Structure for Group Belief Multiple Criteria Decision MakingJiang Jiang，Ying-Wu Chen，Da-Wei Tang，Yu-Wang Chen16.Video Analysis Based on Volumetric Event DetectionJing Wang，Zhi-Jie Xu17.Improving Decision Tree Performance by Exception HandlingAppavu Alias Balamurugan Subramanian，S.Pramala，B.Rajalakshmi，Ramasamy Rajaram18.Robustness Analysis of Discrete-time Indirect Model Reference Adaptive Control with Normalized Adaptive LawsQing-Zheng Gao，Xue-Jun Xie19.A Novel Lifecycle Model for Web-based Application Development in Small and Medium EnterprisesWei Huang，Ru Li，Carsten Maple，Hong-Ji Yang，David Foskett，Vince Cleaver20.Design of a Two-dimensional Recursive Filter Using the Bees AlgorithmD. T. Pham，Ebubekir Ko(c)21.Designing Genetic Regulatory Networks Using Fuzzy Petri Nets ApproachRaed I. Hamed，Syed I. Ahson，Rafat Parveen1.State of the Art and Emerging Trends in Operations and Maintenance of Offshore Oil and Gas Production Facilities: Some Experiences and ObservationsJayantha P.Liyanage2.Statistical Safety Analysis of Maintenance Management Process of Excavator UnitsLjubisa Papic，Milorad Pantelic，Joseph Aronov，Ajit Kumar Verma3.Improving Energy and Power Efficiency Using NComputing and Approaches for Predicting Reliability of Complex Computing SystemsHoang Pham，Hoang Pham Jr.4.Running Temperature and Mechanical Stability of Grease as Maintenance Parameters of Railway BearingsJan Lundberg，Aditya Parida，Peter S(o)derholm5.Subsea Maintenance Service Delivery: Mapping Factors Influencing Scheduled Service DurationEfosa Emmanuel Uyiomendo，Tore Markeset6.A Systemic Approach to Integrated E-maintenance of Large Engineering PlantsAjit Kumar Verma，A.Srividya，P.G.Ramesh7.Authentication and Access Control in RFID Based Logistics-customs Clearance Service PlatformHui-Fang Deng，Wen Deng，Han Li，Hong-Ji Yang8.Evolutionary Trajectory Planning for an Industrial RobotR.Saravanan，S.Ramabalan，C.Balamurugan，A.Subash9.Improved Exponential Stability Criteria for Recurrent Neural Networks with Time-varying Discrete and Distributed DelaysYuan-Yuan Wu，Tao Li，Yu-Qiang Wu10.An Improved Approach to Delay-dependent Robust Stabilization for Uncertain Singular Time-delay SystemsXin Sun，Qing-Ling Zhang，Chun-Yu Yang，Zhan Su，Yong-Yun Shao11.Robust Stability of Nonlinear Plants with a Non-symmetric Prandtl-Ishlinskii Hysteresis ModelChang-An Jiang，Ming-Cong Deng，Akira Inoue12.Stability Analysis of Discrete-time Systems with Additive Time-varying DelaysXian-Ming Tang，Jin-Shou Yu13.Delay-dependent Stability Analysis for Markovian Jump Systems with Interval Time-varying-delaysXu-Dong Zhao，Qing-Shuang Zeng14.H∞ Synchronization of Chaotic Systems via Delayed Feedback ControlLi Sheng，Hui-Zhong Yang15.Adaptive Fuzzy Observer Backstepping Control for a Class of Uncertain Nonlinear Systems with Unknown Time-delayShao-Cheng Tong，Ning Sheng16.Simulation-based Optimal Design of α-β-γ-δ FilterChun-Mu Wu，Paul P.Lin，Zhen-Yu Han，Shu-Rong Li17.Independent Cycle Time Assignment for Min-max SystemsWen-De Chen，Yue-Gang Tao，Hong-Nian Yu1.An Assessment Tool for Land Reuse with Artificial Intelligence MethodDieter D. Genske，Dongbin Huang，Ariane Ruff2.Interpolation of Images Using Discrete Wavelet Transform to Simulate Image Resizing as in Human VisionRohini S. Asamwar，Kishor M. Bhurchandi，Abhay S. Gandhi3.Watermarking of Digital Images in Frequency DomainSami E. I. Baba，Lala Z. Krikor，Thawar Arif，Zyad Shaaban4.An Effective Image Retrieval Mechanism Using Family-based Spatial Consistency Filtration with Object RegionJing Sun，Ying-Jie Xing5.Robust Object Tracking under Appearance Change ConditionsQi-Cong Wang，Yuan-Hao Gong，Chen-Hui Yang，Cui-Hua Li6.A Visual Attention Model for Robot Object TrackingJin-Kui Chu，Rong-Hua Li，Qing-Ying Li，Hong-Qing Wang7.SVM-based Identification and Un-calibrated Visual Servoing for Micro-manipulationXin-Han Huang，Xiang-Jin Zeng，Min Wang8.Action Control of Soccer Robots Based on Simulated Human IntelligenceTie-Jun Li，Gui-Qiang Chen，Gui-Fang Shao9.Emotional Gait Generation for a Humanoid RobotLun Xie，Zhi-Liang Wang，Wei Wang，Guo-Chen Yu10.Cultural Algorithm for Minimization of Binary Decision Diagram and Its Application in Crosstalk Fault DetectionZhong-Liang Pan，Ling Chen，Guang-Zhao Zhang11.A Novel Fuzzy Direct Torque Control System for Three-level Inverter-fed Induction MachineShu-Xi Liu，Ming-Yu Wang，Yu-Guang Chen，Shan Li12.Statistic Learning-based Defect Detection for Twill FabricsLi-Wei Han，De Xu13.Nonsaturation Throughput Enhancement of IEEE 802.11b Distributed Coordination Function for Heterogeneous Traffic under Noisy EnvironmentDhanasekaran Senthilkumar，A. 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FTV118918Rigging Mechanical Objects in 3ds MaxGeorge MaestriLinkedIn Learning / Description Learn techniques for rigging mechanical objects, vehicles, machines, and other inanimate objects in 3ds Max software. Many people normally use 3ds Max software’s rigging tools for characters, but you can also use them to rig things such as vehicles, motors, pistons,assemblies, conveyor belts, and more. This course will demonstrate practical applications of 3ds Max software’s extensive rigging tools to automate the animation of a variety of mechanical objects and systems.Speaker(s)George Maestri is an animation industry professional with extensive experience as a writer, director, and producer, working on a number of hit television shows, including South Park and Rocko’s Modern Life. As an entrepreneur, he built a successful studio that produced several hundred cartoons for broadcast, education, and film. George has written over a dozen books on computer graphics. As an educator, he has taught at several top animation schools, including California Institute of the Arts. He currently authors and develops CAD and 3D content for LinkedIn Learning and Learning Objectives• Learn how to use scripts and expressions to automate rigging •Learn how to use character animation tools to rig mechanical objects • Learn how to automate the animation of a complex assembly • Learn how to create a control panel to manipulate animationMechanical Rigging in 3ds MaxWhat is Rigging?Rigging is a process that makes complex assemblies of objects easier to animate and manage. Rigging is most commonly associated with character animation, but the same tools directly apply to complex mechanical objects and assemblies.A good rig will do the following :Automate as much as possibleIf parts of the assembly always move in relation to other parts, then these can be automated to cut down on animator overhead. Gears in a clock can be rigged to turn in relation to each other, pistons can automatically compress/expand, and so on…Create Realistic MovementParts in an asssembly should move realistically and stay in proper alignmentBe Animator FriendlyA good rig will abstract the animation to a collection of controls that the animator will manipulate. The goal is to have the animator work the controls and never have to touch the mesh.Easy to UnderstandRigging is essentially a computer interface, so good interface guidelines should apply. Make controls clear and easy to understand, provide labels if needed, and color code key parts of the rig.Designing and Creating a RigDesigning and creating a righ is similar to creating a user interface. When considering how to rig an assembly, you need to go through a general iterative design process:Understand How the Object MovesWhen creating a rig for a mechanical object, you need to know how it should move. You need to know exactly how the parts are connected and how they interact. This may require research about the mechanics of the object, and you may have to talk to engineers or designers. You also need to know limitations of the object’s motion – how far is it supposed to bend? Does the dial go in increments or is it continuous? Finally, you should also consider how the object will be presented. If a particular action is never shown in the presentation, you may not need to rig it.Decide what needs to be controlledAs the rigger, you need to make decisions about what can and cannot be animated in an object or assembly. A hydraulic arm may simply need to be positioned in space, so you can give the animator a single position control and rig the rest. Something more complex, such an airplane, may need additional controls for things like landing gear and propeller rotation.Determine what can be automatedThe rig is supposed to make life easier for the animator. This means the rig should control much of the behavior of the assembly. Hydraulic pistons can automatically expand/contract, hoses can bend automatically, gear assemblies can rotate in sync, and so on. Automating these things is where rigging really matters to an animator. Be sure to understand the animator’s role and what needs to be controlled before rigging.Make it animator friendlyYour rig is the user interface for the scene and the objects being animated. Make that interface easy to use. Observe good interface design practices with your rigs. Make controls easy to spot, use shapes to define function, use color as a guide, and be sure to add labels, if needed.Keep it SimpleAs with any good interface design, simpler is almost always better. Keep the controls direct and easy to understand. Don’t add controls for things that do not need to be animated. Make the resulting animation simple to control as well. You don’t want a spaghetti bowl of animation curves for the animator to wade through.Test until it doesn’t breakThink of every possible way to move the rig and run it through its paces before handing it over to the animator. Make sure the rig is rock solid.Creating ControlsWhen building a rig, we need to create controls that are animator friendly. These controls are typically created using non-renderable objects such as curves and text objects. Proxy objects can also be used as controls. These controls should be easy to understand and placed where they are easy to access. Naming schemes and color schemes can also help with readability.On-object controlsThese are controls that are placed on/near the objects being controlled. The scoop of a digging machine may have a rotational control directly above the scoop.Multi-Purpose controlsSome controls may be easier to use if they control more than one parameter. In the above example, the rotational control for the scoop could be used to determine the position of the digger’s arm.Control PanelsYou may need to abstract and/or consolidate controls into a single control panel. This is often useful for assemblies where there are a lot of parameters to manage.In general, the animator should always be operating the controls and not the object itself. This enables the mesh of the object to be frozen so that is not tampered with, moved out of alignment, deleted, and so on…Rigging Tools3ds Max has a number of rigging tools. Some are very easy to use, others are more complex. Typically, we start with the simpler tools and work towards the more complex ones in the rig. Generally, the goal is to keep the rig as simple as possible, so simpler tools tend to fit this goal. Don’t make things more complicated than necessary just because you have a lot of really cool tools in your arsenal. Other people may have to sue/debug your rigs, so easier to understand rigs make sense.That said, more complex tools can provide a lot of functionality and they should absolutely be used when needed.Basic Four Rigging ToolsThese rigging tools are the simplest, but many times, they are all you need. If you learn these, you’ll be able to do a lot of basic rigging.PivotsThese simply define the axis of rotation and scale for any object. Placement of this axis is critical for anything involving rotation. Centering the pivot may not align directly to the volumetric center of the object. If you need multiple pivots, then you must create helper objects such as a dummy or point to position the other axis of rotation.Links/HierarchiesThis simply connects objects together in a hierarchy. The structure of the hierarchy usually reflects how the object will be animated, but novel hierarchies can also be used. Many times, we may link helper objects or animation controls into a hierarchy to make it easier to animate.Inverse KinematicsAllows objects to rotate automatically to meet a goal object. Traditionally used in character animation, it can be used for all sorts of mechanical assemblies, from robotic armatures to pistons. Several types of IK are availableHI (History-Independent) – Normally used for characters and longer sequences, it allows for the IK solution to be calculated on every frame. It does basic joint rotation but no sliding jointsHI (History-Dependent) - The HD Solver is a solver well-suited to use for animating machines,especially ones with sliding parts that require IK animation. It lets you set up joint limits andprecedence. It has performance problems on long sequences, so ideally use it on shortanimation sequences. It is good for animating machines, especially ones with sliding parts.IK Limb – Only works for two-bone chains. Fast to use, but limited in application.Spline IK – Allows a spline curve to control the orientation of the joints/objects. It is good forhoses, springs, and other flexible objects.ConstraintsA method of connecting parameters together outside of traditional hierarchies. Constraints can be used to align an object’s position, rotation, or scale to another. They can also be used to point objects at one another, and attach them to paths or surfaces.Extended Rigging Tools/Concepts3ds Max has a number of sophisticated rigging tools that go beyond the standard four. These can introduce a finer degree of control over your rigs and add much more sophisticated behaviors. Animation ControllersThese are modules that control an object’s motion, and are really the foundation of all animation in 3ds Max. Controllers can use standard keyframing techniques to create motion, but they can also be used to connect objects together using algorithms, procedures, or other processes.Morphs – Morphs are used to control shape. They can be used for flexible objects such as springs and hoses.Wire ParametersWire Parameters lets you link any two object parameters in the viewport, so that adjusting one parameter changes the other automatically. This enables you to set up one- and two-way connections between specified object parameters, or to control any number of objects with a dummy object containing the desired parameters. By wiring parameters, you can set up custom constraints directly without having to go to Track View and assign controllers.You can also add expressions to Wired Parameters to introduce mathematics and logic to object connections.ExpressionsIf you want to dig even deeper, you can use the Expression Controller to more discretely control objects using math, logic, and basic coding.Reaction ManagerA controller that links the behavior of two objects with user-defined curves that can create sophisticated behavior not available with standard expressions. The controller works by linking a Master parameter to a Slave parameter. Moving or adjusting the Master affects the slave in a non-linear way. The link is controlled by an animation curve that specifies behavior at all points throughout the range of motions.。

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。

《基于i-vector的说话人识别的研究》篇一基于i-vector的说话人识别技术研究一、引言随着人工智能技术的不断发展，说话人识别技术已成为生物特征识别领域的重要研究方向之一。

i-vector技术作为一种有效的说话人识别方法，其准确性和鲁棒性在众多研究中得到了验证。

本文旨在探讨基于i-vector的说话人识别技术的研究，从算法原理、数据集、实验设计及结果等方面进行深入分析。

二、i-vector算法原理i-vector算法是一种基于高斯混合模型（GMM）的说话人识别方法，其核心思想是将说话人的语音特征表示为一个固定长度的向量。

该算法首先通过高斯混合模型将语音数据进行建模，提取语音数据的全局特征，然后将这些特征转换为固定维度的i-vector。

i-vector包含了说话人的独特信息，可以有效地用于说话人识别任务。

三、数据集本文采用的数据集为公开的语音数据集，包括不同语言、不同背景的语音数据。

数据集的选取对于说话人识别的准确性和鲁棒性至关重要。

在数据预处理阶段，需要进行语音信号的预加重、分帧、加窗等操作，以提取出高质量的语音特征。

四、实验设计本文通过实验验证了i-vector算法在说话人识别任务中的性能。

实验中，我们采用了不同的参数配置和特征提取方法，以找到最佳的模型参数和特征表示。

同时，我们还对比了其他说话人识别方法，如传统的基于声纹特征的识别方法和深度学习模型等。

五、实验结果与分析实验结果表明，i-vector算法在说话人识别任务中具有较高的准确性和鲁棒性。

我们通过对比不同参数配置和特征提取方法的性能，找到了最佳的模型参数和特征表示。

同时，我们还发现i-vector算法对于不同语言、不同背景的语音数据具有良好的泛化能力。

与其他说话人识别方法相比，i-vector算法在准确性和鲁棒性方面具有明显优势。

六、结论与展望本文研究了基于i-vector的说话人识别技术，通过实验验证了其性能和泛化能力。

i-vector算法通过高斯混合模型将语音数据进行建模，提取出固定维度的i-vector作为说话人的特征表示。

化学交换饱和转移磁共振成像量化方法研究进展介绍化学交换饱和转移磁共振成像（CEST-MRI）是一种独特的磁共振成像技术，它利用化学交换效应，即溶液中特定分子与水分子之间的化学交换反应，来增强溶液成像对比度。

CEST-MRI已经被证明在动物模型和人体中，可以快速检测出各种组织和病变的显微结构、代谢和功能。

研究进展1. CEST-MRI的基本原理CEST-MRI的基本原理是利用化学交换效应使水分子和目标分子（例如代谢产物）之间的振荡磁场不同步，产生一个局域的消化效应。

这种效应需要属于化学交换的自由糖、蛋白质、代谢物、肽、核酸等分子才能发生。

该效应产生的消化谱可被视为与代谢谱相反的功能信息，因此可以构建出非无限制图像。

这种图像又称为化学交换影像，是该技术的核心。

如果一个特定的化合物与水反应的速率快于其试样运动所需的时间，那么磁性的信号占优势，磁共振成像技术就可以用来成像化合物。

2. CEST-MRI的应用相对于传统的MRI技术，CEST-MRI有很多应用。

首先，它可以利用化学交换效应来增强MRI成像的对比度，故而对病变区域成像的精度更高。

其次，它可以对器官、组织的生理和代谢活动进行追踪，尤其是对肿瘤组织的代谢活动更敏感。

此外，CEST-MRI技术还可以用于小鼠实验，既可以检测脑内物质交换活动，也能评估心肌组织的代谢变化，同时可用于活体外分子分析等。

3. CEST-MRI的挑战和发展尽管CEST-MRI技术有很多应用和优势，但这项技术的发展仍然面临一些挑战。

首先，CEST-MRI技术的成像深度受制于液体中的纵弛豫时间（T1）以及化合物的浓度和pH值等因素。

其次，由于化学交换效应的复杂性，CEST-MRI存在许多潜在的亚型，这也增加了压缩成像等数学处理过程的难度。

最后，CEST-MRI技术的临床应用还面临限制，例如磁场均匀性、装置复杂性、物体移动对成像结果的影响等。

结论在过去的几年中，CEST-MRI已经得到了广泛的应用和发展。

A Unified Microwave Radiative Transfer Model for General Planar Stratified Media:Slab Formulation Miao Tian,Student Member,IEEE,and Albin J.Gasiewski,Fellow,IEEEAbstract—A unified microwave radiative transfer(UMRT) model is presented for computing the thermal radiation emit-ted from any geophysical medium composed of planar layers of either densely or tenuously distributed moderately sized spher-ical scatterers.UMRT employs the discrete-ordinate eigenanal-ysis(DOE)method with layer adding to solve the differential radiative transfer equation for such multilayer structures.UMRT inherits the symmetrization and analytical diagonalization and factorization techniques of symmetric and positive definite ma-trices from the discrete-ordinate tangent linear radiative transfer (DOTLRT)model presented by Voronovich et al.These techniques ensure accuracy,numerical stability,and rapid computation for all matrix operations required for DOE along with a fast Jacobian calculation for radiance assimilation purposes.UMRT extends the applicability of DOTLRT by including both the Mie theory and the dense media radiative transfer(DMRT)theory.Other nontrivial extensions within UMRT are the following:1)The vertical and horizontal radiation intensities are coupled within each layer by applying the reduced Mie or DMRT phase matrices, and2)the physical temperature profile of a layer is allowed to be linear in height.The symmetry properties of both the reduced Mie and DMRT phase matrices are proved,and the associated scattering and absorption coefficients are compared and discussed. The UMRT slab formulation is validated by imposing energy conservation,and the numerical results for some nominal cases are produced and discussed.Index Terms—Dense media,dense-medium radiative transfer (DMRT),Jacobian,layered media,microwave remote sensing, Mie,polarization,symmetric and positive definite matrix.I.I NTRODUCTIONC URRENTLY,a major challenge in passive microwaveremote sensing is the accurate and fast-forward numerical modeling of the electromagnetic scattering and emission prop-erties of any geophysical media consisting of soil,water,snow, ice,rain,clouds,fog,etc.Of importance in any such numerical model are accuracy,numerical stability,computational speed, applicability to both dense and tenuous scattering media,and the capability to produce a Jacobian for radiance assimilation purposes.The three primary solution techniques to solve the differ-ential radiative transfer equation(DRTE)for the four Stokes parameters are the following:1)the iterative method[2],[3];Manuscript received April5,2012;revised September19,2012;accepted October29,2012.Date of publication January28,2013;date of current version June20,2013.The authors are with the Center for Environmental Technology,Department of Electrical,Computer,and Energy Engineering,University of Colorado—Boulder,Boulder,CO80309-0425USA(e-mail:Miao.Tian@; Albin.Gasiewski@).Color versions of one or more of thefigures in this paper are available online at .Digital Object Identifier10.1109/TGRS.2012.22273312)the discrete-ordinate eigenanalysis(DOE)method[4]–[7]; and3)the Monte Carlo method[8],[9].Among these,the iterative method is applicable to low-albedo cases or thin layers, and the Monte Carlo method lacks physical insight and conver-gence criteria.The DOE method with layer adding is widely used due to its applicability to layers of arbitrary albedo.In the DOE method,the continuum of propagation directions is described by afinite number of quadrature angles.The resulting system of equations is solved by eigenanalysis,and medium inhomogeneity is accommodated by layer adding.The basic DOE solution for a multilayer structure under the planar stratified approximation follows the formulation developed by Stamnes and Swanson[5].In this work,a matrix-operator method to solve the DRTE as an eigenvalue problem and a technique to reduce the order of the problem by a factor of two were devised.In1986,Nakajima and Tanaka[6]introduced the decomposition of a symmetric transition matrix to provide a nearly stable numerical solution for the DRTE.Matrix-operator representations of the reflection and transmission matrices in the multilayer stack were also introduced in their algorithm.In 1988,the DOE model was summarized by Stamnes et al.[7] for general use in planar multilayer multiple scattering media. Although the aforementioned models have been success-ful,there remained two major problems within the DOE formulation.1)Analytic functions of matrices were required to be com-puted using Taylor series expansions.For example,fora sufficiently small transition matrix argument ABh,one can calculate the cosine hyperbolic operator of this argument ascosh=1+2!h2+A B24!h4+ (1)The above expansion generally requires too many terms for practical implementation.Accordingly,the accuracy of the DOE solution is compromised by accumulated round-off errors.2)The second issue is the well-known matrix inversion in-stability associated with the implementation of the DOE method for high albedo,high opacity,and thick layers. These two attributes have historically limited the applicabil-ity of the DOE method.To circumvent these problems,V oronovich et al.[1]de-veloped the discrete-ordinate tangent linear radiative transfer (DOTLRT)model based on symmetrization of the DRTE and analytical diagonalization and factorization of the resulting0196-2892/$31.00©2013IEEEsymmetric and positive definite matrices to provide inherent computational stability and high computational efficiency for all matrix operations required by the DOE method.The core DOTLRT procedure requires that both transition matrices A and B are symmetric and positive definite,in which case any arbitrary analytic function g operated on the matrix can be readily calculated.Specifically,applying symmetry,the matrix can be represented asA=M1Λ1M T1(2)where1is an orthogonal matrix consisting of eigenvectors of having the following characteristics:M1M T1=M1M−11=I(3)where(·)T denotes the matrix transpose and is the identity matrix.In(2),Λ1is a diagonal matrix of associated eigenval-ues.Since is positive definite,the eigenvalues are positive({1}ii>0),which guarantees that the values of±1/21are allpositive real.Similarly,another set of eigenvalue and eigenvec-tor matrices can be defined using the matrices B and(Λ1,M1) in(2)as1 2 1T11121=22T2.(4)Using(2)and(4),the product of AB can be calculated asAB=M1Λ121M2Λ2M1Λ−121M2T.(5)As a resultg(AB)=M1Λ121M2gΛ2M1Λ−121M2T(6)for any matrix function g.Moreover,by incorporating the derivative chain rule usingfirst-order perturbations of the eigen-values and eigenvectors of a symmetric matrix,DOTLRT pro-vides rapid numerical calculation of the associated Jacobians between the observed brightness temperature and all relevant radiative parameters.Although the DOTLRT algorithm provides a stable,fast,and accurate solution to the DRTE,it was originally developed for atmospheric simulation in which scattering hydrometeors are sparse(e.g.,rain,fog,clouds,and aerosols).It is also based on a single polarization using the Henyey–Greenstein(HG) phase matrix approximation for a planar multilayer structure with nonrefracting layers.Finally,it is based on layers with constant physical temperature.These attributes have limited its application,particularly for cases of dense media(e.g.,snow, ice,and soil)and thick atmospheric or surface layers with strong temperature gradients.In this paper,we present a new“unified microwave radiative transfer(UMRT)model”to extend DOTLRT in all of the aforementioned areas.This model can be applied to widely varying types of media for both forward radiative transfer and radiance assimilation purposes.Within UMRT,we seamlessly partition medium layers into two categories,which are treated distinctively as follows:1)sparse-medium layers,in which scatters are loosely distributed and independent scattering is dominant,and2)dense-medium layers,in which scatters oc-cupy significant volume fraction and volumetric scattering is dominant.For sparse-medium layers,the cross-polarization is considered by using the reduced Mie phase matrix.A proof of the symmetry and positive definite nature of the Mie phase matrix is developed to ensure the applicability of the stable ma-trix operation formulation of DOTLRT.Assuming independent scattering,UMRT sparse-medium layers are parameterized by sets of particle size distribution functions for each of the different scatterer phases,for example,liquid spheres and ice spheres.Calculations of the associated extinction,scattering, and absorption coefficients and the phase matrices are per-formed for each of these phases.For dense-medium layers,the dense-medium radiative trans-fer(DMRT)theory is applied within UMRT.The DMRT theory with the quasi-crystalline approximation(QCA)was developed by Tsang and his colleagues beginning in the early1980s[8], [10].In UMRT,a recent(2007)version of the DMRT-QCA model by Tsang et al.[11]is used.This model uses a sticky par-ticle assumption for moderately sized(i.e.,Mie scale)spherical particles.In this model,the adhesion and aggregation of the sticky particles are simulated by using sticky pair distribution functions based on the Percus–Yevick(PY)approximation.As used within UMRT,the reduced DMRT-QCA phase matrix is included,and its symmetry properties are identified.The associated absorption and scattering coefficients are calculated under the DMRT framework.Other nontrivial extensions to DOTLRT incorporated within UMRT include extending the accuracy of the single-layer DRTE solution by permitting the temperature profile to be lin-ear in height.UMRT also inherits from DOTLRT the capability for rapid Jacobian calculation for a general medium model. During the development of UMRT,we realized that,for a single particle,using a spherical or nonspherical assumption will indeed yield significant brightness differences and also realized that,for certain nonspherical cases such as cylinders and spheroids,the associated scattering problems have been addressed[4].The reason we do not include the nonspherical cases in this work is that an intrinsic problem with nonspherical particle theory will introduce more parameters,e.g.,aspect ratios and orientation distributions,into the problem.How-ever,these parameters for nonspherical particles are difficult to actually measure,and if particles are randomly oriented in a medium,the nonspherical and spherical cases usually give sim-ilar results in the polydispersed scattering case.We thus wanted to focus this modeling effort on the most basic particle type and study the impact of other issues,such as polarization,refraction, discretization,integration accuracy,computation speed,and fast Jacobian development.Our discussion of rapid computation capability directly fol-lows that for DOTLRT in[1,Sec.IX].In UMRT,the number of operations required for the calculation of both the brightness temperature profile and the associated Jacobian for all stream angles(M angles)is NM3,where N is the total number ofTIAN AND GASIEWSKI:UMRT MODEL FOR GENERAL PLANAR STRATIFIED MEDIA 4105layers.Since the same complexity applies,we do not bother to belabor the discussion again.Thus,the previous argument goes as follows:1)For a conventional DOE solution with a divided difference Jacobian,the number of operations required is N 2,and 2)for an iterative perturbation solution,the number of operations is N 3.Normally,N M ;therefore,DOTLRT and UMRT are rapid models in this regard.This paper focuses on the details of the UMRT formulation for a multilayer stack with nonrefractive boundaries.This paper is organized as follows.Section II summarizes the equations for the scattering and absorption coefficients and the phase matrices based on the Mie theory and the DMRT-QCA theory.A proof of the symmetry property of both the Mie and DMRT-QCA phase matrices is included along with a comparison of the two phase matrices.Section III provides the theoretical framework for UMRT,including the DRTE symmetrization,the DOE solution to a single medium layer by using the decompo-sition of a symmetric and positive definite matrix and under the linear profile assumption,and the upward recursive DOE solution to the multilayer stack with nonrefractive boundaries.Section IV provides a validation of the UMRT solution by im-posing energy conservation and also presents numerical results for some nominal environmental scenarios.Section V provides brief conclusion and discussion of related ongoing work.II.E XTINCTION AND S CATTERING C OEFFICIENTSAND P HASE M ATRICES All numerical integrations in UMRT are computed by ap-plying the Gauss–Legendre quadrature with the Christoffel weights [12],although,in principle,any quadrature scheme can be implemented.It is nontrivial to point out that UMRT employs the method derived by Yakimiw [12]to compute the Gauss–Legendre nodes and weights.According to [13],the Yakimiw method reduces both the error growth in the node and weight computations from the orders O (n )and O (n 2)of the eigensystem method to O (1)and O (n ),respectively.Moreover,the Yakimiw method is suitable in terms of accuracy,reliability,and speed for computing the nodes and weights of very high order Gauss quadrature rules with n ∼104,which are currently used for high-resolution global atmospheric models.A.Stokes Matrix:Transformation and SymmetryIn radiative transfer theory,the relationship between the incident and scattered Stokes vectors I i (Θ)and I s (Θ),respec-tively,for a single particle is depicted in the particle-based system of coordinates [Fig.1(a)]and described byI s (Θ)=1r 2·I i (Θ)(7)where L (Θ)is the Stokes matrix for a single particle and has the following simplified form due to the symmetry of spherical particles [4]:L (Θ)=⎡⎢⎣|f 11|20000|f 22|20000Re {f 11f ∗22}−Im {f 11f ∗22}00Im {f 11f ∗22}Re {f 11f ∗22}⎤⎥⎦(8)Fig.1.(a)Particle-based coordinate system (from [4])defined by the scatter-ing plane containing ˆki and ˆk s ,which are the incident and scattered directions,respectively.The angle between ˆki and ˆk s is Θ.(b)Principal coordinate system defined by (θs ,φs ;θi ,φi )and the relationship between the two coordinate systems (from [15]).where f αβ,with α=1or 2and β=1or 2,is the scattering amplitude and represents the scattering between polarizations.Although the particle-based system has advantages in pro-viding simple forms of the scattering amplitudes for particles with symmetry,it is necessary for modeling stratified media to express the scattering amplitudes in the principal coordi-nate system,defined by the scattering and incident angles (θs ,φs ;θi ,φi )[4],[14],[15],shown in Fig.1(b).The transformation between the two coordinate systems is given byL (θs ,φs ;θi ,φi )=L r (−i 2)L (Θ)L r (−i 1)(9)where L r is the rotation matrix [14],[16]L r (i 1,2)=⎡⎢⎣cos 2i 1,2sin 2i 1,20.5sin 2i 1,20sin 2i 1,2cos 2i 1,2−0.5sin 2i 1,20−sin 2i 1,2sin 2i 1,2cos 2i 1,200001⎤⎥⎦(10)and the angles i 1and i 2each are spherical surface angles.From [15],the cosines of these two angles are expressed ascos i 1=cos θs sin θi −cos θi sin θs cos Δφsin Θ(11)cos i 2=cos θi sin θs −cos θs sin θi cos Δφ(12)4106IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,VOL.51,NO.7,JULY 2013whereΔφ=φi −φssin Θ=1−cos 2Θcos Θ=cos θs cos θi +sin θs sin θi cos Δφ.The spherical surface angles i 1and i 2can be computed by the following equations without ambiguity:i 1,2=2π−acos [cos(i 1,2)],π<Δφ<2πacos [cos(i 1,2)],0<Δφ<π.(13)In general,θs ,φs ;θi ,φi )is a full 4×4matrix,whereasL (Θ)has only six nonzero elements,four of which are independent.The analytical diagonalization and factorization technique used within UMRT requires symmetry of the phase (and thus Stokes)matrix in the principal coordinate system under scat-tering path reversal (i.e.,θs ↔θi ).To show this degree of symmetry,the following equalities are examined by applying the coordinate transformation defined within (9)–(13):L (θs ,θi ;Δφ)?= L (θi ,θs ;Δφ)Tθs ,π−θi ;Δφ)?= θi ,π−θs ;Δφ)T(14)for which they would (respectively)follow thatL r (−i 2)L (Θ)L r (−i 1)?= L r (−i 1)L (Θ)L r (−i 2)Tr (−i 1)r (−i 2)?= r (i 2)(Θ)r (i 1) T.(15)Applying a Stokes matrix (Θ)for a spherical particle withform as in (8)–(15),the equalities in (14)and (15)hold for the diagonal and v −h elements,viz.Δ=⎡⎢⎣00Δ13Δ1400Δ23Δ24Δ31Δ320Δ34Δ41Δ42Δ43⎤⎥⎦(16)where Δ L (θs ,θi ;Δφ)−[L (θi ,θs ;Δφ)]T or L (θs ,π−θi ;Δφ)−[(θi ,π−θs ;Δφ)]T and Δij represents a nonzero matrix element.Equation (16)shows that the Stokes matrix L (θs ,θi ;Δφ)for spheres is symmetric for the first two Stokes parameters.More specifically,if (Θ)is calculated from either the Mie or the DMRT scattering theory,the difference matrix is found by numerical calculation for a wide range of parameters and angles comprising nearly one million diverse cases to be=⎡⎢⎣∼0∼0∼0∼0∼0∼0∼0∼0∼0∼0∼0Δ34∼0∼0Δ43∼0⎤⎥⎦(17)where the “∼0”entries are zero within standard IEEE numer-ical precision.While not an absolute proof,the above strongly suggests that both the Mie and DMRT Stokes matrices are symmetric for the first three Stokes parameters.Moreover,if the Stokes matrix L (Θ)has the simplified form of the Rayleigh Stokes matrix (i.e.,for electrically small particles)L (Θ)=32⎡⎢⎣cos 2Θ000010000cos Θ0000cos Θ⎤⎥⎦.(18)Then,it can be shown that Δ=0for all entries.Hence,inthis case of small particles,L (θs ,θi ;Δφ)is symmetric for all four Stokes parameters.B.Mie Phase MatrixFrom [2]and [4],the phase matrix is calculated by integrat-ing the Stokes matrix with respect to an appropriate particle size distribution function n (D )θs ,θi ;Δφ)=∞θs ,θi ;Δφ)·n (D )dD (19)where D is the sphere diameter.The details of various n (D )functions relevant for atmospheric hydrometeors can be found in [2].Due to the azimuthal symmetry of the planar stratified medium model employed in UMRT,the phase matrix can be further simplified by dimensional reduction to a reduced phase matrix [2]P(θs ,θi )≡2πP (θs ,θi ;Δφ)d (Δφ).(20)The reduced phase matrix is only a function of the incident angle θi and the scattered angle θs .Due to the azimuthal symmetry within the Mie and DMRT theories,it can be shown that the reduced phase matrix becomesP (θs ,θi )Mie /DMRT =⎡⎢⎣P 11P 1200P 21P 220000P 33P 3400P 43P 44⎤⎥⎦(21)where it is seen that the first and second Stokes parameters aredecoupled from the third and fourth.The equations related to the Mie phase matrix are summa-rized by noting that the scattering amplitudes from the Mie theory [17],[18]aref 11(Θ)=−j k n maxn =12n +1n (n +1)[a n πn (cos Θ)+b n τn (cos Θ)]f 22(Θ)=−j k n maxn =12n +1n (n +1)[a n τn (cos Θ)+b n πn (cos Θ)](22)TIAN AND GASIEWSKI:UMRT MODEL FOR GENERAL PLANAR STRATIFIED MEDIA4107 where k is the wavenumber in air,a n and b n are the Miescattering coefficients,andπn andτn are the angle-dependentfunctions.The choice of maximum iteration number is com-monly determined by n max=round(x+4x1/3+2),wherex=ka is the size parameter,a is the sphere radius,and theoperation round(·)returns the closest integer less than(·).Accordingly,the Mie phase matrix elements for a specificparticle size distribution function n(D)are computed asP11(Θ)=∞|f11(Θ)|2·n(D)dDP22(Θ)=∞|f22(Θ)|2·n(D)dDP33(Θ)=∞Re{f11(Θ)·f∗22(Θ)}·n(D)dDP44(Θ)=P33(Θ)P34(Θ)=−∞Im{f11(Θ)·f∗22(Θ)}·n(D)dDP43(Θ)=−P34(Θ).(23) In computing the reduced Mie phase matrix elements,it is usually convenient to integrate the above expressions numeri-cally with respect to the azimuthal angle as in(20).It is also convenient to define the reduced normalized phase matrix[2]p (θs,θi)≡P(θs,θi)κs(24)where πp (θs,θi)sinθs dθs=1.Within UMRT,for sparse media,the extinction and scattering coefficientsκe andκs are calculated based on the Mie theory for polydispersed particles[2],while for dense media,they are calculated differently under the DMRT-QCA theory(cf. Section II-C).Using the Mie theory,the efficienciesηe andηs for monodispersed spherical particles are computed asηe=2x2n maxn=1(2n+1)Re(a n+b n)ηs=2x2n maxn=1(2n+1)|a n|2+|b n|2.(25)Given a size distribution function n(D),the coefficients for polydispersed spherical particles are computed asκe=π4∞ηe·D2·n(D)dDκs=π4∞ηs·D2·n(D)dD.(26)As in[2],the upper limit of the above integrations is setto be15times the mean particle diameter D .Since n(D)is typically an exponential function,integrand contributionstypically diminish after a few mean diameters.C.DMRT-QCA Phase MatrixUMRT employs the DMRT-QCA model outlined in[11],which simplifies the calculation of the DMRT-QCA phasematrix relative to previous implementations.The effective prop-agation constant K and the average multipole amplitudes X(M)vand X(N)vare numerically calculated by solving the2N max sys-tem of equations obtained using the Lorentz–Lorentz(L–L)lawand the Ewald–Oseen extinction theorem.The aforementionedquantities are subsequently used to calculate the DMRT-QCAStokes matrix elementsf11(Θ)=−j(1−R)1kK rN maxn=12n+1n(n+1)×a n X(N)nπn(cosΘ)+b n X(M)nτn(cosΘ)f22(Θ)=−j1rN maxn=12n+1×a n X(N)nτn(cosΘ)+b n X(M)nπn(cosΘ)(27)where k is the wavenumber in air,K r=Re{K},and R is acoefficientR=−jπn ok2(k+K r)N maxn=1(−1)nb n X(M)n−a n X(N)n(2n+1)(28)where n o is the particle number density,in units ofm−3·mm−1.The phase matrix elements areP11(Θ)=|f11(Θ)|2q(Θ)P22(Θ)=|f22(Θ)|2q(Θ)P33(Θ)=P44(Θ)=Re{f11(Θ)·f∗22(Θ)}q(Θ)P34(Θ)=−P43(Θ)=−Im{f11(Θ)·f∗22(Θ)}q(Θ)(29)where the factor q(Θ)is obtained using the PY approximationin[11,eqs.10and11].In DMRT-QCA,the scattering andabsorption coefficients are computed as follows:κa=kK r2πk2|1−R|2n o·N maxn=1(2n+1)×X(M)n2·Re{b n}−|b n|2+X(N)n2Re{a n}−|a n|2κs=π∞[P11(Θ)+P22(Θ)]sinΘdΘκe=κa+κs.(30)4108IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,VOL.51,NO.7,JULY2013Fig.2.(a)Scattering and (b)absorption coefficients for polydispersive ice particles computed using the Mie and DMRT-QCA theories.TABLE IC ONDITIONS FOR C ALCULATING THE S CATTERING AND A BSORPTION C OEFFICIENTS FOR V ARIOUS I CE P ARTICLE D ISTRIBUTIONS AND FOR THE (1AND 2)DMRT-QCA T HEORY AND (3–6)M IE T HEORY ,FOR W HICH C ASES 3AND 4U SE S PARSE SS I CE S IZE D ISTRIBUTIONS FOR T WO N OMINAL P RECIPITATION R ATES W HILE C ASES 5AND 6U SE D ENSE E XPONENTIAL S IZE D ISTRIBUTIONS FOR AF IXED V OLUMEF RACTION AND T WO P ARTICLE DIAMETERSD.Results and DiscussionComparisons of the scattering and absorption coefficients from the Mie theory and the DMRT-QCA theory as functions of frequency for several typical conditions illustrate fundamental differences between these distinct models (Table I and Fig.2).For purposes of comparison,cases 2–6use the fixed lossy value of ice permittivity of ice =3.15−j 0.001,while case 1uses the frequency-dependent value obtained from [19].The differences in both κs and κa between the DMRT-QCA and Mie theories for identical ice volume fractions are seen in cases 1-2and 5-6in Fig.2(a)and (b),where DMRT-QCA gen-erally predicts smaller values for κs than the Mie theory for the same mean particle sizes and overall densities (cases 1-2and 6).However,since absorption is more closely related to the internal field amplitude and particle volume,the differences are smaller than those for scattering.This effect is seen more clearly by considering cases 5and 6,where the mean particle size of the Mie simulation is varied by a factor of ten.For these cases,the scattering coefficient for Rayleigh-sized particles increases by D 6/n o ,but there is less effect on the absorption coefficient,particularly for frequencies less than 10GHz.In Fig.2(a),we also note that κs of the Mie theory saturates with larger particles at higher frequencies.This behavior suggests that the Mie scattering coefficient has a weaker frequency dependence than that of DMRT-QCA.As can be expected,Fig.2(a)and (b)also shows that the values of both κs and κa under Mie scattering for a dense distribution (cases 5and 6)are much greater than their corresponding counterparts determined using the sparse Sekhon–Srivastava (SS)[20]distribution (cases 3and 4).This difference is the result of scaling by the volume fraction and is inherent in the Mie theory.However,the DMRT-QCA scattering coefficient depends nonlinearly on f v and is accurately computable to volume fractions of at least ∼20%.Finally,in cases 1and 2,it is noted that the use of the nominal value for the ice dielectric constant in computing the value of κs does not result in obvious differences when compared with the results using the ice dielectric constant values from Warren [19];however,these two dielectric constant models do result in significant differences in the value of κa .Accordingly,improved models of the dielectric constant of homogeneous water ice are suggested to be of interest.The behavior of reduced normalized Mie phase matrices is studied by assuming a rain case with the following con-ditions:1)Marshall–Palmer (MP)size distribution [21]with precipitation rate =10mm/h and 2)mean drop diameter D =2mm.The water dielectric constant is determined us-ing the double Debye model [22]at a temperature of 0◦C.As seen in Fig.3(a)–(c),the reduced normalized Mie phase matrices exhibit the expected symmetry for both vertical and horizontal polarizations.The plots further show that forward scattering relative to backscattering or side scattering increases as frequency increases,gradually becoming dominant above ∼100GHz as suggested by calculations of polydispersive asymmetry in [2].Analogously,Fig.4shows the reduced normalized DMRT-QCA phase matrices computed for a dense snowpack under the following conditions:1)dielectric constant of ice of ice =3.15−j 0.001;2)mean ice diameter of D =1.4mm;3)vol-ume fraction f v =25%;and 4)stickiness parameter τ=0.1.As shown,the reduced DMRT-QCA phase matrices also exhibit the expected symmetry as found for the Mie case,and the forward scattering also increases as frequency increases.More-over,the DMRT-QCA phase matrices present more forwardTIAN AND GASIEWSKI:UMRT MODEL FOR GENERAL PLANAR STRATIFIED MEDIA4109Fig.3.Reduced normalized Mie phase matrices using an MP rain distribution of10mm/h and32quadrature angles.(a)10GHz.(b)100GHz.(c)1000GHz.scattering than that of the comparable Mie cases.However,it is noted that the reduced normalized Mie phase matrix can be steadily and accurately computed over a wide frequency range (in terms of mean size parameter x )since there exist numer-ically stable algorithms[17],[18],[23],[24]for frequencies up to at least∼1000GHz and for practical hydrometeor size distributions.In contrast,there is no conclusive study on the stability of the DMRT-QCA algorithm except for a brief discus-sion of the maximum number N max of L–L equations required for convergence in[11].From this work,N max is suggested to be determined by the relation N max=round(k D )+1.This requirement for N max was studied by computing the DMRT-QCA phase matrices at frequencies up to1000GHz.First,it should be pointed out that,in the three cases in Fig.4,the errors caused by the choice of N max are small(the value of N max is four at100GHz).As the frequency is extended to300GHz,a value of N max=10is needed,thus increasing the computational bur-den.The L–L system of equations becomes ill conditioned at higher frequencies,and a stable numerical solution is currently unavailable.Nonetheless,for microwave remote sensing of snow and ice,DMRT-QCA is still readily computable for the most practical snow and ice sensing frequencies(i.e.,below ∼100GHz).III.UMRT F RAMEWORKA.DRTE SymmetrizationUMRT assumes a planar stratified medium structure and pro-vides a solution for the brightness temperature T B(θ,z)in the upwelling(+)and downwelling(−)directions,accounting for polarization coupling caused by the reduced phase matrix.The DRTE is discretized over a set of quadrature anglesθi,which are determined by the Gauss–Legendre nodes and Christoffel weightsμidT+Bvidz=−k e T+Bvi+⎡⎣Mj=1γj P++vvijT+Bvj+Mj=1γj P+−vvijT−Bvj+Mj=1γj P++vhijT+Bhj+Mj=1γj P+−vhijT−Bhj⎤⎦+k a T(z)(31)−μi dT−Bvidz=−k e T−Bvi+⎡⎣Mj=1γj P−+vvijT+Bvj+Mj=1γj P−−vvijT−Bvj+Mj=1γj P−+vhijT+Bhj+Mj=1γj P−−vhijT−Bhj⎤⎦+k a T(z)(32)。

Discrete OptimizationVehicle routing problem with time windows and alimited number of vehiclesHoong Chuin Laua,*,Melvyn Sim b ,Kwong Meng TeocaDepartment of Computer Science,School of Computing,National University of Singapore,3Science Drive 2,117543SingaporebOperations Research Center,Massachusetts Institute of Technology,Cambridge,MA 02139,USAcSavy Technology Asia Pte Ltd.,Technology Park@Chai Chee,469001SingaporeAbstractThis paper introduces a variant of the vehicle routing problem with time windows where a limited number of vehicles is given (m -VRPTW).Under this scenario,a feasible solution is one that may contain either unserved customers and/or relaxed time windows.We provide a computable upper bound to the problem.To solve the problem,we propose a tabu search approach characterized by a holding list and a mechanism to force dense packing within a route.We also allow time windows to be relaxed by introducing the notion of penalty for lateness.In our approach,customer jobs are inserted based on a hierarchical objective function that captures multiple objectives.Computational results on benchmark problems show that our approach yields solutions that are competitive to best-published results on VRPTW.On m -VRPTW instances,experiments show that our approach produces solutions that are very close to computed upper bounds.Moreover,as the number of vehicles decreases,the routes become more densely packed monotically.This shows that our approach is good from both the optimality as well as stability point of view.Ó2002Elsevier Science B.V.All rights reserved.Keywords:Tabu search;Heuristics;Routing;Combinatorial optimization;Vehicle routing problem with time windows1.IntroductionMany practical transport logistics and distri-bution problems can be formulated as a vehicle routing problem whose objective is to obtain a minimum-cost route plan serving a set of customers with known demands.Each customer is assigned to exactly one vehicle route and the total demand of any route must not exceed the vehicle capacity.To date,most of the proposed algorithms as-sume that the number of vehicles is unlimited,and the objective is to obtain a solution that either minimizes the number of vehicles and/or total travel cost.However,transport operators in the real world face resource constraints such as a fixed fleet.The question we like to ask is,if the given problem is over-constrained in the sense of insuf-ficient vehicles,what constitutes a good solution and how may we find one?In this paper,we provide some insights to this question.In our view point,it is desirable to have an algorithm that not only performs well given a*Corresponding author.E-mail addresses:lauhc@.sg (u),melvyn@ (M.Sim),kmteo@ (K.M.Teo).0377-2217/03/$-see front matter Ó2002Elsevier Science B.V.All rights reserved.doi:10.1016/S0377-2217(02)00363-6European Journal of Operational Research 148(2003)559–569standard VRPTW problem,but also handles over-constrained problems well in the following sense: 1.Optimality:It returns solutions which serve(orpack)as many customers as possible as the pri-mary objective,while optimizing standard crite-ria such as the number of vehicles and distance travelled.2.Stability:It degrades gracefully under con-strainedness,i.e.when the number of vehicles is reduced,the customer packing density,defined as the average number of customers per vehicle in service,must be monotically increasing,al-though the total number of customers served will become less.This paper proceeds as follows.Wefirst introduce the problem(m-VRPTW)and a computable upper bound to the problem.We then present a tabu search approach with the following characteristics: (a)a holding list to accommodate unserved cus-tomers;(b)a mechanism that introduces new vehicles in stages so as to force denser customer packing within a route.We then extend the algo-rithm to a generalization of the problem with re-laxed time windows.In terms of computational results,experiments on VRPTW benchmark problems show that our approach can produce solutions that are very close to previous best-published results.What is more interesting perhaps is the performance on m-VRPTW instances.Results show that our approach produces solutions that are very close to computed upper bounds.Moreover,as the number of vehicles is reduced,the average number of customers per route is monotically increasing.This shows that our approach is good from both the optimality as well as stability point of view.2.Literature reviewThe primary objective of m-VRPTW is to maximize the number of customers served,which is NP-hard,sincefinding it is a generalization of the multiple constrained knapsack problem.Al-though the classical VRPTW has been the subject of intensive research since the80s,to our knowl-edge,there has been little research work on m-VRPTW.We give some research developments in VRPTW.SolomonÕs insertion heuristics[18]is the seminal work behind heuristic construction algo-rithms.Many efficient heuristic and meta-heuristic approaches have been proposed recently,including the works of Chiang and Russell[5],Potvin and Rousseau[14],Rochat and Taillard[15],Taillard et al.[20],and Thangiah et al.[21].More recently, Schulze and Fahle[19],Gehring and Homberger [9]proposed new parallel tabu search heuristics that enable large-scale VRPTW instances to be solved.Several works have been carried out advocat-ing the hybrid use of constraint programming and local search.For example,Pesant and Gendreau [13]applied constraint programming to evaluate the local neighborhood tofind the best local moves.There is also constrained-directed local search proposed in[1,11,17].In[17],for example, the author presented a method called large neigh-borhood search(LNS)for VRP in which a part of a given solution is extracted and then reinserted into the partial solution using a quasi-complete search process.If the reinsertion procedure gen-erated a better solution,then the solution is kept. This process is repeated until certain stopping criterion is met.The result produced with this technique is competitive with other meta-heuristic approaches.In terms of exact algorithms,Desrochers et al.[6]has applied column generation that was able to solve some100-customer problems optimally. Based on this,Kohl et al.[12]developed a more efficient optimization algorithm by introducing a new valid inequality within a branch-and-cut algorithm,called k-path cuts,which solves70of the87Solomon benchmark problems to optimal-ity.However,due to the exponential size of the solution space,it is unlikely that these optimization procedures can be used for larger-scale problems.3.Problem definition and notationThe standard VRPTW problem is defined for-mally as:Given an undirected graph GðV;AÞu et al./European Journal of Operational Research148(2003)559–569where V ¼f v 0;v 1;...;v n g ,v 0is the depot,v i ,i ¼0is a customer with demand d i ,time windows (e i ;l i )and service duration s i ;A ¼fðv i ;v j Þ:i ¼j ,v i ;v j 2V g ,each arc (v i ;v j )having a travel distance (time)t ij ;and vehicle capacity Q ,find a minimum set of vertex-disjoint routes starting and ending at depot v 0such that each customer v i is served by exactly one vehicle within its time windows,P d i for all customers v i served by each vehicle is less than Q ,and the total distance travelled is mini-mized (as the secondary objective function).m -VRPTW is defined formally as:Given m (number of vehicles)and a VRPTW instance,find m or less routes with the primary objective func-tion of maximizing total number of customers served,and the secondary objective function of minimizing the total distance travelled.4.Upper bound for m -VRPTWIn this section,we determine an upper boundfor the total number of customers that can be served by a given fixed number of vehicles.We propose an integer programming (IP)formulation.The IP formulation should be able to solve large-scale problems,yet not be overly simplified such that the gap of the bound from the optimum is too wide.We have adopted a formulation that ac-counts for the capacity constraints of the vehicles as well as the time constraints imposed by the latest return times of every vehicle to the depot.The upper bound is derived by solving a relaxation of m -VRPTW,formulated as follows.Define U ¼f 1;2;...;m g to be the indices of the set of m serving vehicles and V c to be the set of customer nodes (excluding the node at the depot).Define r i ¼min j ;j ¼i t ij ,i ;j 2V which is the travel-ing time from node i to its nearest neighbor.This quantity is used to lower bound the travel time from node i to any other node.Let w i ¼l i þs i þt i 0,i 2V c denote the time of return to the depot after serving node i as its last customer at its latest start time.Without loss of generality,assume that all w i s do not exceed the depot close time.Let G ¼½g 1;g 2;...;g m be a list of m unique customers in V such that w i P w j for all i 2G and j 2G .Since vehicles are identical,the melements of G represent the latest possible times of return to the depot for each of the m vehicles,for a solution to be feasible.The decision variables are denoted by x ij 2f 0;1g ,i 2V c ,j 2U where x ij ¼1if and only if the customer at node i is served by vehicle j .The following IP returns the upper bound of the total number of customers served by all the vehicles:max X i 2V c Xj 2Ux ij s :t :Xj 2Ux ij 61;8i 2V cð1ÞXi 2V cx ij d i 6Q ;8j 2Uð2ÞXi 2V cx ij ðs i þr i Þþr 06w g j ;8j 2Uð3Þx ij 2f 0;1g ;8i 2V c ;j 2UConstraints (1)state that all customers must be assigned to at most 1vehicle.Constraints (2)ensure that the vehicle capacity constraint is not violated.Constraints (3)impose some linear tim-ing constraints:it says that for each vehicle,the earliest possible time of returning to the depot (induced by the assignment x )cannot exceed the latest possible return time (imposed by G ).Note that in this formulation,we ignore the full con-siderations of time window constraints and the actual travel time between two nodes.The bound is thus expected to be less effective on test cases with tight time window constraints.Note that the above formulation gives us a constrained knapsack problem ,which is NP-hard.Fortunately,many variants and generalizations of the knapsack problem have been well-studied there exist exact algorithms which are computationally efficient,such as [22].5.Standard two-phase methodMost of recently published VRPTW heuris-tics are two-phase approaches.First,a construc-tion heuristic is used to generate a feasible and as good as possible initial solution.Then,an iterative improvement heuristic is applied to this solution.u et al./European Journal of Operational Research 148(2003)559–569561It generates successive solutions by searching the neighborhood of the current solution.In the sec-ond phase,various methods are then used to pre-vent the algorithms from being trapped at local optimal and to explore a larger search space.The construction phase involves insertion of all the customers into a set of feasible vehicles routes. The purpose of the construction phase is to pro-vide an initial feasible ually,each customer will be inserted in turn to the route that gives the minimum additional cost or distance at that instance.The order of customer selection then defines the heuristics,some of which are listed follows:•Nearest insertion rule:Next nearest unserved customer will be selected.•Earliest ready time rule:Next unserved cus-tomer with the earliest ready time will be se-lected.•Window tightness rule:Next unserved customer with the tightest time window will be selected. Observe that these heuristics assume that there are enough vehicles to serve all the customers.As such,for over-constrained problems,these heu-ristics may fail to deliver satisfactory solutions.Given the initial feasible solution from phase I, the phase II route improvement phase involves an iteration of moving from a feasible solution to its feasible neighborhood until certain termi-nating condition is met.In this phase,the heuris-tics are defined by the neighborhood structure,the choice of the next move,and the terminating condition.The simplest approach of the steepest descent algorithm chooses the best and improved solution among all the neighboring solutions at every iter-ation.However,the algorithm would very quickly be trapped within a local minimum.The neighborhood structure used is usually a k-opt local search procedure,where k refers to the number of customers/arcs that can be inter-changed from the initial solution to its neighbor-hood solutions.Some of these interchanges are •Relocate:Customer from one route transfers to another route.•Exchange:Customer from one route exchanges position with another customer in another route.The quality of a two-phase method depends on whether the choice of construction and improve-ment heuristics is a goodfit to the nature of the search space.The construction heuristics should produce a good enough initial location such that the improvement algorithm starts in a region where good solutions can be achieved.Subse-quently,the improvement heuristics would need to be able to bypass sufficiently many local minima to terminate at a good solution.6.Proposed algorithmThe above-mentioned two-phase method would normally have to work differently for an over-constrained problem.One way is to use the inser-tion heuristic to determine whether the problem instance is feasible.Following which,we have two sets of heuristics to handle separately the infeasi-ble case and the feasible case.Another approach is to increase enough vehicles so as to serve all customers,and then,through subsequent heuris-tics,try to obtain a subset of the solution that maximizes the number of customers that can be served.However,there are pitfalls in both of these approaches.Though thefirst seems credible,it demands extensive use of heuristics and does not value-add in terms of algorithm development.The second approach is not addressing the issue of infeasibility directly,and is therefore unlikely to give a good solution consistently.We therefore seek to have a generalized method to handle both feasible and infeasible problem in-stances.The approach to such an algorithm lies in the introduction of the holding list,the data structure that contains unserved customers.Al-though the idea of holding list is not new atfirst sight(for example,the ILOG Dispatcher product uses the same idea),our overall algorithmic strat-egy of transferring customers back and forth the holding list under our tabu search strategy(see below)is,to our knowledge,a novel idea.u et al./European Journal of Operational Research148(2003)559–5696.1.Holding listThe holding list contains the list of the cus-tomers that are not served in the current solution. The idea of introducing a holding list is triggered by the role of artificial variables in the phase I of the simplex algorithm.A feasible solution of the VRPTW is found when all the customers are dri-ven out of the holding list,which is analogous to driving out all the artificial variables from the simplex tableau.The holding list will induce an extended neigh-borhood search space,which includes the follow-ing moves,in addition to the basic relocate and exchange moves discussed in Section5:•Relocate from holding list:Transferring a cus-tomer from holding list to an existing route.•Relocate to holding list:Transferring a cus-tomer from an existing route to the holding list.•Exchange with holding list:Exchanging a cus-tomer from an existing route with another cus-tomer in the holding list.The holding list is similar to a‘‘phantom’’route which participates in the regular local search,with a variant that insertion of a customer to the hold-ing list is always feasible and does not incur any cost.The customers of a selected route will be searched completely for possible of transfer to/ from or exchange with customers in the holding list.The next accepted move is determined by using a best improvement strategy depicted in the hierarchical cost structure.6.2.Hierarchical cost structurem-VRPTW introduces the additional objective of maximizing the number of served customers and minimizing lateness(if time windows can be relaxed).This implies that the objective function becomes a composite function:•maximize total number of customers served,•minimize total number of customer served late (if allowed),•minimize total lateness duration(if allowed),•minimize total number of vehicle used,•minimize total distance traveled.One possible way of dealing with multi-criteria objective is to define a composite cost function with different weights for the different cost pa-rameters.However,setting the proper weights becomes a tricky(or almost impossible)mission, and the resulting function becomes meaningless to interpret.Our approach is to define a hierarchical cost structure.For example,serving more customers is always better regardless of the number of vehicles used.Although one can argue that theÔbig MÕapproach on the composite cost function can be used to enforce the hierarchy,we believe our ap-proach is a cleaner way.We propose a hierarchical cost structure in decreasing order of priority of the above list of objectives.In hierarchical cost comparison between two states(during local search),the state with greater preference down the hierarchy of importance as-sumes aÔlower costÕ.As opposed to the composite cost function,where the total composite cost is computed,hierarchical cost is never computed. This is because in local search techniques,com-putation of the absolute cost is not needed. Rather,the hierarchical cost is used for comparing the current solution state with the previous best solution state.6.3.Tabu search strategyAlthough many construction and local im-provement techniques have been reported to solve VRPTW problems,it is unclear how these heuris-tics will behave when constrained by a limited vehiclefleet.Particularly,it is unclear how the two-phase approach can work co-operatively in ensur-ing the ultimate solution to have good customer packing while not exceeding the prescribed vehicle limit.For instance,if we use too few vehicles in the construction phase,it may limit the search space of the local improvement phase;on the other hand,if we use up to the maximum allowable number of vehicles,then it leaves little room for the local search phase to add more unserved customers, since all routes have been used up.The outcomeu et al./European Journal of Operational Research148(2003)559–569563may be a solution where some of the routes are relatively loosely packed,but no more customers can be added to any one of them unless drastic changes are made to the solution,which local search,by its nature,is incapable of realizing.To deal with limited vehiclefleet,our strategy is to meld the two-phase approach into a nested approach.The idea is to increase the number of vehicles in stages and at each stage,apply standard tabu search to maximize the number of customers to be inserted onto those vehicles.Within each stage,the number of vehicles isfixed and hence the search will not consider adding a vehicle to serve unassigned customers.In other words,we steer the tabu search to favor packing of customers within the existing routes.With certain abuse of termi-nology to draw parallelism with duality,the dual of the vehicle minimization problem is the maxi-mization of customer packing problem.Empirical results have shown that this strategy has tremen-dous improvement of packing density on Solomon test cases,without having to rely on a set of good construction heuristics.With the incorporation of the holding list,it is easy to implement the tabu search strategy dis-cussed above.The hierarchical cost structure fa-vors transfer of customers from the holding list to the routes.Unlike many tabu search algorithms, where the search space is always feasible,the holding list is a neat way of incorporating local search towards a path of feasibility that favors customer packing.Let TS denote one iteration of a standard tabu search procedure with the search neighborhood and hierarchical cost structure discussed in Sec-tions6.1and6.2respectively.The tabu list stores customers that have been moved within the preced-ing number of iterations defined by the tabu length.A move is tabu if and if only the customers are in the tabu list and it is not aspired by being better than the best solution so far,in the sense of the hierarchical cost function defined in Section6.2.Let StepSize denote the additional number of vehicles introduced in each stage.Let numVeh denote the current number of vehicles used,ini-tialized to StepSize.Let CountLimit denote the maximum number of non-improving moves using numVeh vehicles.The algorithm proceeds as follows(refer to Algorithm A).The holding list initially contains all customers of the given instance.We introduce StepSize additional number of vehicles(i.e.empty routes)in each stage.Each stage is implemented by the while loop(i.e.steps(3)to(6)).When TS is called in step(4),it will return a solution which differs from the previous solution by a local move made with respect to the neighborhood and the hierarchical cost structure.In step(5),a better solution means that the hierarchical cost objective value is better than that of the best solution found so far.If a better solution has not been found after CountLimit consecutive tries,then the stage ends, and the algorithm proceeds to the next stage by adding more vehicles.Algorithm A(1)until holding list is empty or numVeh¼m(2)set Count¼0(3)while Count6CountLimit(4)call TS based on numVeh vehicles(5)if better solution found then set Count¼0else set Count¼Countþ1(6)endwhile(7)set numVeh¼minðnumVehþStepSize;mÞ(8)end until6.4.m-VRPTW with relaxed TWsThe logical extension to solving m-VRPTW problems is to relax the time window constraints. In other words,we allow late arrivals after the intended due time to increase the number of cus-tomers that could be served.Although one could perceive this as a separate objective that may entail a separate algorithm,our challenge is to incorpo-rate this feature into one seamless generalized al-gorithm.We define late period to be the amount of late-ness between the time windows upper bound and the actual arrival time(and0if the arrival time lies within the time window).We impose the late pe-riod as a soft time constraint which can be vio-lated.This is done by incorporating the number of late arrivals and the total late period into the hi-erarchical cost function.u et al./European Journal of Operational Research148(2003)559–569Intuitively,by relaxing the time window con-straints,we would expect to have improved solu-tions(in terms of its objective value).However, from experimentation with our proposed local search technique,we discovered an anomaly that the solution for some cases become worse if we relax the constraints as it is.This is illustrated in the sample run in solving an over-constrained Solo-mon benchmark problem R103with13vehicles. Without relaxing the time windows,the total late period is0,while with relaxed time windows,the late period became77.8units!This contradicts with the intuition that with relaxed time windows,the solution obtained from local search will always be an improvement.As local search techniques do not guarantee global optimality,it is likely that even with relaxed constraints the reported solution may be worse off.To deal with this anomaly,we propose Algo-rithm B.Here,wefirst solve the problem without relaxing the time windows.The solution then be-comes the initial feasible solution for the problem with relaxed time windows.In the latter case,more customers may be inserted from the holding list, albeit at the expense of relaxed time windows.In this manner,we can always guarantee improve-ment,if any,on the relaxed problem.Table1pre-sents the new result obtained for R103under this new scheme.Algorithm B(1)call Algorithm A(2)if holding list not empty(3)relax time windows constraints(3)set Count¼0(4)repeat steps(2)to(7)of Algorithm A 7.Results and analysisIn this section,we present experimental results of applying our algorithm to solve both the stan-dard VRPTW as well as m-VRPTW problems. In our experiments,we set the tabu length to be 100.We set the values of CountLimit and Step-Size in Algorithm A to be500and1respec-tively.We refer to our implementation as the OV method.7.1.Performance on VRPTW problemsHere,we test the performance of Algorithm A on the set of56SolomonÕs test cases with100 customers.The run time of the algorithm tested on a Pentium II433machine is about1min on average.Wefirst compare our results with the overall best-published heuristics results.1The comparison of solutions is presented in Table2,where the Best and OV columns contain the best published and our results respectively.Next,we also compare our results with the specific results of(a)Rochat and Taillard[15] (RT),(b)Chiang and Russell[3](CR),(c)Taillard et al.[20](TBGGP),(d)Homberger and Gehring [10](HG),and(e)Cordeau et al.[4](CLM).A summary of the comparison is given in Table3. We observe that although our results are in general inferior compared to these results,they are within only several percent worse on average.We believe this is justifiable,in two sense.First,our goal is in obtaining results within1min on average and hence set the maximum number of iterations to 500.This is in contrast to the other methods which typically require hours of CPU time on compatibleTable1Consistency of solution with relax TWsWithout relaxed time windows With relaxed time windows#Vehicles1313#Customersinserted99100#Customersserved late02 Total late period015.7921Contributions to the best-published results are taken from: Cordeau et al.[4]––R107,R108,RC104,RC106,R204,RC201, RC207,Chiang and Russell[3]––R207,Homberger and Geh-ring[10]––R103,R109,R112,R201,R203,R208,R210,R211, C202,RC203,RC204,RC205,R110,Rousseau et al.[16]––R111,RC105,R202,R205,R209,RC206,RC208,Rochat and Taillard[15]––R101,R104,R105,R106,C101,C102,C103, C104,C105,C106,C107,C108,C109,RC103,R206,C201, C202,C203,C204,C205,C206,C207,C208,R102,Taillard et al.[20]––RC101,RC102,RC107,RC108.u et al./European Journal of Operational Research148(2003)559–569565machines.Second,this work is not aimed at beating the best VRPTW results,but rather on proposing an algorithm that works well when limited to a number of vehicles.7.2.Performance on m-VRPTWTo assess the performance of our algorithm on m -VRPTW,we will measure the total number ofTable 3Summary of comparisonRTCR TBGGP HG CLM OV C1mean no of vehicles 10.0010.0010.0010.0010.0010.00C1mean distance832.59828.38828.38828.38828.38832.13C2mean no of vehicles 3.00 3.00 3.00 3.00 3.00 3.00C2mean distance595.38591.42589.86589.86589.86589.86R1mean no of vehicles 12.8312.1712.1711.9212.0812.16R1mean distance1208.431204.191209.351220.971210.141211.55R2mean no of vehicles 3.18 2.73 2.82 2.73 2.73 3.00R2mean distance999.63986.32980.27968.55969.581001.12RC1mean no of vehicles 12.7511.8811.5011.5011.5012.25RC1mean distance1381.331397.441389.221388.241389.781418.77RC2mean no of vehicles 3.62 3.25 3.38 3.25 3.25 3.37RC2mean distance1207.371229.541117.441140.431134.521170.93Table 2OV solutions against best-published resultsBest OV Best OV VehiclesDistance Vehicles Distance Vehicles Distance Vehicles Distance C10110828.9410828.94R11291003.73101021.95C10210828.9410834.64R20141252.3741292.53C10310828.0610834.56R20231191.7031158.98C10410824.7810846.32R2033942.643980.70C10510828.9410828.94R2042849.623847.74C10610828.9410828.94R2053994.4231146.80C10710828.9410828.94R2063912.9731007.00C10810828.9410828.94R2072914.393869.94C10910828.9410828.94R2082731.232790.46C2013591.563591.56R2093909.8631020.06C2023591.563619.36R2103955.3931032.65C2033591.173604.01R2112910.093866.10C2043590.63644.23RC101141696.94151657.46C2053588.883601.43RC102121554.75131535.79C2063588.493588.88RC103111262.02121386.03C2073588.293608.94RC104101135.48101213.25C2083588.323591.83RC105131633.72151625.13R101191650.8201765.00RC106111427.13121426.07R102171486.12181548.61RC107111230.54111330.59R103131292.85141258.34RC108101139.82101175.88R10410982.01101018.48RC20141406.9441468.46R105141377.11151462.69RC20231389.5741222.69R106121252.03121328.66RC20331060.4531171.88R107101113.69121160.08RC2043799.123839.32R1089964.38101045.83RC20541302.4241338.70R109111194.73131259.09RC20631153.9331201.27R110101124.4111127.70RC20731062.0531139.48R111101096.72111097.10RC2083829.693985.60u et al./European Journal of Operational Research 148(2003)559–569。

扫描及重建参数对肺磨玻璃结节体积测量影响的体模研究李艳; 程勇; 冯茜茜; 沈倩; 兰永树【期刊名称】《《中国介入影像与治疗学》》【年(卷),期】2019(016)011【总页数】5页(P691-695)【关键词】肺肿瘤; 体层摄影术;X线计算机; 体模;显像术【作者】李艳; 程勇; 冯茜茜; 沈倩; 兰永树【作者单位】西南医科大学附属医院放射科四川泸州646000【正文语种】中文【中图分类】R734.2; R814.42随着CT筛查的普及CT技术的发展，小、中等肺结节的检出率越来越高，其中多数为良性[1]，无需进一步的临床干预，因此需长期随访观察来评估肺结节。

Walter等[2]认为，结节处理是以体积和倍增时间为基础，因此结节体积测量的可重复性及准确率尤为重要。

相较于人工测量，利用后处理软件进行三维体积测量更具准确性[3]，但在复查随访的临床工作中扫描及重建的条件不一致可能会影响肺结节体积测量的准确率。

本研究采用不同扫描及重建参数对肺磨玻璃结节体模进行扫描并测量，探讨其对结节容积测量的影响，以期为肺磨玻璃结节的随访条件提供参考。

表1 扫描参数分组管电压组(kV)120 10080管电流组(kV)808080管电流(mA)200200200200150100有效剂量(mSv)4.392.801.511.511.130.76图1 肺体模(A)及8个磨玻璃结节(B)1 材料与方法1.1 材料体模采用岛津公司研制的多用途男性胸部模型N1及8个球形小结节体模(图1)，该仿真体模长40 cm、宽43 cm、高48 cm，质量18 kg，胸围94 cm。

根据2017版Fleischer Society指南[4]：对于偶发直径<6 mm(体积<100 mm2)结节的人群，无论有无肺癌高危因素，都无需常规随访，因此本研究中采用直径≥5 mm的结节。

8个结节：2种CT值(-800 HU、-630 HU)、4种直径(5 mm、8 mm、10 mm、12 mm)。

I.J.Mathematical Sciences and Computing,2018, 2, 12-21Published Online April 2018 in MECS ()DOI: 10.5815/ijmsc.2018.02.02Available online at /ijmscA Systematic Expository Review of Schmidt-Samoa CryptosystemQasem Abu Al-Haija a*, Mohamad M.Asad b, Ibrahim Marouf a,b, a,b c Department of Electrical Engineering, King Faisal University, Hufof 31982, Saudi Arabia Received: 21 November 2017; Accepted: 13 February 2018; Published: 08 April 2018AbstractPublic key cryptographic schemes are vastly used to ensure confidentiality, integrity, authentication and non-repudiation. Schmidt-Samoa cryptosystem (SSC) is a public key cryptosystem, which depends on the difficulty of large integer factorization problem. The implementation of SSC to secure different recent communication technologies such as cloud and fog computing is on demand due to the assorted security services offered by SSC such as data encryption/decryption, digital signature and data integrity. In this paper, we provide a systematic review of SSC public key cryptosystem to help crypto-designers to implement SSC efficiently and adopt it in hardware or software-based applications. According to the literature, the effective utilization and design SSC can place it as a viable alternative of RSA cryptosystems and many others.Index Terms: Information Security, Public Key Cryptography, Schmidt-Samoa Cryptosystem, Integer Factorization.© 2018 Published by MECS Publisher. Selection and/or peer review under responsibility of the Research Association of Modern Education and Computer Science1.IntroductionIn the last decades, the communication system over the world has been extremely enlarged where millions of computers were connected to networks and internet to exchange a huge amount of information. This information is vulnerable to interrupt, change, or even seen by unwanted people (i.e. unauthorized). Because of that, secure communication channels were introduced to prevent any third party from reading or changing information. Such prevention is accomplished by setting rules for accessing the confidential data known collectively as Cryptography. Cryptography is the science that concern with encrypting and decrypting data to provide secure transactions between communication parties. Cryptography provides the secure communication networks by a means of cryptographic primitives [1] (listed in table 1) which contributed along with the crypto-* Corresponding author. Tel.: +966-13-589-5400; fax: +966-13-581-7068E-mail address: Qalhaija@.saalgorithms to provide many services such as: confidentiality: To help protect a user's identity or data from being read, data integrity: To help protect data from being changed, authentication: To ensure that data is originated from a certain user, and non-repudiation: To prevent a certain party from being denied of sending messages [1].Table 1. Cryptographic Primitive and Their UseCryptographic primitive UseSecret-key encryption (symmetric cryptography) Performs a transformation on data to keep it from being read by third parties. This type of encryption uses a single shared, secret key to encrypt and decrypt data.Public-key encryption (asymmetric cryptography) Performs a transformation on data to keep it from being read by third parties. This type of encryption uses a public/private key pair to encrypt and decrypt data.Cryptographic signing (Digital Signatures) Helps verify that data originates from a specific party by creating a digital signature that is unique to that party. This process also uses hash functions.Cryptographic hashes (Fixed Size Digesting) Maps data from any length to a fixed-length byte sequence. Hashes are statistically unique; a different two-byte sequence will not hash to the same value.Based on encryption/decryption process, cryptographic algorithms are categorized as Symmetric key algorithms and Public key algorithms (Asymmetric key). Symmetric Key Cryptography (SKC) is a field of cryptography where the same key is shared between both sender and receiver to be used for encryption and decryption processes. SKC ciphers can either be stream cipher which encrypt and decrypts data as bit-by-bit process using bit operations (such as XOR) or block cipher which deals with blocks of fixed length of bits encrypted/decrypted with a key. An examples of stream cipher is LFSR encryption [2] and examples of block cipher are DES, 3DES, Blowfish, and AES. Modern symmetric algorithms such as AES or 3DES are very secure. However, there are several drawbacks associated with symmetric-key scheme like key distribution problem, number of keys or the lack of protection against cheating [3]. In symmetric key algorithms, the key must be established in a secure channel which does not exist in communication channels. Even if this problem solved, substantial number of keys will be needed when each pair needs a separate key in a network. Moreover, any party can cheat and accuse the other party. Hence, asymmetric key algorithms are needed to solve these problems.Public Key Cryptography (PKC) where the two parties (sender and receiver) have two different keys; one public shared key for encryption and one private key for decryption. Public-key algorithms are used mainly for Key Establishment, Identification and Encryption. Diffie-Hellman Key Exchange (DHKE) [4] is an example of an asymmetric key algorithm used for key exchange and RSA is an encryption public-key algorithm [5]. PKC algorithms are fundamental security component in many cryptosystems, applications and such as Transport Layer Security (TLS) protocol [6]. Public key algorithms provide data encryption, key exchange, and digital signatures [7].PCK algorithms can be categorized based on the mathematical problem used in the scheme into [4]: Integer-factorization based schemes such RSA and McEliece [8] algorithms and discrete logarithm-based schemes such as Diffie–Hellman key exchange and ELGamal encryption scheme [4]. Integer factorization is the process where an integer is decomposed to the product of smaller numbers. If these numbers are prime numbers, then it is called prime factorization. The complexity in this method arises when factoring a very large number because there no such known efficient algorithm. However, not all number with the same length are equal in complexity. When the number is the product of two coprime numbers, it is infeasible to factor this kind of numbers using the current technology [9]. Most non-RSA public-key algorithms with practical relevance are based on another one-way function, the discrete logarithm problem [3]. The security of many cryptographic schemes relies on the computational intractability of finding solutions to the Discrete Logarithm Problem (DLP). The discrete logarithm problem is defined in what are called cyclic groups. However, there are four families of alternative public key schemes [10] that are potentially interesting for use in practice: hash based, code-based, lattice-based and multivariate quadratic (MQ) public-key algorithms.Practically, public key schemes are preferred to use due to many reasons such as the non-exitance of thesecure communication channels. Therefore, the efficient implementation of public key cryptosystems is on demand especially if its implemented with appropriate technology with high precision design. In this paper, Schmidt-Samoa Cryptosystem (SSC) [11] will be used analyzed as efficient and comparable alternative to RSA which is a well-known secure and practicable public key scheme that can be used to protect information during the transmission over the insecure channels. SSC Cryptosystem is heavily based on modular arithmetic involving large prime numbers.The remaining of this paper is organized as follows: Section 2 discusses the Schmidt-Samoa Cryptosystem (SSC) in details including SSC crypto-algorithm, the SSC factoring, numerical example of how SSC works, some possible attacks of SSC, and the underlying design issues and requirements followed by conclusions.2.Schmidt-Samoa Cryptosystem (SSC)Schmidt-Samoa Cryptosystem (SSC) is an asymmetric cryptographic technique (public key algorithm) in which security depends on the difficulty of integer factorization problem used for data encryption and decryption. Just like RSA, SSC uses very large prime numbers and modular arithmetic to provide different security services such as conditionality, integrity, and non-repudiation.2.1.SSC AlgorithmTo start the secure communication session, the receiver, who is Alice in this case, starts by choosing two large prime numbers (p, q) and then compute her public key 2=. Alice then share the public key (N) withN p qBob (and even other senders) who will use it to encrypt the plaintext messages communicated with Alice. Again, Alice computes her private key (d) to be used for decryption processes 1=. Next, using the privated N-key, Alice decrypts the ciphertext.Fig.1. Complete Diagram of Schmidt-Samoa Algorithm.Fig.1, shows the complete SSC algorithm diagram which is divided into three stages: key generation stage, Encryption stage, and Decryption stage. The challenge in SSC is the ability to factor out the public key which is the product of two very large primes. As the size of the key is increases, the factorization problem becomes even more complicated [9]. Factoring a number means defining that number as a product of prime numbers. InSSC, factoring the public key (N ) means as breaking the cryptosystem. If an attacker can factor out the public key, he can easily calculate the private key (d ) and decrypt any data. As public key 2N p q =, is known to everyone, therefore factoring (N ) leads to compute p and q . Then the private key can be computed using congruent (1) (where LCM is the least common multiple of two numbers):1mod (1,1)d N LCM p q -≡-- (1) For better understanding, we provide the following simplified numerical example. Let’s assume that the plaintext message m = 2 and the domain parameters (p = 11, q = 17, m = 2), then we run SSC (11,17,2) as follows:22057N p q ==11mod (10,16)2057mod8073d N LCM --≡==2057mod 20571855c m ==731855mod1872m ==2.2. Possible Attacks of SSCReasonably, there is no such a perfect system, but there are systems hard to be attacked. SSC is proved to be very secure [11], however, it is vulnerable to some known attacks such as Brute-force attack, Man-in-the-Middle attack, and Side Channel attack. Generally, all public key cryptography algorithms suffer from these attacks [3].∙ Exhaustive search of SSC: In computer science, brute-force search or exhaustive search, also known asgenerate and test, is a very general problem-solving technique that consists of systematically generating all possible candidates for the solution and checking whether each candidate satisfies the problem's statement. For instance, finding the factorization of a very large number by trying all the numbers less than the asked number. In cryptography, an exhaustive search attack involves checking all possible keys until the correct key is found [12]. This strategy theoretically can be used against any cryptosystem by an attacker who is unable to take advantage of any weakness in the system that would make breaking the system easier. The length of the used key in the encryption process determines the practical feasibility of performing a brute force attack, with larger keys exponentially more difficult to break than smaller ones. One of the measures of the strength of an encryption system is how long it would theoretically take an attacker to successfully mount a brute force attack against it. In Schmidt-Samoa cryptosystem, as the bit size of the key is increased, the time needed to perform an exhaustive search would increase exponentially. It is believed that a 1024-bit key can be factored in period of 10-15 years, where it is possible for some intelligence agencies to compute the key earlier [12]. However, for 2048- bit or more, it is not feasible to factor out SSC key relying on the current technology (computers). Sample example of exhaustive search algorithm (brute force) is illustrated in figure 2 as it shows the possible trial values of simple 4-bit key.Fig.2. Example of Brute Force Attack of 4 bit KeyMan-in-the-Middle Attack [13]: it is a type of cyberattack where a malicious actor inserts him/herself into a conversation between two parties, impersonates both parties and gains access to information that the two parties were trying to send to each other. It allows a malicious actor to intercept, send and receive data meant for someone else, or not meant to be sent at all, without either outside party knowing until it is too late. Man-in-the-middle attacks can be abbreviated in many ways, including MITM, MitM, MiM or MIM. An example of MITM by using SSC scheme is shown in Fig.3 where Alice generates her public and private keys and sends the public key over unsecure channel. However, Trudy interrupts the communication and generates new public key then sends it to Bob. Bob now encrypts data and sends it back to Alice on the unsecure channel, however, only Trudy who can decrypt the message. Trudy can generate new false message and send it to Alice, pass the original message, or just block it where Alice and Bob thinking they are communicating with each other securely.a = K prAA = αa mod(n) = K pubAb = K prBB = αb mod(n) = K pubBA’ = αT1 mod(n)B’ = αT2 mod(n)A BK AT = (B’)a = (αT2)amod(n)K BT = (A’)a = (αT1)bmod(n)A’B’K AT = A T2 = (αa)T2 mod(n)K BT = B T1 = (αb)T1 mod(n) Fig.3. MITM Attack Scheme for SSC.Side Channel Attack: In cryptography, a side-channel attack is an attack based on analyzing the physical implementation gained information of a cryptosystem, rather than a brute-force of any theoretical weakness [12]. They exploit information about the private key which is leaked through physical channels such as the power consumption or the timing behavior. However, to observes such channels, an attacker must have access to the cipher implementation, e.g., in cell phones or smart card. Fig.4 shows the power trace of an RSA implementation on a microprocessor [12], or the drown electric power by the processor to be more precise. The attacker goal is to extract the private key d which is used during the RSA decryption. It can be differentiated between the high and low activity from the graph, this behavior is explained by the square-and-multiply algorithm. If an exponent bit has the value 0, only a squaring is per formed. If an exponent bit has the value 1, a squaring together with a multiplication is computed.Fig.4. The Power Trace of an RSA Implementation.2.3.SSC ServicesSSC is very flexible and can provide the four main cryptographic services: confidentiality, integrity, authentication, and non-repudiation. As for RSA algorithm, SSC algorithm can be used to encrypt and decrypt private message providing, confidentiality and non-repudiation. Also, SSC can be implemented to be used as digital signature (DSA-SSC) as shown in Fig.5, providing integrity. PKI and alternative schemes; hashed-based, coded-based, etc., can be implemented using SSC.2.4. several digital arithmetic and modular arithmetic algorithms as well as different number theory schemes. It employs the properties of prime numbers alongside the congruent to produce a very secure hard to break cryptosystem. Arithmetic operation like multiplication and squaring, and modular exponentiation and modular inverse are involved in the algorithm to add complexity to the cipher. Thus, implementing a SSC coprocessor requires the contribution of many design components as seen in the diagram of figure 6.Fig.6. SSC Underlying Design Requirements Diagram.Number Theory Algorithms: Because of the modular factors (p, q) must be prime, therefore, twocomponents are contributing here generate test a prime number with desired length: a random number generator (RNG) [2] and a prime number tester PNT) [14]. Also, to test the co-prime relativity, a greatest common devisor (GCD) unit [15] is required in Schmidt-Samoa. In addition, to generate the private key modulus, a Least common multiple (LCM) [15] unit is needed.∙Digital Arithmetic Algorithms: in order to compute the public key (N) which is also used as the encryption algorithm modulus, efficient arithmetic digital multiplier (used for squaring as well) unit is required to generate N, such as Karatsuba multiplier [16]. The multiplier is built from fast two operand adder units such as Kogge Stone adder (KSA) [17] as an efficient Parallel prefix adder [18], fast three operand adder such as Carry save adder [18] and multi-operand addition trees such as Wallace trees [18]. ∙Modular Arithmetic Algorithms: As for SSC encryption and decryption processes, an efficient modular expatiation such as [19] should be carefully selected as this operation consumes most of the time in the SSC system. Similarly, another costly operation is needed in the generation of decryption key which is the modular inverse (division by modulus) operation [9] which is well known to be one of the long-time operations performed by the Cryptoprocessor.∙Hardware/Software design tools: SSC Cryptoprocessor can be implemented either in software environment or in hardware platform. However, it’s noted that building Cryptoprocessor via hardware is more secure and efficient than in software [20]. Nowadays, reconfigurable hardware devices are commonly spread to implement various digital applications such as cryptographic coprocessor and embedded systems design. It’s largely recommended to implement SSC using the field programmable gate arrays (FPGA) [21] which provide wide range of flexibility and dynamic control of several design factors such as delay, area and power consumption. The reconfigurability feature of FPGA devices attracted many cryptographic researchers to implement their designs using FPGA devices benefiting from the spacious libraries and modules offered by Computer Aided Design (CAD) [22] tools as well as the flexibility of Hardware description languages (HDLs) [23].Eventually, the adequate adoption of the efficient accelerated built-in units and component along with affordable high technology design platform will result in undoubtedly robust SSC cryptosystem that is comparable and competitive with RSA and many other well-known secure cryptosystems. It can replace RSA Cryptosystem in many applications such as in design of the cryptography system with multi-level crypto-algorithms [24], in the design an effective parallel digital signature algorithm for GPUs [25], in the design of robust image Steganography [26], in the design of an alternative equations for Guillou-Quisquater Signature scheme which is based originally on RSA [27], or many other valid applications.3.Conclusions and RemarksSchmidt-Samoa cryptosystem public key cryptosystem (SSC) with numerical example and sample possible attacks as well as the cryptosystem's design issues has been methodologically analysed and investigated in this paper. Thus, even if you use the best possible random number generators to create candidates for the primes that are needed to make SSC secure, the security of SSC encryption/decryption depends critically on the difficulty of factoring large integers which become easier for shorter key sizes due the existence of powerful computers. Therefore, SSC cryptography has had to rely on increasingly larger values for the integer modulus and, Hence increasingly longer encryption keys. As for RSA, these days you are unlikely to use a key whose length is shorter than 1024 bits for SSC as many people recommended to use 2048 or even 4096-bit keys. References[1]Denning, D.E.R.E, “Cryptography and data security”, Reading, MA: Addison-Welsey.[2]Q. A. Al-Haija, N. A. Jebril, and A. AlShua'ibi. (2015). Implementing variable length Pseudo RandomNumber Generator (PRNG) with fixed high frequency (1.44 GHZ) via Vertix-7 FPGA family. Network Security and Communication Engineering, CRC press, Pp. 105 -108.[3] C. Paar, J. Pelzl, (2010) ‘Understanding Cryptography’. Springer-Verlag Berlin Heidelberg Publisher.https:///10.1007/978-3-642-04101-3.[4]Menezes, A.J., van Oorschot, P.C. and Vanstone, S.A., (1996), 'Handbook of applied cryptography',CRC Press, http://cacr.uwaterloo.ca/hac/[5]Q. 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Abu Al-Haija, "Comparative Study of Efficient Modular ExponentiationAlgorithms", COMPUSOFT, An international journal of advanced computer technology, 6 (8), Pp.2381– 2389, 2017[20]L. Tawalbeh and Q. Abu Al-Haija," Enhanced FPGA Implementations for Doubling Oriented andJacobi-Quartics Elliptic Curves Cryptography,” Journal of Information Assurance and Security (JIAS), By Dynamic Publishers Inc., Vol 6 (3), Pp. 167-175, 2010[21] C. Maxfield, " The Design Warrior’s Guide to FPGAs: Devices, Tools and Flows", Mentor GraphicsCorporation and Xilinx, Elsevier, 2004.[22]Nicos Bilalis, (2000), 'Computer Aided Design CAD', INNOREGIO Project: dissemination ofinnovation and knowledge management techniques, Technical University of Crete.[23]David Harris Sarah Harris, (2012), ‘Digital Design and Computer Architecture’, Imprint: MorganKaufmann, ISBN: 9780123944245, Elsevier.[24]Surinder Kaur, Pooja Bharadwaj, Shivani Mankotia,"Study of Multi-Level Cryptography Algorithm:Multi-Prime RSA and DES", International Journal of Computer Network and Information Security(IJCNIS), Vol.9, No.9, pp.22-29, 2017.DOI: 10.5815/ijcnis.2017.09.03.[25]Sapna Saxena, Neha Kishore," PRDSA: Effective Parallel Digital Signature Algorithm for GPUs ",International Journal of Wireless and Microwave Technologies(IJWMT), Vol.7, No.5, pp. 14-21, 2017.DOI: 10.5815/ijwmt.2017.05.02.[26]M.I.Khalil,"Medical Image Steganography: Study of Medical Image Quality Degradation whenEmbedding Data in the Frequency Domain", International Journal of Computer Network and Information Security(IJCNIS), Vol.9, No.2, pp.22-28, 2017.DOI: 10.5815/ijcnis.2017.02.03[27]J. Ettanfouhi, O. Khadir," Alternative Equations for Guillou-Quisquater Signature Scheme ",International Journal of Computer Network and Information Security, 2016, 9, 27-33, DOI:10.5815/ijcnis.2016.09.04/Authors’ ProfilesQasem Abu Al-Haija is a senior lecturer of Electrical and Computer Engineering at KingFaisal University. Eng. Abu Al-Haija received his B.Sc. in ECE from Mu’tah University inFeb-2005 and M.Sc. in computer engineering from Jordan University of Science &Technology in Dec-2009. His current research Interests: Information Security &Cryptography, Coprocessor & FPGA design, Computer Arithmetic, Wireless SensorNetworks.Muhammad M. Asad is a senior student of Electrical Engineering Department at KingFaisal University. He is a Syrian resident born on Jan-01-1994 and excellent in bothlanguages Arabic and English. His research interests include (but not limited to): PublicKey Cryptography, FPGA Design, Digital Arithmetic, Microcontroller Design, ElectronicDesign.Ibrahim A. Marouf is a senior student of Electrical Engineering Department at KingFaisal University. He is a Syrian resident born on Aug -15-1995 and excellent in bothlanguages Arabic and English. His research interests include (but not limited to): PublicKey Cryptography, FPGA Design, Digital Arithmetic, Microcontroller Design, ElectronicDesign.How to cite this paper: Qasem Abu Al-Haija, Mohamad M.Asad, Ibrahim Marouf,"A Systematic Expository Review of Schmidt-Samoa Cryptosystem", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.4, No.2, pp.12-21, 2018.DOI: 10.5815/ijmsc.2018.02.02。