2011-internetallics-Modeling hardness of polycrystalline materials and bulk metallic glasses

合集下载

基于人工免疫网络的联想记忆器

基于人工免疫网络的联想记忆器

止的条件。当实际应用模 型有时间条件约束 时, 这种限制形式很

人工免疫 网络 ( N) A1 已经 用于解 决优化 问题 】同时 , , 通过
研究人工免疫机制 , 就可以利用这些方法创 建~种新型 的联想记 忆器。这种机制用于定 义在 r维空间上 的点群 。点群表示上述 l
超平面。这拌一个点群 中最佳点等价于吸 引子。
所设计的记忆器学习和记忆 阶段流程 图如图 1 图 2 , 所示。 需要指 出的是, 在学 习阶段首先必须定义形式空间。假设被 记忆摸型是一个用 n维双极性 向量表示 的 P模型集合 。则形式 空间是一个超立 方体 , 它的 中心在轴心, 并且边长为 2 势为 的初始群体 , 是在整个形式空间产生的。对群体 中每一个个体 I , 在那些表示群体中的个体中 , 通过调换一个随机选择的基因位来 确定候选个体 。
生物免疫系统的 自修复机制 。利用这些特性 , 在实数空间 和海 明
空间中都可 以产生新的最优方法。本文 中, 于这些最优方法简 基
述了人工免疫 阿错联想记 忆器模型的设计思想和 实现方法 下
1 T n 瞒 o 8 I e . f m 佃na A p i l n  ̄ lai s c o
( ( “H tm ,『 oi xai J Jl , i r J ” t
o f“
“ Ⅲ
' 。 mMe, z ta p ns f u l ∞ai l r t c i
“m ca i ̄ ek nst m a z l
h  ̄ dt m m ct r . 。 — “ m
方面称为系统 动态性。也就是控制一组确定 的淋 巴细 胞繁殖
和相应的免疫蛋白的数量增减 的微 分方 程。另一方 面称 为系统 的后动态性。这是一种算法。谈算 法控制从群体免疫 细胞 中除 去一定的克隆细胞 , 同时控制来 自骨髓产生的新淋巴细胞池 中的 新繁殖细胞的补充机制。免疫 网络的学 习和 自适 应特 性来 自于

使用中继选择研究半双工非对称双向解码转发中继(IJCNIS-V3-N5-1)

使用中继选择研究半双工非对称双向解码转发中继(IJCNIS-V3-N5-1)
Index Terms—Two-way relaying, decode-and-forward, outage probability, relay selection, asymmetric traffic
I. INTRODUCTION
Cooperative relaying as a promising technology has attracted widespread attentions, since it can achieve higher transmission quality and throughput for wireless networks. In particular, considerable interests have focused on the half-duplex two-hop relaying, where two user terminals communicate with each other enabled by a relay node. Due to the half-duplex constraint, conventionally a direct four time-slot transmission scheme is employed for such a scenario to complete one round information exchange between the two source terminals. Although the conventional scheme is easier to implement, however, it results in a loss in spectrum efficiency due to the fact that more time slots are

BP神经网络与实例讲课文档

BP神经网络与实例讲课文档
(5)a0 ( j ) 表示输入的第j个分量.
第33页,共61页。
在上述假定下网络的输入输出关系可以表示为:
N0
u1 (i) w1 (i, j)a 0 ( j) 1 (i),
j 1
a1 (i) f (u1 (i)),
N1
u 2 (i)
a
2
(i)
w 2 (i, j )a1 ( j) 2 (i),
实现神经计算机的途径。
(3)应用的研究:探讨如何应用ANN解决实际问题,如 模式识别、故障检测、智能机器人等。
第3页,共61页。
研究ANN方法
(1)生理结构的模拟:
用仿生学观点,探索人脑的生理结构,把对人 脑的微观结构及其智能行为的研究结合起来即人工
神经网络(Artificial Neural Netwroks,简称ANN
Vhp Vnp
隐含层LB
V
… a1
… ah
an 输入层LA
a
k 1
a
k h
a
k n
基本BP网络的拓扑结构
第27页,共61页。
四、反向传播算法(B-P算法)
Back propagation algorithm
算法的目的:根据实际的输入与输出数据,计算模型的参数 (权系数) 1.简单网络的B-P算法
图6 简单网络
• 得到的结果见图1
• 图1飞蠓的触角长和翼长
第22页,共61页。
❖ 思路:作一直线将两类飞蠓分开
• 例如;取A=(1.44,2.10)和 B=(1.10,1.16),过
A B两点作一条直线:

y= 1.47x - 0.017
• 其中X表示触角长;y表示翼长.
• 分类规则:设一个蚊子的数据为(x, y) • 如果y≥1.47x - 0.017,则判断蚊子属Apf类; • 如果y<1.47x - 0.017;则判断蚊子属Af类.

a survey of visualizaiton systems for network security

a survey of visualizaiton systems for network security

A Survey of Visualization Systemsfor Network SecurityHadi Shiravi,Ali Shiravi,and Ali A.Ghorbani,Member,IEEE Abstract—Security Visualization is a very young term.It expresses the idea that common visualization techniques have been designed for use cases that are not supportive of security-related data,demanding novel techniques fine tuned for the purpose of thorough analysis.Significant amount of work has been published in this area,but little work has been done to study this emerging visualization discipline.We offer a comprehensive review of network security visualization and provide a taxonomy in the form of five use-case classes encompassing nearly all recent works in this area.We outline the incorporated visualization techniques and data sources and provide an informative table to display our findings.From the analysis of these systems,we examine issues and concerns regarding network security visualization and provide guidelines and directions for future researchers and visual system developers.Index Terms—Information visualization,network security visualization,visualization techniques.Ç1I NTRODUCTIONA LTHOUGH the visualization of network security events isthe subject of this survey,this paper does not focus on designing and developing a specific visualization system. Instead,we consider network security with respect to information visualization and introduce a collection of use-case classes.In this study,we provide an overview of the increasing relevance of security visualization.We explore a novel classification approach and review the artifacts most commonly associated with security visualization systems. We provide a historical context for this emerging practice and outline its surrounding concerns while providing design guidelines for future developments.Visual data analysis help to perceive patterns,trends, structures,and exceptions in even the most complex data sources.As the quantity of network audit traces produced each day grows exponentially,communicating with visuals allows for comprehension of these large quantities of data. Visualization allows the audience to identify concepts and relationships that they had not previously realized.There-by,explicitly revealing properties and relationships inher-ent and implicit in the underlying data.Identifying patterns and anomalies enlightens the user,provides new knowl-edge and insight,and provokes further explorations.It is these fascinating capabilities that influence the use of information visualization for network security.Visualiza-tion is not only efficient but also very effective at communicating information[1].A single graph or picture can potentially summarize a month’s worth of intrusion alerts(depending on the type of network),possibly showing trends and exceptions,as opposed to scrolling through multiple pages of raw audit data with little sense of the underlying events.Security Visualization is a very young term[2],[3].It expresses the idea that common visualization techniques have been designed for use cases that are not supportive of security-related data,demanding novel techniques fine tuned for the purpose of thorough analysis.It may not always be possible to fully predict how an end user will perceive and interpret a design due to the varying nature of the audience’s cognitive characteristics.Yet careful con-sideration of the user’s needs,cognitive skills,and abilities can determine the appropriate content and design.Often associated with human-computer interaction,the philoso-phy of user-centered design places the end user at the center of the design work security is a highly specialized and technical discipline and operation.It deals with packets and flows,intrusion detection and prevention systems,vulnerabilities,exploits,malware,honeypots,and risk management and threat mitigation.The complex, dynamic,and interdependent nature of network security demands extensive research during the development process.Without an in-depth understanding of security operations and extensive hands on experience,developing a security visualization system will not be possible.A design process centered on the needs,behaviors,and expectations of security analysts can greatly influence and impact the usability and practicality of such systems.For best results, security experts and visual designers must thereby colla-borate to complement each other’s skills and expertise to innovate informative,interactive,and exploratory systems that are technically accurate and aesthetically pleasing.In this survey,we begin by looking into different categories of data sources incorporated in the design of security visualizations and provide an informative list of sources accessible to the research community.We continue in Section3by expressing our main contribution in the classification of network security visualization systems. We provide a detailed description of the proposed taxonomy.The authors are with the Information Security Centre of Excellence,Faculty of Computer Science,University of New Brunswick,540WindsorStreet,Gillin Hall,Room E128Fredericton,NB E3B5A3,Canada.E-mail:{hadi.shiravi,ali.shiravi,ghorbani}@unb.ca.Manuscript received30Aug.2010;revised26June2011;accepted12Aug.2011;published online23Aug.2011.Recommended for acceptance by K.-L.Ma.For information on obtaining reprints of this article,please send e-mail to:tvcg@,and reference IEEECS Log Number TVCG-2010-08-0203.Digital Object Identifier no.10.1109/TVCG.2011.144.1077-2626/12/$31.00ß2012IEEE Published by the IEEE Computer Societytogether with an analysis of the derived use-case classes.We follow by giving a thorough description of each system as we outline its strengths and weaknesses.An overall assessment of systems in each use-case class in addition to guidelines and directions for future systems is also provided.We summarize the multiple attributes of recent network security visualization systems in a table for better future references. We continue in Section4by outlining issues and concerns surrounding security visualization by elaborating on seven potential pitfalls.We conclude this research in Section5by summarizing our findings.Papers studied in this survey were selected based on the following metrics:1.Relevance to network security:As the title of thepaper indicates,this study focuses specifically onnetwork security visualization systems.Visualiza-tions of code security,binary files,or visualcryptanalysis are subjects that could span anothervolume of similar size and are thereby not consid-ered in this study.2.Contribution of system and visual techniques:Dueto the chronological study of papers,systems thathave utilized a specific visualization technique ormethod with highly similar characteristics to thoseof previous systems have not been selected for thissurvey.Similarly,visualization systems that lackcontextual,perceptive,and cognitive considerationsare also not considered.3.Satisfactoriness of evaluation:Although most sys-tems surveyed in this paper lack formal evaluation,yet many have been validated through ad hoc use-case attack scenarios.Systems that lack even thisbasic validation strategy are also not considered inthis survey.We believe these three metrics impact the quantity and quality of papers surveyed in this work to resemble systems that are focused explicitly on network security,are novel in their incorporated visual techniques,and are validated on at least a use-case scenario.Systems that do not adhere to these metrics are thereby not considered in this study.2D ATA S OURCESVisualization cannot happen without data or information. Many of the systems surveyed in this paper have been created based on a single source of data.Looking at network events from multiple perspectives by incorporat-ing different data sources into a system can provide an analyst with a richer insight into the underlying events. Therefore,a nonexhaustive list of potential data sources that are available to the research community and may be incorporated in the design of network security visualization systems is given in Table1.The decision on the type and number of incorporated data sources and the set of extracted features from each data source is a critical act. The data sources mentioned in Table1are very generic and in some cases,e.g.,network traces,hundreds of features can be extracted from them.The importance of selecting the appropriate features,as a first step in designing a visualization system,has been extensively studied in theTABLE1Potential Data Sources for Security Visualizationsfields of statistics,pattern recognition,machine learning,and data mining and the resulting efforts have been applied to the fields of artificial intelligence,text categorization,and also intrusion detection.These studies are of great benefit to security visualization researchers as often the required steps of selecting an optimal subset of features (subset generation,subset evaluation,stopping criterion,and result validation)have been examined extensively before.Based on a particular problem a researcher is facing and the data sources available to him or her,a subset of features may be extracted and incrementally validated until a desired optimality is achieved.3C LASSIFICATION A PPROACHThe approach taken in many visualization systems is data driven.In network security for instance,one may take a single data source like packet traces and try to develop a visualization system based on that.The methodology behind the design of visualization systems should be use-case driven.A visualization system should be built to support answering specific questions.In this approach,the system may incorporate one or multiple data sources.Based on this mindset,we have classified the recent works of network security visualization into five use-case classes.We provide a detailed description of each class,discuss several recent examples of each approach,specify the incorporated visualization techniques of each system,and challenge the applicability of each use-case class in regard to modern day networks.Guidelines for future research,and directions for informative and efficient visuals are also provided for each use-case class at the end of each section.3.1Host/Server MonitoringIn this class of visualization,the main display is devoted to the representation of hosts and servers.The intent is to display the current state of a network by visualizing the number of users,system load,status,and unusual or unexpected host or server activities.Systems of this class should also be able to correlate communicating processes of a single host or server with the network traffic.This feature enhances the ability of a user to identify malware as they often manifest themselves in irregular and often anon-ymous system processes.The work of Erbacher et al.[4],[5]constitutes one of the earlier works in this class.As illustrated in Fig.1,hosts are arranged around five concentric circles with the monitored server placed in the center.The ring of a node depicts the difference between its IP address and that of the monitored system,resulting in hosts residing inside the local subnet to appear closest to the monitored system.The position of a host on the circular ring is also recorded to ensure that a specific host always appears in the same position.Multiple visual attributes are assigned to each node as they are depicted using glyphs.For the monitored server,for example,spokes extending from its perimeter represent the number of connected users.As connections are made from hosts to the monitored server and based on the connection type,communication links are shown with different line patterns.These visual illustrations give an analyst an exploratory framework to work with as itstrengthens her abilities to detect unknown relationships within the underlying data.Tudumi [6]is also one of the earlier systems belonging to this category aimed at monitoring and auditing user behavior on a server.In a 3D visualization,Tudumi visualizes connections using lines and system nodes using 3D glyphs as they are displayed on multilayered concentric disks.Similar to Erbacher’s system,Tudumi uses line patterns to encode different access methods including coarse dashed lines to represent a terminal service and thin dashed lines to represent file transfer.The previous two systems are more concerned with the activities of a single or a limited number of hosts or servers rather than incorporating a larger portion of the network.NVisionIP [7],[8]takes on a different approach.It represents an entire class B IP network on a single 256Â256matrix grid with each cell of the matrix representing interactions between the corresponding network hosts.In the galaxy view of the system,all network subnets are listed along the horizontal axis while hosts of each subnet are listed along the vertical axis.As the number of visualized elements increases,inevitably the portion of the screen allocated to each object decreases.NVisionIP uses a magnifier function to allow the user to hover over the display screen.If an analyst is interested in a particular part of the display,she can select it using the magnifier function.A bar graph is then displayed for each host,depicting their activity over common and uncommon port numbers.Portall [9]digs deeper into the monitored hosts and tends to correlate TCP connections with the host processes that generate them,allowing an end-to-end visualization of communications between distributed processes.As dis-played in Fig.2,the main display consists of two parallel axes with the left side representing clients and the right side representing servers and their respective processes.A line is drawn from a client to a server to depict a TCP connection.Portall is one of the first systems that visually correlatesSHIRAVIET AL.:A SURVEY OF VISUALIZATION SYSTEMS FOR NETWORK SECURITY 1315Fig.1.Basic visual representation of network and system activity in [4].network traffic to host processes,allowing spywares and ad-wares to be easily detected.Similar in nature to Portall is the Host Network(HoNe) [10]visualization system that also visualizes communicat-ing processes of a host with network traffic.The authorsargue that the reason behind not being able to correlate processes to network traffic is inherent in the design of the TCP/IP networking model of modern operating systems. The system visualizes client side hosts and their respective processes and port numbers on the left side of the display while external sources and their respective port numbers are displayed on the right side.Different to Portall,HoNe uses splines rather than simple straight lines to connect processes of a client to external servers.Perlman and Rheingans[11]extend existing approaches in host/server monitoring by adding and encoding service and temporal information inside the visualized node itself. Each host inside the network is illustrated using a circular glyph node much like a pie chart.Each glyph represents the existence and amount of activity for a particular service. The size of the glyph represents the total amount of activity of the node,measured by the number of packets.Wedge sizes identify what percentage of the total activity belongs to a particular service.A collection of different colors is used to distinguish between different services of a host.The system also incorporates time by using a stacked pie chart approach where the most outer ring represents the most recent time slice.Hosts are laid out in a simple node-link layout with straight lines connecting the communicating hosts together.The Radial Traffic Analyzer[12]visualizes the distribu-tion of network traffic of a particular host using a radial representation.The system is composed of four concentric circles,each mapped to an attribute of the underlying data. In its default setting,the innermost ring is assigned to source IP addresses,the second ring to destination IP addresses,and the third and fourth rings are mapped to source and destination port numbers,respectively.The notion of assigning port numbers to services and applica-tions,devised in this system,is no longer accurate in modern networks as many applications tend to piggyback or tunnel through common port numbers such as HTTP(80) and HTTPS(443).The work of Mansmann et al.[13]is one of the recent works in this class.In their proposed visual analytics tool, by incorporating a force directed graph layout,host behavior is monitored and irregular positional changes are flagged as suspicious.The authors believe that change in network traffic over time is well suited for detecting uncommon system behaviors.As illustrated in Fig.3,in the first step of the visualization,a set of dimension nodes,each representing a network service are laid out using a circular force directed layout.In the second step,the observation nodes representing a particular host are placed on the display and are connected to their corresponding dimen-sion nodes through virtual springs.Node size is calculated based on the sum of transferred bytes using a logarithmic scale.Since the state of a monitored host is displayed through multiple time stamps and due to the large number of visualized elements on the display,depicting multiple hosts without overlap is a challenge for this visual.Overall assessment of the host/server monitoring class. The ability of the visualization systems of this class in displaying a restrained number of hosts or servers within the monitored network is a perceptible issue.Most,if not all, of the systems of this class are constrained by their incorporated visualization techniques.As networks tend to grow in size and complexity at an exponential rate,there is an unprecedented need to create meaningful contexts.Even the smallest of university campus networks can consist of thousand of hosts,with which the aforementioned systems are less than capable of displaying in a clear and perceivable manner.For an analyst,simpler graphics are easier to understand and interpret than complex ones,since complex-ity can often influence the ability of the viewer in perceiving and decoding a visual.The overwhelming number of hosts in a monitored network,accompanied by the many hundreds of events generated for each,and the complexity of relations between events limit the cognitive process of situation awareness for analysts.For visualizations of this class to be effective and to clearly convey meaning,it is essential for them to devise an automated process that prioritizes situations and projects critical events.If a visualization system,due to its incorporated visualization technique,is limited in displaying a comprehensible range of hosts,envisioning a situation assessment process is inevitable.In this case,the process of identifying hosts with anomalous behavior and the mechanism of correlating1316IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,VOL.18,NO.8,AUGUST2012Fig.2.A screen shot of Portall[9]with monitored hosts and servers stacked on the left and rightsides.Fig.3.A sketch showing the coordinate calculation of a host position at aparticular point of time as depicted in[13].events is partly undertaken in a separate background component,and the processed results are projected to the visualization system.In this manner,the load on the visualization system is reduced considerably;allowing for a near real time analysis of events and a more responsive system.Packet traces,server logs,and network flows constitute primary data sources for this class of visualiza-tions.Node link graphs,glyphs,and scatter plots are also primary visualization techniques incorporated in this class.3.2Internal/External MonitoringVisualizations of this class are concerned with the interac-tion of internal hosts with respect to external IPs.Similar to the above-mentioned class,this class of visualization also incorporates a display of internal hosts,but in relation to communicating external IPs.Since the art of displaying internal hosts in a nonoccluding and meaningful manner is by itself a delicate act,adding the burden of displaying hundreds and thousands of external IPs is a nontrivial process for systems of this class.VISUAL [14]is one of the earliest systems of this class.It is a security visualization system with the goal of allowing an analyst to see communication patterns between an internal network in regard to external sources.As displayed in Fig.4,the internal network is represented by a grid with each cell depicting one of the internal hosts.External sources are represented as squares outside the internal grid with the square size denoting the level of activity.Simple straight lines are used to represent a connection between internal and external hosts.Multiple filtering mechanisms can be used to filter out internal or external hosts leading to a less cluttered display.Various detailed information regarding a host can also be displayed upon user request.VizFlowConnect [15]uses parallel axes to display net-work traffic between internal and external hosts.The goal of the system is to display relationships between communicat-ing machines of a network.The main display consists ofthree distinct parallel axes.The center axis represents internal hosts.The left axis corresponds to machines originating network traffic to the internal network while the right axis represents the destination machines of internal traffic.Each point on an axis represents an IP address and connections between points on parallel axes represent network communication.Time is incorporated in the system by using animation and various multiple views allow for further exploratory analysis.VizFlowConnect also shows individual host statistics,but further drill down depth is desired.Erbacher et al.[16]have come up with a second visualization system;this time aimed at internal/external host monitoring and geared toward filtering unwanted data,allowing focus on more critical events.The visual system incorporates a radial panel design consisting of multiple concentric disks each showing a constant period of time.Local IP addresses are placed around the radial disks while remote hosts are located on the top and bottom of the display.In order to avoid overlapping lines,an IP address located on the top half of the circle is connected to remote hosts located along the top of the display while hosts located on the bottom half of the circle are connected to remote hosts located on the bottom of the display.In the same manner,port numbers are also allocated on the left and right sides.The outer ring of the display shows the most recent period with interior rings displaying previous periods.This feature allows an analyst to see trends and patterns within the communicating hosts.Hosts are identified by dots on the circular rings resulting in difficult user interaction.In a visual network traffic analysis system,TNV [17],Goodall et al.believe that analysts often lose sight of the big picture while examining low-level details of attacks.In order to prevent this loss of context,they propose TNV with the goal of providing a focused view on packet level data in the high-level network traffic context.As illustrated in Fig.5,the main visual component of TNV is a matrix displayingSHIRAVI ETAL.:A SURVEY OF VISUALIZATION SYSTEMS FOR NETWORK SECURITY 1317Fig.4.VISUAL [14]displaying 80hours of network data on a network of 1,020hosts.Fig. 5.TNV [17]showing 50,000network packets in a 90minute time span.network activity of hosts over time,with connections between hosts overlaid on the matrix.TNV is designed based on a focus and context paradigm where the center of the display,the focal area,shows communicating hosts within wider columns.In order to preserve continuity throughout the display,the context area,located to the left and right sides of the display,has gradually decreasing width.Each host inside the matrix is colored according to its level of activity and multiple linked views are used to illustrate port activity and details of raw packets.TNV is one of the few security visualization systems that has been fully implemented and is freely available for download.Overall assessment of the internal/external monitoring class.Similar to the recommendations mentioned for the host/server monitoring class,the visualizations of this class can greatly benefit from a situation assessment component.This component can be defined in two different styles.One,as a process that automatically identifies and evaluates the impact of underlying events and relates them to assets of the monitored network or two,as an exploratory system that provides the facility for an analyst to validate various hypotheses.In the first style,due to the processing of events in a background component,the visual component can focus better toward richer and more responsive user interfaces.A necessity that is lacking and often overlooked in security visualization systems.In the second style,it is the analyst’s job to pose queries,correlate disparate events,and derive insightful meanings from the visualization.These activities impose the need for visual exploration and filtering mechanisms to be implemented.Dynamic queries,details on demand techniques,and linking and brushing interaction techniques are essential concepts that need to be addressed and considered in this class of visualizations.Color maps,radial panels,scatter plots,and parallel coordinates are common visual techniques used in this class.Packet traces and network flows are also used as the main data sources for visualizations of this class.3.3Port ActivityDesigners of this class of visualization argue that various malicious programs like viruses,Trojans,and worms manifest themselves through unusual and irregular port activity.Visualizations of this class can aid in the detection of malicious software running inside a network.Scaling techniques must be incorporated in the design of visualiza-tions of this class,due to the amount of traffic as well as the large range of possible port numbers and IP addresses.One of the earlier visualization systems designed speci-fically for this class is the work of Abdullah et al.[18].In their developed system,a port-based overview of network activity is presented through stacked histograms of aggre-gated port activity.The authors believe that port activity can be used to detect zero-day exploits that are not detectable by conventional methods.As displayed in Fig.6,port numbers are aggregated into multiple groups based on the services provided in the network.Well-known ports (<1;024)are assigned to major services on a system making them more vulnerable to attacks.For this reason,they are placed into bins of 100’s,registered ports (<50;000)are placed into bins of 10,000’s,and the remaining private/dynamic ports (50,000-65,535)are placed into a single bin.Color andscaling methods are also used effectively to distinguish between the aggregated port groups.In their developed system,the user has the ability to drill down in order to view finer details of the visualization.Displaying data over time also helps to highlight any patterns or trends appearing in irregular activities.The visualization is intuitive,easy to work with,and meets its intended design goals.The Spinning Cube of Potential Doom [19]is an interesting example of security visualization.A system that visualizes real-time port and IP data in a three dimensional cube,displayed as a rotating scatter plot.Each axis of the 3D display represents a component of a TCP connection.Destination IP addresses are mapped to the X -axis,port numbers to the Y -axis,and source IP addresses to the Z -axis.TCP connections are displayed as individual dots with color used to distinguish a successful connection from an unsuccessful one.Time is displayed through the use of animation.While quite useful to see coarse trends in large-scale networks,it lacks drill down mechanisms,multiple views,and interactive capabilities.The system is good for solo attacks and can only be used for port scan detection.PortVis [20]employs a colored-based grid visualization to map network activity to cells of a grid.As depicted in Fig.7,the main display contains a 256Â256grid where each point represents one of the possible 65,536port numbers.The location of a port on the gird is determined by breaking the port number into a 2-byte (X,Y)location.X being the high byte of the port number and Y being the low byte.Changes and variations of each point,with respect to time,is depicted using color.Black portrays no variation or change,blue depicts a small level of variance,red refers to a larger level of variance,while white denotes the most variant.The grid can be magnified to provide further detailed information about specific ports.A drawback of the system is seen when a port with suspicious activity is located among a collection of ports with a high,legitimate,level of activity.In this case,the ability to identify and focus on that region is not an easy Bytes Viewer [21]allows a detailed inspection of the behavior of an individual host over time.It facilitates in identifying behavioral changes that manifest themselves as unusual port usage or traffic volume regarding a single Bytes offers multiple views in both two and three dimensions,making it possible for an analyst to view the1318IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,VOL.18,NO.8,AUGUST2012Fig.6.Botnet traffic capture displayed using a cube root scale histogram in [18].。

信息网络安全-7-第7章 数字签名(研究生)

信息网络安全-7-第7章 数字签名(研究生)
对整体消息进行签名;
对压缩的消息进行签名。
按照消息/签名的对应关系划分
确定性( deterministic )数字签名:消息与签名一一对应, 对同一消息的签名永不变化,如RSA和Rabin算法;
随机化(randomized)或概率式数字签名:对同一消息的签 名是变化的。因此,此类签名取决于算法中的随机参数的取 值,如ElGamal算法。
DSS签名算法的实现速度
DSA
密钥生成 预计算 14s 14s
RSA
Off card N/A
证明: v=[(gu1yu2) mod p] mod q =[g H(m)wyrw mod p] mod q =[g H(m)wgxrw mod p] mod q =[g[H(m)+xr]w mod p ] mod q 而: [H(m)+xr]w = [H(m)+xr]s-1=k mod q 所以:v=gk mod q= r
计算:w’=gsy-r mod p
计算:H(w’||m)
验证H(w’||m)=r ?即 Very{(r, s), m}=真
Schnorr签名体制的安全性
Schnorr与ElGamal的区别
在 ElGamal体制中, g为Zp的本原元素;在 Schnorr体制中, g为Zp*中的子集Zq*的本原元,它不是Zp* 的本原元。 Schnorr的签名长度要比ElGamal短,由|q|及|H(m)|决定。 w=gk mod p可以预先计算,签名只需1次乘法和1次加法, 所以签名速度非常快,适用于智能卡应用。
对于课件中出现的缺点和错误,欢迎读者提 出宝贵意见,以便及时修订。
课件制作人:刘建伟 2016年10月25日
第7章 数字签名

基于定性微分博弈的通信网络攻击预警方法

基于定性微分博弈的通信网络攻击预警方法

DCWTechnology Study技术研究23数字通信世界2023.06通信网络在广义上指的是在各个孤立的通信设备之间建立物理连接,形成信息交换链路,通过链路,实现网络资源实时共享与高效通信的目标[1]。

通常情况下,通信网络由传输、交换与终端设备共同组成,能够为各个行业领域提供通信服务[2-3]。

在通信网络运行过程中,其环境条件较为复杂[4]。

由于受到网络干扰因素的影响,整体网络防御能力较低,无法高效地应对各类复杂多变的网络攻击,从而无法保证通信网络的安全性[5]。

因此,开发高效的通信网络攻击预警方法至关重要。

为了提高预警方法在实际应用中的性能优势,本文特引入定性微分博弈原理,提出一种全新的通信网络攻击预警方法。

通过定性微分博弈模型的连续决策作用,划分通信网络安全状态空间,有针对性地预测各个状态空间的攻击与威胁状态演化情况,为网络攻击预警提供精确的数据支持,提高预警结果的准确性。

1 通信网络攻击预警方法1.1 建立通信网络安全状态演化分析模型本文建立的通信网络安全状态演化分析模型中包含4种不同的节点状态,分别为正常状态、攻击感染状态、攻击修复状态以及攻击失效状态[6-7]。

各个网络节点之间通过状态转移,反映网络安全状态。

模型中通信网络节点状态转移如图1所示。

基于定性微分博弈的通信网络攻击预警方法程书红(闽南理工学院,福建 石狮 362700)摘要:通信网络攻击预警方法覆盖面不足,无法预警位于通信网络隐藏区内的攻击情况,导致通信网络攻击预警结果准确率较低。

文章提出基于定性微分博弈的通信网络攻击预警方法。

首先,建立通信网络安全状态演化分析模型,获取网络运行的实时工况以及存在攻击威胁的路径。

其次,基于定性微分博弈原理,划分通信网络攻击多维状态空间,全面设计网络攻击预警算法。

最后,在此基础上,建立网络攻击预警指标体系,设计预警等级。

实验结果表明,应用新的预警方法后,预警精度优势显著,准确率达到95%以上。

社会化问答平台的答案质量评估_以_知乎_百度知道_为例_贾佳

社会化问答平台的答案质量评估_以_知乎_百度知道_为例_贾佳
以 信 息 为 中 心 的 研 究 ,主 要 包 括 围 绕 答 案 展开的答案质量评估和用 户 满 意 度 研 究、以 及 问题推荐和答案检索等,其 中 答 案 质 量 评 估 和 用户 满 意 度 是 目 前 国 外 研 究 的 热 点。Agich- tein等将文本特征、用 户 关 系 以 及 使 用 统 计 特 征,输入到一个分类器中产 出 对 答 案 质 量 评 价 的结果 。 [3] 而 Shah发现人 工 标 注 者 之 间 对 相 同数据集的评分有很高的相关性。他们采用 基于人工打 分 的 多 维 度 模 型,从 原 创 性、简 洁 性、相关性等 13 个 维 度 来 预 测 雅 虎 的 最 佳 答 案[4]。Kim 和 Oh根据提问 者 的 评 论 总 结 了 判 断最佳答案的评价标准,并 提 炼 出 一 个 分 析 框 架,其中包括内容价值、认 知 价 值、社 会 情 感 价 值、信 息 源 价 值、外 在 价 值、效 用,并 分 析 了 各 个标准的 影 响 因 素[5]。Jeon 等 根 据 社 区 问 答
在用 户 满 意 度 研 究 方 面,Agichtein 等 以 Yahoo!Answers数据 为 样 本,使 用 分 类 算 法 预 测用 户 对 答 案 的 满 意 度,并 比 较 了 决 策 树、 SVM、叶 贝 斯 等 分 类 器 在 预 测 用 户 满 意 度 中 的 不 同 [11]。Shah以 雅 虎 知 识 堂 为 对 象 ,研 究 发 现 超 过 30% 的 问 题 在 5 分 钟 之 内 可 以 收 到 回 答 , 92% 的 问 题 在 一 小 时 之 内 可 以 收 到 回 答 。 但 最 佳问题的回复时间往往会更久 。 [12]
Jia Jia Song Enmei Su Huan

Globally networked risks and how to respond

Globally networked risks and how to respond
Many disasters in anthropogenic systems should not be seen as ‘bad luck’, but as the results of inappropriate interactions and institutional settings. Even worse, they are often the consequences of a wrong understanding due to the counter-intuitive nature of the underlying system behaviour. Hence, conventional thinking can cause fateful decisions and the repetition of previous mistakes. This calls for a paradigm shift in thinking: systemic instabilities can be understood by a change in perspective from a component-oriented to an interaction- and network-oriented view. This also implies a fundamental change in the design and management of complex dynamical systems.
BOX 1
Risk, systemic risk and hyper-risk
According to the standard ISO 31000 (2009; /iso/ catalogue_detail?csnumber543170), risk is defined as ‘‘effect of uncertainty on objectives’’. It is often quantified as the probability of occurrence of an (adverse) event, times its (negative) impact (damage), but it should be kept in mind that risks might also create positive impacts, such as opportunities for some stakeholders.

A Survey on the SIC Performance for Single-Antenna and Multiple-Antenna OFDM Systems

A Survey on the SIC Performance for Single-Antenna and Multiple-Antenna OFDM Systems

A Survey on the Successive Interference Cancellation Performance for Single-Antenna and Multiple-Antenna OFDM SystemsNikolaos I.Miridakis and Dimitrios D.Vergados,Senior Member,IEEEAbstract—Interference plays a crucial role for performance degradation in communication networks nowadays.An ap-pealing approach to interference avoidance is the Interference Cancellation(IC)methodology.Particularly,the Successive IC (SIC)method represents the most effective IC-based reception technique in terms of Bit-Error-Rate(BER)performance and, thus,yielding to the overall system robustness.Moreover,SIC in conjunction with Orthogonal Frequency Division Multiplexing (OFDM),in the context of SIC-OFDM,is shown to approach the Shannon capacity when single-antenna infrastructures are applied while this capacity limit can be further extended with the aid of multiple antennas.Recently,SIC-based reception has studied for Orthogonal Frequency and Code Division Multiplex-ing or(spread-OFDM systems),namely OFCDM.Such systems provide extremely high error resilience and robustness,especially in multi-user environments.In this paper,we present a comprehensive survey on the perfor-mance of SIC for single-and multiple-antenna OFDM and spread OFDM(OFCDM)systems.Thereby,we focus on all the possible OFDM formats that have been developed so far.We study the performance of SIC by examining closely two major aspects, namely the BER performance and the computational complexity of the reception process,thus striving for the provision and optimization of SIC.Our main objective is to point out the state-of-the-art on research activity for SIC-OF(C)DM systems, applied on a variety of well-known network implementations, such as cellular,ad hoc and infrastructure-based platforms. Furthermore,we introduce a Performance-Complexity Tradeoff (PCT)in order to indicate the contribution of the approaches studied in this paper.Finally,we provide analytical performance comparison tables regarding to the surveyed techniques with respect to the PCT level.Index Terms—Orthogonal Frequency Division Multiplexing (OFDM),Successive Interference Cancellation(SIC),Multiple-Input Multiple-Output(MIMO),Iterative Reception,Successive Decoding.I.I NTRODUCTIONI N MODERN wireless communication networks,Orthog-onal Frequency Division Multiplexing(OFDM)has been proposed as one of the key technologies for modulation and signal propagation.Recently,most of the research concern Manuscript received11July2011;revised15December2011.N.I.Miridakis is with the Department of Informatics,University of Piraeus,80Karaoli and Dimitriou St.GR-18534,Piraeus,Greece and the Department of Computer Engineering,Technological Education Institute of Piraeus,250Thivon and P.Ralli St.GR-12244,Aegaleo,Greece(e-mail: nikozm@unipi.gr).D. D.Vergados is with the Department of Informatics,University of Piraeus,80Karaoli and Dimitriou St.GR-18534,Piraeus,Greece(e-mail: vergados@unipi.gr).Correpsonding author;Tel:+302104142479,Fax:+30 2104142119.Digital Object Identifier10.1109/SURV.2012.030512.00103has focused on its multi-user access method,Orthogonal Frequency Division Multiple-Access(OFDMA),as it provides acceptable performance on numerous applications[1].IEEE 802.20Mobile Broadband Wireless Access(MBW A)[2],[3], Worldwide interoperability for Microwave Access(WiMAX) [4],3GPP Long-Term Evolution(LTE)[5]and next-generation Wireless Wide Area Network(WW AN)[6]are some of the most representative OFDM-enabled network standards.The main reasons for the OFDM popularity are(a)the achievement of a high data rate performance due to the provision of spectral efficiency in comparison to prior modulation schemes,such as Code Division Multiple Access(CDMA)and(b)the efficient adaptation to the frequency selectivity of the channel,due to the orthogonality principle.Nevertheless,the growing need for Quality-of-Service (QoS)enhancements along with the dense multi-user tenet in recent OFDM(A)infrastructures contradict mainly to capacity limitations and thereby encloses potential user demands or application perspectives.Interference plays a crucial role in the above mentioned limitations,while induces a typical upper bound to the system performance.More than any other single effect,interference can lead to quite catastrophic results at a typical OFDM receiver[7],[8].Since the outage probability is predominantly caused by the interference appearance,an appealing alternative to interference avoidance is Interference Cancellation(IC).IC is divided into two main categories,namely pre-IC and post-IC.Pre-IC represents a family of techniques established at the transmitter side,which are focused on the cancellation or the suppression of interference on a priori basis.An essential precondition for pre-IC to cancel the interference effect is the establishment of the appropriate precoding technique. Some representative precoding examples applied on OFDM systems are the Selected Mapping(SLM),the Partial Transmit Sequences(PTS)and the Dirty Paper Coding(DPC)[9],[10]. Especially DPC represents quite an effective pre-IC precoding method[11],which is implemented at the transmitter by taking into consideration the interference amount(experienced at the receiver)before the signal transmission.Thereupon,a suppressed from the ongoing interference signal is transmitted accordingly.In order for the transmitter to efficiently pre-estimate the level of the ongoing interference,reliable Channel State Information(CSI)via signaling is more than a prerequi-site.Hence,feedback and/or feed-forward signaling overhead is necessary in order to preserve critical up-to-date interference information at the transmitter side,constantly.As CSI is more1553-877X/13/$31.00c 2013IEEEaccurate and reliable,the pre-IC techniques become more error resilient.However,perfect CSI is very difficult to accomplish in real conditions and,therefore,a potential error at the pre-IC process may occur with high probability.The imperfect CSI gets more emphatic as user mobility is introduced or the number of potential system users is increased,i.e.within a multi-user environment.Furthermore,keeping a detailed interference profiling for all the transmitting users induces the error probability on the precoding process while enormously increases the signaling overhead,yielding to an overall system inefficiency.On the other hand,post-IC represents a family of techniques established at the receiver side,which are focused on the inter-ference cancellation on a posteriori basis.In general,it should be expounded as the class of techniques that decode desired information and then use this information along with channel estimates to cancel a fraction of the received interference from the overall received signal[7],[12],[13].Therefore,signal processing is required after signal detection in order to classify the system as post-IC.Unlike pre-IC,in a post-IC framework the signaling overhead between the transmitter and the receiver side is not necessary.The entire processing takes place at the receiver side and the presence of CSI is only optional (e.g.blind IC-based reception).Due to these reasons,post-IC represents quite an adaptable IC methodology,appropriate for numerous OFDM implementations.Post-IC methodology can generally be broken into two major categories,namely parallel and successive,although re-cent developments in an iterative post-IC regime have blurred the distinction.Parallel Interference Cancellation(PIC)fun-damentally operates by detecting all the users simultaneously. This quite coarse estimation can be used to cancel some interference whereas the parallel detection can be repeated in a number of stages to improve both the system reliability and robustness with respect to the error resilience and the Bit-Error-Rate(BER)probability[14].However,this approach causes a rather inefficient reception performance as it is sus-ceptible to errors and the probability for inaccurate detection is quite high.Furthermore,PIC requires precious hardware gear in order to operate in parallel,which makes it unprofitable for numerous practical implementations[7],[15].A particularly interesting type of IC reception which over-comes the above mentioned restrictions is Successive Inter-ference Cancellation(SIC),first suggested in[16].The key idea of SIC is that users are decoded successively.After one user is decoded,its signal is stripped away from the aggregate received signal before the next user is decoded.When SIC is applied,one of the users,say user1,is decoded treating user2as interference,but user2is decoded with the benefit of the signal of user1already removed.In contrast,using conventional reception,every user is decoded treating the other interfering users as noise.It is then straightforward that the later scheme is suboptimal in comparison to SIC in terms of reliability,system robustness and,hence,capacity with respect to the aggregated throughput at the receiver[17].In order to further enhance the performance and the accuracy of SIC,an optimal decision ordering can be potentially applied on the signal detection process which will correspondingly result to the decoding of the strongest userfirst,i.e.the user which experiences the best Signal-to-Interference-plus-Noise-Ratio (SINR)and/or Signal-to-Noise-Ratio(SNR).In general,users should be decoded in the order of their received powers(even though this is not always the most preferable choice from an information theoretic perspective[18]).From the above mentioned discussion,we state that SIC aims to efficiently turn the interference problem into an interference advantage in order to achieve capacity and performance gain,as compared to the conventional non-SIC reception.In this paper,an illustrative analysis of SIC-enabled recep-tion is thoroughly provided for the prominent wireless OFDM communication networks,namely the SIC-OFDM systems,as they represent a major topic for research and development currently.The study concerns the conventional single-antenna and the propitious multiple-antenna OFDM transceiver modes for SIC reception.In addition,both the unspread and the more robust Orthogonal Frequency and Code Division Multiplexing (OFCDM or spread OFDM)design versions are considered in order to provide a rather exhaustive analysis of thefield, resulting to a compact study of the SIC performance at all the available OFDM formats so far.Most of the research community,which has focused on the SIC-OFDM amelioration,tends to optimize two factors.These are(a)the BER performance and(b)the overall computational complexity reduction,which both represent cornerstone re-quirements for the SIC efficiency.Unfortunately,the enhance-ment of the former factor contradicts the later and vice versa. In fact,as the SIC becomes more robust and accurate in terms of BER performance,the overall complexity of the iterative detection and decoding process increases dramatically.We, therefore,introduce the term Performance-Complexity Trade-off,namely PCT,to point out the above mentioned fragility. All the surveyed contributions into this paper have classified with respect to PCT.In this paper,we refer to the system performance,accuracy,reliability and robustness with respect to the BER performance and error resilience.Furthermore,we refer to the system capacity with respect to the overall system throughput and/or the maximization of the number of system users.II.P RELIMINARIESIn OFDM systems,the interference effect is generated mainly due to the channel radio conditions and/or the user transmissions occurring on adjacent subcarriers,regarding either single or multiple access environments.A sophisticated design of the OFDM transceiver plays,therefore,a crucial role to the interference suppression and,thus,to the com-munication establishment successfully, e.g.the appropriate adoption of encoding,interleaving or spreading methods. In this section we briefly describe fundamental OF(C)DM concepts of the transmitter and the receiver side(from the interference cancellation viewpoint)as well as basic channel influences responsible for signal degradation scenarios,since they represent significant impacts on the SIC performance.A.NotationThe notations used throughout this paper are the following ones.Vectors and matrices are represented by lowercase boldFig.1.Block diagram of a typical OFDM transmitter.The Interleaver and Spreader components indicated by dashed lines are present only for BICM and OFCDM transmissions,respectively.Likewise,the Spatial Encoder and the Multiple-Antenna RF Transmitter components are utilized only in multiple-antenna infrastructures,which are thoroughly discussed in Section V.typeface and uppercase bold typeface letters,respectively.A a,b denotes the(a,b)th element of A.E{.}stands for the statistical mean.A complex Gaussian random variable with mean m and varianceσ2is denoted by G(m,σ2).Superscripts(.)T and (.)H denote the transposition and the conjugate(or Hermitian) transposition,respectively.B.OFDM TransmitterFigure1depicts the structure of the transmitter block for a typical OFDM system.First,the information input bits are appropriately encoded through a channel encoder.Afterwards, they are bit-by-bit interleaved and then converted to QAM symbols according to a Gray-coded constellation Bit-Mapper (BMAP).This scheme is also known as Bit-Interleaved Coded Modulation(BICM)[19]and provides further robustness compared to the conventional transmission schemes in terms of BER performance,due to the successful combination of coding and interleaving before the bit mapping procedure. In fact,BICM optimizes the system accuracy and robustness since severe channel selectivity-dominant to current and fu-ture network designs-determines the propagation attenuation behavior of the OFDM signals.Especially when both time and frequency(i.e.double)selectivity is present,both coding and interleaving represent an essential parameter that allows for efficiency enhancement in OFDM systems.Nevertheless,it represents only an optional selection which aims to optimize the OFDM transceiver block in terms of BER performance and system robustness.When OFCDM is used instead of the unspread conventional transmission,the subsequent procedure takes place before the OFDM modulation.The encoded symbols are being spread symbol-by-symbol by a particular code C SF.The objective of the OFCDM transmission-reception mode is to enhance the system accuracy and to efficiently exploit multi-user diversity, with respect to the conventional(unspread)OFDM approach, especially in dense multiple access environments.In order to improve the quality of the signal and,therefore,to reduce the interference level at the receiver,the mutual information must be kept at a minimum level.Hence,the C SF codes have cho-sen to be orthogonal(e.g.Walsh-Hadamard codes)or quasi-orthogonal(e.g.PN-sequences),while a unique signature codeword is assigned to each user.In general,orthogonality is one of the most important principles in OFCDM,borrowed by the conventional CDMA,which isolates user signals according to their signature codewords at the receiver and preserving all the extrinsic information at the appropriate noise level.Then, the output signal is serial-to-parallel converted for OFDM modulation according to the N available subcarriers,as shown infigure1.For notational simplicity,at the OFCDM transmit-ter case we assume that the number of OFDM subcarriers is equal to the spreading code length(i.e.N=C SF), where each information sequence transmitted from a specific user comprises an individual OFDM symbol.Otherwise,(if N>C SF),each OFDM symbol may consist of several parts of different users’information bits.OFDM modulation is accomplished using the N-point In-verse Fast Fourier Transform(IFFT).In order to avoid the Inter-Symbol Interference(ISI)and Inter-Carrier Interference (ICI)effects,dominant in OFDM systems,a guard interval, e.g.a Cyclic Prefix(CP),is appropriately added to each IFFT sequence before the OFDM block transmission.Then,all the sequences are parallel-to-serial converted to form an OFDM block(or stream).Finally,in case of single-antenna infrastruc-tures,the output OFDM block is transmitted to the wireless channel via an RF transmitter.In case of multiple-antenna infrastructures,the output OFDM block passes through the appropriate spatial encoder and then to a multiple-antenna RF transmitter(both components are thoroughly discussed in section V),as shown infigure1.C.Basic OFDM Channel ConditionsThe frequency selectivity of the wireless channel is a crucial parameter for QoS degradation in modern OFDM systems.Especially when such systems support high mobility, double selectivity is present.In addition,the provision of the performance in these schemes is further challenged in urban terrestrials where the existence of Rayleigh fading,due to the rich scattering environments,and the lack of Line-Of-Sight (LOS)signal transmissions,determines the amount of signal decay at the receiver.In particular,the most crucial perfor-mance degradation influences in the OFDM transmissions are listed below:•the propagation attenuation(mostly due to the distance between the transmitter and the receiver)•the ISI effect(due to the multipath propagation)•the ICI effect (mainly due to the loss of the tight frequency synchronization between the transmitter and receiver,which results in the loss of subcarrier orthogo-nality)•the existence of the unavoidable Additive White Gaussian Noise (AWGN)Figure 2shows the major impacts experienced by a typical OFDM receiver,from the interference viewpoint,whereas the included numerous interference in fluences are discussed in detail subsequently,as it is the main subject of this paper.Assuming that ISI and multipath fading can be eliminated by choosing a suitable size for the CP pre fix,a sophisticated decision for the length of this size is a rather determinant criterion for QoS provision in OFDM systems.In general,ICI represents the main performance degradation in fluence in a typical OFDM receiver.Two essential reasons for its realiza-tion are the self-interference and the so-called Multiple Access Interference (MAI).The former is due to the power leakage to/from adjacent subcarriers of the same user and the later is due to the power leakage to/from adjacent subcarriers caused by other users’transmissions,when multiuser environments are considered.Despite the interference suppression by the CP,the ICI effect and the AWGN aggregation to the received information still remain the main challenges for an OFDM receiver to be dismantled.Moreover,from the frequency synchronization perspective,a typical OFDM channel is assumed to be synchronous for the forward link transmissions while is usually assumed to be asynchronous or quasi-synchronous for the reverse link transmissions (i.e.all uplink transmissions are assumed to be synchronous since they are bounded within the CP margin).The later distinction plays an important role on infrastructure-based and capacity-limited OFDM systems,such as the cel-lular networks,in terms of ef ficient reception,as it is further discussed in the next section.D.OFDM ReceiverFor each OFDM block the input-output relationship can bedescribed,after the CP extraction,as [20]-[22]y =FG t F H x +Fw t =FG t x t +Fw t =Gx +w ,(1)where y =[y 1,y 2,...,y N ]T ,x =[x 1,x 2,...,x N ]T and w =[w 1,w 2,...,w N ]T are the N ×1received signal vector,the transmitted signal vector and the AWGN received vector in the frequency domain,respectively.Likewise,x t and w t represent the N ×1received signal vector and AWGN received vector in the time-domain,respectively.G t is the N ×N channel matrix in the time-domain.F =(1/√N )[exp (−j 2π(a −1)(b −1)/N )]a,b =1,2,...,N and G denote the N ×N Fast Fourier Transform (FFT)matrix and N ×N channel matrix in the frequency-domain,respectively.In ideal channel conditions,G is typically a diagonal matrix.However,severe channel selectivity,present in numerous modern network applications,makes the ICI effect feasible mostly on adjacent OFDM subcarriers.Since the off-diagonal channel matrix elements cause the occurrence of ICI,G is typically a non-diagonal matrix and that is the main reason for performance degradation in general.Hence,taking intoaccount the ICI contribution and focusing on the decoding of the i -th user,the received signal can further decomposed to the following expression as y = Gx+N i−1 j =0N −1k =0I ij,kself −interference+U −1 p =0,p =iN p−1 j =0N −1k =0I p j,kMAI+w ,(2)where Gdenotes the interference-free channel matrix,U denotes the total number of users,N i denotes the numberof subcarriers assigned to the i -th user,and I ij,kdenotes the ICI contribution of the j -th subcarrier of the i -th user on the k -th subcarrier,under multiuser network scenarios.In case of single-user scenarios,all the OFDM subcarriers are assigned to the i -th user (i.e.N i =N ,U =1)and MAI is ually,the above mentioned interference contributions are modeled as Gaussian processes since they are considered as random events.Moreover,the modeling of such interference events is crucial for the overall reception performance and thus represents one of the main aspects considered into this paper,which is further discussed and analyzed in the next sections.Upon the received mutual information and before the signal detection and decoding,OFDM demodulation is accomplished via the N -point F matrix,as figure 3shows.Thereupon,only in case of OFCDM,an appropriate despreading is necessary in order to recompose the initial information,transmitted by each user.Then,SIC is responsible for the appropriate detection and decoding of the output data by decomposing the overall signal in the useful information (each user’s data)and the extrinsic information.SIC can be directly applied on both Single-User Detectors (SUD)and Multi-User Detectors (MUD)for OFDM applications.Recently,MUD has dominated over the prior SUD reception type,by simultaneously receiving multiple in-terfering users,mostly due to the achievement of performance and capacity gain [23].The received signal is then regenerated taking into account both CSI and the extrinsic information while the most dominant interferer is being canceled according to speci fic detection ordering criteria and sent back to the detector for sampling evaluation and so on,until all interfering users have been canceled.The number of iterations is not determined only by the number of the interfering users but most importantly by the considered number of SIC stages,which are mainly predetermined by the system engineer or the network manufacturer.The equalization strategy,i.e.the front-end of a receiver,may have any type of structure.However,the selection of the appropriate equalizer for OFDM systems plays a crucial role to SIC performance as it is further discussed in the next sections.Typically,the most common equalization techniques used for detection and decoding at OFDM receivers are the optimal Maximum Likelihood (ML)criterion and the suboptimal linear Minimum Mean Squared Error (MMSE)and Zero Forcing (ZF)strategies.ML achieves the best performance since it is the most error resilient equalizer at the expense of the highest computational complexity.It represents quite an exhaustive detection method by searching between the overall received signal and the most appropriate symbol estimation for allFig.2.Representation of the major interference influences in OFDM systems.the possible combinations on a given constellation alphabet. ZF,on the other hand,has the slightest complexity but it is susceptible to errors.It is performed by estimating the Moore-Penrose pseudoinverse of a given channel matrix.MMSE balances appropriately the benefits and the drawbacks of the two above mentioned techniques,regarding the PCT level. It calculates an appropriate matrix inversion by taking into consideration both the channel status and the noise variance. These methods are further discussed and analyzed in the following sections by illustrating several case studies. Particularly,there are two different types of SIC strate-gies,namely the hard-and soft-SIC,as shown infigure3. These terms refer to the decision policy or strategy which is used in the equalization and the detection ing a hard decision policy,the detection and,thus,the decoding process is implemented by conventional reception strategies, e.g.hard Viterbi decoding.A soft decision policy is a more sophisticated reception strategy which aims to optimize the BER performance.In this case,the received signal is demod-ulated by an iterative Soft-Input-Soft-Output(SISO)inverse bit mapper.Particularly,in BICM schemes,the received QAM symbols arefirst demodulated by a soft-output demapper and de-interleaved,and then passed to a standard binary soft-input Viterbi decoder[24].The main difference between a soft and a hard Viterbi decoder is that the soft values have the same sign as the later decoder whereas their absolute values indicate the reliability of the decision[25].A Maximum a Posteriori(MAP)estimator is usually adopted based on a Log-Likelihood Ratio(LLR)value approximation,in order to accomplish soft detection.Even though the hard decision is less complex and less time consuming,it provides significant performance degradation compared to the soft decision policy. It is,therefore,clear that the appropriate selection for SIC, i.e.to be either hard-or soft-enabled,debates for the PCT optimality and depends mostly on the application require-ments.As the appropriate decision policy for equalization or data decoding does not represent the primary subject of this paper,no further analysis is given for hard or soft equalization methodologies.A detailed analysis on the performance of hard and soft decision approximation for M-ary constellations,used constantly in OFDM systems,may be found in[25]-[27]. Finally,the reconstructed hard or soft output passes through a channel re-estimator,where the received signal is regen-erated including all the extrinsic information but without the interference contribution of the last decoded and already canceled symbol.Thereupon,at the next SIC stages the remaining users signals go through a more advantageous decoding process in terms of accuracy and BER performance since the interference level at the receiver is somehow relaxed. Afterwards,the same procedure follows on for the next interfering user and so on,until the extrinsic information from all the available interferers has been canceled out. Overall,we highlight the most important steps of the SIC-based reception more specifically as1)Upon a signal reception,calculate the equalizationN×N matrix J,where J could be either an ML,a ZF or an MMSE detector(J is represented by various forms depending on the detection policy,as analytically described in the next section)2)Apply an optional detection ordering B l on J,l∈(0,N]3)Calculate J y l,where . l denotes the l-th row of amatrix.The resulting term denotes an estimation of the detected symbol x,which can subsequently be decoded according to the modulation type which is used.4)Subtract the decoded information from the remainingsignal as y new=y previous−x[G]l,where[.]l denotes the l-th column of a matrix5)Relax the channel matrix in terms of interference contri-bution as G new=[G]l ,where l is the deflated version of a matrix whose1,2,...,l-th columns have been zeroed 6)Repeat steps1to5until all the OFDM symbols havebeen decodedFig.3.Block diagram of a typical OFDM SIC-based receiver:(a)Soft-SIC,(b)Hard-SIC.The Interleaver/de-Interleaver and Despreader components indicated by dashed line are present only for BICM and OFCDM receptions respectively.Likewise,the Spatial Decoder and the Multiple-Antenna RF Receiver components are utilized only in multiple-antenna infrastructures,which are thoroughly discussed in Section V.SIC-enabled receivers provide extremely high QoS pro-visioning in terms of the system robustness and the BER performance,under the fundamental assumption of perfect signal decoding.However,this ideal condition is overopti-mistic for realistic network scenarios where the probability of potential errors at the decoding process is quite high.If a symbol is decoded incorrectly,all the subsequent symbols are affected irreparably and the error propagates to all the remaining SIC stages rapidly[18].Hence,error propagation is a crucial parameter for system performance degradation and determines the PCT effectiveness.The limitation of the error propagation represents a major research topic nowadays. In order to suppress the error occurrence probability at each SIC stage,either the simultaneous transmissions from different users or the SIC stages should be upper bounded appropriately. In addition,the decision on the appropriate ordering of the cancelling users plays a significant role for the limitation of the error propagation.The above mentioned solutions are analytically discussed in the next sections with respect to the PCT performance,under both single-and multiple-antenna OFDM infrastructures.Figure4gives a representative example of a typical SIC algorithm.III.S UCCESSIVE I NTERFERENCE C ANCELLATION ON S INGLE-A NTENNA OFDM S YSTEMS Typically,OFDM provides great spectrum efficiency by al-lowing adjacent subchannels1to spectrally overlap,yet remain 1The terms subchannel and tone will be used interchangeably in the paper, indicating an OFDM carrier.orthogonal in time[28].Moreover,the CP addition apart from preventing the ISI effect,it also converts the linear convolution of the data sequence and the impulse response of the channel to a circular convolution[29].Nevertheless,time variations of the channel within an OFDM frame could still lead to a loss of subcarrier orthogonality resulting mainly in ICI and, thus,to the system degradation.In general,the ICI effect is assumed a random event and,therefore,can be modeled as an additive Gaussian process leading to an irreducible errorfloor. Subsequently,we show that one of the most crucial factors responsible for the ICI generation is accomplished by the Carrier Frequency Offset(CFO)effect.Thus,wefirst analyze CFO and we provide SIC-based solutions,afterwards.A.Interference Enhancement due to the Carrier Frequency OffsetOFDM presents a high sensitivity to the frequency offsets among the subcarriers.CFO along with time variations of the channel are the most crucial effects for ICI realization. CFO is mainly generated either by local oscillator mismatches which cause synchronization errors between the transmitter(s) and the receiver(s)or by the Doppler shift introduced by the user mobility.CFO estimation can be subdivided in two phases,namely acquisition and tracking.When a user initially enters an OFDM system may experience a large instantaneous frequency offset.An appropriate acquisition algorithm is nec-essary to detect and correct this CFO initially.After the acqui-sition phase,the residual CFO is well bounded to a given range (e.g.within a±0.5subcarrier spacing)and the exact CFO。

当代大学生利用网络教学资源状况探析

当代大学生利用网络教学资源状况探析

第31卷第3期教学研究V ol.31No.32008年5月Research in TeachingMay 2008随着网络信息时代的到来,网络学习资源成为大学生学习、生活中的重要依托,网络教学信息资源建设也成为提高教育教学质量的重要途径。

投资不断增加的校园网建设与学生利用校园网的实际效果之间是否存在差异?校园网络教学信息资源建设过程中应注意哪些问题?为此,笔者对某地方综合大学的二年级余名大学生进行了涉及利用网络教学信息资源共计个问题的问卷调查。

本次调查共发放问卷份,回收有效问卷份。

名回答问卷的学生中有男生人,有女生人,男女生比例差异不显著;其中包括教育、人文、管理、政法等文科学生人,包括数学与计算机、电信等理工科学生人。

经对调查结果进行分析,以下三点应引起人们的共同思考。

校园网络资源已经成为当代大学生学习、生活的重要依托与互联网相连的校园网络在今天的大学校园中显得尤为重要,很难想象没有网络资源的大学将是一种怎样的场景。

从一定程度上讲,没有互联网就没有现代意义上的大学。

在本次问卷调查中,有的学生平均每周上网~个小时,有的学生平均每周上网~个小时,有的学生平均每周上网个小时以上。

其中,男生每周上网超过小时的占,而女生则为;男生每周上网~小时的有,而女生则为。

收稿日期作者简介胡东东(),女,河北保定人,馆员,主要研究方向为图书资料管理。

这说明,大学生利用网络资源是十分普遍的现象。

而且男女生在上网时间方面存在一定差异,男生上网时间明显多于女生(见表)。

表学生每周花费在网络上的时间统计表人数上网时间比例~小时~小时小时以上男生数比例女生数比例总人数总比例表学生在网上进行的主要活动统计表人数主要活动比例进入休闲学习男生数比例女生数比例总人数总比例关于利用校园网络主要从事的哪些活动,的学生回答主要是参加的讨论与网友进行互动,的学生主要是在网上看大片、玩游戏和听音乐,的学生主要是利用网络查询学习资料。

其中,的男生主要用于休闲,的男生主要用于查询学习资料;而女生则是主要用于休闲,用于查询学习资料。

[JMAA384(2)(2011)683-689]Sums of weighted composition operators on H(infinity)

[JMAA384(2)(2011)683-689]Sums of weighted composition operators on H(infinity)

1
j
N,
N j=1
u
j
C
ϕ
j
is
compact
on
H∞
if
and
only if
(
N j=1
u j Cϕ j )(
f
)

A(D)
for
every
f
∈ H∞. This is a generalization
of Theorem 2 given in [11].
We denote by B(H∞) the closed unit ball of H∞. For z, w ∈ D, the pseudo-hyperbolic distance between z and w is given
for
every
1
j,
N.
Note that if |ϕ j(zn)| → 1 as n → ∞ for some 1 j N, then it is easy to see that there exists a subsequence {zn } of {zn}n satisfying {zn } ∈ Z .
Sums of weighted composition operators on H∞
Kei Ji Izuchi a,1, Shûichi Ohno b,∗,2
a Department of Mathematics, Niigata University, Niigata 950-2181, Japan b Nippon Institute of Technology, Miyashiro, Minami-Saitama 345-8501, Japan

网络成瘾者HANOI塔和威斯康星卡片分类测验(M—WCST)的对照研究

网络成瘾者HANOI塔和威斯康星卡片分类测验(M—WCST)的对照研究
a dc ( 2 d it s n= 6) a d n No — d it ( 2 n a dcs n= 6) w r asse u ig ee se sd sn HANOI o r n d T we a M— WCS ts 。 Re u t T ee s T et s sl s: h r i a
【 e od 】 t c adci i re; xcte fntn K yw rsI e t d i o ds d rE eui u c o nr a tn o v i
网络 对 于 青 少 年 心 理 健 康 的 巨 大影 响 吸 引 了 医学 界乃至 全社会 广 泛 的关注 与研 究 。美 国心理 学
【 bt c】O jcie T xlr te eeui u ci iee csb ten i e tadcsad N n adc . to s It t A s at be t : oepoe h xct e f t n d rne e e n me d i n o —d is Meh d :n me r v v n o f w t t t e
A o to ld s u y o x c t e f n t n i h n e n t a dc s c n r l t d f e e u i u c i n t e it r e d it e v o
G u n ,t0 A0 Yf g e 1 e
( e tro na Heh , e Frt f l td Ho i , h n qn dclU i ri ) C ne Me t a h t i f i e s t C o g ig Me ia nv st f l h s Ai a pa l e y
40 1) 0 0 6
( 庆 医科 大学 附属 第 一 医院 心 理 卫 生 中心 , 庆 重 重

Datafusion on wireless sensor and actuator networks powered by the system--P.A.C.S.Neves--2011

Datafusion on wireless sensor and actuator networks powered by the  system--P.A.C.S.Neves--2011

Published in IET CommunicationsReceived on15th July2010Revised on24th November2010doi:10.1049/iet-com.2010.0644In Special Issue on Distributed Intelligence and Data Fusion for SensorSystemsISSN1751-8628 Data fusion on wireless sensor and actuator networks powered by the ZenSens systemP.A.C.S.Neves1,2J.J.P.C.Rodrigues1K.Lin31Instituto de Telecomunicac¸o˜es,University of Beira Interior,Covilha˜,Portugal2Polytechnic Institute of Castelo Branco,Castelo Branco,Portugal3Dalian University of Technology,Dalian,Liaoning,People’s Republic of ChinaE-mail:joeljr@Abstract:Wireless sensor and actuator networks(WSANs)provide sensor and actuator services through small smart nodes with very limited hardware and power rmation fusion must be considered a critical step in WSAN design,enabling energy saving,increasing data quality and deeper insight of the monitored environment.This study presents experimental results over real hardware on inner-node fusion in the context of the ZensSens system.ZenSens provides user-centric WSAN functionality,with IPv6support at the sensor node level,automatic node attachment and suitable software tools for multi-channel access.This study presents some material about information fusion,the ZenSens system and possible solutions for inner-node information fusion based on real temperature sensor data.The approach proves the feasibility of inner-node fusion on constrained hardware with IPv6support.1IntroductionWireless sensor and actuator networks(WSANs)are composed of small smart nodes with sensing and/or actuation capabilities,thus extending the applicability of wireless sensor networks(WSNs)to scenarios where actuation is mandatory[1].Sensors capture environment data,actuators make decisions based on available data and take actions,while one or more sink(s)help achieve the desired network’s goals[2].A WSN/WSAN can be designed with different goals, namely it is common that deployments are application specific,such as medicine,agriculture,environment, military,intrusion detection,and among many others[3].A WSAN smart sensor node is typically composed of one or more sensors or actuators,a processing core with programme memory(flash)and execution memory(RAM), a wireless transceiver and a battery.The battery is always a concern on every deployment,since many scenarios do not enable battery replacement or recharging.Since a battery-depleted node is unusable,it can jeopardise the network’s goals and operation.This pervasive WSN/WSAN challenge leads to many research papers on MAC[4],routing[5]and transport[6]layer protocols to minimise energy consumption on nodes.In this regard,information and data fusion can play a major role on mitigating energy consumption,by reducing the amount of data transferred between the network nodes.WSN nodes are prone to failures,even more when deployed in hostile environments.Sensor data raw measurements may suffer from technological limitations such as sensor accuracy,sensor node availability (destruction,energy depleted and others)and cost constraints.As a result,cooperation,redundancy and complementarily are three key features that WSN must present[7].Information and data fusion can contribute to achieve these goals,for example,by joining data from multiple sensor sources in different locations,composing a complete view of the scene from the partial reality of each node.Information and data fusion must be considered as a very important step in any WSAN-based solution,enabling network lifetime extension,while helping to achieve network’s application goals,let it be target tracking,smart homes,event tracking or others[8,9].This paper tackles the implementation of fusion over WSANs,in the context of the ZenSens system.The ZenSens system,presented in[10,11],provides a user-centric approach to WSN with multi-channel monitoring tools,IPv6-enabled WSAN capable of Internet connectivity and Plug-and-Play(PnP)functionality with automatic and transparent attachment of new nodes,independent of its sensor/actuator hardware assets.The approach was validated through a testbed with Crossbow TelosB motes running the Contiki operating system[12,13].ZenSens belongs to a new era of WSN/WSANs that employ IPv6at the sensor level,thus enabling Internet connectivity from the ground up,opposed to other approaches that employ a protocol mapping on the sink[14].In terms of distributed computing paradigms,ZenSen’s client/server computing paradigm is suited for small-scale networks[15],such as body sensor networks(BSN)[16].However,it is also possible to extend for hierarchical networks,where typically a two-hop communication is employed[9].IET Commun.,2011,Vol.5,Iss.12,pp.1661–16681661 doi:10.1049/iet-com.2010.0644&The Institution of Engineering and Technology2011The remainder of the paper is organised as follows.In Section2,information and data fusion background material and related work are presented,whereas Section3 elaborates on the ZenSens system.Section4discusses possible approaches for information and data fusion, whereas Section5dwells into implementation details of inner-node fusion on ZenSens.Finally,Section6concludes this paper,pointing guidelines for future works.2Background and related workThis section presents some background material on information and data fusion,namely concept explanation, classification and an overview on some related work.A different plethora of different names is sometimes used related to fusion,such as data fusion,information fusion, data aggregation and sensor fusion,among others.As a result,Nakamura et al.[9]provide some explanations on these terms,although considering that‘systems, architectures,applications,methods and theories about the fusion of data from multiple sources are not unified’.2.1Common languageData fusion and information fusion are usually accepted as overall terms,with the idea that fusion on WSN must result on‘better quality data’.Such‘better quality data’greatly depend on the application,and as a result the term quality is employed in its generic sense.Moreover,data fusion in WSN must address two important goals:accuracy improvement and energy saving.Some authors also consider that data from the same source at different time instants is different data.Other terms like‘multisensor fusion’and‘multisensor integration’are also employed by some authors to refer to data fusion in WSN.Multisensor integration is also used in robotics,computer vision and industrial automation areas.However,‘so far,uniform and accurate definition about information fusion of WSN is rare’[17].Finally,data aggregation is becoming popular in WSN research as a synonym for data fusion,where aggregation is faced as‘a way to summarise data’.Data aggregation in WSN begins with a collection of raw data from pervasive sources,obtaining less voluminous but more refined data, thus enhancing data quality while reducing its size. Information fusion classification,according to[9],can be divided into three classification types–based on relationship among the sources,based on levels of abstraction and based on input and output.When information fusion is based on the relationship between sources,it can be complementary,redundant and/or cooperative.When sensor data sources present information of different parts of a scene,information fusion can be complementary;joining information from the different sources to draw the big picture.If a given set of two or more sensors provides the same view,these pieces can be fused to obtain higher-quality data.Finally,two independent sources can be cooperative when the information they provide is fused to obtain new, application-suited information.Information fusion based on different levels of abstraction can be classified in three main levels–low,medium and high.Low-level fusion,also referred as signal level fusion, receives raw sensor data as input and produces pieces of data that are more accurate than individual inputs.This approach typically addresses measurement noise and sensor inaccuracy.Medium-level fusion,also referred to as feature/ attribute-level fusion,grabs attributes or features of an entity(e.g.shape,texture,position and so on),to obtain a feature map that can be applied to other tasks such as segmentation or detection of an object.High-level fusion, also known as symbol or decision level fusion,works on decisions or symbolic representations to obtain a more confident or even global decision.Finally,fusion classification based on input and output comprisesfive levels.Data In–Data Out(DAI–DAO) where information fusion deals with raw data,resulting in more accurate/reliable sensor data.Data In–Feature Out (DAI–FEO)processes raw data to extract features or attributes that describe an entity(object,situation or world abstraction).On Feature In–Feature Out(FEI–FEO)fusion works on a set of features to provide a more refined feature or extract new ones.In Feature In–Decision Out(FEI–DEO),fusion takes a set of features to generate symbolic representations or decisions.Finally,Decision In–Decision Out(DEI–DEO)fuses decisions to obtain new decisions or give emphasis on previous ones.Authors of[17]provide a similar view on information fusion,with a three-level classification–raw data level, feature level and decision level.They also introduce a classification based on information type–temporal fusion, spatial fusion and temporal–spatial fusion.Temporal fusion is considered at the sensor level,where data from the same source at different time intervals is fused.Spatial fusion picks data from several sensor nodes but at the same time instant.Finally,temporal and spatial fusion considers different sensors data that is fused over a period of time. Furthermore,a classification on the base of user requirements is also ers may be interested on single information about a concrete place,for example, acquired by a single sensor(as is the case of BSNs),new information about a certain area or even the complete network view.2.2Research overviewA smart home control system with information fusion is presented in[18].Authors present a comparison between a fuzzy logic approach and a fuzzy neural network approach.A smart alarm clock is used as a case study,with explanation on the developed dedicated hardware.However, results stem from simulation.Authors prove that fuzzy neural network with a weekly update presents the best results,in terms of accuracy,reliability and cost.Fire detection is a case study that information fusion can be applied,and on[19]authors present a multi-sensor fusion approach with underground trackless equipment.The structure model offire detection system is provided, performing several levels of fusion over temperature,gas, smoke and infrared sensors,as Fig.1presents.After a pre-treatment phase,feature extraction modules provide input to an expert system and neural network. Fuzzy inference fusion is used for the output of decision-making,based on expert system and neural network’s outputs.The approach proves to be feasible in theory,but lacks practical application feasibility assessment.Another example of information fusion addresses ground vehicle classification based on acoustic information[20]. Authors employ modified Bayesian decision fusion to combine two sets of features–a key frequency feature vector and a harmonic feature vector.1662IET Commun.,2011,Vol.5,Iss.12,pp.1661–1668 &The Institution of Engineering and Technology2011doi:10.1049/iet-com.2010.0644 However,as can be seen from the examples outlined before,focus is given on applications,not the user.Reconfiguration of fusion schemes,because the specific nature is sometimes not trivial.Moreover,owing to mathematical complexity many approaches must be implemented outside the sensor node world,namely on a Desktop computing platform.3ZenSens systemThe ZenSens system puts the user in control of WSANs with minimal effort.Enabling multi-channel operation,namely Desktop,mobile and Web browser,ZenSens presents the user with WSAN data almost everywhere.ZenSens features transparent node attachment and hardware profile recognition,enabling Plug-and-Play support from the WSAN level upwards.3.1System architectureFig.2shows the ZenSens system architecture,which is divided into three functional levels –the WSAN level composed of sink and remote nodes,the data management and WSAN interface level,and data presentation levels through SenseLab (Desktop and Mobile)and WebSensor.The architecture features two communication assets:IPv6over IEEE 802.15.4between the WSAN nodes and the sink,and USB between the sink and a personal computing platform,running SenseLab Desktop software.ZenSens WSAN’s presents a client /server approach,suitable for low-scale WSANs,where the sink acts as a server to sensor and actuator node’s requests.The sink is also the interface to WSAN’s data and state information,receiving,parsing andreplying to SenseLab USB requests.As a result,no internal routing is performed,since nodes exchange information through the sink in a single-hop fashion.A dedicated ‘instruction set’was developed for the USB communication and another for the UDP communication.The SenseLab application is responsible for communication with the sink,data gathering and presentation and dissemination to the WebSensor server.SenseLab mobile enables,at the time of writing,both iPhone /iPod touch (IOS 3)and Windows Mobile (WM 6.0×)devices.A view on SenseLab Desktop is shown in Fig.3,with a three-node network.Each node’s data can be visualised and stored locally for latter retrieval,namely with chart visualisation.SenseLab Desktop pushes XML data files based on three different XML schemas –networks found,specific network data and mote hardware data,to the server.The WebSensor solution is a Web-based system that stores the collected data from a WSAN.3.2WSAN levelThe lowest level of ZenSens is the WSAN level.It is composed of two main types of nodes –the sink node and remote smart sensor nodes.The nodes communicate wirelessly through IEEE 802.15.4,employing the uIPv6[21]Contiki stack that enables IPv6over IEEE 802.15.4through the implementation of the 6LoWPAN specification.The sink acts as a UDP server,listening for incoming packets from the clients.The developed software over ContikiOS features several protothreads on the sink and remote nodes.On the sink a protothread implements the UDP server,anotherimplementsFig.2ZenSens systemarchitectureFig.1Structure model of fire detection presented in [19]IET Commun.,2011,Vol.5,Iss.12,pp.1661–16681663doi:10.1049/iet-com.2010.0644&The Institution of Engineering and Technology 2011the USB parsing and commands reply,another is responsible for actuation,and another for initialisation.On the remote sensor nodes,one protothread is responsible for UDP client, and another for initialisation.Depending on the node’s assets,it may also contain one or both sensing and actuating protothreads–sensing for sensor data gathering and actuation for deciding and forcing actuators on the node. The node’s software also includes data structures to store sensor and/or actuator data on sink and remote nodes.On the sink a linked list is used to keep track of attached remote node’s data,expanding and collapsing as needed. For a remote node to be part of the network,it must send an attachment request to the sink.This process is critical for the PnP process,since the remote nodes present their hardware features at this stage,enabling hardware abstraction.4Data fusion on the ZenSens systemThis section elaborates on a previous study for implementing data fusion on the ZenSens system.Three areas where data fusion can be applied were identified–at the sensor node, in the sink and on the SenseLab Desktop application.Since ZenSens is focused on the user,we adopted the guidelines of user requirement-based fusion classification from[17], namely the interest on a single entity,the interest on changes on the network,and the complete information about the network.ZenSens is also prepared to present the user with single-sensor and multiple sensor data;however, it is not‘fusion-enabled’.On the current work we focus on inner-node fusion techniques.Fig.4presents an approach to information fusion at the WSAN level.For the sake of simplicity,the design considers that nodes have similar hardware.However,owing to the hardware-agnostic nature of ZenSens,nodes may present different sensor hardware,provided that at the sink level fusion is performed over similar sensors,that is,a common subset of sensors is present.As an example,if interest is on temperature, all interesting nodes must have temperature sensors.However, they may present other kinds of sensors as well.Each node performs internal DAI-DAO-type fusion on each hardware sensor,increasing sensor data quality that is sent to the sink.On the sink multi-sensor fusion may be employed,and more refined global view data can be fed to the control algorithm.The RAIS(redundant array of inexpensive sensors)controller present on the sink basically decides if a given node should be put to sleep based on a cost function over energy and/or data quality.This module can therefore order a node or set of nodes to shutdown temporarily for a given large amount of time,while fusion is performed on data from other nodes.A WSN node must be cost-efficient to be placed unattended for long periods of time,facing issues such as destruction, theft,among others.On several scenarios it is not possible to consider the reuse of an energy-depleted node;thus, easily discarded nodes must be employed.The lower cost of the hardware may present trade-offs on sensor quality,leading to deployment of lower quality devices, introducing a degree of uncertainty on raw sensed data.Inner-node fusion can address this issue,by aggregating and processing a given number of sensor data samples internally and providing the sink with higher-quality sensor data. Several approaches can be equated for inner-node fusion; take into account that the very constrained hardware of the smart sensor node is mandatory.A simple and feasible approach is to employ a simple arithmetic function over a set of samples,such as average,maximum or minimum. Although simple,such functions can provide afiltering effect sometimes suitable for several non-critical scenarios, such as temperature monitoring for heating,ventilation and air-conditioning(HVAC)systems.Another approach is to consider the simple moving average (SMA),an unweighted mean of the previous n raw sensor data points.For example,sensor data can be periodically gathered,being d(t)the raw sensor value at time t.As a result an SMA overfive consecutive sampling periods can be expressed bySMA=155i−0d(i)(1)Equation(1):SMA calculation over afive-sample interval. In terms of implementation,when a new sample is gathered, the last sample can be discarded from memory,similar to a sliding window effect.As a result,it is not necessary to store all previous values,only the n last ones.n is the number of samples to be fused(five in the previous example)and SMA can be rewritten asSMA t+1=SMA t−d(t−n)+d(t)(2) Fig.3SenseLab Desktop view1664IET Commun.,2011,Vol.5,Iss.12,pp.1661–1668 &The Institution of Engineering and Technology2011doi:10.1049/iet-com.2010.0644 Equation (2):another approach for SMA.This approach seems suited for inner-node fusion for smaller values of n (the average window),where memory is low.More elaborated filtering techniques,using estimators,can be equated based on the remaining code and RAM space on the WSAN node,namely,the moving average filter.ThisFig.5CuteCom screenshot presenting output from remotenodeFig.4WSAN level ZenSens information fusionIET Commun.,2011,Vol.5,Iss.12,pp.1661–16681665doi:10.1049/iet-com.2010.0644&The Institution of Engineering and Technology 2011filter takes the input signal z ¼(z (1),z (2),...)and obtains a ‘true signal’,according to (3),for every k ,Mˆx (k )=1M M−1i =0z (k −1)(3)Equation (3):moving average filter.‘M ’is the filter’s window,which can be programmed to suit a specific need.A lower M value tends to introduce a sharper step edge,while if M is increased,the obtained d samples will be ‘cleaner’.It has been proven that if the input signal has random white noise,the moving average filter can reduce noise variance by a factor of M √[22].Another possible approach is to implement an exponential moving average (EMA),also known as exponentially weighted moving average (EWMA).This estimatorendorses weighting factors,which decrease exponentially.This approach gives more importance to recent observations than previous onesS t =a ×d (t −1)+(1−a )×S t −1(4)Equation (4):formula to calculate the EMA.According to (4),0≤a ≤1expresses the degree of weighting decrease,a high value discounts older observations faster,whereas a lower value takes more into account historic data.5ImplementationThis section elaborates on the implementation of data fusion on the ZenSens system.The implementation of data fusion implicates the addition of more protothreads on the sink and remote smart sensor nodes,implementing newprocessingFig.6Application of average function to real sensor data,consideringa 4samples with 0.5secondsb 8samples with 0.5secondsc 4samples with 2.0secondsd 8samples with 2.0secondse 4samples with 4.0seconds f8samples with 4.0seconds1666IET Commun.,2011,Vol.5,Iss.12,pp.1661–1668&The Institution of Engineering and Technology 2011doi:10.1049/iet-com.2010.0644modules identified in Fig.4.It is also important to monitor remote node’s status to obtain raw sensor data to compare with fused data for latter analysis.Results for inner-node fusion are provided for two fusion scenarios–average over a set of samples and SMA.For the sake of simplicity tests were issued over TelosB temperature sensor,and results captured with CuteCom for Linux and output to afile,as in Fig.5.The generatedfile,a CSV–comma separated valuesfile,was fed into a Microsoft w Excel spreadsheet to produce chart visualisations.5.1Average over afixed set of samplesThefirst and more simplistic fusion involves gathering n continuous samples of raw data to produce a single fused value,which corresponds to the average(AVR)of all data on series.Several experiments were carried out with variation on the number of samples(n)per fused data value,and the time interval between samples(T–sampling time).Fig.6presents charts obtained directly from a mote.On these charts the x-axis represents a fused data sample, combining n raw sensor values into a single value sent to the sink node.On these experiments,sampling time is changed from0.5s (a and b)to2s(c and d),up to4s(e and f).The number of samples(real sensor measurements)used to calculate the average value varies from four(a,c and e)up to eight samples(b,d and f).Charts present discrete sample values and the fused data in line form.From thisfirst study one can conclude that,as expected,the average information fusion function can severely diminish the amount of data to be transferred from a specific node, sacrificing time information.It must be pointed here that for each chart different data was used,at different roomtemperature conditions.Moreover,the fusion process smoothes obtained values,filtrating erroneous values that the sensor might introduce.As sampling time increases,the temperature value may change.However,even for abrupt changes the fused data greatly approximates the signal transition.As for the number of samples,the average function performed better infiltrating erroneous values with eight samples,for example,a single value is far from the rest.The average information fusion functionfilters data that may come from a low-quality sensor,or be smeared by environmental conditions.As expected,this function has no prediction capability,since the next result is not affected by the previous.However,because of implementation simplicity it is very well suited to low processing power cores of sensor nodes.5.2Exponentially weighted moving averageThe second more elaborated approach is based on the EWMA estimator.For this scenario,source data taken from Fig.6d were used.Fig.7presents the comparison results between the EWMA estimator,AVR over eight samples,AVR over four samples and raw sensor sample.The construction of this chart is substantially different from the previous,since the EWMA estimator produces a fused sample for each raw sensor sample,while AVR fuses n samples into one(four and eight samples in the current case).As a result x-axis now contains all raw sensor values,thus Figs.6and7cannot be directly compared.The three charts differ on the value of the EWMA.As expected,when a values increase,the EWMA effectively approaches the real sensor data,since previous values are discarded more rapidly.Thus,lower levels of a are more affected from the previous EWMA results.Second,EWMA produces a much softer response than AVR,and as such can easilyfilter sensor anomalies.Third,when compared to the average,it gives a clearer view of the environment,without the harsh edges that average can introduce.As for disadvantages,it requires more processing resources than the previous approach,while by softening the response it may not be suited for alarm detection.Moreover,using this fusion technique alone does not diminish the amount of data to be transmitted;it only improves the‘quality of data’, eliminating possible‘spikes’without loss of time information. 6Conclusions and future workThis paper presented the introduction of data fusion on the ZenSens system at the inner-node level,presenting different approaches based on real gathered data.ZenSens is suitable for small WSANs,where client/server distributed computing paradigm is typically used.In terms of inner-node fusion,simple functions like average may suit simpler scenarios,but more elaborated solutions like EWMA prove to be much more appellative andflexible.Authors believe that a solution combiningboth Fig.7EWMA results fora a¼0.1b a¼0.2c a¼0.4IET Commun.,2011,Vol.5,Iss.12,pp.1661–16681667 doi:10.1049/iet-com.2010.0644&The Institution of Engineering and Technology2011a data preparation phase with an AVR function and an estimator afterwards can lead to reasonable gains in both data quality and energy consumption.As a future work,the introduction of information and data fusion at the other layers of ZenSens may be achieved, namely the introduction of DAI-FEO at the sink level and FEI-FEO at the Desktop level.7AcknowledgmentsPart of this work was supported by the Instituto de Telecomunicac¸o˜es,Next Generation Networks and Applications Group(NetGNA),Portugal,in the framework of BodySens Project,and by the Euro-NF Network of Excellence of the Seventh Framework Programme of EU,in the framework of the Specific Joint Research Project PADU.8References1Xia,F.,Tian,Y.-C.,Li,Y.,Sun,Y.:‘Wireless sensor/actuator network design for mobile control applications’,Sensors J.,2007,7,(10), pp.2157–21732Rezgui,A.,Eltoweissy,M.:‘Service-oriented sensor-actuator networks: Promises,challenges,and the road ahead’,mun.Elsevier, 2007,30,(13),pp.2627–26483Baronti,P.,Pillai,P.,Chook,V.,Chessa,S.,Gotta,A.,Hu,Y.F.:‘Wireless sensor networks:a survey on the state of the art and the 802.15.4and ZigBee standards’,mun.,2007,30,(7), pp.1655–16954Demirkol,I.,Ersoy,C.,Alago¨z,F.:‘MAC protocols for wireless sensor networks:a survey’,IEEE Commun.Mag.,2006,44,(4),pp.115–121 5Akkaya,K.,Younis,M.:‘A survey on routing protocols for wireless sensor networks’,Elsevier Ad Hoc Netw.J.,2005,3/3,pp.325–349 6Wang,C.,Sohraby,K.,Li,B.,Daneshmand,M.,Hu,Y.:‘A survey of transport protocols for wireless sensor networks’,IEEE Netw.,2006, 20,(3),pp.34–407Luo,R.C.,Yih,C.-C.,Su,K.L.:‘Multisensor fusion and integration: approaches,applications and future research directions’,IEEE Sens.J.,2002,2,(2),pp.107–1198Nakamura,E.F.,Loureiro,A.:‘Information fusion in wireless sensor networks’.ACM SIGMOD/PODS Conf.,Vancouver,Canada,9–12 June20089Nakamura,E.F.,Loureiro,A.,Frery,A.:‘Information fusion for wireless sensor networks:methods,models,and classifications’,ACM Comput.Surv.,2007,39,(3),Article910Neves,P.A.C.S.,Vaidya,B.,Rodrigues,J.J.P.C.:‘User-centric plug-and-play functionality for IPv6-enabled wireless sensor networks’.IEEE Int.Conf.on Communications(ICC2010),Cape Town,South Africa,201011Neves,P.A.C.S.,Esteves,A.F.F.,Cunha,R.M.F.,Rodrigues,J.J.P.C.:‘User-centric data gathering multi-channel system for IPv6-enabled wireless sensor networks’,w.–Special Issue on Technologies,Recent Advances in Sensor Integration,2010,9,(1), pp.13–2312Dunkels,A.,Gronvall,B.,Voigt,T.:‘Contiki–A lightweight and flexible operating system for tiny networked sensors’.Proc.29th Annual IEEE Int.Conf.on Local Computer Networks,2004, pp.455–46213ContikiOS:‘Contiki operating system homepage’.Available at:http:// www.sics.se/contiki/,accessed September200914Rodrigues,J.J.P.C.,Neves,P.A.C.S.:‘A survey on IP-based wireless sensor networks solutions’,mun.Syst.,Wiley,2010,23,(8),pp.963–98115Xu,Y.,Qi,H.:‘Distributed computing paradigms for collaborative signal and information processing in sensor networks’,J.Parallel put.,2004,64,(8),pp.945–95916Neves,P.,Stachyra,M.,Rodrigues,J.:‘Application of wireless sensor networks to healthcare promotion’,mun.Software Syst., Croatian Communications and Information Society,2008,4,(3), pp.181–19017Zhao,C.,Wang,Y.:‘A new classification method on information fusion of wireless sensor networks’.Int.Conf.on Software and Systems Symposia(ICESS1008),Sichuan,China,29–31July2008, pp.231–23618Zhang,L.,Leung,H.,Chan,K.C.:‘Information fusion based smart home control system and its application’,IEEE Trans.Consum.Electron.,2008,54,(3),pp.1157–116519Liu,X.,Hu,T.,Li,X.:‘Information fusion technology for underground trackless equipment onfire detection’.Ninth Int.Conf.on Electronic, Beijing,China,16–19August2009,pp.3-109–3-11520Guo,B.,Nixon,M.S.,Damarla,T.R.:‘Acoustic information fusion for ground vehicle classification’.11th Int.Conf.on Information Fusion(FUSION‘08),Cologne,Germany,30June–3July2008, pp.1–721Durvy,M.,Abeille´,J.,Wetterwald,P.,et al.:‘Making sensor networks IPv6ready’.Proc.Sixth ACM Conf.on Networked Embedded Sensor Systems(ACM SenSys2008),Raleigh,North Carolina,USA, 200822Smith,S.W.:‘Digital signal processing:a practical guide for engineers and scientists’(Newnes,2003)1668IET Commun.,2011,Vol.5,Iss.12,pp.1661–1668 &The Institution of Engineering and Technology2011doi:10.1049/iet-com.2010.0644 。

ccnp题库

ccnp题库

FCAPSFault Management ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ FConfiguration Management ‐‐‐‐‐‐‐ CAccounting Management ‐‐‐‐‐‐‐‐‐‐ AQuestion 2FCAPS–model defined by the International Organization for Standardization (ISO).ITIL–framework for it profTNM–network management model is the Telecommunications Standardization Sector’s (ITU-T) Cisco lifecycle–model is often referred to as the PPDIOO modelQuestion 3EEM .IP SLA‐‐‐‐‐‐‐‐‐‐‐‐‐‐ CLISDM .CNA‐‐‐‐‐‐‐‐‐‐‐‐‐‐ GUIFTP ,TFTP,SCP‐‐‐‐‐‐‐‐‐‐‐‐‐‐ BackupCLI(command-line interface,命令行界面)GUI(Graphical User Interface,图形用户界面)SDM(Security Device Manager)是Cisco公司提供的全新图形化路由器管理工具;EEM(Embeded Event Manager)是Cisco IOS的嵌入式事件管理器;1)FCAPS (network maintenance model defined by the ISO)F – FaultC – ConfigA – Accounting2) What happens when running the command: logging console warnings.1-2- warning, notification, error, debugging…3- just warning logging4- warning, critical, alert, emergenciesAnswer:warning, critical, alert, emergencies(Notice this line doesn’t have the word “error”)3) what will be alternative for:ip ftp username xxxxxxip ftp password yyyyyyAnswer:ip http client username xxxxxxip http client password yyyyyy4) Network Maintenance: Choose from the list 2 network maintaining types.Answer:Structured and Interrupt Driven5) access-list 199 permit tcp host 10.1.1.1 host 172.16.1.1access-list 199 permit tcp host 172.16.1.1 host 10.1.1.1debug ip packet 199What would be the output shown on the console?Only communication between host 10.1.1.1 and host 172.16.1.16) what will happen if u configure two router as NTP server (something like that )Answer:The router will choose the best reliable server and will synchronise with it.7) The interface is up and protocol is up. When do u get these messages.%LINEPROTO‐5‐UPDOWN: Line protocol on Interface FastEthernet0/14, changed state to up %LINKDOWN‐3‐SERIAL:Answer:Emergency 0 Alerts 1 Critical 2 Errors 3 Warning4Notification 5 Informational 6 Debugging 78)Serial line is up,protocol is also up?But cdp neighbor not working?Answer:Data link layer.T1:ospf authentication1.Client is unable to ping R1’s serial interface from the client.Problem was disable authentification on R1, check where authentication is not given under router ospf of R1. (use ipv4 Layer 3)conf R1 was:interface Serial0/0.12 point-to-pointip address 10.1.1.1 255.255.255.252ip nat insideip ospf message-digest-key 1 md5 TSHOOTrouter ospf 1log-adjacency-changesnetwork 10.1.1.0 0.0.0.3 area 12default-information originate alwaysconf R2 was:interface Serial0/0.12 point-to-pointip address 10.1.1.2 255.255.255.252ip ospf authentication message-digestip ospf message-digest-key 1 md5 TSHOOTAnswer: on R1 need comand in router modearea 12 authentication message-digestAns1) R1Ans2) ipv4 OSPFAns3) ip ospf authentication message-digest command must be given on s0/0/0T2:HSRP TRACKHSRP: DSW1 does not become active.conf on dw1:track 1 ip route 10.1.21.128 255.255.0.0 metric thresholdthreshold metric up 1 down 2track 10 ip route 10.2.21.128 255.255.255.0 metric thresholdthreshold metric up 63 down 64interface Vlan10ip address 10.2.1.1 255.255.255.0standby 10 ip 10.2.1.254standby 10 priority 200standby 10 preemptstandby 10 track 1 decrement 60Answer: (use IPv4 Layer 3 Topology)On dsw 1 interface vlan 10 mode run:no standby 10 track 1 decrement 60standby 10 track 10 decrement 60(ip for track command not exact for real exam)Ans1) DSW1Ans2) HSRPAns3) delete the command with track 1 and enter the command with track 10.T3:BGP NeighborProblem: R1 is not able to ping 209.65.200.226.configuration on R1:router bgp 65001no synchronizationbgp log-neighbor-changesnetwork 209.65.200.224 mask 255.255.255.252neighbor 209.56.200.226 remote-as 65002no auto-summarycheck bgp neighborship. **** show ip bgp sum****The neighbor’s address in the neighbor command is wrong under router BGP. (use ipv4 Layer 3) Answer: need change on router mode on R1 neighbor 209.65.200.226Ans1) R1Ans2) BGPAns3) delete the wrong neighbor statement and enter the correct neighbor address in the neighbor command (change 209.56.200.226 to 209.65.200.226)T4:NAT ACLClient is not able to ping the web server, but the routers can ping the server. NA T problem. (use ipv4 Layer 3)problem on R1 Nat aclAnswer:add to acl 1 permit ip 10.2.1.0 0.0.0.255Ans1) R1Ans2) IP NA TAns3) under NA T access list, enter the command permit 10.2.0.0 0.0.255.255T5:R1 ACLClient is not able to ping the server. Except for R1, no one else can ping the server. (use ipv4 Layer 3)Problem:on R1 acl blocking ipacl something like this:deny 10.2.1.0deny 10.1.4.0deny 10.1.1.0Answer: add permit 209.65.200.224 0.0.0.3command to R1′s ACLAns1) R1Ans2) IPv4 Layer3 SecurityAns3) Add permit 209.65.200.224 0.0.0.3 to R1′s ACLT6: VLAN filterClient 1 is not able to ping the server. Unable to ping DSW1(Use L2 Diagram).Vlan Access map is applied on DSW1 blocking the ip address of client 10.2.1.3Ans1) DSW1Ans2) Vlan access mapAns3)No vlan filter 10T7:Port SecurityClient 1 is not able to ping the serverSituation: Unable to ping DSW1(User layer 2).On ASW1 portsecurity mac 0000.0000.0001, interface in err-disable stateAnswer:on asw1 delele portsecurity & do on interfaces shutdown, no shutdownAns1)ASW1Ans2)Port securityAns3)On fa1/0/1 and fa1/0/2 do disable port security and do shut, no shut.T8:SWItchport vlan 10Client 1 is not able to ping the serverSituation: Unable to ping DSW1 & in port channel configuratioin of ASW1 vlan 10 is not allowed. (Use L2 Diagram)On ASW1, on interfaces fa0/1, fa0/2 switchport access vlan 1Answer: on ASW1 change switchport access vlan 1 to switchport access vlan 10Ans1)ASW1Ans2)Access vlanAns3)give command: interface range fa1/0/1-/2 switchport access vlan 10T9:Switchport trunk.cant ping to web server 209.65.200.241Situation: Unable to ping DSW1 & in port channel configuratioin of ASW1 vlan 10 is not allowed. (Use L2 Diagram)question was about EtherChanelclient can’t obtain ip address(169.x.x.x)on ASW1 trunks allow vlan 20,200Answ: on port channel 13, 23 disables all vlans and give switchport trunk allowed vlan 10,200 Ans1)ASW1Ans2)Switch to switch connectivityAns3)int range portchannel13,portchannel23switchport trunk allowed vlan noneswitchport trunl allowed vlan 10,200T10:Eigrp asClient 1 is not able to ping the serverSituation: Unable to ping R4 fast ethernet port from dsw1.Check ip eigrp neighbors from DSW1 you will not see R4 as neighbor.(use ipv4 Layer 3)On DSW1 & DWS2 the EIGRP AS number is 10 (router eigrp 10) but on R4 it is 1 (router eigrp 1)Answ: change router AS on R4 from 1 to 10Ans1) R4Ans2) IP4 EIGRPAns3) Change eigrp AS number from 1 to 10T11:eitrp to ospfClient 1 is not able to ping the serverSituation: Unable to ping serial interface of R4 from the clients.On R4 in router eigrp:redistribute ospf 1 route-map EIGRP_to_OSPFBUT route-map was named:route-map EIGRP->OSPFAnswer:change in router eigrp router-map nameAns1) R4Ans2) route redistributionAns3) change the name of the route-map under the router EIGRP or router OSPF process from ‘to’to ‘->’.T12:IPV6 ospfIPV6 loopback of R2 cannot be pinged from DSW1’s loopback.Situation: ipv6 ospf was not enabled on R2’s serial interface connecting to R3. (use ipv6 Layer 3) Answer:interface configuration mode:ipv6 ospf 6 area 12Ans1) R2Ans2) IPV6 ospfAns3) on the serial interface of R2, enter the command ipv6 ospf 6 area 0 (make sure to check the IPV6 topology before choose Answer 3 because the options look similar)Device Error DescriptionASW11. Access port not in VLAN 102. Port Channel not allowing VLAN 103. Port SecurityDSW1 1. HSRP track 102. VLAN filterR1 1. Wrong IP of BGP neighbor2. NAT – Access list3. Redistribute access-listR2 1. IPv6: enable OSPF2. OSPF AuthenticationR4 1. EIGRP – wrong AS2. Redistribute (“to” & -> )1、access vlan的vlan 给错了2、port-security导致端口被errdisable3、V ACL4、EIGRP的AS号配置错误5、OSPF的authentication有问题,6、OSPF到EIGRP的redistribut的route-map名字写过了7、NA T的inside pool没有包含client的网段8、BGP的neighbor ip写错了,209.65.200.226写成了209.56.200.2269、到ISP的出接口的ACL少了一条permit10、其中一台接入交换机的Trunk allowed的VLAN少了VLAN 1011、HSRP的track语句指定有问题12、IPV6,R2的其中一个接口没有enable IPV6 OSPF。

001 (ISSCC tutorial)Noise Analysis in Switched-Capacitor Circuits

001 (ISSCC tutorial)Noise Analysis in Switched-Capacitor Circuits
PSD(f) f
© 2011 IEEE
IEEE International Solid-State Circuits Conference
© 2011 IEEE
Thermal Noise Power
• Nyquist showed that
PSD ( f ) = 4kT
• The total average noise power of a resistor in a certain frequency band is therefore
– Examples: Audio systems, wireless transceivers, sensor interfaces
• Electronic noise directly trades with power dissipation and speed • Electronic noise is a major concern in modern technologies with reduced VDD
• The noise of a MOSFET operating in the triode region is approximately equal to that of a resistor • In the saturation region, the thermal noise can be modeled using a drain current source with power spectral density
• We can model the noise using an equivalent voltage or current generator
2 vn
= Pn ⋅ R = 4kT ⋅ R ⋅ Δf

A Survey on Wireless Body Area Networks

A Survey on Wireless Body Area Networks

A survey on wireless body area networksBenoıˆt Latre ´•Bart Braem •Ingrid Moerman •Chris Blondia •Piet DemeesterPublished online:11November 2010ÓSpringer Science+Business Media,LLC 2010Abstract The increasing use of wireless networks and the constant miniaturization of electrical devices has empow-ered the development of Wireless Body Area Networks (WBANs).In these networks various sensors are attached on clothing or on the body or even implanted under the skin.The wireless nature of the network and the wide variety of sen-sors offer numerous new,practical and innovative applica-tions to improve health care and the Quality of Life.The sensors of a WBAN measure for example the heartbeat,the body temperature or record a prolonged ing a WBAN,the patient experiences a greater physical mobility and is no longer compelled to stay in the hospital.This paper offers a survey of the concept of Wireless Body Area Networks.First,we focus on some applications with special interest in patient monitoring.Then the communi-cation in a WBAN and its positioning between the different technologies is discussed.An overview of the current research on the physical layer,existing MAC and network protocols is given.Further,cross layer and quality of service is discussed.As WBANs are placed on the human body and often transport private data,security is also considered.An overview of current and past projects is given.Finally,the open research issues and challenges are pointed out.Keywords Wireless body area networks ÁRouting ÁMAC1IntroductionThe aging population in many developed countries and the rising costs of health care have triggered the introduction of novel technology-driven enhancements to current health care practices.For example,recent advances in electron-ics have enabled the development of small and intelligent (bio-)medical sensors which can be worn on or implanted in the human body.These sensors need to send their data to an external medical server where it can be analyzed and ing a wired connection for this purpose turns out to be too cumbersome and involves a high cost for deployment and maintenance.However,the use of a wireless interface enables an easier application and is more cost efficient [1].The patient experiences a greater physical mobility and is no longer compelled to stay in a hospital.This process can be considered as the next step in enhancing the personal health care and in coping with the costs of the health care system.Where eHealth is defined as the health care practice sup-ported by electronic processes and communication,the health care is now going a step further by becoming mobile.This is referred to as mHealth [2].In order to fully exploit the benefits of wireless technologies in telemedicine and mHealth,a new type of wireless network emerges:a wire-less on-body network or a Wireless Body Area Network (WBAN).This term was first coined by Van Dam et al.[3]and received the interest of several researchers [4–8].A Wireless Body Area Network consists of small,intelligent devices attached on or implanted in the body which are capable of establishing a wireless communica-tion link.These devices provide continuous health moni-toring and real-time feedback to the user or medical personnel.Furthermore,the measurements can be recorded over a longer period of time,improving the quality of the measured data [9].tre´(&)ÁI.Moerman ÁP.Demeester Department of Information Technology,Ghent University/IBBT,Gaston Crommenlaan 8,Box 201,9050Gent,Belgium e-mail:tre@intec.ugent.beB.Braem ÁC.BlondiaDepartment of Mathematics and Computer Science,University of Antwerp/IBBT,Middelheimlaan 1,2020Antwerp,Belgium e-mail:bart.braem@ua.ac.beWireless Netw (2011)17:1–18DOI 10.1007/s11276-010-0252-4Generally speaking,two types of devices can be dis-tinguished:sensors and actuators.The sensors are used to measure certain parameters of the human body,either externally or internally.Examples include measuring the heartbeat,body temperature or recording a prolonged electrocardiogram(ECG).The actuators(or actors)on the other hand take some specific actions according to the data they receive from the sensors or through interaction with the user,e.g.,an actuator equipped with a built-in reservoir and pump administers the correct dose of insulin to give to diabetics based on the glucose level measurements.Inter-action with the user or other persons is usually handled by a personal device,e.g.a PDA or a smart phone which acts as a sink for data of the wireless devices.In order to realize communication between these devi-ces,techniques from Wireless Sensor Networks(WSNs) and ad hoc networks could be used.However,because of the typical properties of a WBAN,current protocols designed for these networks are not always well suited to support a WBAN.The following illustrates the differences between a Wireless Sensor Network and a Wireless Body Area Network:•The devices used have limited energy resources avail-able as they have a very small form factor(often less than1cm3[10]).Furthermore,for most devices it is impossible to recharge or change the batteries althougha long lifetime of the device is wanted(up to severalyears or even decades for implanted devices).Hence, the energy resources and consequently the computa-tional power and available memory of such devices will be limited;•All devices are equally important and devices are only added when they are needed for an application(i.e.no redundant devices are available);•An extremely low transmit power per node is needed to minimize interference and to cope with health concerns[11];•The propagation of the waves takes place in or on a (very)lossy medium,the human body.As a result,the waves are attenuated considerably before they reach the receiver;•The devices are located on the human body that can be in motion.WBANs should therefore be robust against frequent changes in the network topology;•The data mostly consists of medical information.Hence,high reliability and low delay is required;•Stringent security mechanisms are required in order to ensure the strictly private and confidential character of the medical data;•Andfinally the devices are often very heteroge-neous,may have very different demands or may requiredifferent resources of the network in terms of data rates, power consumption and reliability.When referring to a WBAN where each node comprises a biosensor or a medical device with sensing unit,some researchers use the name Body Area Sensor Network (BASN)or in short Body Sensor Network(BSN)instead of WBAN[12].These networks are very similar to each other and share the same challenges and properties.In the following,we will use the term WBAN which is also the one used by the IEEE[13].In this article we present a survey of the state of the art in Wireless Body Area Networks.Our aim is to provide a better understanding of the current research issues in this emergingfield.The remainder of this paper is organized as follows.First,the patient monitoring application is dis-cussed in Sect.2.Next,the characteristics of the commu-nication and the positioning of WBANs amongst other wireless technologies is discussed in Sect.4.Section5 gives an overview of the properties of the physical layer and the issues of communicating near or in the body. Existing protocols for the MAC-layer and network layer are discussed in Sects.6and7,respectively.Section8 deals with cross-layer protocols available for WBANs.The Quality of Service(QoS)and possible security mechanisms are treated in Sects.9and10.An overview of existing projects is given in Sect.11.Finally,the open research issues are discussed in Sects.12and13concludes the paper.2Patient monitoringThe main cause of death in the world is CardioVascular Disease(CVD),representing30%of all global deaths. According to the World Health Organization,worldwide about17.5million people die of heart attacks or strokes each year;in2015,almost20million people will die from CVD.These deaths can often be prevented with proper health care[14].Worldwide,more than246million people suffer from diabetes,a number that is expected to rise to 380million by2025[15].Frequent monitoring enables proper dosing and reduces the risk of fainting and in later life blindness,loss of circulation and other complications [15].These two examples already illustrate the need for continuous monitoring and the usefulness of WBANs. Numerous other examples of diseases would benefit from continuous or prolonged monitoring,such as hypertension, asthma,Alzheimer’s disease,Parkinson’s disease,renal failure,post-operative monitoring,stress-monitoring,pre-vention of sudden infant death syndrome,etc[9,16,17]. These applications can be considered as an indicator for thesize of the market for WBANs.The number of people suffering from diabetics or CVD and the percentage of people in the population age60years and older will grow in the future.Even without any further increase in world population by2025this would mean a very large number of potential customers.WBAN technology could provide the connectivity to support the elderly in managing their daily life and medical conditions[18].A WBAN allows continuous monitoring of the physiological parameters. Whether the patient is in the hospital,at home or on the move,the patient will no longer need to stay in bed,but will be able to move around freely.Furthermore,the data obtained during a large time interval in the patient’s natural environment offers a clearer view to the doctors than data obtained during short stays at the hospital[9].An example of a medical WBAN used for patient moni-toring is shown in Fig.1.Several sensors are placed in clothes,directly on the body or under the skin of a person and measure the temperature,blood pressure,heart rate,ECG, EEG,respiration rate,SpO2-levels,etc.Next to sensing devices,the patient has actuators which act as drug delivery systems.The medicine can be delivered on predetermined moments,triggered by an external source(i.e.a doctor who analyzes the data)or immediately when a sensor notices a problem.One example is the monitoring of the glucose level in the blood of diabetics.If the sensor monitors a sudden drop of glucose,a signal can be sent to the actuator in order to start the injection of insulin.Consequently,the patient will experience fewer nuisances from his disease.Another example of an actuator is a spinal cord stimulator implanted in the body for long-term pain relief[19].A WBAN can also be used to offer assistance to the disabled.For example,a paraplegic can be equipped with sensors determining the position of the legs or with sensors attached to the nerves[20].In addition,actuators posi-tioned on the legs can stimulate the muscles.Interaction between the data from the sensors and the actuators makes it possible to restore the ability to move.Another example is aid for the visually impaired.An artificial retina,con-sisting of a matrix of micro sensors,can be implanted into the eye beneath the surface of the retina.The artificial retina translates the electrical impulses into neurological signals.The input can be obtained locally from light sen-sitive sensors or by an external camera mounted on a pair of glasses[21].Another area of application can be found in the domain of public safety where the WBAN can be used byfire-fighters,policemen or in a military environment[22].The WBAN monitors for example the level of toxics in the air and warns thefirefighters or soldiers if a life threatening level is detected.The introduction of a WBAN further enables to tune more effectively the training schedules of professional athletes.Next to purely medical applications,a WBAN can include appliances such as an MP3-player,head-mounted (computer)displays,a microphone,a camera,advanced human-computer interfaces such as a neural interface,etc [20].As such,the WBAN can also be used for gaming purposes and in virtual reality.This small overview already shows the myriad of pos-sibilities where WBANs are useful.The main characteristic of all these applications is that WBANs improve the user’s Quality of Life.3Taxonomy and requirementsThe applications described in the previous section indicate that a WBAN consists of several heterogeneous devices.In this section an overview of the different types of devices used in a WBAN will be given.Further the requirements and challenges are discussed.These include the wide var-iability of data rates,the restricted energy consumption,the need for QoS and reliability,ease-of-use by medical pro-fessionals and security and privacy issues.3.1Types of devices(Wireless)sensor node:A device that responds to and gathers data on physical stimuli,processes the data if necessary and reports this information wirelessly.It consists of several components:sensor hardware,a power unit,a processor,memory and a transmitter or transceiver[23].(Wireless)actuator node:A device that acts according to data received from the sensors or throughinteractionwith the user.The components of an actuator are similar to the sensor’s:actuator hardware(e.g.hardware for medicine administration,including a reservoir to hold the medicine),a power unit,a processor,memory and a receiver or transceiver.(Wireless)personal device(PD):A device that gathers all the information acquired by the sensors and actuators and informs the user(i.e.the patient,a nurse,a GP,etc.) via an external gateway,an actuator or a display/LEDS on the device.The components are a power unit,a (large)processor,memory and a transceiver.This device is also called a Body Control Unit(BCU)[4],body-gateway or a sink.In some implementations,a Personal Digital Assistant(PDA)or smart phone is used.Many different types of sensors and actuators are used in a WBAN.The main use of all these devices is to be found in the area of health applications.In the following,the termnodes refers to both the sensor as actuator nodes.The number of nodes in a WBAN is limited by nature of the network.It is expected that the number of nodes will be in the range of20–50[6,24].3.2Data ratesDue to the strong heterogeneity of the applications,data rates will vary strongly,ranging from simple data at a few kbit/s to video streams of several Mbit/s.Data can also be sent in bursts,which means that it is sent at higher rate during the bursts.The data rates for the different applications are given in in Table1and are calculated by means of the sampling rate,the range and the desired accuracy of the measure-ments[25,26].Overall,it can be seen that the application data rates are not high.However,if one has a WBAN with several of these devices(i.e.a dozen motion sensors,ECG, EMG,glucose monitoring,etc.)the aggregated data rate easily reaches a few Mbps,which is a higher than the raw bit rate of most existing low power radios.The reliability of the data transmission is provided in terms of the necessary bit error rate(BER)which is used as a measure for the number of lost packets.For a medical device,the reliability depends on the data rate.Low data rate devices can cope with a high BER(e.g.10-4),while devices with a higher data rate require a lower BER(e.g. 10-10).The required BER is also dependent on the criti-calness of the data.3.3EnergyEnergy consumption can be divided into three domains: sensing,(wireless)communication and data processing [23].The wireless communication is likely to be the most power consuming.The power available in the nodes is often restricted.The size of the battery used to store the needed energy is in most cases the largest contributor to the sensor device in terms of both dimensions and weight. Batteries are,as a consequence,kept small and energy consumption of the devices needs to be reduced.In some applications,a WBAN’s sensor/actuator node should operate while supporting a battery life time of months or even years without intervention.For example,a pacemaker or a glucose monitor would require a lifetime lasting more than5years.Especially for implanted devices,the lifetime is crucial.The need for replacement or recharging induces a cost and convenience penalty which is undesirable not only for implanted devices,but also for larger ones.The lifetime of a node for a given battery capacity can be enhanced by scavenging energy during the operation of the system.If the scavenged energy is larger than the average consumed energy,such systems could run eternally.How-ever,energy scavenging will only deliver small amounts of energy[5,28].A combination of lower energy consumption and energy scavenging is the optimal solution for achieving autonomous Wireless Body Area Networks.For a WBAN, energy scavenging from on-body sources such as body heat and body vibration seems very well suited.In the former,a thermo-electric generator(TEG)is used to transform the temperature difference between the environment and the human body into electrical energy[27].The latter uses for example the human gait as energy source[29].During communication the devices produce heat which is absorbed by the surrounding tissue and increases the temperature of the body.In order to limit this temperature rise and in addition to save the battery resources,the energy consumption should be restricted to a minimum. The amount of power absorbed by the tissue is expressed Table1Examples of medical WBAN applications[21,25,26,27] Application Data rate Bandwidth(Hz)Accuracy(bits) ECG(12leads)288kbps100–100012ECG(6leads)71kbps100–50012EMG320kbps0–10,00016EEG(12leads)43.2kbps0–15012 Blood saturation16bps0–18 Glucose monitoring1600bps0–5016 Temperature120bps0–18 Motion sensor35kbps0–50012 Cochlear implant100kbps––Artificial retina50-700kbps––Audio1Mbps––Voice50-100kbps––by the specific absorption rate(SAR).Since the device may be in close proximity to,or inside,a human body,the localized SAR could be quite large.The localized SAR into the body must be minimized and needs to comply with international and local SAR regulations.The regulation for transmitting near the human body is similar to the one for mobile phones,with strict transmit power requirements [11,30].3.4Quality of service and reliabilityProper QoS handling is an important part in the framework of risk management of medical applications.A crucial issue is the reliability of the transmission in order to guarantee that the monitored data is received correctly by the health care professionals.The reliability can be con-sidered either end-to-end or on a per link base.Examples of reliability include the guaranteed delivery of data(i.e. packet delivery ratio),in-order-delivery,…Moreover, messages should be delivered in reasonable time.The reliability of the network directly affects the quality of patient monitoring and in a worst case scenario it can be fatal when a life threatening event has gone undetected [31].3.5UsabilityIn most cases,a WBAN will be set up in a hospital by medical staff,not by ICT-engineers.Consequently,the network should be capable of configuring and maintaining itself automatically,i.e.self-organization an self-mainte-nance should be supported.Whenever a node is put on the body and turned on,it should be able to join the network and set up routes without any external intervention.The self-organizing aspect also includes the problem of addressing the nodes.An address can be configured at manufacturing time(e.g.the MAC-address)or at setup time by the network itself.Further,the network should be quickly reconfigurable,for adding new services.When a route fails,a back up path should be set up.The devices may be scattered over and in the whole body.The exact location of a device will depend on the application,e.g.a heart sensor obviously must be placed in the neighborhood of the heart,a temperature sensor can be placed almost anywhere.Researchers seem to disagree on the ideal body location for some sensor nodes,i.e.motion sensors,as the interpretation of the measured data is not always the same[32].The network should not be regarded as a static one.The body may be in motion(e.g.walking, running,twisting,etc.)which induces channel fading and shadowing effects.The nodes should have a small form factor consistent with wearable and implanted applications.This will make WBANs invisible and unobtrusive.3.6Security and privacyThe communication of health related information between sensors in a WBAN and over the Internet to servers is strictly private and confidential[33]and should be encrypted to protect the patient’s privacy.The medical staff collecting the data needs to be confident that the data is not tampered with and indeed originates from that patient.Further,it can not be expected that an average person or the medical staff is capable of setting up and managing authentication and authorization processes. Moreover the network should be accessible when the user is not capable of giving the password(e.g.to guarantee accessibility by paramedics in trauma situations).Security and privacy protection mechanisms use a significant part of the available energy and should therefor be energy efficient and lightweight.4Positioning WBANsThe development and research in the domain of WBANs is only at an early stage.As a consequence,the terminology is not always clearly defined.In literature,protocols devel-oped for WBANs can span from communication between the sensors on the body to communication from a body node to a data center connected to the Internet.In order to have clear understanding,we propose the following defi-nitions:intra-body communication and extra-body com-munication.An example is shown on Fig.2.The former controls the information handling on the body between the sensors or actuators and the personal device[34–37],the Fig.2Example of intra-body and extra-body communication in a WBANlatter ensures communication between the personal device and an external network[32,38–40].Doing so,the medical data from the patient at home can be consulted by a phy-sician or stored in a medical database.This segmentation is similar to the one defined in[40]where a multi-tiered telemedicine system is presented.Tier1encompasses the intra-body communication,tier2the extra-body commu-nication between the personal device and the Internet and tier3represents the extra-body communication from the Internet to the medical server.The combination of intra-body and extra-body communication can be seen as an enabler for ubiquitous health care service provisioning.An example can be found in[41]where Utility Grid Com-puting is combined with a WBAN.Doing so,the data extracted from the WBAN is sent to the grid that provides access to appropriate computational services with highbandwidth and to a large collection of distributed time-varying resources.To date,development has been mainly focused on building the system architecture and service platform for extra-body communication.Much of these implementa-tions focus on the repackaging of traditional sensors(e.g. ECG,heart rate)with existing wireless devices.They consider a very limited WBAN consisting of only a few sensors that are directly and wirelessly connected to a personal device.Further they use transceivers with a large form factor and large antennas that are not adapted for use on a body.In Fig.3,a WBAN is compared with other types of wireless networks,such as Wireless Personal(WPAN), Wireless Local(WLAN),Wireless Metropolitan(WMAN) and Wide Area Networks(WAN)[42].A WBAN is operated close to the human body and its communication range will be restricted to a few meters,with typical values around1–2m.While a WBAN is devoted to intercon-nection of one person’s wearable devices,a WPAN is a network in the environment around the person.The com-munication range can reach up to10m for high data rate applications and up to several dozens of meters for low data rate applications.A WLAN has a typical communi-cation range up to hundreds of meters.Each type of net-work has its typical enabling technology,defined by the IEEE.A WPAN uses IEEE802.15.1(Bluetooth)or IEEE 802.15.4(ZigBee),a WLAN uses IEEE802.11(WiFi)and a WMAN IEEE802.16(WiMax).The communication in a WAN can be established via satellite links.In several papers,Wireless Body Area Networks are considered as a special type of a Wireless Sensor Network or a Wireless Sensor and Actuator Network(WSAN)with its own requirements1.However,traditional sensor networks do not tackle the specific challenges associated with human body monitoring.The human body consists of a complicated internal environment that responds to and interacts with its external surroundings,but is in a way separate and self-contained.The human body environment not only has a smaller scale,but also requires a different type and fre-quency of monitoring,with different challenges than those faced by WSNs.The monitoring of medical data results in an increased demand for reliability.The ease of use of sensors placed on the body leads to a small form factor that includes the battery and antenna part,resulting in a higher need for energy efficiency.Sensor nodes can move with regard to each other,for example a sensor node placed on the wrist moves in relation to a sensor node attached to the hip.This requires mobility support.In brief,although challenges faced by WBANs are in many ways similar to WSNs,there are intrinsic differences between the two,requiring special attention.An overview of some of these differences is given in Table2.A schematic overview of the challenges in a WBAN and a comparison with WSNs and WLANs is given in Fig.4.5Physical layerThe characteristics of the physical layer are different for a WBAN compared to a regular sensor network or an ad-hoc network due to the proximity of the human body.Tests with TelosB motes(using the CC2420transceiver)showed lack of communications between nodes located on the chest and nodes located on the back of the patient[46]. This was accentuated when the transmit power was set to a minimum for energy savings reasons.Similar conclusions where drawn with a CC2420transceiver in[47]:when a person was sitting on a sofa,no communication was pos-sible between the chest and the ankle.Better results were obtained when the antenna was placed1cm abovethe Fig.3Positioning of a Wireless Body Area Network in the realm of wireless networks1In the following,we will not make a distinction between a WSAN and a WSN although they have significant differences[43].body.As the devices get smaller and more ubiquitous,a direct connection to the personal device will no longer be possible and more complex network topologies will be needed.In this section,we will discuss the characteristics of the propagation of radio waves in a WBAN and other types of communication.5.1RF communicationSeveral researchers have been investigating the path loss along and inside the human body either using narrowband radio signals or Ultra Wide Band(UWB).All of them came to the conclusion that the radio signals experience great losses.Generally in wireless networks,it is known that the transmitted power drops off with d g where d represents the distance between the sender and the receiver and g the coefficient of the path loss(aka propagation coefficient)[48].In free space,g has a value of2.Other kinds of losses include fading of signals due to multi-path propagation.The propagation can be classified according to where it takes place:inside the body or along the body.5.1.1In the bodyThe propagation of electromagnetic(EM)waves in the human body has been investigated in[49,50].The body acts as a communication channel where losses are mainly due to absorption of power in the tissue,which is dissipated as heat.As the tissue is lossy and mostly consists of water, the EM-waves are attenuated considerably before they reach the receiver.In order to determine the amount of power lost due to heat dissipation,a standard measure of how much power is absorbed in tissue is used:the specific absorption rate(SAR).It is concluded that the path loss is very high and that,compared to the free space propaga-tion,an additional30–35dB at small distances is noticed.A simplified temperature increase prediction scheme based on SAR is presented in[50].It is argued that considering energy consumption is not enough and that the tissue is sensitive to temperature increase.The influence of a patient’s body shape and position on the radiation pattern from an implanted radio transmitter has been studied in [51].It is concluded that the difference between bodyTable2Schematic overview of differences between Wireless Sensor Networks and Wireless Body Area Networks,based on[45] Challenges Wireless sensor network Wireless body area networkScale Monitored environment(m/km)Human body(cm/m)Node number Many redundant nodes for wide area coverage Fewer,limited in spaceResult accuracy Through node redundancy Through node accuracy and robustnessNode tasks Node performs a dedicated task Node performs multiple tasksNode size Small is preferred,but not important Small is essentialNetwork topology Very likely to befixed or static More variable due to body movementData rates Most often homogeneous Most often heterogeneousNode replacement Performed easily,nodes even disposable Replacement of implanted nodes difficultNode lifetime Several years/months Several years/months,smaller battery capacityPower supply Accessible and likely to be replaced moreeasily and frequentlyInaccessible and difficult to replaced in an implantable setting Power demand Likely to be large,energy supply easier Likely to be lower,energy supply more difficultEnergy scavenging source Most likely solar and wind power Most likely motion(vibration)and thermal(body heat) Biocompatibility Not a consideration in most applications A must for implants and some external sensorsSecurity level Lower Higher,to protect patient informationImpact of data loss Likely to be compensated by redundant nodes More significant,may require additional measures to ensure QoSand real-time data deliveryWireless technology Bluetooth,ZigBee,GPRS,WLAN,…Low power technologyrequired。

A Unified Model of Internet Scale Alerting Services

A Unified Model of Internet Scale Alerting Services

A Unified Model of Internet Scale AlertingServicesAnnika Hinze,Daniel FaensenInstitute of Computer ScienceFreie Universit¨a t Berlin,Germanyhinze,faensen@inf.fu-berlin.deAbstractIn the last years,alerting systems have gained strengthened attention.Sev-eral systems have been implemented.For the evaluation and cooperation of thesesystems,the following problems arise:The systems and their models are not com-patible,and existing models are only appropriate for a subset of conceivable ap-plication domains.Due to modeling differences,a simple integration of differentalerting systems is impossible.What is needed,is a unified model that covers thewhole variety of alerting service applications.This paper provides a unified model for alerting services that captures the spe-cial constraints of most application domains.The model can serve as a basis foran evaluation of alerting service implementations.In addition to the unified model,we define a general profile structure by which clients can specify their interest.This structure is independent of underlying pro-file definition languages.To eliminate drawbacks of the existing non-cooperatingsolitary services we introduce a new technique,the Mediating Alerting Service(MediAS).It establishes the cooperation of alerting services in an hierarchical andparallel way.1IntroductionThe number of scientific publications doubles every10-15years[Odl95].Electronic publication becomes very popular.Since the readers do not want to be forced to re-gularly search for information about new documents,there is strong need for alerting services(AS).An alerting service keeps its clients informed about new documents and events they are interested in.But alerting services are not restricted to the area of scientific publications.Examples for applications that could benefit from alerting services are applications such as digital libraries,stock tickers,and traveler infor-mation systems.Currently,several implementations of alerting services already exist for the different applicational domains,such as Salamander[MJS97],Siena[Car98], Keryx[BK97a,BK97b,LRW97]or OpenCQ[LPT99,PL98,LPR98]and Conquer [LPTH99].The underlying models of these services do not meet all requirements found in applications suitable for wide area networks,such as digital libraries.Ad-ditionally,the models for existing alerting services mainly cover the applications the services are designed for.In this paper,we provide a unified model for alerting services that considers the special constraints of the different application domains.The interests of clients are1defined as so-called profiles.Since several profile definition languages are used in the different services,we give a general structure of profiles for alerting services,indepen-dently from the profile definition language.The large number of existing alerting services for a certain application domain has several drawbacks.The users have to define their interest at different services in different ways.The available notifications are mostly bound to the supply of individual services,information from different suppliers is not combined.We therefore introduce and propose the use of a Mediating Alerting Service(MediAS),that connects several suppliers and clients.The remainder of this paper is structured as follows:In Section2,we provide an overview of the structure and tasks of an alerting service.In Section3,we introduce scenarios for the conceivable application domains of alerting services and name the problems with existing alerting services in detail.Section4introduces our architectural model for alerting services and Section5outlines our event-based model.In Section6, we propose the use of a mediating alerting service.Section7provides an overview of some related systems and models.Section8gives some directions for our future work. 2Event Notification ServiceIn this section,we introduce the general structure and tasks of an alerting service.Alert-ing services connect suppliers of information and interested clients.In our example of scientific papers the suppliers are publishing houses and the clients are the interested scientists.Alerting services inform the clients about the occurrences of events on ob-jects of interest.Objects of interest are located at the supplier’s side.Events can be for example changes on existing objects or the creation of new objects,e.g.,the publication of a journal article.Clients define their interest by personal profiles.The information about the occurring events isfiltered according to these profiles and notifications are sent to the interested users(clients).Figure1depicts the data-flow in a high-level ar-chitecture of an alerting service.Keep in mind that the data-flow is independent from the delivery mode,such as push or pull.Figure1:Data-flow in an Alerting ServiceThe tasks of an alerting service can be subdivided into the following steps:First, the observable event classes are to be determined and offered to the clients.Then, the client’s profiles have to be defined and stored.The occurring events have to be observed andfiltered.Before creating notifications,the events are integrated in order to detect combinations of events(e.g.,two conferences happen to be at the same time). After duplicate recognition the messages can be buffered in order to enable efficient notification(e.g.,by merging several messages into one notification).According to a given schedule,the clients have to be notified.23Scenarios and Arising ProblemsWe present a collection of possible scenarios in which Internet-scale event services are applicable.The main purposes of this collection are the motivation and validation of an event model that covers all applications.Based on the given scenarios and the derived common requirements for alerting services,we will point out the problems with existing alerting services and their models.3.1ScenariosEvent services are applicable in a variety of scenarios of wide area network usage.In this section,we present a selection of these applications to demonstrate the need for a unified and extended event model and a Mediating Alerting Service.Stock ticker Selected stock values are pushed to registered clients.The clients sub-scribe to selected stocks.Notifications are delivered only to the subscribers.Delivery can be immediate(to paying customers)or deferred(by20min).Clients can be off-line.In that case,notifications are lost without consequences.A client can be a PC with an analysis software that reacts on events like threshold crossing of a share value by notifying its user or by reacting autonomously.Cardinality of the relation between supplier and client is usually,size of notification messages is small(a few bytes)but they are sent with high frequency. Encryption can be required.Objects of interest are identifiable in advance,the clients subscribe to objects by selecting from a given list of objects(out of).Digital Library In a digital library,users want to be notified on new publications they are interested in.They define their interest by specifying certain bibliographical meta-data(e.g.,a journal or an author)or by Information Retrieval-like r-mation suppliers can be publishing houses or universities’technical report servers.The offered documents reside on publisher’s side within a database,file systems,or other repositories.Within the profiles,the clients have to specify the source.Notifications can include the full document or a pointer to it(DOI,URL).To avoid an unnecessary high frequency of notification deliveries,users can specify a time in-terval(e.g.,weekly)within which notifications are collected and then delivered alto-gether.Since users do not know each supplier and do not want to register at differ-ent suppliers’interfaces,a service covering many suppliers and unified access to their repositories operates between clients and information sources.Departments or work-ing groups have overlapping user profiles.That allows for hierarchical cooperating alerting services to ensure scalability.Cardinality between suppliers and clients can be,notifications can be large (several MB).Frequency of notifications is low,delivery has to be guaranteed.Objects of interest are unknown at the time of profile definition and usually come into existence later.An arising problem is the notion of composed objects:A mathematical proof is part of an article,which is part of a journal issue,which in turn is part of a journal. Software Update Registered users of software(programs,data)automatically get updates pushed from their vendors via the Internet.To avoid too frequent delivery, users can specify that only every second update is really of interest.3While notification frequency is low,their size can become huge.It depends on previous events whether an update event is to be forwarded to the client.We call dependence of events on other events event patterns.Remote monitoring and control A power station is equipped with a variety of probes and sensors.Multiple devices of the same type ensure reliability by redun-dant measurement.Measured values are pushed in real-time to the monitors.Monitors are displaying devices supervised by a human,or software agents that evaluate the data and react,e.g.,by alerting a technician or by shutting down parts of or the whole power plant.Redundancy is not restricted to the probes and sensors.The bus for sending mea-surement values is multiplied.Monitoring is done at different places,several control rooms and replicated software agents.A client can request information from certain probes or probe classes.Additionally,it can require to be notified only if two or more probes deliver the same value or if a probe did not deliver new measurements for a certain time interval.Cardinality of supplier to client can be out of,that means1of re-dundant clients has to handle en event.Reliable connections and real-time delivery is required.Notification delivery in the case of at least two related event occurrences is another example of an event pattern.Events that indicate that nothing happened in a time interval are called passive events.Replication Services In a replication service,a DBS(orfile system or Web site) has knowledge of several mirror sites.These have to be notified on any changes that occur.With the notification,a change log(e.g.,transaction log)is delivered that allows the mirrors to update themselves.There is not necessarily a master.That means any “mirror”can accept local changes and notifies the other mirrors.Mirrors can define a profile to subscribe to subsets of the masters(or peer’s)repository(e.g.,“sports-related Web pages”,“transactions on private customer accounts”,or“small documents daily, larger ones only weekly”).To ensure consistency,the receiver can acknowledge the notification.Cardinality can range from to.Event producer and subscriber roles can switch continuously.Notifications can occur frequently(and then are small)or less frequently(bundling changes to large notifications).Mobile Computing Portable end-user devices lack the power to compute complex tasks,e.g.,the detection of certain conditions to alarm a share holder.Transfer of appli-cation logic to a server is a typical such scenario.Moreover,mobile devices cannot be online permanently.That rises(i)the need to buffer notifications and(ii)the necessity of defining complex event patterns in the profile.Mobile users of a digital library avoid automatic delivery of large documents by stating in their profile that only pointers to the objects are to be delivered.3.2Dimensions for Model EvaluationFrom the scenarios described in the previous section,the following dimensions to clas-sify and evaluate event models and alerting services emerge:Cardinality Associations between suppliers and clients cover the range from to .The Remote Monitoring and Control scenario shows that the notion of aout of cardinality is useful.4Notification size Depending on application type,the size of a notification can range from a few bytes(e.g.,stock ticker)to several megabytes(digital library,soft-ware update).By delivering pointers to objects instead of the objects themselves, the size can be reduced significantly.Notification frequency Can vary from high frequent(in the range of seconds)to,say, once a year or only once at all.Guaranteed delivery In a digital library,for instance,it is necessary to guarantee delivery of notifications even if clients are offline.Real time Remote monitoring and control can require real time delivery of notifica-tions.Passive events In some cases,it is useful to be notified if during a specified interval nothing happened,e.g.,if a server does not handle requests anymore.Event pattern Clients register for events that depend on other events.Composed objects Objects do not need to be atomic,but can consist of other objects(e.g.,journals consists of articles).Object repositories Clients can subscribe to repositories to get informed about the changes within that repository.To subscribe to information objects that do not exist at the time of the profile definition,clients refer to the repository the object will appear in.Profile definition Clients can subscribe to concrete objects(e.g.,by referring to their identifier),by specifying meta-data that describe the objects of interests or(in the case of digital libraries)using an IR-like query.Scalability Can be achieved by redundant alerting services(or duplicated parts of them).If profiles of different clients are overlapping,a hierarchy of cooperat-ing alerting services can improve scalability.Encryption Scenarios that cover delivery of privacy data or data that are liable for costs can require encrypted delivery.Encryption is handled on protocol level.Reliability and Acknowledgment In the case of remote monitoring and control,reli-able connections are required.Acknowledgments can be used to implement re-liable delivery.These characteristics will not be considered in our model,since they have no influence within the modeling level used here.In the following part,we show the drawbacks of the existing models for alerting ser-vices in covering the requirements derived from the different scenarios.1.Terminology:On the one hand there exist several names for this kind of service(Alerting Service,Notification Service,Profile Service,etc.),while on the other hand several different concepts are called notification service(see Section7).Additionally,the different models for alerting systems use identical terms to describe different concepts.For example consider the term Channel:In the CORBA model,an event channel is an intervening object that allows multiple suppliers to communicate with multiple consumers asynchronously[OMG97].5CDF[Ell97]or Netcaster Channel[Net]are similar to television broadcast chan-nels.In contrast to CORBA,a CDF-Channel has an observer function for the channel objects.Further evaluation of the implementation of alerting services with channels can be found in[FHS98].2.States of non-existing objects:In most event-based models,an event is definedas a state transition of an object of interest at a particular time,where the state of an object is the current value of all its attributes(e.g.,[OMG97,KR95]).Other definitions refer more to the observation of events and therefore identify events by their physical representation as messages(e.g.,[Car98,TIB]).Here,we tend to thefirst approach,as it is more complete and also covers the existence of un-observed events and is therefore the superordinate case.The binding of events to the object of interest cannot be weakened in general as the events are strongly related to the objects(opposite to clock-time events).Consider the case of a scientific paper or article that is published.This object (publication)appears at a specific time,the state of the object is then the content of the paper and its meta-data such as author and title.But what is the state of the object before it exists?So the terms of an event as a state transition of the object is not appropriate here.Rosenblum and Wolf[RW97]define an event as an instantaneous effect of the termination of an invocation of an operation on an object.This definition associates the event with the invoker of the operation instead of with the object of interest.As a consequence,the invoker has to com-municate with the observer in order to announce the event.It cannot be generally presupposed that invokers actively announce events to observers,due to several reasons,e.g.,they are not known to each other or suppliers of documents refuse to support the observer.posed objects:The notifications sent to the clients are the messages thatare seen as the physical representation of the events[Car98]that the clients are subscribed to.Since the events relate to identifiable objects of interest,the no-tifications contain or refer to these objects.(Example:The client subscribes to all articles by author X,the publishing house publishes a journal(the object)that contains an article by X,the client gets the journal,or rather the information about the journal.)However,clients are often not interested in whole documents or sites(they are interested in an article instead of the whole journal,or even ina single mathematical proof instead of the article),therefore,substructures needto be identifiable as objects.4.How to register that nothing happened:Example:“Send message if the valueof share S does not change for a period of days”(see Section3.1).Existing models of alerting services cannot handle this kind of profile,as neither is an operation performed on the object of interest,nor does the object of interest change its state.We are aware of the fact that this construct is contradictory to the intuitive notion of an event as something that happens.4Architectural ModelIn this section we introduce a general architectural model for alerting services that can be applied for existing implementations and is used for the identification of compo-nents involved in the event model presented in Section5.It serves as a basis for the6development of MediAS,the Mediating Alerting Service that notifies users of a digi-tal library of electronically available scientific publications from different suppliers.A diagram that shows the involved components and their relations is shown in Figure2.Figure2:Architectural Model of an Alerting Service Objects of interest are so-called information objects that are located at the sup-plier’s side,optionally in an object rmation objects can be persistent (e.g.,documents)or transient(e.g.,measured values).The objects can be organized hierarchically.Changes of theses objects(creation,update,deletion)are induced by an invoker. Responsibility of the observer is the detection of changes of single objects or in the object repositories.Change detection can be an active task of the observer,performed periodically,if the invoker does not inform the observer by itself.Any change is an event.A detailed definition of the term event will be given in Section5.1.Events are reported as materialized event messages to thefilter.Thefilter has knowledge of the client’s profiles and compares the event with the query part of the profiles.If a profile and event match,thefilter creates an event message and delivers it to the notifier.For the detection of event patterns,events are stored in the event repository.The notifier in turn checks the schedule part of the profile.If immediate delivery is demanded,the event message is edited according to the format specified by the client and delivered.Otherwise,it is buffered until the notifications become due.The notifier keeps track of the due-dates.The buffering of notifications is also needed for the notification of offline clients to guarantee delivery.Client’s profiles consist of two parts(see Section5.1).The query-profile(used by thefilter)specifies the set of information objects the client is interested in.In the meta-profile,a schedule,a notification protocol,and a notification format(e.g.,for data type conversion)are defined.Schedule,protocol and format are attributes used by the notifier.The components of the alerting service can be(and usually are)deployed and du-plicated for scalability and reliability.Invoker and object repository usually reside at the supplier’s side.Not all information suppliers implement an observer;an alerting service that covers this type of suppliers could implement an observer as a wrapper for each supplier.The observer is enforced to keep various information on the repository (e.g.,previous states)if it is not notified by the invoker and the supplier’s interface7does not offer a search for changes since a specified date.Alternatively,the observer can be moved to the supplier’s side(if allowed)and perform its tasks as an agent of the alerting service there.5Unified Event ModelIn this section,we introduce our event-based model for alerting services.First,we de-fine the terms used within the model,then we formally describe the tasks of a MediAS.5.1Terms and DefinitionsObject:In correspondence to other models,we use objects to encapsulate the func-tionality of model participants.In our model,an object can be any logical entity resid-ing on a hardware component within a network,such asfiles and processes.Hardware and human beings can also participate but are represented by their software-based prox-ies.Each object is uniquely identifiable,for simplicity reasons,we refer to the identifier as a handle(already used in[CGM97]).A handle can be,e.g.,a URL or a DOI.Con-siderations of a naming model for alerting services can be found in[RW97].The objects that are offered by suppliers,such as journals,news-pages,or movies,we call objects of interest.Objects have a state.The state of an object is the value of its attributes.A set of objects offered by a supplier is referred to as repository.A sup-plier can offer one or more repositories,examples are databases,web-sites,or a set of documents on an ftp-server.Since repositories can also be seen as objects of interest, we consider a hierarchy of objects,whereas the items within the repositories are called information rmation objects can also be composed of other objects,e.g., journals consist of articles.Therefore,objects need to carry information about their position within the hierarchy.Event:Based on the scenarios,we get a set of possible events regarding information objects:A new information object appears;existing information objects are changed; existing information objects are deleted;for a certain interval of time the information object remains unchanged.Similar to the model used for Event Ac-Figure3:Event Types tion Systems[KR95]we divide events into two classes:time events and object events (see Figure3).Time events involve clock times,dates,and time intervals.Object events involve changes of non-temporal objects.We additionally distinguish active and passive events.Active events are state transitions of the repository at a particular time;they are observer independent.State transitions can be actions such as insertion,deletion,or change of a data-object.In the context ofdatabases,as in digital libraries,the notion of state transition is in close relation to integrity constraints.Each transaction on the database underlies several constraints (e.g.,the key of a tuple has to be unique)and the operations are accepted only if the constraints are fulfilled.Allowed operations transfer the database from a valid state to another valid state.This process is called a state transition.Asfinal consequence,8both constraints and given set of values ensure an invariant state space.A state of a particular information object is a defined attribute of an object that has afinite number of predefined values(e.g.,the output of a logic circuit can be high,low,or high-Z, an interrupt-flag can be set or not).A state transition occurs if the attribute value is changed.Passive events involve counters and object properties at a specified time. They have to be observed.Passive events model the fact that for a given time interval an object did not change.Examples are also given in Section3.Profile:A profile is the formally defined information need of a client.Each profile consists of two parts,the description of the events the client wants to get notified about (query-profile)and the conditions for the notification(meta-profile).Within the query-profile,the clients specify the events they are interested in.For time events,we can distinguish between events given as points(),as intervals (,,or)or as frequencies(e.g.,weekly).Here the events are given as absolute values in time.These points,intervals,or frequencies can also be given in relation to another event,e.g.,“X weeks after the conference”(rela-tive).Other time events can be formed by using combinations.Relative time events and combinations can be seen as forms of event patterns,which are described later.For time events,client and server need to define a reference(e.g.,a common time zone). For the object events,clients have to define the objects,the attribute values they are interested in and the state transistion to observe on the object.Additionally,the repos-itory of the objects has to be defined(by giving the repository identity in the profile or indirectly by subscribing to services from special suppliers).The definition of the object repository is independent from the object itself since the same object can reside as duplicate on different repositories.Identification of objects can be done by giving1.the object handles,2.metadata about the objects,3.values of attributes of the objects.For example,if clients define their interest by giving similar objects(“Notify about all objects similar to THIS one”),this can be seen as a handling of(2)and/or(3). The subscription to subject-based Internet-channels is covered by(2),subscription to Internet-sites(favorites)refers to(1).For composed objects,the level of the object and the concerned attributes within the hierarchy should also be given(e.g.consider the journal-example).For active object events,clients have to define the objects as described above,further attribute values they are interested in and the type of state transition to be observed on the object.Possible state transitions are the occurrence of a new object,the change of an object,or the removal of an object.The change of an object can concern the structure(changing set of attributes,changing range of attributes),or the values of the attributes.Additionally,clients can be interested in the number of values,if it changes or in different changes of the value itself.Conceivable are values that change from point or interval to point or interval(e.g.,“Notify,if value X is no longer in”, change from interval to interval).Passive events need to specify the objects and their attributes(see active events),and constraints as time or counters.9In addition to primitive events(time and object events),clients can specify event pat-terns.Event patterns are combinations of events.A pattern can include any number of events combined with unary,binary,n-ary-operators.Conceivable operators are, for instance,the sequence operator or Boolean operators in combination with time constrains.The sequence operator reflects the temporal order of the events(e.g.imple-mented in Siena[Car98]).Examples of query-profiles are:-“Notify,when in an issue of journal X an article about topic Z is published”:object event,object identification by metadata,state transition:new object ap-pears),-“Notify,when in database X the value of attribute Y is larger than Z”:object event,object identification by handle,state transition:change of object, change:attribute value from interval to interval),-“Notify,if the temperature in room X is constant for the time period Y”:passive event,object identification by handle-“Notify,iffile X is changed after message Y appeared”:event pattern of two object events,sequence operator,object identification by handle-“Notify,if two articles with title X appear in different journals”:event pattern of two object events,Boolean AND with unlimited time period, object identification by meta-dataThe following items have to be defined within a meta-profile:1.Content of notification(e.g.,object-handle,object itself,meta-data describingthe object/event and/or their number),2.Structure of notification(number of events reported in the message,instructionsfor the merging of notifications,ranking mechanism),3.Notification-protocol(e.g.e-mail,desktop-icon,download),4.Time-policy for event detection(frequency of observations),5.Time-policy for notification such as scheduled(e.g.,daily)or event-dependent(e.g.,on events or depending on event-attributes,such as“X weeks before theconference”).Observer:Observers detect events.The event observation can be triggered by the invoker,by a time policy for observation or by a profile(for passive events).Observers can be part of the alerting service or reside on suppliers side.A MediAS can be dis-tributed so that it employs several observers.Notification:A notification is a message reporting about events.Clients are notified according to the time-policy given in their profiles.Notifications created by observers have to be evaluated to discover patterns or duplicates.Before sending notifications, depending on the profile,they have to be edited(e.g.,duplicate removal,merging, formating)in order to ensure that clients get only one notification at the time.10。

EN 10083-1

EN 10083-1
Supersedes ÖNORM EN 10083-1:1997-05
Steels for quenching and tempering
Part 1: General technical delivery conditions
Vergütungsstähle – Teil 1: Allgemeine technische Lieferbedingungen Aciers pour trempe et revenu – Partie 1: Conditions techniques générales de livraison
1
Scope ......................................................................................................................................................4
ON-Download Service 04.05.2010
Continuation EN 10083-1 pages 1 to 25
Publisher and printing: Österreichisches Normungsinstitut, 1020 Wien Copyright © ON - 2006. All rights reserved; No part of this publication may be reproduced or utilized in any form or by any means – electronic, mechanical, photocopying or any other data carriers without prior permission from ON! Sale and distribution of national and foreign standards and technical regulations via Österreichisches Normungsinstitut (ON), Heinestraße 38, 1020 Wien Tel.: (+43 1) 213 00-805, Fax: (+43 1) 213 00-818, E-Mail: sales@on-norm.at, Internet: http://www.on-norm.at
相关主题
  1. 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
  2. 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
  3. 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。

Modeling hardness of polycrystalline materials and bulk metallic glassesXing-Qiu Chen *,Haiyang Niu,Dianzhong Li *,Yiyi LiShenyang National Laboratory for Materials Science,Institute of Metal Research,Chinese Academy of Sciences,Wenhua Road No.72,Shenyang 110016,Liaoning,PR Chinaa r t i c l e i n f oArticle history:Received 24February 2011Accepted 23March 2011Available online 2June 2011Keywords:E.Mechanical properties,theory B.Elastic propertiesa b s t r a c tThough extensively studied,hardness,de fined as the resistance of a material to deformation,still remains a challenging issue for a formal theoretical description due to its inherent mechanical complexity.The widely applied Teter ’s empirical correlation between hardness and shear modulus has been considered to be not always valid for a large variety of materials.The main reason is that shear modulus only responses to elastic deformation whereas the hardness links both elastic and permanent plastic properties.We found that the intrinsic correlation between hardness and elasticity of materials correctly predicts Vickers hardness for a wide variety of crystalline materials as well as bulk metallic glasses (BMGs).Our results suggest that,if a material is intrinsically brittle (such as BMGs that fail in the elastic regime),its Vickers hardness linearly correlates with the shear modulus (H v ¼0.151G ).This correlation also provides a robust theoretical evidence on the famous empirical correlation observed by Teter in 1998.On the other hand,our results demonstrate that the hardness of polycrystalline materials can be correlated with the product of the squared Pugh ’s modulus ratio and the shear modulus (H v ¼2ðk 2G Þ0:585À3where k ¼G /B is Pugh ’s modulus ratio).Our work combines those aspects that were previously argued strongly,and,most importantly,is capable to correctly predict the hardness of all hard compounds known included in several pervious models.Ó2011Elsevier Ltd.All rights reserved.1.IntroductionDespite the great efforts,to understand the theory of hardness and to design new ultrahard materials are still very challenging for materials scientists [1e 4].During the past few years,several semi-empirical theoretical models [5e 9]have been developed to estimate hardness of materials based on:(i )the bond length,charge density,and ionicity [5],(ii )the strength of the chemical bonds [6],(iii )the thermodynamical concept of energy density per chemical bonding [7],and (iv )the connection between the bond electron-holding energy and hardness through electronegativity [8],and (v )the temperature-dependent constraint theory for hardness of multicomponent bulk metallic glasses (BMGs)[9].Experimentally,hardness is a highly complex property since the applied stress may be dependent on the crystallographic orientations,the loading forces and the size of the indenters.In addition,hardness is also characterized by the ability to resist to both elastic and irreversible plastic deformations and can be affected signi ficantly by defects (i.e.,dislocations)and grain sizes [10].Therefore,hardness is not a quantity that can be easily determined in a well-de fined absolutescale [1].It has been often argued [13]that hardness measurements unavoidably suffer from an error of about 10%.All these aspects add huge complexity to a formal theoretical de finition of hardness [5e 9].Within this context,to find a simple way to estimate hardness of real materials is highly desirable.Unlike hardness,the elastic properties of materials can be measured and calculated in a highly accurate manner.Therefore,it has been historically natural to seek a correlation between hardness and elasticity.The early linear correlation between the hardness and bulk modulus (B)for several covalent crystals (diamond,Si,Ge,GaSb,InSb)was successfully established by Gilman and Cohen since 1950s [10,11].Nevertheless,successive studies demonstrated that an uniformed linear corre-lation between hardness and bulk modulus does not really hold for a wide variety of materials [1,12,13],as illustrated in Fig.1(a).Subsequently,Teter [12]established a better linear correlation between hardness and shear modulus (G ),as illustrated in Fig.1(b).This correlation suggests that the shear modulus,the resistance to reversible deformation under shear strain,can correctly provide an assessment of hardness for some materials.However,this correla-tion is not always successful,as discussed in Refs.[5,13,14].For instance,tungsten carbide (WC)has a very large bulk modulus (439GPa)and shear modulus (282GPa)but its hardness is only 30GPa [15],clearly violating the Teter ’s linear correlation [see*Corresponding authors.E-mail addresses:xingqiu.chen@ (X.-Q.Chen),dzli@ (D.Li).Contents lists available at ScienceDirectIntermetallicsjou rn al homepage:/locate/intermet0966-9795/$e see front matter Ó2011Elsevier Ltd.All rights reserved.doi:10.1016/j.intermet.2011.03.026Intermetallics 19(2011)1275e 1281Fig.1(b)][5].Although the link between hardness and elastic shear modulus can be arguable,it is certain to say that the Teter ’s correlation grasped the key.In this manuscript,following the spirit of Teter ’s empirical correlation,we successfully established a theoretical model on the hardness of materials through the introduction of the classic Pugh modulus ratio of G /B proposed in 1954[16].We found that the intrinsic correlation between hardness and elasticity of materials correctly predicts Vickers hardness for a wide variety of crystalline materials as well as BMGs.Our results suggest that,if a material is intrinsically brittle (such as BMGs that fail in the elastic regime),its Vickers hardness linearly correlates with the shear modulus (H v ¼0.151G ).This correlation also provides a robust theoretical evidence for the famous empirical correlation observed by Teter in 1998.On the other hand,our results demonstrate that the hardness of crystalline materials can be correlated with the product of the squared Pugh ’s modulus ratio and the shear modulus (H v ¼2ðk 2G Þ0:585À3where k is Pugh ’s modulus ratio).This formula provides the firm evidence that the hardness not only correlates with shear modulus as observed by Teter,but also with bulk modulus as observed by Gilman et al.Our work combines those aspects that were previously argued strongly,and,most importantly,is capable to correctly predict the hardness of all compounds included in Teter ’s [12],Gilman ’s [4,10],Gao et al.’s [5]and Sim u nek and Vacká r ’s [6]sets.Also,our model clearly demonstrates that the hardness of bulk metallic glasses is intrin-sically based on the same fundamental theory as the crystalline materials.We believe that our relation represents a step forward for the understanding and predictability of hardness.2.Model and resultsAccording to Vicker [10],the hardness of H v is the ratio between the load force applied to the indenter,F ,and the indentation surface area:H v ¼2F sin ðq =2Þd 2;(1)where d and q are the mean indentation diagonal and angle between opposite faces of the diamond squared pyramid indenter,respectively (Fig.2).In order to derive our model,we first assume that (i)the diamond squared pyramid indenter can be divided into four triangular based pyramid indenters and that (ii)the Vickers hardness is measured within the elastic scale.Then,for each triangular based pyramid,one can de fine the shear modulus G as,G ¼F4A tan ða Þ(2)which speci fies the ratio between shear stress and the shear strain.In terms of our model the exact shear area A on which the shear force (F )acts is unknown.But,the deformation area A *[A *¼1/8d 2tan(a )]delimited by the klO 0triangle is well de fined by the indentation geometry.Therefore,we can express the exact shear area (A )as:A ¼cA *¼c d 2tan ða Þ;(3)where c is the proportional coef ficient.It is clear that under elastic shear deformation the deformation area (A *)will be extremely small.However,upon real hardness measurements the deforma-tion area (A *)should be large enough so that the coef ficient c can be safely neglected and A z A *.Under this assumption,equation (2)can be revised as following,G ¼2F d 2tan 2ða Þ(4)Combining equations (1)and (4),the Vickers hardness readsH v ¼G tan 2ða Þsin ðq =2Þ¼0:92G tan 2ða Þ;(5)where the term sin(q /2)is intrinsically determined by the indenter itself,which can be considered as a constant (originated from the Vickers hardness,see equation (1)).For the diamond squared pyramid indenter with q ¼136 ,sin(q /2)is equal to 0.92for Vickers hardness measurement.In an ideal form of indentation,tan(a )¼0.404because of a ¼(p Àq )/2.0(c.f.,Fig.2).Therefore,equation (5)can be simpli fiedas,abFig.1.Correlation of experimental Vickers hardness (H v )with (a)bulk modulus (B )and with (b)shear modulus (G )for 39compounds (Table 2).Inset of panel (b):H v vs.G for 37BMGs (see Table 1).The solid line denotes empirical Teter ’s fitting values,whereas dashed lines correspond to the value derived from Eq.(6).The black and hollow squares denote data taken from Refs.[1,12].Fig.2.Illustration of indentation in terms of the squared diamond pyramid indenter.The red framework highlights one of four triangular based pyramid indenters.(For interpretation of the references to colour in this figure legend,the reader is referred to the web version of this article.)X.-Q.Chen et al./Intermetallics 19(2011)1275e 12811276H v¼0:151G:(6) Equation(6)represents a robust theoretical evidence of the linear correlation behavior observed by Teter[12],as reflected by the data shown in Fig.1(b).Residual discrepancies should be mainly attributed to the neglection of plastic deformation effects. Remarkably,we found that Eq.(6)is also valid for ing the experimental shear modulus G¼38.6(36.6)GPa[17]for Pd40Ni40P20BMG,the estimated Vickers hardness is5.83(5.53) GPa,in consistency with the experimental value of5.38GPa[17]. Similarly,for Fe41Co7Cr15Mo14C15B6Y2BMG by using the experi-mental G(84.3GPa[18])we obtained H v¼12.7GPa,in nice accord with the measured Vickers hardness of12.57Æ0.22GPa[18].As illustrated in the inset of Fig.1(b),the agreement with the experi-mental values is highly satisfactory for all37BMGs collected here (see Table1).Considering that BMGs are brittle materials without plastic fractures the data in the inset of Fig.1(b)strongly convey that our proposed formula(Eq.(6))is intrinsically connected to the shear modulus of materials if they fail in an elastic regime.It is highly difficult to realistically take plastic deformation into account in our modeling scheme.However,the indentation after a real hardness measurement shows the permanent plastic effect,which is,of course,reflected by the ratio l OO0=l OO00[namely, equal to tan(a)](see Fig.2).Note that the depth of the indenta-tion,l OO0,is parallel to the direction of shear deformation.We reasonably assume that its size should be closely correlated with the shear modulus of G,whereas the expansion wideness of the indentation,l OO00,is perpendicular to the direction of the loading force,hence,with almost little connection to the shear defor-mation.Therefore,the expansion wideness seems to reflect the ability to resist to compression effects,a property that should be related to bulk modulus,B.Accordingly,we proposed the following relation,tanðaÞf G=B:(7) Finally,combining equations(5)and(7),the hardness can be written as,H v f GðG=BÞ2:(8) It is interesting to note that in Eq.(8)the ratio of G/B is the famous modulus ratio proposed by Pugh in as early as1954[16].In his pioneer work,Pugh derived that the strain at fracture can be measured as3f(B/G)2.Indeed,hardness can be defined as the resistance to the applied stress at the critical strain of3that the system can sustain before yielding it to fracture.This clearly provides fundamental support for our model(Eq.(8)).Importantly, Pugh also highlighted a relation between the elastic and plastic properties of pure polycrystalline metals and stated that G/B is closely correlated to the brittle and ductility of materials[16]:the higher the value of G/B is,the more brittle the materials would be [16].Otherwise,the materials are expected to deform in a ductile manner with a low G/B value.This relation has been extensively accepted and applied not only to metal but also to high-strength materials[25e27].In principles,the covalent materials(such as diamond and c-BN)have the highest hardness but they are obvi-ously brittle with a larger Pugh modulus ratio.The strong covalent bonds indeed create a significant resistance to initialize the plastic flow to pin the dislocation,resulting in a quite high hardness. Conversely,ductile materials with a low Pugh’s modulus ratio are characterized by metallic bonding and low hardness.It is thus highly reasonable to establish a correlation between hardness and the modulus ratio G/B in Eq.(8).Thus,we revise further Eq.(8)in the following form,H v¼Ck m G n;ðk¼G=BÞ;(9)where H v,G and B are the hardness(GPa),shear modulus(GPa)and bulk modulus(GPa),respectively.The parameter k is the Pugh’s modulus ratio,namely,k¼G/B.C is a proportional coefficient.In order to derive the parameters C,m and n,wefirst selected ten materials with diamond-like(diamond,c-BN,b-SiC,Si and Ge), zinc-blende(ZrC and AlN)and rock-salt structures(GaP,InSb and AlSb)because their hardness,bulk and shear moduli are well-known(see Table2).By analyzing these data we found that C¼1.887,m¼1.171and n¼0.591.Hence,Eq.(9)becomes,H v¼1:887k1:171G0:591z1:887k2G0:585:(10) In order to assess the validity of this relation,we plot in Fig.3H v against k2G for a series of compounds.These data show a clear and systematic trend with k2G andfirmly establish a direct relation between hardness and k2G.Byfitting the data of Fig.3and revising further Eq.(10)we arrive to thefinal formula:Table1The comparison between the predicted Vickers hardness by Eq.(6)[H v¼0.151G]andthe experimental data for37bulk metallic glasses is shown,together with experi-mental Young modulus(E)and Shear modulus(G).The[XX];[YY]in thefirst columndenotes the reference numbers:XX is the reference for elastic constants and YY isthe reference for the Vickers hardness.Compounds E G H calc H expFe41Co7Cr15Mo14C15B6Y2[18]22684.312.7312.57Ni50Nb50[19]13248.27.268.93Ni40Cu5Ti17Zr28Al10[20]133.949.77.508.45Ni39.8Cu5.97Ti15.92Zr27.86Al9.95Si0.5[20]11743 6.498.13Ni40Cu5Ti16.5Zr28.5Al10[20]12245.2 6.837.84Ni45Ti20Zr25Al10[20]11442 6.347.76Ni40Cu6Ti16Zr28Al10[20]11140.9 6.187.65{Zr41Ti14Cu12.5Ni10Be22.5}98Y2[21]107.640.3 6.09 6.76Zr54Al15Ni10Cu19Y2[21]92.133.8 5.10 6.49Zr53Al14Ni10Cu19Y4[21]8631.5 4.76 6.44Zr41Ti14Cu12.5Ni8Be22.5C1[21]10639.5 5.96 6.13Zr46.75Ti8.25Cu7.5Ni10Be27.5[19]10037.2 5.62 6.1Zr48Nb8Cu14Ni12Be18[21]93.734.2 5.16 6.09Zr34Ti15Cu10Ni11Be28Y2[21]109.841 6.19 6.07Zr57Nb5Cu15.4Ni12.6Al10[19]87.331.9 4.82 5.9Zr48Nb8Cu12Fe8Be24[21]95.735.2 5.32 5.85Zr40Ti15Cu11Ni11Be21.5Y1Mg0.5[21]94.234.7 5.24 5.74Zr41Ti14Cu12.5Ni10Be22.5[19];[21]10137.4 5.65 5.97Zr41Ti14Cu12.5Ni10Be22.5[19];[22]10137.4 5.65 5.4Zr41Ti14Cu12.5Ni10Be22.5[19];[23]10137.4 5.65 5.88Zr41Ti14Cu12.5Ni10Be22.5[19]10137.4 5.65 5.23Zr65Al10Ni10Cu15[19]8330.3 4.58 5.6Zr65Al10Ni10Cu15[19]8331 4.58 5.6Zr57Ti5Cu20Ni8Al10[19]8230.1 4.55 5.4Cu60Hf10Zr20Ti10[19]10136.9 5.577Cu50Zr50[19]88.732.4 4.83 5.8Cu50Zr50[19]8532 4.83 5.8Cu50Zr45Al5[19]10233.3 5.03 5.4Pd40Ni40P20[19];[17]10838.6 5.83 5.38Pd40Ni40P20[17]e36.6 5.53 5.38Pd40Ni40P20[19]10838.6 5.83 5.3Pd40Ni10Cu30P20[19]9835.1 5.305Pd77.5Si16.5Cu6[19]92.932.9 5.25 4.5Pd77.5Si16.5Cu6[19]9634.8 5.25 4.5Pt60Ni15P25[19]9633.8 5.10 4.1Mg65Cu25Tb10[19]51.319.6 2.96 2.83Nb60Al10Fe20Co10[19]51.219.4 2.93 2.2Ce70Al10Ni10Cu10[19]3011.5 1.74 1.5Er55Al25Co20[24]70.7227.08 4.09 5.45Dy55Al25Co20[24]61.3623.52 3.55 4.7Tb55Al25Co20[24]59.5322.85 3.45 4.42Ho55Al25Co20[24]66.6425.42 3.84 4.14La55Al25Co20[24]40.915.42 2.33 3.48La55Al25Cu10Ni5Co5[24]41.915.6 2.363Pr55Al25Co20[24]45.917.35 2.62 2.58X.-Q.Chen et al./Intermetallics19(2011)1275e12811277H v ¼2 k 2G 0:585À3(11)from which we see that the hardness correlates not only with the shear modulus but also with the bulk modulus.Physically,the bulk modulus only measures isotropic resistance to volume change under hydrostatic strain,whereas shear modulus responses to resistance to anisotropic shear strain.Although the bulk modulus was thought to be less directly connected with hardness [5],the Pugh ’s modulus ratio k clearly contributes to the Vickers hardness.Equation (11)demonstrates that,if bulk modulus increases,hard-ness would decrease as long as the shear modulus remains unchanged,and vice versa.This behavior can be understood by the fact that if the Pugh ’s modulus ratio G /B gets smaller with increasing bulk modulus,the material would become more ductile.Its hardness can be thus expected to have a lower value.Taking the example,the compounds TiN and b -SiC have almost the same experimental shear moduli [28,29]of 187.2GPa and 191.4GPa,Table 2Comparison between theoretical values within our current model and experimental values as compared with available theoretical findings obtained through the modelsof Gao et al.[5]and of Sim u nek and Vack a r [6].Furthermore,the bulk and shearmoduli are compiled in this table.In the last column,“e ”and “c ”denote elastic data (G and B )from direct experimental and theoretical calculations,respectively.The Pugh ’s modulus ratio k is compiled in this table.For details,see text.The [XX];[YY]in the first column denotes the reference numbers:XX is the reference for elastic constants and YY is the reference for the Vickers hardness.Note that,as the experimentally measured hardness is a highly complex property,which depends on the loading force and the quality of samples.For instance,the hardness of diamond was reported in the range from 60GPa to 120GPa.For the sake of convenientcomparisons with the known Gao et al.[5]and Sim u nek and Vack a r ’s [6]models,here we also quoted the same values as previous models quoted [5,6,7,8,9].If compounds were not discussed in the previous theoretical models [5,6,7,8],here the experimental Vicker ’s hardness was selected,mainly based on the saturated Vickers hardness value (namely load-invariant indentation hardness)at a relatively large loading force.For instance,for ReB 2,we selected the Vicker ’s hardness of 30.1GPa at the large loading forces of 4.9N as discussed in Ref.[46].Compounds G B k H calc H exp H Gao H SimunekDiamond [28];[5]535.5442.3 1.21195.79693.695.4e Diamond [28];[5]548.3465.5 1.17893.99693.695.4c Diamond [35];[5]520.3431.9 1.20593.59693.695.4c Diamond [12];[5]535.0443.0 1.20895.49693.695.4e BC 2N [36];[37]446.0403.0 1.10776.9767871.9c BC 2N [12];[37]445.0408.0 1.09175.4767871.9e BC 5[38];[32]394.0376.0 1.04866.771c c-BN [39];[5]405.4400.0 1.01465.26664.563.2e c-BN [28];[5]403.4403.70.99963.86664.563.2c c-BN [28];[5]382.2375.7 1.01763.16664.563.2c c-BN [40];[5]404.4384.0 1.05368.26664.563.2c c-BN [12];[5]409.0400.0 1.02366.26664.563.2e g -B 28[41];[33]236.0224.0 1.05449.050c B 60[12];[42]204.0228.00.89536.438e b -SiC [28];[5]191.4224.70.85232.83430.331.1e b -SiC [28];[5]196.6224.90.87434.53430.331.1c b -SiC [43];[5]190.2209.20.90935.53430.331.1c b -SiC [44];[5]186.5220.30.84632.03430.331.1e b -SiC [12];[5]196.0226.00.86734.13430.331.1e SiO 2[12];[12]220.0305.00.72129.03330.4e ReB 2[45];[46]273.0382.00.71532.930.1e WC [47];[5]301.8438.90.68833.43026.421.5e WC [5]282.0439.00.64229.33026.421.5e B 4C [48];[12]192.0226.00.85032.830a e VC [This work];[6]209.1305.50.68526.22927.2c ZrC [49];[30]169.7223.10.76126.325.8e ZrC [50];[30]182.5228.30.79929.425.8c ZrC [50];[30]185.9228.00.81530.525.8c ZrC [51];[30]169.6223.30.75926.225.8e ZrC [12];[30]166.0223.00.74425.225.8e TiC [49];[6]182.2242.00.75327.124.718.8e TiC [52];[6]176.9250.30.70724.524.718.8c TiC [15];[6]198.3286.00.69325.824.718.8c TiC [53];[6]187.8241.70.77728.824.718.8e TiC [12];[6]188.0241.00.78029.024.718.8e TiN [54];[30]183.2282.00.65022.52318.7c TiN [29];[30]187.2318.30.58820.02318.7e TiN [55];[30]205.8294.60.69926.72318.7c TiN [56];[30]207.9326.30.63723.82318.7c RuO 2[57];[5]142.2251.30.56615.72020.6c RuO 2[58];[5]173.0248.00.69823.72020.6c AlO 2[40];[5]161.0240.00.67121.52020.6c AlO 2[40];[5]160.0259.00.61819.22020.6c AlO 2[59];[5]164.0254.00.64620.72020.6e AlO 2[12];[5]162.0246.00.65921.12020.6e NbC [60];[6]171.0333.00.51315.51818.3c NbC [51];[6]171.7340.00.50515.21818.3e AlN [40];[5]134.7206.00.65418.41821.717.6c AlN [61];[5]130.2212.10.61416.51821.717.6c AlN [62];[5]123.3207.50.59415.21821.717.6c AlN [63];[5]132.0211.10.625171821.717.6e AlN [12];[5]128.0203.00.63116.91821.717.6e NbN [64];[6]155.9292.00.53415.41719.5e NbN [12];[6]156.0315.00.49513.91719.5e HfN [28];[30]186.3315.50.59120.017c HfN [28];[30]164.8278.70.59118.417c GaN [65];[5]105.2175.90.59813.715.118.118.5e GaN [12];[5]120.0210.00.57114.115.118.118.5eTable 2(continued )Compounds G B k H calc H exp H Gao H SimunekZrO 2[40];[5]88.0187.00.4718.41310.8c ZrO 2[66];[5]93.0187.00.4979.51310.8e Si [49];[5]66.697.90.68011.81213.611.3e Si [67];[5]64.097.90.65410.91213.611.3c Si [67];[5]63.290.70.69711.81213.611.3c Si [68];[5]61.796.30.64010.21213.611.3c Si [68];[5]61.789.00.69311.51213.611.3c GaP [51];[5]55.788.20.6319.39.58.98.7e GaP [49];[5]55.888.80.6289.29.58.98.7e GaP [47];[5]56.188.60.6339.49.58.98.7e GaP [70];[5]61.989.70.69011.59.58.98.7c AlP [71];[5]49.086.00.5707.19.49.67.9e AlP [70];[5]51.890.00.5757.59.49.67.9c AlP [72];[5]48.886.00.5677.09.49.67.9c InN [65];[6]55.0123.90.444 5.1910.48.2c InN [71];[6]77.0139.60.5529.7910.48.2c Ge [67];[6]53.172.20.73611.38.811.79.7c Ge [67];[6]43.860.30.7269.58.811.79.7c GaAs [47];[5]46.575.00.6207.87.58.07.4e GaAs [69];[5]46.775.50.6197.87.58.07.4e GaAs [49];[5]46.775.40.6197.87.58.07.4e YO 2[28];[5]72.5166.00.437 6.37.57.7c YO 2[28];[5]62.7146.50.428 5.37.57.7c YO 2[73];[5]66.5149.30.445 6.07.57.7e InP [69];[5]34.371.10.483 3.8 5.4 6.0 5.1e InP [47];[5]34.472.50.475 3.6 5.4 6.0 5.1e AlAs [74];[5]44.877.90.575 6.758.5 6.8e AlAs [69];[5]44.678.30.569 6.558.5 6.8e GaSb [69];[6]34.256.30.607 5.8 4.5 6.0 5.6e GaSb [49];[6]34.156.40.606 5.8 4.5 6.0 5.6e GaSb [47];[6]34.356.30.608 5.8 4.5 6.0 5.6e AlSb [75];[5]31.556.10.561 4.64 4.9 4.9c AlSb [69];[5]31.958.20.549 4.54 4.9 4.9e AlSb [49];[5]32.559.30.548 4.64 4.9 4.9e AlSb [47];[5]31.958.20.549 4.54 4.9 4.9e InAs [69];[5]29.557.90.509 3.6 3.8 5.7 4.5e InAs [49];[5]29.559.10.499 3.4 3.8 5.7 4.5e InSb [47];[5]23.046.90.490 2.4 2.2 4.3 3.6e InSb [69];[5]22.946.50.492 2.4 2.2 4.3 3.6e InSb [49];[5]22.946.00.498 2.5 2.2 4.33.6e ZnS [49];[6]32.878.40.418 2.5 1.8 2.7e ZnS [47];[6]31.577.10.408 2.3 1.8 2.7e ZnSe [47];[6]28.863.10.456 2.7 1.4 2.6e ZnTe [47];[6]23.451.00.459 2.11 2.3e ZnTe [49];[6]23.451.00.4592.112.3eaB 4C was suggested to be very hard in Ref.[76].Mukhanov et al.recently pre-dicted that the Vickers hardness of B 4C was 45.0GPa [85]in agreement with the reported experimental data of 45GPa in Ref.[77](see Table 1in Ref.[85]).However,we also noted that Teter [12]ever summarized the hardness of B 4C with a value of 30Æ2GPa.In addition,the experimental value of 32e 35GPa was recently summarized in the handbook [78].Therefore,here we quoted the experimental Vickers hardness of 30GPa,as summarized by Teter in Ref.[12].X.-Q.Chen et al./Intermetallics 19(2011)1275e 12811278respectively.However,the experimental bulk modulus of TiN (318.3GPa)[28,29]is larger by about 42%than that of b -SiC (224.7GPa).In terms of our formula,b -SiC is found to be harder than TiN,in agreement with the experimental observations [5,30][Expt (Calc in this work):TiN with H v ¼23(20)GPa and b -SiC with H v ¼34(33)GPa].To further assess the performance of our model (Eq.(11))we show in Fig.4a comparison between the estimated and experi-mental values for a series of compounds (see Table 2),con firming a good agreement.Also WC,which is wrongly found to be a superhard (49GPa)material within Teter ’s linear correlation,is now predicted to have a Vickers hardness of 29.3GPa in very good accordance with experimental value (30GPa [15]).In particular,Figs.3and 4convey that our proposed formula reproduced very well the Vickers hardness for all well-known superhard materials (Diamond [5,6],BC 2N [5,6,12,31],c-BN [5,6],c-BC 5[32],and g -B 28[33,34]).The interesting case is the compound of ReB 2,which was thought to be superhard [79].Although its Vickers hardness was debated extensively [79e 81],there is now a wide-accepted consensus that its Vickers hardness of 30.1GPa at the large loading force of 4.9N [45,46].Using the experimentally measured bulk and shear moduli [45](B ¼273GPa and G ¼382GPa)and interms of our Eq.(11),the Vickers hardness is derived to be 32.9GPa,in nice agreement with the experimental data [45,46].In addition,we also noted that Qin et al.claimed that the Vickers ’hardness of ReB 2is only about 18GPa for a densi fied compact sample [82].However,this value was very recently demonstrated experimen-tally not to re flect well the intrinsic hardness of ReB 2[83]because that the amorphous boron,which is not detectable only by powder X-ray diffraction (Fig.2in Ref.[82]),exists in the synthetic samples in the Qin et al.’s experiment [82].As reported in Ref.[83],the ReB 2sample synthesized by spark plasma sintering exhibits a Vickers hardness of 27.0Æ 4.7GPa.Through a tri-arc crystal-growing furnace the synthesized high-purity ReB 2crystal with grains oriented with respect to the c-axis has a high hardness value of 39.5Æ2.5GPa [83].Again,our estimated Vickers hardness value is still within these experimental values.Another attention has to be paid to the case of B ing the experimental bulk and shear moduli (B ¼230GPa and G ¼206GPa)[84],its Vickers hardness is calculated to be 36.7GPa within our current model.This value is well within the scale of the experimentally measured results from 32to 38GPa [42,84]for polycrystalline boron suboxide sintered samples,although a Vick-ers hardness of 45GPa was reported for the single crystals under a loading force of 0.98N [42].Indeed,the light loading force of 0.98N is not large enough to obtain a real hardness.It is thus ex-pected to have a smaller hardness if a loading force larger than 0.98N is applied.Our estimated value for B 6O is also in good agreement with the derived value of 37.3GPa through a very recent thermodynamic model of hardness [83].We further estimated two more phases of carbon (C 4and M -carbon),which were suggested to be superhard [86e 90].Utilizing elastic shear and bulk moduli obtained in Ref.[88],the Vickers hardness of C 4is calculated to be 69.0GPa (c.f.,Fig.3)that is comparable to the superhard c-BN.Moreover,through using the calculated bulk and shear moduli (B ¼415GPa and G ¼468GPa [91])for the M -carbon phase,we obtained its Vickers hardness of 81.0GPa (c.f.,Fig.3),placing M-carbon in between BC 2N and dia-mond,agreeing well with the value (83.1GPa)obtained by Simunek ’s model [87].In addition,from Fig.4all estimated data are in good agreement with those obtained from pervious models [5,6].Nevertheless,we would like to emphasize that,although our proposed model canreproduce well the results obtained by Gao et al.’s [5]and Sim u nekand Vack a r [6]models,the underlying mechanism is substantiallydifferent.Gao ’s and Sim u nek and Vack a r ’s models are based onbond properties such as bond length,charge density,ionicity and their strengths and coordinations in crystalline materials.Differ-ently,our model depends totally on the so-called polycrystalline moduli (bulk and shear moduli as well as Pugh ’s modulus ratio),which indeed response directly to the abilities of resistance under loading forces for polycrystalline materials.As demonstrated above,for polycrystalline materials the introduced Pugh ’s modulus ratio in our model plays a crucial role in elucidating plastic deformation,which is intrinsically different from all known semi-empirical hardness models [5e 9].3.Discussion and remarksThe hardness of a material is the intrinsic resistance to defor-mation when a force is applied [1].Currently,a formal theoretical de finition of hardness is still a challenge for materials scientists.The need for alternative superhard and ultrahard materials for modern technology has brought a surge of interest on modeling and predicting the hardness of real materials.In particular,in recent years several different semi-empirical models for hardness of polycrystalline covalent and ionic materials have beenproposed.Fig.4.Correlation between experimental and theoretical Vickers hardness (H v )for 39compounds,as compared with the estimated data from the models [5,6](see Table 2).Fig.3.Experimental Vickers hardness as a function of the product k 2G (k ¼G /B ).All data are collected from literature (see Table 2).X.-Q.Chen et al./Intermetallics 19(2011)1275e 12811279。

相关文档
最新文档