10-Intergrade Example

CHALMERSWireless Networks ProjectReportMobile Ad-hoc Network (MANET)Group 5Azadeh Hosseinzadeh 810911-6742Molood Noori Alavijeh 850913-5789Wenjing Li 860323-T120Yang Wang 850426-5110Table of contentsTable of contents (2)Review Question (3)Introduction (4)MANET (4)MANET Features (5)Mobile IP (6)Problems and issues concerning combination of MANET andMobile IP (6)Mobile IP for ad hoc network (8)Features of the suggested method (8)Adapting Mobile IP to MANET (9)1.Advertisements broadcasting by Foreign Agents: (9)2.Detecting nodes movement: (9)Summary of Study of Simulation (11)Results (12)Conclusion (12)Future work (13)Reference: (13)Review QuestionHow Mobile IP for ad hoc network method does works in short?IntroductionIn today’s communication world, wireless technique and mobility computing are gaining more and more popularity. Wireless devices are widely accepted and used by movable devices such as cell phones, laptops, and PDAs.A recent very hot research area concerning IP mobility is mobile ad hoc network (MANET). It is a self-configuring network of mobile devices which are connected by wireless links. Outstanding features of MANET compared to normal wireless network are that no fixed infrastructure is needed, it is easy to set up and it can transmit beyond the range of normal fixed access point. In recent years, there have been tremendous researches in developing MANET routing in order to achieve mobility feature. Many routing protocols have been proposed including several using on-demand routing ones. [1] In our study of simulation session, an on-demand routing protocol named AODV will be applied to the emulation environment.On the other hand, in wireless internet environment Mobile IP is a standard protocol to support IP mobility which has been wildly accepted. Mobile IP allows a host to roam from one subnet to another while retaining the same IP-address. Within MANET, IP mobility is restricted to the ad hoc network area and because nodes in ad hoc network are mobile, roaming of nodes between different ad hoc networks cannot be avoided.Therefore there is a need to make use of Mobile IP and MANET together.In this project we have looked at how ad hoc networks in which on-demand routing is used can be connected to the Internet and how to provide nodes in the ad hoc network with the roaming services that Mobile IP enables.Mobile IP for Mobile Ad Hoc Networks is a solution to use Mobile IP to provide nodes within MANET with internet. From a technical point of view, there can be a simulation environment for testing the suggested solution for combining MANET and MIP. Since the expenses of the simulation is too high for a university term paper,in this project the results of the simulation that has been performed in Carnegie Mellon University about this issue is also going to be studied. MANETMANET is a self-configuring network and it is a type of wireless ad hoc networks that became popular due to the increasing application of laptops and 802.11/Wi-Fiwireless networking since 1990s. In a MANET, mobile devices are connected by any number of wireless links, every device is free to move independently in any arbitrary direction and thus its links to other devices will be changed on a regular basis. Therefore, each device is also a router to forward traffic to other devices. A MANET may operate in isolation, or may be connected to a fixed network. MANET FeaturesMANETs have several important characteristics different from the higher-speed, semi-static topology of the fixed Internet. These characteristics create some basic assumptions and performance concerns for MANET’s protocol design.(1)Dynamic topologies: Devices are mobile and free to move arbitrarily andthe change of the connectivity among the devices and network topology may be unpredictable.(2)Multi-hop routing: a MANET uses multi-hop routing instead of staticnetwork infrastructure to provide network connectivity. Multi-hop is more complex than single-hop in terms of structure and implementation, but even more in functionality and applicability.(3)Bandwidth-constrained, variable capacity links: Wireless links will havelower capacity than their hardwired counterparts. In addition to the effects of multiple access, fading, noise, and interference conditions.(4)Energy-constrained: Some or all of the devices in a MANET may rely onbatteries.(5)Limited physical security: The increased possibility of security threatsshould be carefully considered. Mobile wireless networks are usually easier to threats than are fixed cable nets. Existing link security techniques are often applied within wireless networks.MANET routing protocol AODV is an on-demand distance vector protocol. Therefore periodic routing table exchanges do not exist. As soon as a node requires communicating with another node, the routes are set up. When a node does not have any information about the route to the node it wants to communicate with, a rout discovery starts in order to set up a path from the source to the destination and vice versa.[6]Mobile IPThe Mobile IP is a standard defined by IETF to support IP mobility for mobile device users when they are moving from one network to another.[3]Transparent to higher level protocols like TCP and UDP and applications it allows users with mobile devices to stay connected without changing IP address when roaming between networks with a different IP addresses.In order to keep existing transport layer connections, like TCP, during roaming every mobile node get a home address which is connected with the endpoint of a tunnel to mobile node home agent,.With this home address the mobile devices become able to receive data in such a way that it was on its home network ignoring its current location on the Internet.[4]When a user leaves its home network and enters a foreign network it uses an IP address valid on foreign network called care-of address which recognize its current location. The care of address must be changed whenever a mobile node moves from one network to another. A mobile node in a foreign network is named a visiting node.A visiting node has to register its current care of address with its home agent while visiting a foreign network to represent it within its home network and makes it able to receive data. It will be done by registering through a foreign agent that is located in foreign network.Problems and issues concerning combination of MANET and Mobile IPSince an ad hoc network has special characteristics and it is substantially different from those of the fixed internet, connecting an ad hoc network to the internet raise several issues concerning routing and providing nodes with IP addresses with possibility to route from fix internet. Also, Mobile IP for MANET solution brings up some problems because it has been based on using foreign care-of address.In this part, some of these routing and addressing issues are going to be argued and also problems of using Mobile IP within a multi-hop ad hoc network are going to be discussed.On the other hand, since ad hoc network (MANET) is a self-configuring network of mobile devices which can come and go as they want the two aspects of fix network which are the capability to use one route to entire network instead of having one route per host and the capability to use default routes have not been required. Moreover, mobile ad hoc network must be able to perform without any centralized control and it should be formed by any number of nodes without necessity to use any special network ID and unconcerned of addresses they use.It indicates that it is not possible to understand if a node is a part of special network by looking at the network ID. Also default routes are not useful since ad hoc network doesn`t have hierarchy and it is totally autonomous. Consequently, routing in MANET is usually accomplished using just host specific routes.IP multi-hop communication within ad hoc network is another characteristic of MANET that differentiates it from a fixed network. IP layer routing must be used by nodes to attain a gateway between fixed internet and ad hoc network since they do not have link-layer connectivity with all other nodes in the ad hoc network. Another problem that has to be concern is, it is impossible for nodes to find out the location of the destination by looking at the destination`s network ID due to do not having any assumptions about their network ID`s. Moreover, since on-demand routing has been used in ad hoc network and routs are only set up when they are needed, nodes are not able to expect to have routes to all obtainable hosts within the ad hoc network.The route discovery mechanism of MANET protocol has to look for the destination inside the ad hoc network before making decision about if the destination is in the ad hoc network or not because do not having a host route does not necessarily mean that it is impossible for nodes to reach it inside the ad hoc network.Furthermore, using Mobile IP for internet access in a multi-hop ad hoc network also brings several problems since IP mobile is designed generally for fixed internet and wireless leaf networks. It has been designed to have the foreign agent and the visiting node on the same link. Packets to the visiting node are forwarded by the foreign agent during link layer connectivity .On the other hand, in ad hoc network, the foreign agent and a visiting node might not have link-layer connectivity and it use multi-hop communication instead. So Mobile IP has to rely on the network routing protocol that has been used in ad hoc network for routing packets between the foreign agent and mobile node when it applied to MANET. Multi-hop communication has also influence on the movement detectionmechanism supported by Mobile IP.A visiting node has to count on the routing protocol to decide if there is a root to the foreign agent or not since it doesn`t have access to foreign agent by using link-layer feedback .[2][8]Mobile IP for ad hoc networkIn our project we studied a method to enable MANET nodes to access Internet and obtain mobility services of Mobile IP which is called Mobile IP for ad hoc network. This method uses Mobile IP foreign agents as access points to the Internet.The way this method works as follows in short:1) Any node within an ad hoc network that wants to access Internet always uses their home address for any interactions and communications. When the node roams from the home network to a new network it registers with a foreign agent.2) For any node to send a packet to a host on the Internet, tunnel the packet to the currently registered foreign agent.3) For any node to receive packets from hosts on the Internet. The packets will be delivered to foreign agent through ordinary Mobile IP mechanisms. The foreign agent will then send the packet to the node using ad hoc routing protocol. Features of the suggested methodThis method allows a host to move from one subnet to another while retaining the same IP-address. Since the host retains the same address all the time it can be used to contact the host without knowing its physical location. A session between two hosts can continue even if one of them moves. Each host has a router called its home agent. The home agent is the router for the subnet belonging to the host’s home address. When a host moves, it registers with a foreign agent who is willing to serve and informs its home agent of its new address, called it's care of address. When a host B wants to send a packet to another host A the packet is routed to the home agent of A. The agent knows the care of address of A and forwards the packet there. When host A sends packets to host B it uses its home address as the source address. The pattern of the routing is that of a triangle, from host B to the home agent of A, and finally back to host B.Tunneling mechanism makes it possible that this method can incorporate the default route concept into AODV without big modifications, when a node tries to send a packet, the packet is firstly delivered within the ad hoc network, if the destination cannot be found, it will be tunneled to the foreign agent preserving that it has already registered with the foreign agent.When a packet is tunneled to the foreign agent, the IP address of that foreign agent is required to be encapsulated into the packet outer IP header. The ad hoc routing protocols will tunnel the packet by looking at the source address and destination address. If no route to the foreign agent can be found, a searching mechanism directed by ad hoc routing protocols will be activated. If the node does not have any connection with any foreign agent, it considers the destination as unreachable.A feature called reverse tunneling is used by having the Foreign Agent tunnel packets back to the Home Agent when it receives them from the Mobile Node. Adapting Mobile IP to MANETWhen a visiting node roams from one subnet to another using Mobile IP foreign agent, this node must have link-layer connectivity with the foreign agent. At the meanwhile, link-layer connecting cannot be obtained in a mobile ad-hoc network. In order to identify the connection an upper layer-network layer is used, i.e. IP addresses.In the following sections the different Mobile IP mechanisms that are affected by mobile IP for ad hoc network will be discussed further.1.Advertisements broadcasting by Foreign Agents:Broadcasting advertisements is a very important feature in wireless communication which enables all related units keeps information updated. In Mobile IP the minimal time interval between two advertisements is 1 second.This can lead to a message over flooding problem in Mobile ad hoc network considering MANET uses multi-hop mechanism. Though the most suitable time interval has not been derived, a period of 5 seconds will be used in the study of simulation section later on.2.Detecting nodes movement:Mobile IP provides several algorithms on detecting nodes movement i.e., Lazy Cell Switching (LCS), Eager Cell Switching (ECS). However, none of themcould meet the requirement of MANET since MANET uses multi-hop mechanism in which several hops may occur between the foreign agent and the visiting node. For example, LCS principle suggests that a node should use the same foreign agent for as long time as possible. However as MANET uses multi-hop, this creates time delay problems, if a new foreign agent is discovered to be much closer (for example 3 hops less) than the one that the node is currently registered, the node can still not switch to it. Problems when applying ECS to MANET emerges as that it assumes movement along a straight line. [8] ECS does not allow a visiting node switch freely between different agents which may cause the same time delay problems as described in LCS.Mobile IP for ad hoc network provides a new method using hop counts to decide which agent should be chosen. The mobile IP for MANET Cell Switching (MMCS) algorithm suggests that a registered visiting node should register with another foreign agent if it is at least two hops closer to this foreign agent than the foreign agent that it is currently registered through, for two consecutive agent advertisements.(a)All nodes are currently registered with Foreign Agent 1. Node D is moving towards Foreign Agent 2.(b) Node D has registered to Foreign Agent 2 now since there exist two more hops than to connect to Foreign Agent 1Figure.1 Mobile IP for MANET Cell Switching Algorithm ExampleMMCS is an extension of ECS. It helps visiting nodes easier to connect them to a more efficient foreign agent and also helps prevent frequent switching and minimize the chances that the visiting node will connect to a temporary seemingly stable agent. Figure.1 shows how MMCS works.Summary of Study of SimulationIn order to study the different mechanisms of Mobile IP for MANET the Carnegie Mellon University has set up the Monarch project in Network Simulator 2which actually simulates this mechanism. The routing protocol they have used is AODV. The periodic agent advertisement is the main mechanism that is studied in the simulations.The mechanisms for adapting Mobile IP to an on-demand ad hoc network include broadcast and unicast approaches. Switching between foreign agents is impossible in the unicast approach.The study of simulation presented in this section intends to evaluate the differences between the two approaches of broadcast and unicast.This experience includes 15 mobile nodes that move randomly over a rectangular (1000m x 500m) flat space for 900 seconds and two foreign agents, one on each side of the rectangle and there are a number of wired nodes as well i.e. home agents and their corresponding nodes but our focus will be on the wireless part only. The total number of visiting nodes in the network is the core parameter in the simulations which demonstrates the relative movement between nodes.ResultsFor both the broadcast and unicast approaches, the number of visiting nodes that reply to the request for registration is supposed to be almost linear.In the broadcast approach the two foreign agents flood the network with their agent advertisements periodically. In the unicast approach foreign agents only send agent advertisements to those nodes which are registered with them.Since unicast generates more control packets the AODV overhead is higher. Therefore when the percentage of visiting nodes is low the unicast approach is better than the broadcast approach. But this statement is only true when more than half of the nodes are visiting nodes and so the broadcast approach generates less overhead than the unicast approach. So when there are no visiting nodes the broadcast approach generates more traffic than the unicast approach.Because the overhead of the protocol is larger in the unicast approach,the total number of transmissions is higher.A lost packet might be a result of a broken link and therefore the overhead of routing is larger in the unicast approach.In the broadcast approach typically the nodes switch about 2 times but in the unicast approach they are not able to reregister with a closer foreign agent. Although in the unicast approach, a node might switch a foreign agent can continue to send agent advertisements to it which is why sometimes a visiting node is reregistered with two foreign agents at the same time. Switching to the closest foreign agent is good because the delay of traverse will be kept at minimum. ConclusionIn this project, we have studied on the theory of MANET, Mobile IP and make our focus on the solution of integrating these two techniques which enables IP mobility of mobile nodes in MANET between networks. Regarding to the issues when applying Mobile IP to mobile ad hoc networks, this method uses Mobile IP foreign agents as access points to the Internet and ad hoc routing protocol when information sent between the foreign agent and the visiting node. Also a scheme of movement detection and the use of reverse tunneling to Internet access points are included. The simulation study has evaluated from the two different approaches, namely, unicast and broadcast. Broadcasting is better than unicasting at all times in sending agent advertisements periodically except when no visiting nodes are in MANET.The outcomes of the simulation illustrate that being close to a foreign agent leads to reduction of traffic load and delay of packets. Therefore spreadingthe information about the existence of foreign agents is valuable even though it has some disadvantages.Future workBecause of the lack of experiences and time in the study of MANET area, we didn’t implement the simulation ourselves. This might be some future work for us. At the same time, there are some related areas we didn’t work through but are still very interested in. For instance, Optimized Link State Routing Protocol (OLSR), Study of Node Mobility, Multicast and Dynamic address allocation. Reference:[1]David B. Johnson and David A. Maltz, Mobile computing,chapter 5, pp. 153-181, Kluwer Academic Publishers,1996[2]Yu-Chee Tseng, Chia-Ching Shen, and Wen-Tsuen Chen," Mobile IP and Ad Hoc Networks: An Integration and Implementation Experience", Computer, 2003[3] Charles E. Perkins, “Mobile IP,” IEEE Communications Magazine, May 1997.[4] Hui Lei and Charles E. Perkins, “Ad hoc networking with Mobile IP,” in Proceedings of 2nd European Personal Mobile Communication Conference, 1997[5] “The Internet Engineering Task Force (IETF),” Webpage, /.[6] David A. Maltz, Josh Broth, a d David B. Johnson, “The effects of on-demand behaviour in routing protocols for ad hoc networks,” IEEE Journal on Selected Areas of Communications, 1999[7] Charles E. Perkins and Elizabeth M. Royer, “Ad-hoc on-demand distance vector routing,” in Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, Feb. 1999.[8] Ulf Jonsson, Fredrik Alriksson, “MIPMANET -Mobile IP for Mobile Ad Hoc Networks”, IEEE, 2000.。

英语作文,写关于互联网的发明1971年全文共6篇示例，供读者参考篇1The Amazing Story of How the Internet Was BornHi there! My name is Jamie and I'm going to tell you all about how the internet was invented way back in 1971. It's a pretty wild story involving lots of really smart people and some huge computers. Get ready, because this is going to blow your mind!It all started in the 1960s when computers were these massive machines that took up entire rooms. Scientists and researchers used them to do lots of heavy duty calculations and store information. However, each computer could only work on its own. There was no way for the different computers to share data and talk to each other over long distances.Some super geniuses at places like MIT and the University of California had a brilliant idea - what if they could connect all these mighty computers together into one giant network? That way, researchers across the country could easily share information and work together on projects. A scientist inCalifornia could send data straight to a computer in Massachusetts! Revolutionary, right?The guy who really got the ball rolling on this "computer network" concept was a mathematician named Leonard Kleinrock. In 1961, he wrote the first paper about something called "packet switching" which is a fancy way to transmit data between computers. Kleinrock's theories became the basis for how information would travel through this future network.A few years later in 1965, an MIT computer expert named Lawrence G. Roberts took Kleinrock's ideas and proposed an actual technological plan to build this network. He called it the "ARPANET" which stood for the "Advanced Research Projects Agency Network." The special agency in charge of developing new technology for the military loved Roberts' networked computer concept and gave him a bunch of funding to make it happen.After years of tireless work engineering both the software and hardware, the first two nodes of the ARPANET were established in 1969 at UCLA and Stanford. A "node" is just a single computer connected to the network. By the end of 1969 there was a tiny ARPANET made up of four nodes located atUCLA, Stanford, UC Santa Barbara, and the University of Utah. Baby steps, but it was the birth of the internet!Over the next couple years, the ARPANET kept adding more nodes at universities and research centers across the United States. By 1971, there was a fully operational network of 15 nodes all sharing resources and data across the country. It was slow, clunky, and certainly not user-friendly. But it was officially the first working model of the internet as we know it today!One of the key breakthroughs that really expanded the ARPANET's capabilities came in 1971 when a computer programmer named Ray Tomlinson invented a way for network messages to be sent between different computers. Up until that point, the messaging capabilities of the ARPANET were pretty limited. But Tomlinson figured out how to send messages from one node to another by putting the recipient's address and the sender's address separated by an @ sign.Forexample:raymailbox@************************** format for email addresses to this day! Tomlinson's invention of network email kicked the ARPANET into overdrive and totally transformed how researchers could collaborate together over the network. No more having to mail documents or transfer datausing physical storage media. You could just type out a message and instantly send it through the network!By the mid 1970s, the ARPANET had proven that a functional computer network for sharing data was not only possible, but incredibly useful. However, it was still pretty exclusive and limited to the military, universities, and research labs working on defense-funded projects. That all changed in the late 1970s and into the 1980s when protocol standards like TCP/IP were developed, allowing different networks to join together and form an "internet."More user-friendly tools like the World Wide Web, web browsers, search engines and graphical user interfaces were also created in the 1980s and 90s. This transformed the internet into a global network open to the public, not just restricted to the military or academics. Ordinary people could now access this wealth of information and communicate across the world!So while the roots of the internet trace back to 1971 and the ARPANET, it really took about 20 years of continuous innovation, engineering and improvement by thousands of people before the modern internet emerged. And now here we are today where the internet impacts just about every aspect of our lives - how we work, study, shop, get entertainment, socialize with friends, andso much more! All because a bunch of big brains decades ago had a vision for an interconnected world of shared knowledge.It still blows my mind just how far the internet has come in my lifetime. From those first few clunky nodes sending basic messages and data over old school modems and phone lines. To today's high speed fiber optic networks beaming all of human knowledge to your smartphones and laptops with just a few taps. The story of the internet's invention and evolution is one of remarkable teamwork, perseverance and human ingenuity. And I'm excited to see what incredible new internet technologies get invented next!篇2The Awesome Invention of the InternetDid you know that the internet was invented way back in 1971? That was over 50 years ago! It's hard to imagine life without being able to go online, watch videos, play games, and chat with friends any time we want. But there was a time when the internet didn't exist at all!It all started with a computer scientist named Leonard Kleinrock. He was one of the first people to have the crazy idea that computers should be able to talk to each other and shareinformation over long distances. Up until then, computers just worked alone without being connected.In 1969, a team led by Kleinrock set up the first computer network called ARPANET. It connected computers at universities and research labs across the United States. ARPANET sent its first message in 1969, but it had some issues at first. The first message they tried to send was "LOGIN" but the system crashed after the first two letters "LO"! They had to reboot everything and try again.Two years later in 1971, a computer programmer named Ray Tomlinson invented a way for people to send messages between computers on ARPANET. He came up with the idea of the @ symbol for email addresses. So the first email in history might have looked something like "************************." Pretty cool, right?At first, ARPANET was mostly used by scientists, researchers, and universities to share data and research. But more people started joining the network and by the 1980s, there were thousands of computers connected! This eventually led to the internet we know today.In 1990, a computer scientist in Switzerland named Tim Berners-Lee took ARPANET to the next level and invented the World Wide Web. He came up with rules for formatting pages with text, images, and links using HTML and HTTP protocols. This made it much easier to view and navigate between pages on the internet.Berners-Lee's boss didn't think the World Wide Web was a good idea at first! He wrote in his notebook "Vague, but exciting..." Luckily, he let Berners-Lee keep working on it, and the World Wide Web has grown into the amazing resource we use every day.From those early days of connecting just a few university computers, the internet has expanded all around the world. Now there are over 4.5 billion people using the internet! We can go online from computers, phones, tablets, gaming systems, and even watches.The internet connects people for communication, education, entertainment, business, and so much more. We can learn about any topic, watch movies and shows, play multiplayer games with friends across the world, read news from any country, take classes online, and discover amazing things 24/7.Just think how different your life would be without the internet! You couldn't video chat with your cousins, watch unboxing videos of the latest toys, or download new games and apps. Your parents and teachers wouldn't be able to quickly look up information or do online research.Basically, the world has been completely transformed because of the invention of the internet back in 1971. What started as a small network called ARPANET to connect a few university computers has grown into a global phenomenon used by billions every single day.The internet opens up a world of knowledge, connections and opportunities that didn't exist before the 1970s. We owe a lot to the computer scientists, programmers and inventors who pioneered things like ARPANET, email, and the World Wide Web. Their important work shaped the modern internet age we live in today.I can't even imagine trying to write a report or do homework without being able to go online and access information from anywhere in the world. The internet brings the world to our fingertips! It has made learning, communicating, and being entertained so much easier.While the internet can have some downsides if it's not used properly, overall it has been an amazing advancement for the world. The next time you go online to watch a video, chat with friends, or look something up, remember that it all started with an idea over 50 years ago to connect computers together. Thank you Leonard Kleinrock, Ray Tomlinson, Tim Berners-Lee and the other internet pioneers!篇3The Amazing Story of the InternetHi friends! Today I'm going to tell you all about one of the most incredible inventions of the 20th century - the internet! The internet has changed our lives in so many ways. We use it for school projects, playing games, watching videos, and keeping in touch with friends and family. But where did this awesome technology come from?It all started way back in 1971 at a research lab in California. There was a computer scientist named Ray Tomlinson who sent the very first email! Can you believe people used to communicate without email? It must have been so slow having to wait for letters to arrive in the mail.Ray's email was just a simple test message to himself, but it proved that computers could talk to each other over a network. This was huge news! Up until then, computers could only do calculations on their own. Connecting them together opened up a whole new world of possibilities.After Ray's successful test, other scientists quickly realized how useful this computer network could be. They started connecting more and more computers located at different universities and research centers. By 1973, this early version of the internet linked almost 40 different machine language computer systems!As the network grew bigger, scientists had to find better ways to organize and route all the data being sent across the connections. They created protocols, which are kind of like the internet's language and traffic rules. These protocols help all the computers communicate properly.One of the most important protocols was called TCP/IP, created in 1978. It's kind of like the internet's address system that lets data find its way to the right destination. Another key part of the internet was created in 1991 - the World Wide Web. This allowed words, pictures, sounds and videos to be shared easily between computers, not just plain data.Once the World Wide Web arrived, the internet really started taking off. More and more people began connecting their home computers and using the web to find information on almost any topic imaginable. Businesses also rushed to create websites to attract customers.The web was made even awesomer in 1993 with the release of Mosaic, one of the first easy-to-use web browsers with menus and clickable links. Up until then, using the web had been a lot more complicated with just text and codes. But Mosaic helped open up the internet to everyone, not just experts.As the 90s rolled along, the internet grew like crazy! More homes got online, email became a daily thing, and Google launched in 1998 to help people search the massive new ocean of info on the world wide web.And this incredible resource all started with one simple test email sent by a scientist named Ray Tomlinson. If he could see how the internet has transformed our lives, education, business, entertainment and more, I bet he'd be totally blown away!I'm sure the internet will keep evolving and getting even smarter as new technologies are invented. Maybebrain-computer links or something crazy like that! For now, I'mjust grateful we have this awesome tool to learn, create, and connect with others near and far. Thanks, internet!篇4The Amazing Internet: How It All BeganHave you ever wondered how the Internet started? It's hard to imagine a world without it, isn't it? Well, get ready to be amazed because the story of how the Internet was invented is like something straight out of a sci-fi movie!It all began a long, long time ago, way back in 1971. That's like, a gazillion years ago in grown-up years! Back then, computers were these massive, room-filling machines that looked like they came from outer space. They were nothing like the sleek laptops and smartphones we have today.In those days, scientists and researchers were trying to figure out how to connect these giant computers so they could share information. You see, each computer had its own little world of data, and they couldn't talk to each other. It was like having a bunch of kids at a playground, but they all spoke different languages and couldn't understand each other.That's when a brilliant scientist named Ray Tomlinson came up with a mind-blowing idea. He invented something called "email," which allowed people to send messages from one computer to another. It was like having a secret code that every computer could understand!But Ray wasn't done yet. He realized that if computers could send messages to each other, they could probably share all kinds of information too. That's when the idea for the Internet was born!Now, you might be thinking, "But how did they actually build this Internet thingy?" Well, it was a team effort by some of the smartest people on the planet. They were like a group of superheroes, each with their own special power.There was Vint Cerf, who helped create the rules and protocols that allowed computers to communicate with each other. Then there was Bob Kahn, who figured out how to break up information into tiny packets that could be sent across the network. And let's not forget Leonard Kleinrock, who did a bunch of mathematical calculations to make sure the whole system would work smoothly.Together, these brilliant minds (and many others) worked tirelessly to make the Internet a reality. They had to overcome allsorts of challenges, like figuring out how to route information across different networks and making sure everything was secure.Finally, after years of hard work, the Internet was born! It was like a brand new world, where information could travel at the speed of light and people could connect with each other from anywhere on the planet.At first, the Internet was just a small network used by scientists and researchers. But as more and more people discovered its awesomeness, it started to grow and grow. Pretty soon, universities, businesses, and even regular folks like you and me were getting connected.Nowadays, the Internet is everywhere. We use it to do our homework, watch funny videos, play online games, and so much more. It's like having a magical portal to all the knowledge and entertainment in the entire universe, right at our fingertips.But we can't forget the pioneers who made it all possible. People like Ray Tomlinson, Vint Cerf, Bob Kahn, and Leonard Kleinrock are the real superheroes of the Internet age. Without their brilliant ideas and hard work, we might still be living in a world where computers couldn't talk to each other.So, the next time you're surfing the web, playing an online game, or video chatting with your friends, take a moment to appreciate the amazing invention that is the Internet. It all started with a dream, a bunch of really smart people, and a whole lot of hard work. Who knows what other mind-blowing inventions are waiting to be discovered in the future?篇5The Awesome Invention of the InternetHi everyone! Today I want to tell you all about one of the most awesome inventions ever - the internet! The internet is this really cool thing that lets computers all around the world connect and share information. Isn't that just mind-blowing?It all started way back in 1971. That was over 50 years ago, even before my parents were born! Back then, computers were these huge, room-sized machines. They took up so much space and weren't very powerful compared to our phones and laptops today. Pretty crazy, right?There was this guy named Ray Tomlinson who helped create the first email program. Email is where you can send messages to people over the internet. Ray sent the first email ever to himselfin 1971! I can't even imagine a world without email. How did people communicate before that?Another really important person was Vint Cerf. He's known as one of the "fathers of the internet." In 1973, he helped develop something called the Transmission Control Protocol (TCP). This protocol defined how data could be transmitted between multiple networks. Basically, it's what allowed computers on different networks to connect and share data with each other over the internet.Things really started taking off in the 1980s and 1990s. More and more people started getting personal computers and hooking them up to the internet. Websites started popping up left and right. You could suddenly access all this information from anywhere with an internet connection!One of the earliest websites was the CERN website launched in 1991. CERN is this big science lab in Switzerland. Their website gave instructions on how to access other websites and even let people make their own web pages. It helped spread website creation tools to the public.Then in 1993, things got even crazier when a student created a web browser called Mosaic. A web browser is like a window that lets you view and navigate different websites. Mosaic madethe internet way more user-friendly with its point-and-click interface. More and more people started "surfing" the web after that.Nowadays, the internet is absolutely everywhere! We use it for school projects, playing games, watching videos, listening to music, and so much more. Internet companies like Google, Facebook, and Amazon have become some of the biggest companies in the world.We can video chat with our friends and family across the world with just a few clicks. We can learn about any topic imaginable by looking it up online. Heck, I'm even writing this essay about the internet's history while connected to the internet! How meta is that?The internet has changed our lives in so many amazing ways. And to think it all started with those early computer scientists just trying to connect a few machines together. Just imagine how it will keep evolving over the next 50 years! Who knows what awesome new internet inventions are coming our way?I don't know about you, but I'm super grateful for the internet. Giving us the ability to access infinite information and connect with others globally is one of the greatest inventions of all time in my book. Three cheers for the internet!篇6The Awesome Invention of the InternetHave you ever thought about how the internet started? It's such a big part of our lives now - we use it for school, games, watching videos, and keeping in touch with friends and family. But it wasn't always around! The internet as we know it today was invented way back in 1971. That's over 50 years ago! Can you imagine life without it?It all began with a computer scientist named Leonard Kleinrock. He was one of the first people to start thinking about how to connect computers together so they could share information. In 1969, he helped develop the first computer network called ARPANET. This allowed computers at different universities to send messages to each other.Two years later in 1971, another very smart man named Ray Tomlinson invented a way for people to send messages between these connected computers. He came up with the idea of an "@" symbol to separate the username from the computer name. This became the first email system! Pretty cool, right?With email, ARPANET grew quickly as more computers joined the network. Scientists used it to share research and data.But back then, you couldn't just sign up for an email account like Gmail or Outlook. Only certain universities, companies, and government organizations had access.In the 1980s, some researchers wanted to make the network more open and user-friendly. They created new protocols and systems for linking computers around the world. One of the key people was a computer scientist named Vint Cerf. Some even call him one of the "Fathers of the Internet!"By the 1990s, this globally connected network of computers had become known as the "internet." More regular people started to use it, not just scientists. Can you believe that in 1992 there were only about 1 million internet users worldwide? That's a tiny fraction of the billions today!One of the biggest changes came in 1994 when the World Wide Web became popular. This is the internet system of websites, hyperlinks, and browsers that we're all familiar with now. A computer programmer in Switzerland named Tim Berners-Lee is credited with inventing the World Wide Web.Once the web took off, the internet grew at an absolutely mind-blowing rate. More and more people wanted to use it for communication, information, entertainment, shopping, you name it. Companies raced to set up websites. Schools startedteaching students computer skills. The internet changed everything!Nowadays, the internet connects nearly every computer and mobile device on the planet. Billions of people use it every single day. We can instantly access any information, communicate with anyone, watch or listen to anything, play games with friends across the world, and so much more. It's hard to imagine modern life without it.The birth of the internet was a really remarkable point in human history. What started as a small computer network for researchers transformed into a global system that puts the world's knowledge at our fingertips. Whenever I'm watching a video, playing an online game, or video chatting with my cousins overseas, I'm amazed at how far the internet has come in just the last 50 years or so.Who knows what amazing new internet technologies will be invented in our lifetimes? The possibilities seem endless as more of the world comes online. The internet has already opened up so many opportunities for learning, connecting people across distances, and exploring our world in ways our grandparents could have never imagined. I can't wait to see what future innovations will change our lives next!。

The Value of the Fourth Year of Mathematics Too many students and educators view the senior year and graduation from high school as an end point, rather than one vital step along the education pipeline. Students who engage in a fourth year of math tap into and build upon their advanced analytic skills and are more likely to have better success in postsec-ondary course work, as they have maintained their momentum and continued to practice mathematics throughout their high school experience.Math is a continuum of learning.n R esearchers who study learning and cognition describemathematical learning as a progression in which conceptualunderstanding builds logically, and expertise is developedgradually.1n W hen students are not directly engaged in instruction, they suffer a learning loss. Just over an average summer, students lose approximately 2.6 months of grade-level equivalency in mathematics.2 The learning loss during a student’s senior year similarly has the potential to be very significant.n A dditionally, 67 percent of middle school teachers rank math as the single most difficult subject for students to re-engage in when returning to school after the summer break and 50 percent claimed that students’ math skills regress the most, compared to other subjects, during that time off.3n A ll students gain more advanced math skills later in high school, but the most significant gains are found among students who take rigorous math during their junior and senior years. The largest learning gains made in advanced skill proficiency—such as complex multi-step analysis—were among students who took pre-calculus and another course during 11th and 12th grade.The largest gains in intermediate math skills–such as simpleoperations and problem solving—were made by students who took Geometry and Algebra II during the last two years of high school.4 n U nsurprisingly, the smallest gains at all proficiency levels were made were among students who took no math or only one math course during 11th and 12th grade.5math works1775 Eye Street NW n Suite 410 n Washington, DC 20006 n Phone (202) 419-1540 n /mathworksFOURTH YEAR MATH ALTERNATIVES Many students who complete a three-course sequence, such as Algebra I, Geometry, and Algebra II, go on to take Pre-Calculus and Calculus. Yet, for those students who choose not to follow that track, there need to be options for fourth year courses that include rich and meaningful mathematics. Students not intending to pursue math-intensive majors should be able to select from a number of fourth year “capstone” courses to maintain and extend their prior mathematical knowledge and connect mathematics instruction with other interests. Effective capstone courses can keep students engaged in learning and ensure a smoother transition into postsecondary education and the workplace.10A fourth year of math improves students’ college readiness.n A recent report from ACT finds that a fourth year of math is associated positively with students’ college readiness. While only 16 percent of students taking three years of math met the readiness benchmarks on the ACT in math, 62 percent of students taking four years, and 75 percent of students taking four and a half years of math met that benchmark.6n S imilarly, on average, students with four years of high school math score 63 points higher on the SAT-I quantitative section than students with only three years of math. Students who take more than four years of math, such as students who complete Algebra I in middle school, score 52 points higher on the SAT-I quantitative section than students with exactly four years of math.7n I n one study of students from three states who had taken the ACT, 26 percent of students who took three years of math in high school (including Algebra, Geometry and Algebra II) required remediation upon entering college, while taking a fourth year of advanced math reduced the remediation rate to 17 percent.8n 74 percent of recent high school graduates surveyed believe that requiring four years of math and science would have better prepared them for life after high school. Additionally, about 80 percent of graduates say they would have worked harder had their high schools demanded more of them.9ENDNOTES1 NRC, 2005 /openbook.php?record_id=11101&page=432 Cooper, Harris et al (1996). The Effects of Summer Vacation on Achievement Test Scores: A Narrative and Meta-Analytic Review. Review of Educational Research, v66 n3 p227-68 Fall 1996.3 The Raytheon MathMovesU Back-to-School Survey, Nov 2006.4 Bozick, R., and Ingels, S.J. (2008). Mathematics Coursetaking and Achievement at the End of High School: Evidence from the Education Longitudinal Study of 2002. (NCES 2008-319). Washington, DC: U.S. Department of Education, National Center for Education Statistics.5 Bozick, R., and Ingels, S.J. (2008). Mathematics Coursetaking and Achievement at the End of High School: Evidence from the Education Longitudinal Study of 2002. (NCES 2008-319). Washington, DC: U.S. Department of Education, National Center for Education Statistics.6 ACT, Inc. (2007). Rigor At Risk: Reaffirming Quality in the High School Core Curriculum. /research/policymakers/pdf/rigor_ report.pdf7 College Board. (2006). 2006 College-Bound Seniors Total Group Profile Report. /prod_downloads/about/news_ info/cbsenior/yr2006/national-report.pdf.8 ACT, Inc. (2007). Rigor At Risk: Reaffirming Quality in the High School Core Curriculum. /path/policy/pdf/rigor_report.pdf9 Peter D. Hart Research Associates/Public Opinion Strategies. (2005). Rising to the Challenge: Are High School Graduates Prepared for College and Work? Washington, DC: Achieve.10 To see examples of fourth year capstone courses, see /k12mathbenchmarks/resources/capstone.php1775 Eye Street NW n Suite 410n Washington, DC 20006n Phone (202) 419-1540n /mathworks© November 2008。

CHAPTER6ARITHMETIC AND LOGIC OPERA TIONSWHAT WILL WE LEARN?•Which arithmetic and logic operations can be applied to digital images?•How are they performed in MATLAB?•What are they used for?6.1ARITHMETIC OPERATIONS:FUNDAMENTALS AND APPLICATIONSArithmetic operations involving images are typically performed on a pixel-by-pixel basis;that is,the operation is independently applied to each pixel in the image.Given a2D array(X)and another2D array of the same size or a scalar(Y),the resulting array,Z,is obtained by calculatingX opn Y=Z(6.1) where opn is a binary arithmetic(+,−,×,/)operator.This section describes each arithmetic operation in more detail,focusing on how they can be performed and what are their typical applications.Practical Image and Video Processing Using MATLAB®.By Oge Marques.©2011John Wiley&Sons,Inc.Published2011by John Wiley&Sons,Inc.103104ARITHMETIC AND LOGIC OPERATIONSFIGURE6.1Adding two images:(a)first image(X);(b)second image(Y);(c)result(Z= X+Y).6.1.1AdditionAddition is used to blend the pixel contents from two images or add a constant value to pixel values of an image.Adding the contents of two monochrome images causes their contents to blend(Figure6.1).Adding a constant value(scalar)to an image causes an increase(or decrease if the value is less than zero)in its overall brightness, a process sometimes referred to as additive image offset(Figure6.2).Adding random amounts to each pixel value is a common way to simulate additive noise(Figure6.3). The resulting(noisy)image is typically used as a test image for restoration algorithms such as those described in Chapter12.FIGURE6.2Additive image offset:(a)original image(X);(b)brighter version(Z=X+ 75).ARITHMETIC OPERATIONS:FUNDAMENT ALS AND APPLICA TIONS105FIGURE 6.3Adding noise to an image:(a)original image (X );(b)zero-mean Gaussian white noise (variance =0.01)(N );(c)result (Z =X +N ).In MATLABMATLAB’s Image Processing Toolbox (IPT)has a built-in function to add two images or add a constant (scalar)to an image:imadd .In Tutorial 6.1(page 113),you will have a chance to experiment with this function.When adding two images,you must be careful with values that exceed the maxi-mum pixel value for the data type being used.There are two ways of dealing with this overflow issue:normalization and truncation .Normalization consists in storing the intermediate result in a temporary variable (W )and calculating each resulting pixel value in Z using equation (6.2).g =L maxf max min(f −f min )(6.2)where f is the current pixel in W ,L max is the maximum possible intensity value (e.g.,255for uint8or 1.0for double ),g is the corresponding pixel in Z ,f max is the maximum pixel value in W ,and f min is the minimum pixel value in W .Truncation consists in simply limiting the results to the maximum positive number that can be represented with the adopted data type. EXAMPLE 6.1For the two 3×3monochrome images below (X and Y ),each of which represented as an array of unsigned integers,8-bit (uint8),calculate Z =X +Y ,using (a)normalization and (b)truncation.X =⎡⎢⎣2001001000105050250120⎤⎥⎦106ARITHMETIC AND LOGIC OPERATIONSY=⎡⎢⎣100220230 4595120 2051000⎤⎥⎦SolutionThe intermediate array W(an array of unsigned integers,16-bit,uint16)is obtained by simply adding the values of X and Y on a pixel-by-pixel basis:W=⎡⎢⎣300320330 45105170 255350120⎤⎥⎦(a)Normalizing the[45,350]range to the[0,255]interval using equation(6.2), we obtainZ a=⎡⎢⎣213230238050105 17525563⎤⎥⎦(b)Truncating all values above255in W,we obtainZ b=⎡⎢⎣255255255 45105170 255255120⎤⎥⎦MATLAB code:X=uint8([200100100;01050;50250120])Y=uint8([100220230;4595120;2051000])W=uint16(X)+uint16(Y)fmax=max(W(:))fmin=min(W(:))Za=uint8(255.0*double((W-fmin))/double((fmax-fmin))) Zb=imadd(X,Y)6.1.2SubtractionSubtraction is often used to detect differences between two images.Such differences may be due to several factors,such as artificial addition to or removal of relevant contents from the image(e.g.,using an image manipulation program),relative object motion between two frames of a video sequence,and many others.Subtracting aARITHMETIC OPERATIONS:FUNDAMENT ALS AND APPLICA TIONS107FIGURE6.4Subtractive image offset:(a)original image(X);(b)darker version(Z= X−75).constant value(scalar)from an image causes a decrease in its overall brightness,a process sometimes referred to as subtractive image offset(Figure6.4).When subtracting one image from another or a constant(scalar)from an image, you must be careful with the possibility of obtaining negative pixel values as a result. There are two ways of dealing with this underflow issue:treating subtraction as absolute difference(which will always result in positive values proportional to the difference between the two original images without indicating,however,which pixel was brighter or darker)and truncating the result,so that negative intermediate values become zero.In MATLABThe IPT has a built-in function to subtract one image from another,or subtract a constant from an image:imsubtract.The IPT also has a built-in function to cal-culate the absolute difference of two images:imabsdiff.The IPT also includes a function for calculating the negative(complement)of an image,imcomplement.In Tutorial6.1(page113),you will have a chance to experiment with these functions. EXAMPLE6.2For the two3×3monochrome images below(X and Y),each of which represented as an array of unsigned integers,8-bit(uint8),calculate(a)Z=X−Y,(b)Z= Y−X,and(c)Z=|Y−X|.For parts(a)and(b),use truncation to deal with possible negative values.X=⎡⎢⎣20010010001050 50250120⎤⎥⎦108ARITHMETIC AND LOGIC OPERATIONSY=⎡⎢⎣100220230 4595120 2051000⎤⎥⎦SolutionMATLAB’s imsubtract will take care of parts(a)and(b),while imabsdiff will be used for part(c).(a)Z a=⎡⎢⎣100000000150120⎤⎥⎦(b)Z b=⎡⎢⎣0120130 458570 15500⎤⎥⎦(c)Z c=⎡⎢⎣100120130 458570 155150120⎤⎥⎦MATLAB code:X=uint8([200100100;01050;50250120])Y=uint8([100220230;4595120;2051000])Za=imsubtract(X,Y)Zb=imsubtract(Y,X)Zc=imabsdiff(Y,X)Image subtraction can also be used to obtain the negative of an image(Figure6.5):g=−f+L max(6.3) where L max is the maximum possible intensity value(e.g.,255for uint8or1.0for double),f is the pixel value in X,g is the corresponding pixel in Z.ARITHMETIC OPERATIONS:FUNDAMENT ALS AND APPLICA TIONS109FIGURE6.5Example of an image negative:(a)original image;(b)negative image.6.1.3Multiplication and DivisionMultiplication and division by a scalar are often used to perform brightness adjust-ments on an image.This process—sometimes referred to as multiplicative image scaling—makes each pixel value brighter(or darker)by multiplying its original value by a scalar factor:if the value of the scalar multiplication factor is greater than one,the result is a brighter image;if it is greater than zero and less than one,it results in a darker image(Figure6.6).Multiplicative image scaling usually produces better subjective results than the additive image offset process described previously.In MATLABThe IPT has a built-in function to multiply two images or multiply an image by a constant:immultiply.The IPT also has a built-in function to divide one imageFIGURE6.6Multiplication and division by a constant:(a)original image(X);(b)multipli-cation result(X×0.7);(c)division result(X/0.7).110ARITHMETIC AND LOGIC OPERATIONS into another or divide an image by a constant:imdivide.In Tutorial6.1(page113), you will have a chance to experiment with these functions.6.1.4Combining Several Arithmetic OperationsIt is sometimes necessary to combine several arithmetic operations applied to one or more images,which may compound the problems of overflow and underflow discussed previously.To achieve more accurate results without having to explicitly handle truncations and round-offs,the IPT offers a built-in function to perform a linear combination of two or more images:imlincomb.This function computes each element of the output individually,in double-precisionfloating point.If the output is an integer array,imlincomb truncates elements that exceed the range of the integer type and rounds off fractional values.EXAMPLE6.3Calculate the average of the three3×3monochrome images below(X,Y,and Z), each of which represented as an array of unsigned integers,8-bit(uint8),using(a) imadd and imdivide without explicitly handling truncation and round-offs;(b) imadd and imdivide,but this time handling truncation and round-offs;and(c) imlincomb.X=⎡⎢⎣20010010001050 50250120⎤⎥⎦Y=⎡⎢⎣100220230 4595120 2051000⎤⎥⎦Z=⎡⎢⎣200160130 145195120 105240150⎤⎥⎦Solution (a)S a=⎡⎢⎣858585 638585 858585⎤⎥⎦LOGIC OPERA TIONS:FUNDAMENTALS AND APPLICA TIONS111(b)S b=⎡⎢⎣167160153 6310097 12019790⎤⎥⎦(c)S c=⎡⎢⎣167160153 6310097 12019790⎤⎥⎦MATLAB code:X=uint8([200100100;01050;50250120])Y=uint8([100220230;4595120;2051000])Z=uint8([200160130;145195120;105240150])Sa=imdivide(imadd(X,imadd(Y,Z)),3)a=uint16(X)+uint16(Y)b=a+uint16(Z)Sb=uint8(b/3)Sc=imlincomb(1/3,X,1/3,Y,1/3,Z,’uint8’)The result in(a)is incorrect due to truncation of intermediate results.Both(b)and (c)produce correct results,but the solution using imlincomb is much more elegant and concise.6.2LOGIC OPERATIONS:FUNDAMENTALS AND APPLICATIONS Logic operations are performed in a bit-wise fashion on the binary contents of each pixel value.The AND,XOR,and OR operators require two or more arguments, whereas the NOT operator requires only one argument.Figure6.7shows the most common logic operations applied to binary images,using the following convention: 1(true)for white pixels and0(false)for black pixels.Figures6.8–6.11show examples of AND,OR,XOR,and NOT operations on monochrome images.The AND and OR operations can be used to combine images for special effects purposes.They are also used in masking operations,whose goal is to extract a region of interest(ROI)from an image(see Tutorial6.2).The XOR operation is often used to highlight differences between two monochrome images.It is,therefore,equivalent to calculating the absolute difference between two images. The NOT operation extracts the binary complement of each pixel value,which is equivalent to applying the“negative”effect on an image.112ARITHMETIC AND LOGIC OPERATIONSFIGURE6.7Logic operations on binary images.FIGURE6.8The AND operation applied to monochrome images:(a)X;(b)Y;(c)X AND Y.FIGURE6.9The OR operation applied to monochrome images:(a)X;(b)Y;(c)X OR Y.In MATLABMATLAB has built-in functions to perform logic operations on arrays:bitand, bitor,bitxor,and bitcmp.In Tutorial6.2(page118),you will have a chance to experiment with these functions.TUTORIAL6.1:ARITHMETIC OPERA TIONS113FIGURE6.10The XOR operation applied to monochrome images:(a)X;(b)Y;(c)X XOR Y.FIGURE6.11The NOT operation applied to a monochrome image:(a)X;(b)NOT X.6.3TUTORIAL6.1:ARITHMETIC OPERATIONSGoalThe goal of this tutorial is to learn how to perform arithmetic operations on images.Objectives•Learn how to perform image addition using the imadd function.•Explore image subtraction using the imsubtract function.•Explore image multiplication using the immultiply function.•Learn how to use the imdivide function for image division.What You Will Need•cameraman2.tif•earth1.tif•earth2.tif•gradient.tif•gradient_with_text.tif114ARITHMETIC AND LOGIC OPERATIONSProcedureThe IPT offers four functions to aid in image arithmetic:imadd,imsubtract, immultiply,and imdivide.You could use MATLAB’s arithmetic functions (+,−,*,/)to perform image arithmetic,but it would probably require additional coding to ensure that the operations are performed in double precision,as well as setting cutoff values to be sure that the result is within grayscale range.The functions provided by the IPT do this for you automatically.Image addition can be used to brighten(or darken)an image by adding(subtracting) a constant value to(from)each pixel value.It can also be used to blend two images into one.e the imadd function to brighten an image by adding a constant(scalar)value to all its pixel values.I=imread(’tire.tif’);I2=imadd(I,75);figuresubplot(1,2,1),imshow(I),title(’Original Image’); subplot(1,2,2),imshow(I2),title(’Brighter Image’);Question1What are the maximum and minimum values of the original and the adjusted image?Explain your results.Question2How many pixels had a value of255in the original image and how many have a value of255in the resulting image?e the imadd function to blend two images.Ia=imread(’rice.png’);Ib=imread(’cameraman.tif’);Ic=imadd(Ia,Ib);figureimshow(Ic);Image subtraction is useful when determining whether two images are the same. By subtracting one image from another,we can highlight the differences between the two.3.Close all openfigures and clear all workspace variables.4.Load two images and display them.I=imread(’cameraman.tif’);J=imread(’cameraman2.tif’);TUTORIAL6.1:ARITHMETIC OPERA TIONS115 figuresubplot(1,2,1),imshow(I),title(’Original Image’); subplot(1,2,2),imshow(J),title(’Altered Image’);While it may not be obvious atfirst how the altered image differs from the orig-inal image,we should be able to see where the difference is located after using the imsubtract function.5.Subtract both images and display the result.diffim=imsubtract(I,J);figuresubplot(2,2,1),imshow(diffim),title(’Subtracted Image’);e the zoom tool to zoom into the right area of the difference image abouthalfway down the image.You will notice that a small region of pixels is faintly white.7.To zoom back out,double-click anywhere on the image.Now that you know where the difference is located,you can look at the original images to see the change.The difference image above does not quite seem to display all the details of the missing building.This is because when we performed image subtraction,some of the pixels resulted in negative values,but were then set to0by the imsubtract function(the function does this on purpose to keep the data within grayscale range).What we really want to do is calculate the absolute value of the difference between two images.8.Calculate the absolute difference.Make sure Figure2is selected before exe-cuting this code.diffim2=imabsdiff(I,J);subplot(2,2,2),imshow(diffim2),title(’Abs Diff Image’);e the zoom-in tool to inspect the new difference image.Even though the new image may look the same as the previous one,it represents both positive and negative differences between the two images.To see this difference better,we will scale both difference images for display purposes,so their values occupy the full range of the gray scale.10.Show scaled versions of both difference images.subplot(2,2,3),imshow(diffim,[]),...title(’Subtracted Image Scaled’);116ARITHMETIC AND LOGIC OPERATIONS subplot(2,2,4),imshow(diffim2,[]),...title(’Abs Diff Image Scaled’);e the zoom tool to see the differences between all four difference images. Question3How did we scale the image output?Question4What happened when we scaled the difference images?Question5Why does the last image show more detail than the others?Multiplication is the process of multiplying the values of each pixel of same coor-dinates in two images.This can be used for a brightening process known as dynamic scaling,which results in a more naturally brighter image compared to directly addinga constant to each pixel.12.Close all openfigures and clear all workspace variables.e immultiply to dynamically scale the moon image.I=imread(’moon.tif’);I2=imadd(I,50);I3=immultiply(I,1.2);figuresubplot(1,3,1),imshow(I),title(’Original Image’); subplot(1,3,2),imshow(I2),title(’Normal Brightening’); subplot(1,3,3),imshow(I3),title(’Dynamic Scaling’); Question6When dynamically scaling the moon image,why did the dark regions around the moon not become brighter as in the normally adjusted image?Image multiplication can also be used for special effects such as an artificial3D look.By multiplying aflat image with a gradient,we create the illusion of a3D textured surface.14.Close all openfigures and clear all workspace variables.15.Create an artificial3D planet by using the immultiply function to multiplythe earth1and earth2images.I=im2double(imread(’earth1.tif’));J=im2double(imread(’earth2.tif’));K=immultiply(I,J);figuresubplot(1,3,1),imshow(I),title(’Planet Image’);subplot(1,3,2),imshow(J),title(’Gradient’);subplot(1,3,3),imshow(K,[]),title(’3D Planet’);TUTORIAL6.1:ARITHMETIC OPERA TIONS117Image division can be used as the inverse operation to dynamic scaling.Image division is accomplished with the imdivide function.When using image division for this purpose,we can achieve the same effect using the immultiply function.16.Close all openfigures and clear all workspace variables.e image division to dynamically darken the moon image.I=imread(’moon.tif’);I2=imdivide(I,2);figuresubplot(1,3,1),imshow(I),title(’Original Image’);subplot(1,3,2),imshow(I2),title(’Darker Image w/Division’) 18.Display the equivalent darker image using image multiplication.I3=immultiply(I,0.5);subplot(1,3,3),imshow(I3),...title(’Darker Image w/Multiplication’);Question7Why did the multiplication procedure produce the same result as division?Question8Write a small script that will verify that the images produced from division and multiplication are equivalent.Another use of the image division process is to extract the background from an image.This is usually done during a preprocessing stage of a larger,more complex operation.19.Close all openfigures and clear all workspace variables.20.Load the images that will be used for background subtraction.notext=imread(’gradient.tif’);text=imread(’gradient_with_text.tif’);figure,imshow(text),title(’Original Image’);This image could represent a document that was scanned under inconsistent light-ing conditions.Because of the background,the text in this image cannot be processed directly—we must preprocess the image before we can do anything with the text.If the background were homogeneous,we could use image thresholding to extract the text pixels from the background.Thresholding is a simple process of converting an image to its binary equivalent by defining a threshold to be used as a cutoff value: anything below the threshold will be discarded(set to0)and anything above it will be kept(set to1or255,depending on the data class we choose).118ARITHMETIC AND LOGIC OPERATIONS 21.Show how thresholding fails in this case.level=graythresh(text);BW=im2bw(text,level);figure,imshow(BW)Although the specifics of the thresholding operation(using built-in functions graythresh and im2bw)are not important at this time,we can see that even though we attempted to segregate the image into dark and light pixels,it produced only part of the text we need(on the upper right portion of the image).If an image of the background with no text on it is available,we can use the imdivide function to extract the letters.To obtain such background image in a real scenario,such as scan-ning documents,a blank page that would show only the inconsistently lit background could be scanned.22.Divide the background from the image to get rid of the background.fixed=imdivide(text,notext);figuresubplot(1,3,1),imshow(text),title(’Original Image’); subplot(1,3,2),imshow(notext),title(’Background Only’); subplot(1,3,3),imshow(fixed,[]),title(’Divided Image’) Question9Would this technique still work if we were unable to obtain the back-ground image?6.4TUTORIAL6.2:LOGIC OPERATIONS AND REGION OF INTEREST PROCESSINGGoalThe goal of this tutorial is to learn how to perform logic operations on images.Objectives•Explore the roipoly function to generate image masks.•Learn how to logically AND two images using the bitand function.•Learn how to logically OR two images using the bitor function.•Learn how to obtain the negative of an image using the bitcmp function.•Learn how to logically XOR two images using the bitxor function.What You Will Need•lindsay.tif•cameraman2.tifTUTORIAL6.2:LOGIC OPERA TIONS AND REGION OF INTEREST PROCESSING119 ProcedureLogic operators are often used for image masking.We will use the roipoly function to create the image mask.Once we have a mask,we will use it to perform logic operations on the selected image.e the MATLAB help system to learn how to use the roipoly function whenonly an image is supplied as a parameter.Question1How do we add points to the polygon?Question2How do we delete points from the polygon?Question3How do we end the process of creating a polygon?e the roipoly function to generate a mask for the pout image.I=imread(’pout.tif’);bw=roipoly(I);Question4What class is the variable bw?Question5What does the variable bw represent?Logic functions operate at the bit level;that is,the bits of each image pixel are compared individually,and the new bit is calculated based on the operator we are using(AND,OR,or XOR).This means that we can compare only two images that have the same number of bits per pixel as well as equivalent dimensions.In order for us to use the bw image in any logical calculation,we must ensure that it consists of the same number of bits as the original image.Because the bw image already has the correct number of rows and columns,we need to convert only the image to uint8, so that each pixel is represented by8bits.3.Convert the mask image to class uint8.bw2=uint8(bw);Question6In the above conversion step,what would happen if we used the im2uint8function to convert the bw image as opposed to just using uint8(bw)? (Hint:after conversion,check what is the maximum value of the image bw2.)e the bitand function to compute the logic AND between the originalimage and the new mask image.I2=bitand(I,bw2);imshow(I2);120ARITHMETIC AND LOGIC OPERATIONS Question7What happens when we logically AND the two images?To see how to OR two images,we mustfirst visit the bitcmp function,which is used for complementing image bits(NOT).e the bitcmp function to generate a complemented version of the bw2mask.bw_cmp=bitcmp(bw2);figuresubplot(1,2,1),imshow(bw2),title(’Original Mask’);subplot(1,2,2),imshow(bw_cmp),title(’Complemented Mask’); Question8What happened when we complemented the bw2image?We can now use the complemented mask in conjunction with bitor.e bitor to compute the logic OR between the original image and the com-plemented mask.I3=bitor(I,bw_cmp);figure,imshow(I3)Question9Why did we need to complement the mask?What would have hap-pened if we used the original mask to perform the OR operation?The IPT also includes function imcomplement,which performs the same operation as the bitcmp function,complementing the image.The function imcomplement allows input images to be binary,grayscale,or RGB,whereas bitcmp requires that the image be an array of unsigned integers.plement an image using the imcomplement function.bw_cmp2=imcomplement(bw2);Question10How can we check to see that the bw_cmp2image is the same as the bw_cmp image?The XOR operation is commonly used forfinding differences between two images.8.Close all openfigures and clear all workspace variables.e the bitxor function tofind the difference between two images.I=imread(’cameraman.tif’);I2=imread(’cameraman2.tif’);I_xor=bitxor(I,I2);figuresubplot(1,3,1),imshow(I),title(’Image1’);TUTORIAL6.2:LOGIC OPERA TIONS AND REGION OF INTEREST PROCESSING121 subplot(1,3,2),imshow(I2),title(’Image2’);subplot(1,3,3),imshow(I_xor,[]),title(’XOR Image’);Logic operators are often combined to achieve a particular task.In next steps,we will use all the logic operators discussed previously to darken an image only withina region of interest.10.Close all openfigures and clear all workspace variables.11.Read in image and calculate an adjusted image that is darker using theimdivide function.I=imread(’lindsay.tif’);I_adj=imdivide(I,1.5);12.Generate a mask by creating a region of interest polygon.bw=im2uint8(roipoly(I));e logic operators to show the darker image only within the region of interest,while displaying the original image elsewhere.bw_cmp=bitcmp(bw);%mask complementroi=bitor(I_adj,bw_cmp);%roi imagenot_roi=bitor(I,bw);%non_roi imagenew_img=bitand(roi,not_roi);%generate new image imshow(new_img)%display new image Question11How could we modify the above code to display the original image within the region of interest and the darker image elsewhere?WHAT HAVE WE LEARNED?•Arithmetic operations can be used to blend two images(addition),detect differ-ences between two images or video frames(subtraction),increase an image’s average brightness(multiplication/division by a constant),among other things.•When performing any arithmetic image processing operation,pay special attention to the data types involved,their ranges,and the desired way to handle overflow and underflow situations.•MATLAB’s IPT has built-in functions for image addition(imadd),subtraction (imsubtract and imabsdiff),multiplication(immultiply),and divi-sion(imdivide).It also has a function(imlincomb)that can be used to perform several arithmetic operations without having to worry about underflow or overflow of intermediate results.122ARITHMETIC AND LOGIC OPERATIONS •Logic operations are performed on a bit-by-bit basis and are often used to maskout a portion of an image(the region of interest)for further processing.•MATLAB’s IPT has built-in functions for performing basic logic operations on digital images:AND(bitand),OR(bitor),NOT(bitcmp),and XOR (bitxor).6.5PROBLEMS6.1What would be the result of adding a positive constant(scalar)to a monochrome image?6.2What would be the result of subtracting a positive constant(scalar)from a monochrome image?6.3What would be the result of multiplying a monochrome image by a positive constant greater than1.0?6.4What would be the result of multiplying a monochrome image by a positive constant less than1.0?6.5Given the3×3images X and Y below,obtain(a)X AND Y;(b)X OR Y;(c) X XOR Y.X=⎡⎢⎣20010010001050 50250120⎤⎥⎦Y=⎡⎢⎣100220230 4595120 2051000⎤⎥⎦6.6What happens when you add a uint8[0,255]monochrome image to itself?6.7What happens when you multiply a uint8[0,255]monochrome image by itself?6.8What happens when you multiply a double[0,1.0]monochrome image by itself?6.9What happens when you divide a double[0,1.0]monochrome image by itself?6.10Would pixel-by-pixel division be a better way tofind the differences between two monochrome images than subtraction,absolute difference,or XOR?Explain.PROBLEMS123 6.11Write a MATLAB function to perform brightness correction on monochrome images.It should take as arguments a monochrome image,a number between0and 100(amount of brightness correction,expressed in percentage terms),and a third parameter indicating whether the correction is intended to brighten or darken the image.6.12Write a MATLAB script that reads an image,performs brightness correction using the function written for Problem6.11,and displays a window with the image and its histogram,1before and after the brightness correction operation.What do the histograms tell you?Would you be able to tell from the histograms alone what type of brightness correction was performed?Would you be able to estimate from the histogram information how much brightening or darkening the image experienced? 1Histograms will be introduced in Chapter9,so you may try this problem after reading that chapter.。

Chapter 10 Postsecondary Education: AdmissionsIn this lecture, I’m going to talk to you about postsecondary education in the United States. Today I’ll give you some facts and figures about colleges and universities in the United States and some general information about admission policies. I will also make a few remarks about community colleges and finish up by giving you an idea of what kinds of students make up the student body on a typical U.S. campus.Let’s begin with some facts and figures. The most recent figures I have reveal there are 4,182 public and private four-year and two-year colleges in the United States. These range from full universities with diverse programs to smaller four-year colleges to two-year community colleges. Most of them are accredited, which means the schools meet certain standards set by institutional and private evaluators. When applying to a school, you would probably want to make sure it was accredited. Even though there are more private colleges than public ones, over three-quarters of students, precisely 78 percent, are enrolled at public colleges and universities. Some of the small private schools may have fewer than 100 students, whereas some of the large state university systems may have 50,000 or more students. Most of these schools are coeducational although some of them are primarily for women and others are primarily for men. Some schools may offer only one program of study and others have a great variety of programs. The total cost for attending one of these schools may be less than $5,000 a year or as much as 30 or 40 thousand dollars a year for one of the prestigious private schools. These schools are located all over—in industrial areas, agricultural areas, large cities, and small towns in a wide variety of climates.With such a wide variety of sizes, kinds, and locations of schools, it probably won’t surprise you to find out that admissions requirements at these colleges anduniversities vary greatly also. Some are relatively easy to be admitted to whereas others are highly competitive. However, most schools will ask undergraduate applicants to submit their high school transcripts with a record of their grades and test results from one of the standardized tests regularly offered to high school students. The most common of these standardized exams is the Scholastic Aptitude Test, commonly known as the SAT. Students who are applying to graduate school are usually asked to take other, more specific standardized exams depending on which college they are applying to. For example, some students are required to take the Graduate Record Exam, or the GRE. Students applying to a business college will probably have to take the GMAT, and students applying to law college will have to take the LSAT. You probably know about the TOEFL exam, which most foreign students have to take before being admitted to American colleges or universities. These exams, including the TOEFL, are all prepared by a company that is independent of the school system. These exams have come under a lot of criticism lately, but they are still widely used as one way to determine who will be admitted to various schools. However, most schools try to look at the whole student and consider factors other than simply grades and test scores. Some of these factors may be extracurricular activities in school, ethnic background, work experience, and so on. Some schools will have personal interviews with students they are considering for admission. Many schools, private as well as public, try very hard to have a student population with a wide variety of backgrounds and ages. Even the most prestigious and most highly competitive colleges and universities will not take only those students with the highest grades and standardized test scores but will consider these other factors. Nevertheless, schools of this type, such as Stanford and Harvard, have so many more people applying than they can possibly accept that students who want to get into such schools take grades and SAT exams very seriously. In general, medical and law colleges, both private and public, are very difficult to get into, and, once again,test scores on standardized exams can be extremely important to those applying to these schools.However, for students who want to attend a state college or university in their own state, it may be enough to graduate from high school in the upper third or even upper half of their high school class. This may surprise those of you who come from an educational system that is highly competitive, a system in which only a small percentage of students who pass a very difficult nationwide standardized high school examination can enter a university. You may be even more surprised by what I have to tell you about community colleges.An interesting feature of education in the United States is the two-year community college. Community colleges that are publicly supported offer somewhat different educational opportunities than those offered by a senior college or a university. First, admissions requirements at public community colleges are usually much more lenient than those at a four-year college or university. It’s usually enough to have graduated from an American high school to be admitted. Second, it is also cheaper to attend a community college. The tuition and fees are usually quite a bit lower. Students often live at home because this type of school does not have dormitories. For these two reasons, many people who are unable to go to a four-year college or university can have an opportunity to take classes for college credit. Finally, community colleges offer two-year programs that can lead to an Associate of Arts degree. Many of these programs, but not all of them, are vocational in nature. People attend community colleges for many different purposes. Some people may be taking on a course or two in some field that particularly interests them and may not be planning on getting a degree. Other people may be going to community college full-time and planning to transfer to a four-year college or university upon successful completion of two years at a community college. Well, so much for community colleges.I promised to tell you a little about the actual student body on a typical U.S. campus. Let’s start with some statistics, and then we’ll discuss two items that surprise many foreign students. Among the million high school graduates in 2002, percent were enrolled in college the following October. More than 90 percent of those attended full time. Young men represented half of the high school graduates, but more women than men went on to college. The exact statistics are: percent of female high school graduates and percent of male high school graduates. If we break down the statistics racially, we find that white students enrolled in college in greater proportions than black or Hispanic students. The figures are percent for white graduates, percent of black graduates, and percent of Hispanic students. My next statistic may be surprising. p ercent of full-time students in 2002 were either employed or looking for work. That number jumps to percent for part-time students. That last statistic makes more sense when we consider that besides the students who are from eighteen to twenty-two years old that one expects to find on a college campus, there are also many older married students. They may be people who attend part-time to upgrade their skills, people who are changing careers, or retired people who still have a desire to learn. Also, foreign students are often surprised at how poorly prepare American students are when they enter a university. Actually, at very select schools the students are usually very well prepared, but at less selective schools, they may not be as well prepared as students in your country are. If you will remember the educational philosophy we discussed in the last lecture, you will understand why. Schools in the States simply admit a lot more students than is usual in most other countries.Also, most young American university students have not traveled in other countries and are not very well versed in international matters and do not know a lot about people from other countries. Foreign students usually find them friendly but not very well informed about their countries or cultures.In brief, you can see that educational opportunities and admissions standardsvary greatly in the United States. While it may be quite difficult to gain admission to some colleges and universities because of the very large number of applicants, probably any student graduating from high school with reasonable grades can find some accredited university or college to attend. Those students hoping to enter graduate school will often face very stiff competition, whether at private or public schools. Many students who start at a college or university will not finish in four years. Some will drop out to work or travel and may never finish. Others will return to school a few months or a few years later. Some will go to school full-time and others part-time. Some will not work while going to school, but most will work at some time or other during their school years.We’re out of time, I see. In my next lecture, I’ll talk to you about a relatively new development in education, distance learning. It should be of interest to those of you who want to attend college but can’t because of living far from a colle ge, busy schedules, or for other reasons.。

NCHRP Project 3-92Production of the 2010 Highway Capacity ManualTRB Delivery Draft Chapter 10Freeway FacilitiesPrepared for:National Cooperative Highway Research ProgramTransportation Research BoardNational Research CouncilTransportation Research BoardNAS‐NRCLIMITED USE DOCUMENTThis draft material, not released for publication, is furnished only for review tomembers of, or participants in the work of, the National Cooperative HighwayResearch Program and the Transportation Research Board. It is to be regarded asfully privileged, and dissemination of the information included herein must beapproved by the NCHRP.This is an uncorrected draft as submitted by the research agency. The opinionsand conclusions expressed or implied are those of the research agency. They arenot necessarily those of the Transportation Research Board, the NationalAcademies, or the program sponsors. The information, data, and procedurescontained herein have not been incorporated into the published HighwayCapacity Manual and are not recommended for the analysis of transportationfacilities.February 8, 2010Kittelson & Associates, Inc.Polytechnic Institute of NYUTexas Transportation InstituteUniversity of Florida2010 Highway Capacity Manual CHAPTER 10FREEWAY FACILITIESCONTENTS1.INTRODUCTION..................................................................................................10‐1Segments and Influence Areas..........................................................................10‐2Free‐Flow Speed.................................................................................................10‐3Capacity of Freeway Facilities..........................................................................10‐4Level of Service: Component Segments and the Freeway Facility..............10‐8Service Flow Rates, Service Volumes, and Daily Service Volumes for aFreeway Facility.........................................................................................10‐10Generalized Daily Service Volumes for Freeway Facilities........................10‐11Active Traffic Management and Other Measures to ImprovePerformance...............................................................................................10‐142.METHODOLOGY...............................................................................................10‐16Scope of the Methodology...............................................................................10‐16Limitations of the Methodology.....................................................................10‐17Computational Steps........................................................................................10‐183.APPLICATIONS..................................................................................................10‐40Operational Analysis........................................................................................10‐40Traffic Management Strategies.......................................................................10‐41Use of Alternative Tools..................................................................................10‐424.EXAMPLE PROBLEMS.......................................................................................10‐48Example Problem 1: Evaluation of an Undersaturated Facility.................10‐48Example Problem 2: Evaluation of an Oversaturated Facility...................10‐54Example Problem 3: Capacity Improvements to an OversaturatedFacility.........................................................................................................10‐585.REFERENCES.......................................................................................................10‐62Chapter 10/Freeway Facilities Page 10-i Contents DRAFT February 20102010 Highway Capacity ManualLIST OF EXHIBITSExhibit 10‐1 Influence Areas of Merge, Diverge, and Weaving Segments........10‐2Exhibit 10‐2 Basic Freeway Segments on an Urban Freeway..............................10‐3Exhibit 10‐3 Ramp Density Determination.............................................................10‐4Exhibit 10‐4 Example of the Effect of Segment Capacity on a FreewayFacility..................................................................................................................10‐5Exhibit 10‐5 Free‐Flow Speed vs. Base Capacity for Freeways............................10‐6Exhibit 10‐6 Base Capacity vs. Total Ramp Density..............................................10‐7Exhibit 10‐7 Level‐of‐Service Criteria for Freeway Facilities...............................10‐9Exhibit 10‐8 Generalized Daily Service Volumes for Urban Freeway Facilities(1,000 veh/day)..................................................................................................10‐13Exhibit 10‐9 Generalized Daily Service Volumes for Rural Freeway Facilities(1,000 veh/day)..................................................................................................10‐14Exhibit 10‐10 Freeway Facility Methodology.......................................................10‐18Exhibit 10‐11 Example Time‐Space Domain for Freeway Facility Analysis....10‐20Exhibit 10‐12 Defining Analysis Segments for a Ramp Configuration............10‐22Exhibit 10‐13 Defining Analysis Segments for a Weaving Configuration.......10‐23Exhibit 10‐14 Capacity of Long‐Term Construction Zones................................10‐28Exhibit 10‐15 Capacity Reductions Due to Weather and EnvironmentalConditions in Iowa...........................................................................................10‐29Exhibit 10‐16 Capacities on German Autobahns Under Varying Conditions(veh/h/ln)...........................................................................................................10‐29Exhibit 10‐17 Proportion of Freeway Segment Capacity Available Under IncidentConditions.........................................................................................................10‐30Exhibit 10‐18 Illustration of Speed‐Flow Curves for Different WeatherConditions.........................................................................................................10‐31Exhibit 10‐19 Illustration of Adjusted Speed‐Flow Curves for IndicatedCapacity Reductions........................................................................................10‐32Exhibit 10‐20 Node‐Segment Representation of a Freeway Facility.................10‐35Exhibit 10‐21 Mainline and Segment Flow at On‐ and Off‐Ramps...................10‐35Exhibit 10‐22 Required Input Data for Freeway Facility Analysis....................10‐40Exhibit 10‐23 Limitations of the HCM Freeway Facilities AnalysisProcedure..........................................................................................................10‐43Exhibit 10‐24 List of Example Problems...............................................................10‐48Exhibit 10‐25 Freeway Facility in Example Problem 1........................................10‐48Exhibit 10‐26 Geometry of Directional Freeway Facility for ExampleProblem 1...........................................................................................................10‐48Exhibit 10‐27 Demand Inputs for Example Problem 1.......................................10‐50Exhibit 10‐28 Segment Capacities for Example Problem 1.................................10‐50Contents Page 10-ii Chapter 10/Freeway FacilitiesDRAFT February 20102010 Highway Capacity ManualExhibit 10‐29 Segment Demand‐to‐Capacity Ratios for Example Problem 1..10‐51Exhibit 10‐30 Volume‐Served Matrix for Example Problem 1...........................10‐51Exhibit 10‐31 Speed Matrix for Example Problem 1...........................................10‐52Exhibit 10‐32 Density Matrix for Example Problem 1........................................10‐52Exhibit 10‐33 LOS Matrix for Example Problem 1..............................................10‐52Exhibit 10‐34 Facility Performance Measure Summary for ExampleProblem 1...........................................................................................................10‐53Exhibit 10‐35 Demand Inputs for Example Problem 2.......................................10‐55Exhibit 10‐36 Segment Capacities for Example Problem 2................................10‐55Exhibit 10‐37 Segment Demand‐to‐Capacity Ratios for Example Problem 2..10‐56Exhibit 10‐38 Volume‐Served Matrix for Example Problem 2...........................10‐57Exhibit 10‐39 Speed Matrix for Example Problem 2...........................................10‐57Exhibit 10‐40 Density Matrix for Example Problem 2........................................10‐57Exhibit 10‐41 Expanded LOS Matrix for Example Problem 2...........................10‐57Exhibit 10‐42 Facility Performance Measure Summary for ExampleProblem 2...........................................................................................................10‐58Exhibit 10‐43 Freeway Facility in Example Problem 3.......................................10‐58Exhibit 10‐44 Geometry of Directional Freeway Facility in ExampleProblem 3...........................................................................................................10‐58Exhibit 10‐45 Segment Capacities for Example Problem 3................................10‐60Exhibit 10‐46 Segment Demand‐to‐Capacity Ratios for Example Problem 3..10‐60Exhibit 10‐47 Speed Matrix for Example Problem 3...........................................10‐61Exhibit 10‐48 Density Matrix for Example Problem 3........................................10‐61Exhibit 10‐49 LOS Matrix for Example Problem 3..............................................10‐61Exhibit 10‐50 Facility Performance Measure Summary for ExampleProblem 3...........................................................................................................10‐61Chapter 10/Freeway Facilities Page 10-iii Contents DRAFT February 20102010 Highway Capacity ManualThis page intentionally left blank.Contents Page 10-iv Chapter 10/Freeway FacilitiesDRAFT February 20102010 Highway Capacity Manual1. INTRODUCTIONVOLUME 2: UNINTERRUPTED FLOW 10. Freeway Facilities 11. Basic Freeway Segments 12. Freeway Weaving Segments 13. Freeway Merge and Diverge Segments 14. Multilane Highways 15. Two-Lane HighwaysA freeway is a separated highway with full control of access and two or more lanes in each direction dedicated to the exclusive use of traffic. Freeways are comprised of various uniform segments that may be analyzed to determine capacity and level of service. There are three types of segments found on freeways:• Freeway Merge and Diverge Segments: Segments where two or more traffic streams combine to form a single traffic stream (merge), or where a single traffic stream divides to form two or more separate traffic streams(diverge).• Freeway Weaving Segments: S egments in which two or more traffic streams traveling in the same general direction cross paths along a significantlength of freeway without the aid of traffic control devices (except forguide signs). Weaving segments are formed when a diverge segmentclosely follows a merge segment, or when a one ‐lane off ‐ramp closelyfollows a one ‐lane on ‐ramp and the two are connected by a continuous auxiliary lane.• Basic Freeway Segments: All segments that are not merge, diverge, or weaving segments.Analysis methodologies are detailed for basic freeway segments in Chapter 11, for weaving segments in Chapter 12, and for merge and diverge segments in Chapter 13.Chapter 10, Freeway Facilities, provides a methodology for analyzingextended lengths of freeway comprised of continuously connected basic freeway, weaving, merge, and diverge segments. Such extended lengths are referred to as a freeway facility. Note that in this terminology, the term facility does not refer to an entire freeway from beginning to end; instead, it refers to a specific set of connected segments that have been identified for analysis. In addition, the term does not refer to a freeway system consisting of several interconnected freeways.The methodologies of Chapters 11, 12, and 13 all focus on a single time period of interest, generally the peak 15 min within a peak hour. This chapter’s methodology allows for the analysis of multiple and continuous 15‐min time periods, and is capable of identifying breakdowns and the impact of such breakdowns over space and time.The methodology is integral with the FREEVAL 2010 model, whichimplements the complex computations involved. This chapter discusses the basic principles of the methodology and its application. Chapter 25, Freeway Facilities: Supplemental, contains a complete and detailed description of all of the algorithms that define the methodology. The Technical Reference Library in Volume 4 contains a user’s guide to FREEVAL 2010, and an executable spreadsheet that implements the methodology. Chapter 10/Freeway Facilities Page 10-1 IntroductionDRAFT February 20102010 Highway Capacity ManualSEGMENTS AND INFLUENCE AREASIt is important that the definition of freeway segments and their influenceareas is clearly understood. The influence areas of merge, diverge, and weavingsegments are as follows:•Weaving Segment: The base length of the weaving segment itself plus 500 ftupstream of the entry point to the weaving segment and 500 ftdownstream of the exit point from the weaving segment; entry and exitpoints are defined as the points where the appropriate edges of themerging or diverging lanes meet.•Merge Segment: From the point at which the edges of the travel lanes of themerging roadways meet to a point 1,500 ft downstream of that point;•Diverge Segment: From the point at which the edges of the travel lanes ofthe merging roadways meet to a point 1,500 ft upstream of that point.Points at which the “edges of travel lanes” meet are most often defined bypavement markings.The influence areas of merge, diverge, and weaving segments are illustratedin Exhibit 10‐1.Exhibit 10-1Influence Areas of Merge,Diverge, and WeavingSegments(a) Merge Influence Area (b) Diverge Influence Area(c) Weaving Influence AreaBasic freeway segments are any other segments along the freeway that arenot within these defined influence areas. This is not to say that basic freewaysegments are not affected by the presence of adjacent and nearby merge, diverge,and weaving segments. Particularly when a segment breaks down, its effects willpropagate to both upstream and downstream segments, regardless of type.Furthermore, there is the general impact of the frequency of merge, diverge, andweaving segments on the general operation of all segments, which is taken intoaccount by the free‐flow speed (FFS) of the facility.Basic freeway segments, therefore, do exist even on urban freeways wheremerge and diverge points (most often ramps) are closely spaced. Exhibit 10‐2illustrates this point. It shows a 9,100‐ft (1.7‐mi) length of freeway with fourramp terminals, two of which form a weaving segment. Even with an averageramp spacing less than 0.5 mi, this length of freeway contains three basic freewaysegments. The lengths of these segments are relatively short, but in terms ofIntroduction Page 10-2 Chapter 10/Freeway FacilitiesDRAFT February 20102010 Highway Capacity Manualanalysis methodologies, these must be treated as basic freeway segments. Thus, while it is true that many urban freeways will be dominated by frequent merge, diverge, and weaving segments, there will still be segments classified and analyzed as basic freeway segments.Exhibit 10-2Basic Freeway Segments on an Urban Freeway1,500 ft 1,600 ft 2,000 ft 2,500 ft 1,500 ft1,000 ft Basic 2,600 ft Weaving 1,500 ft Basic 1,500 ft Merge 1,000 ft Basic 1,500 ftMergeFREE-FLOW SPEEDFree ‐flow speed is strictly defined as the theoretical speed when the density and flow rate on the study segment are both zero. Chapter 11, Basic Freeway Segments, presents speed ‐flow curves that indicate that the free ‐flow speed on freeways is expected to prevail at flow rates between 0 and 1,000 pc/h/ln. In this broad range of flows, speed is insensitive to flow rates. This characteristic simplifies and permits the measurement of free ‐flow speeds in the field.Chapter 11 also presents a methodology for estimating the free ‐flow speed of a basic freeway segment in cases in which it cannot be directly measured. Itindicates that the free ‐flow speed of a basic freeway segment is sensitive to three variables:• Lane widths,• Lateral clearances, and• Total ramp density.Of these, the most critical is total ramp density. Total ramp density is defined as the average number of on ‐ramp, off ‐ramp, major merge, and major diverge junctions per mile. It applies to a 6‐mi segment of freeway facility, 3 mi upstream and 3 mi downstream of the midpoint of the study segment.While the methodology for determining free ‐flow speed is provided in Chapter 11, Basic Freeway Segments, it is also applied in Chapter 12, Freeway Weaving Segments and Chapter 13, Freeway Merge and Diverge Segments. Thus, the free ‐flow speed affects the operation of all basic, weaving, merge, and diverge segments on a freeway facility.Chapter 10/Freeway Facilities Page 10-3 IntroductionDRAFT February 20102010 Highway Capacity ManualThe free‐flow speed is an important characteristic, as the capacity c, serviceflow rates SF, service volumes SV, and daily service volumes DSV all dependupon the free‐flow speed.Exhibit 10‐3 illustrates the determination of total ramp density on a 6‐milength of freeway facility.Exhibit 10-3Ramp Density DeterminationAs illustrated in Exhibit 10‐3, there are four ramp terminals and one majordiverge point in the 6‐mi segment illustrated. The total ramp density is,therefore, 5 / 6 = 0.83 ramps/mi.CAPACITY OF FREEWAY FACILITIESCapacity has been traditionally defined for segments of uniform roadway,traffic, and control conditions. When facilities consisting of a series of connectedsegments are considered, the concept of capacity is more complicated.The methodologies of Chapters 11, 12, and 13, allow the capacity of eachbasic freeway, freeway weaving, freeway merge, or freeway diverge segment tobe estimated. It is highly unlikely that every segment of a facility will have thesame roadway, traffic, and control conditions, and even less likely that they willhave the same capacity.Conceptual Approach to the Capacity of a Freeway FacilityConsider the example shown in Exhibit 10‐4. It illustrates five consecutivesegments that are to be analyzed as one “freeway facility.” Demand flow rates v d,capacities c, and actual flow rates v a are shown, as are the resulting v d /c and v a /cratios. A lane is added in segment 3 (even though this segment begins with anoff‐ramp), providing higher capacities for segments 3, 4, and 5 than in segments1 and 2. The example analyzes three scenarios.In Scenario 1, none of the demand flow rates exceed the capacities of thesegments comprising the facility. Thus, no breakdowns occur, and the actualflow rates are the same as the demand flow rates (i.e., v d = v a for this scenario).None of the v d /c or v a /c ratios exceed 1.00, although the highest ratios (0.978)occur in segment 5.Scenario 2 adds 200 veh/h of demand to each segment (essentially another200 veh/h of through freeway vehicles). In this case, segment 5 will experience abreakdown, i.e., the demand flow rate will exceed the capacity. In this segment,demand flow rate v d is different from the actual flow rate v a, as the actual flowrate v a can never exceed the capacity c.In Scenario 3, all demand flow rates are increased by 10%. This, in effect,keeps the relative values of the segment demand flow rates constant. In this case,Introduction Page 10-4 Chapter 10/Freeway FacilitiesDRAFT February 20102010 Highway Capacity Manualdemand flow rate will exceed capacity in both segments 4 and 5. Again, the demand flow rates and actual flow rates will be different in these segments.Exhibit 10-4 Example of the Effect of Segment Capacity on a Freeway Facility 12345 Freeway Segment ScenarioPerformance Measures 1 2 3 4 5 Scenario 1(Stable Flow) Demand v d , veh/h Capacity c , veh/h Volume v a , veh/h v d /c ratio v a /c ratio3,400 4,000 3,400 0.850 0.850 3,500 4,000 3,500 0.875 0.875 3,400 4,500 3,400 0.756 0.756 4,200 4,500 4,200 0.933 0.933 4,400 4,500 4,400 0.978 0.978 Scenario 2 (Add 200 veh/h to each segment) Demand v d , veh/hCapacity c , veh/hVolume v a , veh/h v d /c ratio v a /c ratio3,600 4,000 3,600 0.900 0.900 3,700 4,000 3,700 0.925 0.925 3,600 4,500 3,600 0.800 0.800 4,400 4,500 4,400 0.978 0.978 4,600 4,500 4,500 1.022 1.000 Scenario 3 (Increase demand by 10% in all segments)Demand v d , veh/hCapacity c , veh/hVolume v a , veh/h v d /c ratio v a /c ratio 3,740 4,000 3,740 0.935 0.935 3,850 4,000 3,850 0.963 0.963 3,740 4,500 3,740 0.831 0.831 4,840 4,500 4,500 1.078 1.000 5,060 4,500 4,500 1.120 1.000 Note: Shaded cells indicate segments where demand exceeds capacity.This example highlights a number of points that make the analysis of freeway facilities very complicated:1. It is critical to this methodology that the difference between demand flow rate v d and actual flow rate v a be highlighted, and that both values be clearly and appropriately labeled.2. In Scenarios 2 and 3, the analysis of Exhibit 10‐4 is inadequate andmisleading. In Scenario 2, when segment 5 breaks down, queues begin to form, and to propagate upstream. Thus, even though the demands in segments 1 through 4 are less than the capacity of those segments, the queues generated by segment 5 will, over time, propagate throughsegments 1 through 4 and significantly affect their operation. In Scenario 3, segments 4 and 5 fail, and queues are generated, which also propagate upstream over time.3. It might be argued that the analysis of Scenario 1 is sufficient to understand the facility operation as long as all segments areundersaturated (i.e., all segment v d /c ratios are less than or equal to 1.00). However, when any segment v d /c ratio exceeds 1.00, such a simpleanalysis ignores the spreading impact of breakdowns in both space and time.4. In Scenarios 2 and 3, the segments downstream of segment 5 will also be affected, as demand flow is prevented from reaching those segments by the segment 5 (and segment 4 in Scenario 3) breakdowns and queues.5. In this example, it is also important to note that the segment(s) that break down first do not have the lowest capacities. Segments 1 and 2, with Chapter 10/Freeway Facilities Page 10-5 Introduction DRAFT February 2010lower capacities, do not break down in any of the scenarios. Breakdown occurs first in Segment 5, which has one of the higher capacities.Considering all these complications, the capacity of a freeway facility is defined as:Freeway facility capacity is the capacity of the critical segment among thosesegments comprising the defined facility. This capacity must, for analysis purposes, be compared to the demand flow rate on the critical segment.The critical segment is defined as the segment that will break down first, given that all traffic, roadway, and control conditions do not change, including the spatial distribution of demands on each component segment. This is not a simple definition. It depends upon the relative demand characteristics, and can change over time as the demand pattern changes. Facility capacity may be more than the capacity of the component segment with the lowest capacity. Because of this, it is important that individual segment demands and capacities beevaluated. The fact that one of these will be the critical segment and will define the facility capacity does not diminish the importance of the capacities of other segments in the defined facility.Base Capacity of Freeway FacilitiesIn the methodologies of Chapters 11, 12, and 13, a base capacity is used. The base capacity represents the capacity of the facility, assuming that there are no heavy vehicles in the traffic stream and that all drivers are regular users of the segment. The base capacity for all freeway segments varies with the free ‐flow speed, as indicated in Exhibit 10‐5.Exhibit 10-5Free-Flow Speed vs. Base Capacity for Freeways Free-Flow Speed (mi/h) Base Capacity (pc/h/ln) 75706560 55 2,400 2,400 2,350 2,300 2,250The equation given in Chapter 11, Basic Freeway Segments, for estimating the free ‐flow speed of a segment is as shown in Equation 10‐1:Equation 10-1 84.022.34.75TRD f f FFS LC LW −−−=whereFFS = free ‐flow speed (mi/h),f LW = adjustment for lane width (mi/h), f LC = adjustment for lateral clearance (mi/h), andTRD = total ramp density (ramps/mi).The process for determining the value of adjustment factors is described in Chapter 11.Because the base capacity of a freeway segment is directly related to the free ‐flow speed, it is possible to construct a relationship between base capacity and the lane width, lateral clearance, and total ramp density of the segment. If thelane width and lateral clearance are taken to be their base values (12 ft and 6 ft, respectively), a relationship between base capacity and total ramp density emerges, as shown in Exhibit 10‐6.Base capacity is expressed as a flow rate for a 15‐min analysis period, not a full ‐hour volume. It also represents a flow rate in pc/h, with no heavy vehicles, and a driver population familiar with the characteristics of the analysis segment.Exhibit 10-6Base Capacity vs. Total Ramp DensitySegment Capacity vs. Facility CapacityFree ‐flow speed is a characteristic of a length of freeway extending three miles upstream and three miles downstream of the center point of an analysis segment. The segment may be a basic freeway segment, a weaving segment, a merge segment, or a diverge segment. In essence, it is a measure of the impact of overall facility characteristics on the operation of the individual analysis segment centered in the defined 6‐mi range.This concept can be somewhat generalized where freeway facility analysis is involved. If conditions (particularly ramp density) are similar along a longer length of freeway, it is acceptable to compute the total ramp density for the longer length, and apply it to all segments within the analysis length. Thisassumes that moving the “center” of a 6‐mi length for each component segment would not result in a significant change in the free ‐flow speed.The capacity of a nearly homogeneous freeway facility is, for all practical purposes, the same as the capacity of a basic freeway segment with the same roadway and traffic characteristics. Consider that:• Merge and diverge segments have the same capacity as a similar basic freeway segment. As discussed in Chapter 13, the presence of merge and diverge segments on a freeway may affect operating characteristics,generally reducing speeds and increasing densities, but does not reduce capacity.•Weaving segments often have per‐lane capacities that are less than the per‐lane capacities of the entering and leaving basic freeway segments. In almost all cases, however, weaving segments have more lanes than theentering and leaving basic freeway segments. Thus, the impact on thecapacity of the mainline freeway is most often negligible.This does not mean, however, that the capacity of each component segment of a facility is the same. Each segment has its own demand and demand characteristics. Demand flow rate can change at every entry or exit point along the freeway, and the percent of heavy vehicles can change too. Terrain can change at various points along the freeway.Changes in heavy vehicle presence can change the capacity of individual segments within a defined facility. Changes in the split of movements in a weaving segment can change its capacity. In the same way, changes in the relative demand flows at on‐ and off‐ramps can change the location of the critical segment within a defined facility, and its capacity.As noted previously, the capacity of a freeway facility is defined as the capacity of its critical segment.LEVEL OF SERVICE: COMPONENT SEGMENTS AND THE FREEWAY FACILITYLevel of Service of Component SegmentsChapters 11, 12, and 13 provide methodologies to determine the level of service (LOS) in basic, weaving, merge, and diverge segments. In all cases, LOS F is identified when v d /c is greater than 1.00. Such breakdowns are simply identified, and users are referred to this chapter.This chapter’s methodology provides an analysis of breakdown conditions, including the spatial and time impacts of a breakdown. Thus, when doing a facility‐level analysis, LOS F in a component segment can be identified (a) when the segment v d /c is greater than 1.00, and (b) when a queue from a downstream breakdown extends into an upstream segment. The latter cannot be done using the individual segment analysis procedures of Chapters 11–13.Thus, when facility‐level analysis is undertaken using the methodology of this chapter, LOS F for a component segment will be identified in two different ways:•When v/c is greater than 1.00, ord•When the density is greater than 45 pc/mi/ln for basic freeway segments or 43 pc/mi/ln for weaving, merge, or diverge segments.The latter identifies segments in which queues have formed as a result of downstream breakdowns.Level of Service for a Freeway FacilityBecause LOS for basic, weaving, merge, and diverge segments on a freeway is defined in terms of density, LOS for a freeway facility is also defined on the basis of density.。

Microeconometrics Using StataContentsList of tables xxxv List of figures xxxvii Preface xxxix 1Stata basics1............................................................................................1.1Interactive use 1..............................................................................................1.2 Documentation 2..........................................................................1.2.1Stata manuals 2...........................................................1.2.2Additional Stata resources 3.......................................................................1.2.3The help command 3................................1.2.4The search, findit, and hsearch commands 41.3 Command syntax and operators 5...................................................................................................................................1.3.1Basic command syntax 5................................................1.3.2 Example: The summarize command 61.3.3Example: The regress command 7..............................................................................1.3.4Abbreviations, case sensitivity, and wildcards 9................................1.3.5Arithmetic, relational, and logical operators 9.........................................................................1.3.6Error messages 10........................................................................................1.4 Do-files and log files 10.............................................................................1.4.1Writing a do-file 101.4.2Running do-files 11.........................................................................................................................................................................1.4.3Log files 12..................................................................1.4.4 A three-step process 131.4.5Comments and long lines 13......................................................................................................1.4.6Different implementations of Stata 141.5Scalars and matrices (15)1.5.1Scalars (15)1.5.2Matrices (15)1.6 Using results from Stata commands (16)1.6.1Using results from the r-class command summarize (16)1.6.2Using results from the e-class command regress (17)1.7 Global and local macros (19)1.7.1Global macros (19)1.7.2Local macros (20)1.7.3Scalar or macro? (21)1.8 Looping commands (22)1.8.1The foreach loop (23)1.8.2The forvalues loop (23)1.8.3The while loop (24)1.8.4The continue command (24)1.9 Some useful commands (24)1.10 Template do-file (25)1.11 User-written commands (25)1.12 Stata resources (26)1.13 Exercises (26)2 Data management and graphics292.1Introduction (29)2.2 Types of data (29)2.2.1Text or ASCII data (30)2.2.2Internal numeric data (30)2.2.3String data (31)2.2.4Formats for displaying numeric data (31)2.3Inputting data (32)2.3.1General principles (32)2.3.2Inputting data already in Stata format (33)2.3.3Inputting data from the keyboard (34)2.3.4Inputting nontext data (34)2.3.5Inputting text data from a spreadsheet (35)2.3.6Inputting text data in free format (36)2.3.7Inputting text data in fixed format (36)2.3.8Dictionary files (37)2.3.9Common pitfalls (37)2.4 Data management (38)2.4.1PSID example (38)2.4.2Naming and labeling variables (41)2.4.3Viewing data (42)2.4.4Using original documentation (43)2.4.5Missing values (43)2.4.6Imputing missing data (45)2.4.7Transforming data (generate, replace, egen, recode) (45)The generate and replace commands (46)The egen command (46)The recode command (47)The by prefix (47)Indicator variables (47)Set of indicator variables (48)Interactions (49)Demeaning (50)2.4.8Saving data (51)2.4.9Selecting the sample (51)2.5 Manipulating datasets (53)2.5.1Ordering observations and variables (53)2.5.2Preserving and restoring a dataset (53)2.5.3Wide and long forms for a dataset (54)2.5.4Merging datasets (54)2.5.5Appending datasets (56)2.6 Graphical display of data (57)2.6.1Stata graph commands (57)Example graph commands (57)Saving and exporting graphs (58)Learning how to use graph commands (59)2.6.2Box-and-whisker plot (60)2.6.3Histogram (61)2.6.4Kernel density plot (62)2.6.5Twoway scatterplots and fitted lines (64)2.6.6Lowess, kernel, local linear, and nearest-neighbor regression652.6.7Multiple scatterplots (67)2.7 Stata resources (68)2.8Exercises (68)3Linear regression basics713.1Introduction (71)3.2 Data and data summary (71)3.2.1Data description (71)3.2.2Variable description (72)3.2.3Summary statistics (73)3.2.4More-detailed summary statistics (74)3.2.5Tables for data (75)3.2.6Statistical tests (78)3.2.7Data plots (78)3.3Regression in levels and logs (79)3.3.1Basic regression theory (79)3.3.2OLS regression and matrix algebra (80)3.3.3Properties of the OLS estimator (81)3.3.4Heteroskedasticity-robust standard errors (82)3.3.5Cluster–robust standard errors (82)3.3.6Regression in logs (83)3.4Basic regression analysis (84)3.4.1Correlations (84)3.4.2The regress command (85)3.4.3Hypothesis tests (86)3.4.4Tables of output from several regressions (87)3.4.5Even better tables of regression output (88)3.5Specification analysis (90)3.5.1Specification tests and model diagnostics (90)3.5.2Residual diagnostic plots (91)3.5.3Influential observations (92)3.5.4Specification tests (93)Test of omitted variables (93)Test of the Box–Cox model (94)Test of the functional form of the conditional mean (95)Heteroskedasticity test (96)Omnibus test (97)3.5.5Tests have power in more than one direction (98)3.6Prediction (100)3.6.1In-sample prediction (100)3.6.2Marginal effects (102)3.6.3Prediction in logs: The retransformation problem (103)3.6.4Prediction exercise (104)3.7 Sampling weights (105)3.7.1Weights (106)3.7.2Weighted mean (106)3.7.3Weighted regression (107)3.7.4Weighted prediction and MEs (109)3.8 OLS using Mata (109)3.9Stata resources (111)3.10 Exercises (111)4Simulation1134.1Introduction (113)4.2 Pseudorandom-number generators: Introduction (114)4.2.1Uniform random-number generation (114)4.2.2Draws from normal (116)4.2.3Draws from t, chi-squared, F, gamma, and beta (117)4.2.4 Draws from binomial, Poisson, and negative binomial . . . (118)Independent (but not identically distributed) draws frombinomial (118)Independent (but not identically distributed) draws fromPoisson (119)Histograms and density plots (120)4.3 Distribution of the sample mean (121)4.3.1Stata program (122)4.3.2The simulate command (123)4.3.3Central limit theorem simulation (123)4.3.4The postfile command (124)4.3.5Alternative central limit theorem simulation (125)4.4 Pseudorandom-number generators: Further details (125)4.4.1Inverse-probability transformation (126)4.4.2Direct transformation (127)4.4.3Other methods (127)4.4.4Draws from truncated normal (128)4.4.5Draws from multivariate normal (129)Direct draws from multivariate normal (129)Transformation using Cholesky decomposition (130)4.4.6Draws using Markov chain Monte Carlo method (130)4.5 Computing integrals (132)4.5.1Quadrature (133)4.5.2Monte Carlo integration (133)4.5.3Monte Carlo integration using different S (134)4.6Simulation for regression: Introduction (135)4.6.1Simulation example: OLS with X2 errors (135)4.6.2Interpreting simulation output (138)Unbiasedness of estimator (138)Standard errors (138)t statistic (138)Test size (139)Number of simulations (140)4.6.3Variations (140)Different sample size and number of simulations (140)Test power (140)Different error distributions (141)4.6.4Estimator inconsistency (141)4.6.5Simulation with endogenous regressors (142)4.7Stata resources (144)4.8Exercises (144)5GLS regression1475.1Introduction (147)5.2 GLS and FGLS regression (147)5.2.1GLS for heteroskedastic errors (147)5.2.2GLS and FGLS (148)5.2.3Weighted least squares and robust standard errors (149)5.2.4Leading examples (149)5.3 Modeling heteroskedastic data (150)5.3.1Simulated dataset (150)5.3.2OLS estimation (151)5.3.3Detecting heteroskedasticity (152)5.3.4FGLS estimation (154)5.3.5WLS estimation (156)5.4System of linear regressions (156)5.4.1SUR model (156)5.4.2The sureg command (157)5.4.3Application to two categories of expenditures (158)5.4.4Robust standard errors (160)5.4.5Testing cross-equation constraints (161)5.4.6Imposing cross-equation constraints (162)5.5Survey data: Weighting, clustering, and stratification (163)5.5.1Survey design (164)5.5.2Survey mean estimation (167)5.5.3Survey linear regression (167)5.6Stata resources (169)5.7Exercises (169)6Linear instrumental-variables regression1716.1Introduction (171)6.2 IV estimation (171)6.2.1Basic IV theory (171)6.2.2Model setup (173)6.2.3IV estimators: IV, 2SLS, and GMM (174)6.2.4Instrument validity and relevance (175)6.2.5Robust standard-error estimates (176)6.3 IV example (177)6.3.1The ivregress command (177)6.3.2Medical expenditures with one endogenous regressor . . . (178)6.3.3Available instruments (179)6.3.4IV estimation of an exactly identified model (180)6.3.5IV estimation of an overidentified model (181)6.3.6Testing for regressor endogeneity (182)6.3.7Tests of overidentifying restrictions (185)6.3.8IV estimation with a binary endogenous regressor (186)6.4 Weak instruments (188)6.4.1Finite-sample properties of IV estimators (188)6.4.2Weak instruments (189)Diagnostics for weak instruments (189)Formal tests for weak instruments (190)6.4.3The estat firststage command (191)6.4.4Just-identified model (191)6.4.5Overidentified model (193)6.4.6More than one endogenous regressor (195)6.4.7Sensitivity to choice of instruments (195)6.5 Better inference with weak instruments (197)6.5.1Conditional tests and confidence intervals (197)6.5.2LIML estimator (199)6.5.3Jackknife IV estimator (199)6.5.4 Comparison of 2SLS, LIML, JIVE, and GMM (200)6.6 3SLS systems estimation (201)6.7Stata resources (203)6.8Exercises (203)7Quantile regression2057.1Introduction (205)7.2 QR (205)7.2.1Conditional quantiles (206)7.2.2Computation of QR estimates and standard errors (207)7.2.3The qreg, bsqreg, and sqreg commands (207)7.3 QR for medical expenditures data (208)7.3.1Data summary (208)7.3.2QR estimates (209)7.3.3Interpretation of conditional quantile coefficients (210)7.3.4Retransformation (211)7.3.5Comparison of estimates at different quantiles (212)7.3.6Heteroskedasticity test (213)7.3.7Hypothesis tests (214)7.3.8Graphical display of coefficients over quantiles (215)7.4 QR for generated heteroskedastic data (216)7.4.1Simulated dataset (216)7.4.2QR estimates (219)7.5 QR for count data (220)7.5.1Quantile count regression (221)7.5.2The qcount command (222)7.5.3Summary of doctor visits data (222)7.5.4Results from QCR (224)7.6Stata resources (226)7.7Exercises (226)8Linear panel-data models: Basics2298.1Introduction (229)8.2 Panel-data methods overview (229)8.2.1Some basic considerations (230)8.2.2Some basic panel models (231)Individual-effects model (231)Fixed-effects model (231)Random-effects model (232)Pooled model or population-averaged model (232)Two-way-effects model (232)Mixed linear models (233)8.2.3Cluster-robust inference (233)8.2.4The xtreg command (233)8.2.5Stata linear panel-data commands (234)8.3 Panel-data summary (234)8.3.1Data description and summary statistics (234)8.3.2Panel-data organization (236)8.3.3Panel-data description (237)8.3.4Within and between variation (238)8.3.5Time-series plots for each individual (241)8.3.6Overall scatterplot (242)8.3.7Within scatterplot (243)8.3.8Pooled OLS regression with cluster—robust standard errors ..2448.3.9Time-series autocorrelations for panel data (245)8.3.10 Error correlation in the RE model (247)8.4 Pooled or population-averaged estimators (248)8.4.1Pooled OLS estimator (248)8.4.2Pooled FGLS estimator or population-averaged estimator (248)8.4.3The xtreg, pa command (249)8.4.4Application of the xtreg, pa command (250)8.5 Within estimator (251)8.5.1Within estimator (251)8.5.2The xtreg, fe command (251)8.5.3Application of the xtreg, fe command (252)8.5.4Least-squares dummy-variables regression (253)8.6 Between estimator (254)8.6.1Between estimator (254)8.6.2Application of the xtreg, be command (255)8.7 RE estimator (255)8.7.1RE estimator (255)8.7.2The xtreg, re command (256)8.7.3Application of the xtreg, re command (256)8.8 Comparison of estimators (257)8.8.1Estimates of variance components (257)8.8.2Within and between R-squared (258)8.8.3Estimator comparison (258)8.8.4Fixed effects versus random effects (259)8.8.5Hausman test for fixed effects (260)The hausman command (260)Robust Hausman test (261)8.8.6Prediction (262)8.9 First-difference estimator (263)8.9.1First-difference estimator (263)8.9.2Strict and weak exogeneity (264)8.10 Long panels (265)8.10.1 Long-panel dataset (265)8.10.2 Pooled OLS and PFGLS (266)8.10.3 The xtpcse and xtgls commands (267)8.10.4 Application of the xtgls, xtpcse, and xtscc commands . . . (268)8.10.5 Separate regressions (270)8.10.6 FE and RE models (271)8.10.7 Unit roots and cointegration (272)8.11 Panel-data management (274)8.11.1 Wide-form data (274)8.11.2 Convert wide form to long form (274)8.11.3 Convert long form to wide form (275)8.11.4 An alternative wide-form data (276)8.12 Stata resources (278)8.13 Exercises (278)9Linear panel-data models: Extensions2819.1Introduction (281)9.2 Panel IV estimation (281)9.2.1Panel IV (281)9.2.2The xtivreg command (282)9.2.3Application of the xtivreg command (282)9.2.4Panel IV extensions (284)9.3 Hausman-Taylor estimator (284)9.3.1Hausman-Taylor estimator (284)9.3.2The xthtaylor command (285)9.3.3Application of the xthtaylor command (285)9.4 Arellano-Bond estimator (287)9.4.1Dynamic model (287)9.4.2IV estimation in the FD model (288)9.4.3 The xtabond command (289)9.4.4Arellano-Bond estimator: Pure time series (290)9.4.5Arellano-Bond estimator: Additional regressors (292)9.4.6Specification tests (294)9.4.7 The xtdpdsys command (295)9.4.8 The xtdpd command (297)9.5 Mixed linear models (298)9.5.1Mixed linear model (298)9.5.2 The xtmixed command (299)9.5.3Random-intercept model (300)9.5.4Cluster-robust standard errors (301)9.5.5Random-slopes model (302)9.5.6Random-coefficients model (303)9.5.7Two-way random-effects model (304)9.6 Clustered data (306)9.6.1Clustered dataset (306)9.6.2Clustered data using nonpanel commands (306)9.6.3Clustered data using panel commands (307)9.6.4Hierarchical linear models (310)9.7Stata resources (311)9.8Exercises (311)10 Nonlinear regression methods31310.1 Introduction (313)10.2 Nonlinear example: Doctor visits (314)10.2.1 Data description (314)10.2.2 Poisson model description (315)10.3 Nonlinear regression methods (316)10.3.1 MLE (316)10.3.2 The poisson command (317)10.3.3 Postestimation commands (318)10.3.4 NLS (319)10.3.5 The nl command (319)10.3.6 GLM (321)10.3.7 The glm command (321)10.3.8 Other estimators (322)10.4 Different estimates of the VCE (323)10.4.1 General framework (323)10.4.2 The vce() option (324)10.4.3 Application of the vce() option (324)10.4.4 Default estimate of the VCE (326)10.4.5 Robust estimate of the VCE (326)10.4.6 Cluster–robust estimate of the VCE (327)10.4.7 Heteroskedasticity- and autocorrelation-consistent estimateof the VCE (328)10.4.8 Bootstrap standard errors (328)10.4.9 Statistical inference (329)10.5 Prediction (329)10.5.1 The predict and predictnl commands (329)10.5.2 Application of predict and predictnl (330)10.5.3 Out-of-sample prediction (331)10.5.4 Prediction at a specified value of one of the regressors (321)10.5.5 Prediction at a specified value of all the regressors (332)10.5.6 Prediction of other quantities (333)10.6 Marginal effects (333)10.6.1 Calculus and finite-difference methods (334)10.6.2 MEs estimates AME, MEM, and MER (334)10.6.3 Elasticities and semielasticities (335)10.6.4 Simple interpretations of coefficients in single-index models (336)10.6.5 The mfx command (337)10.6.6 MEM: Marginal effect at mean (337)Comparison of calculus and finite-difference methods . . . (338)10.6.7 MER: Marginal effect at representative value (338)10.6.8 AME: Average marginal effect (339)10.6.9 Elasticities and semielasticities (340)10.6.10 AME computed manually (342)10.6.11 Polynomial regressors (343)10.6.12 Interacted regressors (344)10.6.13 Complex interactions and nonlinearities (344)10.7 Model diagnostics (345)10.7.1 Goodness-of-fit measures (345)10.7.2 Information criteria for model comparison (346)10.7.3 Residuals (347)10.7.4 Model-specification tests (348)10.8 Stata resources (349)10.9 Exercises (349)11 Nonlinear optimization methods35111.1 Introduction (351)11.2 Newton–Raphson method (351)11.2.1 NR method (351)11.2.2 NR method for Poisson (352)11.2.3 Poisson NR example using Mata (353)Core Mata code for Poisson NR iterations (353)Complete Stata and Mata code for Poisson NR iterations (353)11.3 Gradient methods (355)11.3.1 Maximization options (355)11.3.2 Gradient methods (356)11.3.3 Messages during iterations (357)11.3.4 Stopping criteria (357)11.3.5 Multiple maximums (357)11.3.6 Numerical derivatives (358)11.4 The ml command: if method (359)11.4.1 The ml command (360)11.4.2 The If method (360)11.4.3 Poisson example: Single-index model (361)11.4.4 Negative binomial example: Two-index model (362)11.4.5 NLS example: Nonlikelihood model (363)11.5 Checking the program (364)11.5.1 Program debugging using ml check and ml trace (365)11.5.2 Getting the program to run (366)11.5.3 Checking the data (366)11.5.4 Multicollinearity and near coilinearity (367)11.5.5 Multiple optimums (368)11.5.6 Checking parameter estimation (369)11.5.7 Checking standard-error estimation (370)11.6 The ml command: d0, dl, and d2 methods (371)11.6.1 Evaluator functions (371)11.6.2 The d0 method (373)11.6.3 The dl method (374)11.6.4 The dl method with the robust estimate of the VCE (374)11.6.5 The d2 method (375)11.7 The Mata optimize() function (376)11.7.1 Type d and v evaluators (376)11.7.2 Optimize functions (377)11.7.3 Poisson example (377)Evaluator program for Poisson MLE (377)The optimize() function for Poisson MLE (378)11.8 Generalized method of moments (379)11.8.1 Definition (380)11.8.2 Nonlinear IV example (380)11.8.3 GMM using the Mata optimize() function (381)11.9 Stata resources (383)11.10 Exercises (383)12 Testing methods38512.1 Introduction (385)12.2 Critical values and p-values (385)12.2.1 Standard normal compared with Student's t (386)12.2.2 Chi-squared compared with F (386)12.2.3 Plotting densities (386)12.2.4 Computing p-values and critical values (388)12.2.5 Which distributions does Stata use? (389)12.3 Wald tests and confidence intervals (389)12.3.1 Wald test of linear hypotheses (389)12.3.2 The test command (391)Test single coefficient (392)Test several hypotheses (392)Test of overall significance (393)Test calculated from retrieved coefficients and VCE (393)12.3.3 One-sided Wald tests (394)12.3.4 Wald test of nonlinear hypotheses (delta method) (395)12.3.5 The testnl command (395)12.3.6 Wald confidence intervals (396)12.3.7 The lincom command (396)12.3.8 The nlcom command (delta method) (397)12.3.9 Asymmetric confidence intervals (398)12.4 Likelihood-ratio tests (399)12.4.1 Likelihood-ratio tests (399)12.4.2 The lrtest command (401)12.4.3 Direct computation of LR tests (401)12.5 Lagrange multiplier test (or score test) (402)12.5.1 LM tests (402)12.5.2 The estat command (403)12.5.3 LM test by auxiliary regression (403)12.6 Test size and power (405)12.6.1 Simulation DGP: OLS with chi-squared errors (405)12.6.2 Test size (406)12.6.3 Test power (407)12.6.4 Asymptotic test power (410)12.7 Specification tests (411)12.7.1 Moment-based tests (411)12.7.2 Information matrix test (411)12.7.3 Chi-squared goodness-of-fit test (412)12.7.4 Overidentifying restrictions test (412)12.7.5 Hausman test (412)12.7.6 Other tests (413)12.8 Stata resources (413)12.9 Exercises (413)13 Bootstrap methods41513.1 Introduction (415)13.2 Bootstrap methods (415)13.2.1 Bootstrap estimate of standard error (415)13.2.2 Bootstrap methods (416)13.2.3 Asymptotic refinement (416)13.2.4 Use the bootstrap with caution (416)13.3 Bootstrap pairs using the vce(bootstrap) option (417)13.3.1 Bootstrap-pairs method to estimate VCE (417)13.3.2 The vce(bootstrap) option (418)13.3.3 Bootstrap standard-errors example (418)13.3.4 How many bootstraps? (419)13.3.5 Clustered bootstraps (420)13.3.6 Bootstrap confidence intervals (421)13.3.7 The postestimation estat bootstrap command (422)13.3.8 Bootstrap confidence-intervals example (423)13.3.9 Bootstrap estimate of bias (423)13.4 Bootstrap pairs using the bootstrap command (424)13.4.1 The bootstrap command (424)13.4.2 Bootstrap parameter estimate from a Stata estimationcommand (425)13.4.3 Bootstrap standard error from a Stata estimation command (426)13.4.4 Bootstrap standard error from a user-written estimationcommand (426)13.4.5 Bootstrap two-step estimator (427)13.4.6 Bootstrap Hausman test (429)13.4.7 Bootstrap standard error of the coefficient of variation . . (430)13.5 Bootstraps with asymptotic refinement (431)13.5.1 Percentile-t method (431)13.5.2 Percentile-t Wald test (432)13.5.3 Percentile-t Wald confidence interval (433)13.6 Bootstrap pairs using bsample and simulate (434)13.6.1 The bsample command (434)13.6.2 The bsample command with simulate (434)13.6.3 Bootstrap Monte Carlo exercise (436)13.7 Alternative resampling schemes (436)13.7.1 Bootstrap pairs (437)13.7.2 Parametric bootstrap (437)13.7.3 Residual bootstrap (439)13.7.4 Wild bootstrap (440)13.7.5 Subsampling (441)13.8 The jackknife (441)13.8.1 Jackknife method (441)13.8.2 The vice(jackknife) option and the jackknife command . . (442)13.9 Stata resources (442)13.10 Exercises (442)14 Binary outcome models44514.1 Introduction (445)14.2 Some parametric models (445)14.2.1 Basic model (445)14.2.2 Logit, probit, linear probability, and clog-log models . . . (446)14.3 Estimation (446)14.3.1 Latent-variable interpretation and identification (447)14.3.2 ML estimation (447)14.3.3 The logit and probit commands (448)14.3.4 Robust estimate of the VCE (448)14.3.5 OLS estimation of LPM (448)14.4 Example (449)14.4.1 Data description (449)14.4.2 Logit regression (450)14.4.3 Comparison of binary models and parameter estimates . (451)14.5 Hypothesis and specification tests (452)14.5.1 Wald tests (453)14.5.2 Likelihood-ratio tests (453)14.5.3 Additional model-specification tests (454)Lagrange multiplier test of generalized logit (454)Heteroskedastic probit regression (455)14.5.4 Model comparison (456)14.6 Goodness of fit and prediction (457)14.6.1 Pseudo-R2 measure (457)14.6.2 Comparing predicted probabilities with sample frequencies (457)14.6.3 Comparing predicted outcomes with actual outcomes . . . (459)14.6.4 The predict command for fitted probabilities (460)14.6.5 The prvalue command for fitted probabilities (461)14.7 Marginal effects (462)14.7.1 Marginal effect at a representative value (MER) (462)14.7.2 Marginal effect at the mean (MEM) (463)14.7.3 Average marginal effect (AME) (464)14.7.4 The prchange command (464)14.8 Endogenous regressors (465)14.8.1 Example (465)14.8.2 Model assumptions (466)14.8.3 Structural-model approach (467)The ivprobit command (467)Maximum likelihood estimates (468)Two-step sequential estimates (469)14.8.4 IVs approach (471)14.9 Grouped data (472)14.9.1 Estimation with aggregate data (473)14.9.2 Grouped-data application (473)14.10 Stata resources (475)14.11 Exercises (475)15 Multinomial models47715.1 Introduction (477)15.2 Multinomial models overview (477)15.2.1 Probabilities and MEs (477)15.2.2 Maximum likelihood estimation (478)15.2.3 Case-specific and alternative-specific regressors (479)15.2.4 Additive random-utility model (479)15.2.5 Stata multinomial model commands (480)15.3 Multinomial example: Choice of fishing mode (480)15.3.1 Data description (480)15.3.2 Case-specific regressors (483)15.3.3 Alternative-specific regressors (483)15.4 Multinomial logit model (484)15.4.1 The mlogit command (484)15.4.2 Application of the mlogit command (485)15.4.3 Coefficient interpretation (486)15.4.4 Predicted probabilities (487)15.4.5 MEs (488)15.5 Conditional logit model (489)15.5.1 Creating long-form data from wide-form data (489)15.5.2 The asclogit command (491)15.5.3 The clogit command (491)15.5.4 Application of the asclogit command (492)15.5.5 Relationship to multinomial logit model (493)15.5.6 Coefficient interpretation (493)15.5.7 Predicted probabilities (494)15.5.8 MEs (494)15.6 Nested logit model (496)15.6.1 Relaxing the independence of irrelevant alternatives as-sumption (497)15.6.2 NL model (497)15.6.3 The nlogit command (498)15.6.4 Model estimates (499)15.6.5 Predicted probabilities (501)15.6.6 MEs (501)15.6.7 Comparison of logit models (502)15.7 Multinomial probit model (503)15.7.1 MNP (503)15.7.2 The mprobit command (503)15.7.3 Maximum simulated likelihood (504)15.7.4 The asmprobit command (505)15.7.5 Application of the asmprobit command (505)15.7.6 Predicted probabilities and MEs (507)15.8 Random-parameters logit (508)15.8.1 Random-parameters logit (508)15.8.2 The mixlogit command (508)15.8.3 Data preparation for mixlogit (509)15.8.4 Application of the mixlogit command (509)15.9 Ordered outcome models (510)15.9.1 Data summary (511)15.9.2 Ordered outcomes (512)15.9.3 Application of the ologit command (512)15.9.4 Predicted probabilities (513)15.9.5 MEs (513)15.9.6 Other ordered models (514)15.10 Multivariate outcomes (514)15.10.1 Bivariate probit (515)15.10.2 Nonlinear SUR (517)15.11 Stata resources (518)15.12 Exercises (518)16 Tobit and selection models52116.1 Introduction (521)16.2 Tobit model (521)16.2.1 Regression with censored data (521)16.2.2 Tobit model setup (522)16.2.3 Unknown censoring point (523)。

A NGEWANDTE M ATHEMATIK UNDI NFORMATIKU NIVERSIT¨AT ZU K¨OLNReport No.97.294Duality in Combinatorial Optimization–Sometimes it Works,Sometimes it Won’tbyW.Hochst¨a ttler1997Winfried Hochst¨a ttlerZPR,Zentrum f¨u r paralleles RechnenUniversit¨a t zu K¨o lnAlbertus-Magnus-PlatzD-50923K¨o lnGermanyTelefon(0221)470–6024(60212)e–Mail WH@ZPR.UNI-KOELN.DE)1991Mathematics Subject Classification:05B35,06C10,52B40,12E15,90C27, 90D12Keywords:duality,linear programming,matroids,oriented matroids,polarity,ad-joints,skewfields,pseudomodular matroids,cooperative games,core,Held-Karp Bound,MST,TSP,Nukleon,MatchingContents1Introduction12Projective Duality in Matroids and Oriented Matroids72.1Geometric Programming (7)2.1.1From Linear Programming to Signed V ectors (7)2.1.2Arrangements of Pseudospheres (9)2.1.3Oriented Matroids (10)2.1.4Matroids (11)2.1.5Desargues’Theorem I (12)2.1.6The Combinatorial Program (13)2.1.7Improving Directions (14)2.1.8Duality (15)2.1.9Linear Programming Duality (15)2.2Euclideaness (17)2.2.1Euclidean Oriented Matroids (17)2.2.2Euclidean Intersection Property (18)2.2.3Pseudomodular Matroids (20)2.3Polarity (22)2.3.1Pseudoconfigurations of Points (22)2.3.2The Adjoint of a Matroid (23)2.3.3Adjoints of Pseudomodular Matroids (25)2.3.4(Oriented)Matroids from Skew Fields (26)iii CONTENTS2.4Double Adjoints and Desargues’Theorem Revisited (29)2.4.1Infinite Chains of Double Adjoints and Projective Spaces.292.4.2A Matroid without Double Adjoint from Desargues’The-orem (30)2.4.3“Coextending a pseudoconfiguration of points” (31)2.5Remarks and Open Problems (33)3Computational Aspects of Combinatorial Cooperative Games353.1Solution Concepts (36)3.1.1The Core (36)3.1.2-cores (37)3.1.3The Nucleolus and the Nucleon (37)3.1.4Computational Aspects of the Nucleon (39)3.1.5When the Nucleon is not a Unique Point (40)3.2Linear Production Games (41)3.3Minimum Spanning Tree Games (42)3.3.1Core membership testing is co--complete (43)3.4Traveling Salesman Games (44)3.4.1A minimum Euclidean example with empty core (44)3.4.2Approximately fair allocations (46)3.4.3ZusammenfassungIn der vorliegenden Arbeit berichten wir ¨uber unsere Untersuchungen in zwei Ge-bieten der kombinatorischen Optimierung.Der erste Teil ist Ergebnissen zur Exi-stenz eines Adjoints (projektiven Duals)in Matroiden und orientierten Matroiden gewidmet.Diese Untersuchungen sind etwas theoretischerer Natur,gewinnen aber ihre Bedeutung f¨u r die Anwendung aus den vielf¨a ltigen Situationen,in denen ori-entierte Matroide in der angewandten und reinen Mathematik und der Computatio-nal Geometry auftreten.Das andere Thema,das wir eingehender behandeln,sind algorithmische und komplexit¨a tstheoretische Aspekte von L¨o sungskonzepten f¨u r einige kooperative Spiele,deren charakteristische Funktion durch kombinatorische Optimierungsprobleme definiert ist.Dualit¨a t in der kombinatorischen Optimierung wird vielfach mit der linearen Pro-grammierung identifiziert.Obwohl diese f¨u r die Anwendung das wichtigste Dua-lit¨a tskonzept darstellt,gibt es sowohl Ans¨a tze,die diese verallgemeinern,als auch Dualit¨a tsbegriffe,die in gewissem Sinne mit der Dualit¨a t der linearen Program-mierung unvertr¨a glich sind.Hierunter z¨a hlt auch der erste Dualit¨a tsbegriff,mit dem man als Mathematiker konfrontiert wird,n¨a mlich der des dualen V ektorrau-mes in seiner Erscheinungsform als Dualit¨a t in projektiven R¨a umen.Aus der Dualit¨a t der linearen Programmierung l¨a ßt sich auf nat¨u rliche Weise die kombinatorische Struktur des orientierten Matroids axiomatisieren (vgl.z.B.[7]).Orientierte Matroide wurden 1978von Bland und Las V ergnas [13]und Folkman und Lawrence [25]eingef¨u hrt.Letztere Arbeit enthielt bereits das “Topological Representation Theorem”,welches besagt,daßorientierte Matroide ¨a quivalent zu orientierten Arrangements von Hypersph¨a ren auf der Standardsph¨a re (Satz 2.1.5)sind.Dies bedeutet,daßorientierte Matroide sich stets als Hyperebenenarrange-ments ”mit kleinen Dellen“modellieren lassen.Einem orientierten Matroid liegt stets die (gr¨o bere)Struktur eines Matroids zu Grunde.Matroide wurden 1935von Hassler Whitney [60]eingef¨u hrt und sind auch bekannt als kombinatorische Geo-metrien oder geometrische V erb¨a nde [10].Die Dualit¨a tss¨a tze der linearen Programmierung (starke Dualit¨a t und der Satziiivom komplement¨a ren Schlupf)behalten ihre G¨u ltigkeit in orientierten Matroiden.Hingegen kann es beim klassischen L¨o sungsverfahren,dem Simplexalgorithmus,durch ”die kleinen Dellen“zu verbessernden Zykeln kommen.Geometrisch l¨a ßt sich das in etwa wie folgt erkl¨a ren:¨u berstreicht man die Ecken des zul¨a ssigen Be-reiches eines linearen Programmes mit Isokostenhyperebenen der Zielfunktion,so kann man die Ecken des Bereiches in der Reihenfolge ihres Auftretens sortieren.Dies l¨a ßt sich nicht auf beliebige orientierte Matroide ¨u bertragen,da zu einer vor-gegebenen Hyperebene nicht immer f¨u r jeden Punkt eine Parallele durch diesen Punkt zu der Konfiguration hinzugenommen werden kann.Allerdings ist eine solche Erweiterung der Konfiguration stets m¨o glich,falls das (orientierte)Matroid eine polare Dualit¨a t,einen Adjoint,erlaubt.Im Matroidfall bedeutet dies,daßsich das verbandstheoretische Dual in ein Matroid ”einbetten“l¨a ßt,im orientierten Fall,daßdas orientierte Matroid sich als Pseudopunktkonfigu-ration modellieren l¨a ßt.Ein weiterer Aspekt und eine wesentliche Motivation f¨u r die Untersuchung von orientierten Adjoints ist die Fragestellung nach verbands-theoretischer Dualit¨a t (Polarit¨a t)in Seitenfl¨a chenverb¨a nden von ”Matroid Polyto-pen“.Die klassischen S¨a tze der Polyedertheorie von Minkowski und Weyl besa-gen,daßdie Klasse der Polyeder,die man als Schnitte von abgeschlossenen Halbr¨a um-en erh¨a lt,die gleiche ist,wie die derjenigen,welche man als konvexe H¨u lle von Punkten und Halbstrahlen definiert.Das Dual des Seitenfl¨a chenverbandes eines Polyeders ist also wiederum ein Seitenfl¨a chenverband.Beth Munson untersuchte,inwieweit sich dieses Resultat auf ”volldimensionale Zellen“in orientierten Ma-troiden ¨u bertragen l¨a ßt [43].Zur Angabe eines Gegenbeispiels nutzte sie eine Kon-struktionsidee von Lawrence,mit deren Hilfe sich die Existenz einer Polaren auf die Existenz eines Adjoints [15]im zugrundeliegenden Matroid reduzieren l¨a ßt.Im folgenden wurden orientierte Adjoints von Bachem und Kern eingef¨u hrt [6].Aller-dings besch¨a ftigte man sich in dieser Theorie in der Anfangszeit im wesentlichen mit notwendigen Bedingungen f¨u r die Existenz eines solchen Adjoints.Nicht-triviale hinreichende Bedingungen waren nicht bekannt.Dies f¨u hrte sogar dazu,daßN.Mn¨e v eine topologische V ermutung ¨a ußerte (brave version),die implizierte,daßzusam-menh¨a ngende orientierte Matroide vom Rang nur dann einen orientierten Ad-joint haben,wenn sie linear sind.In dieser Arbeit geben wir drei hinreichende Bedingungen f¨u r die Existenz eines Adjoints in nicht-linearen Matroiden an.Ausgangspunkt f¨u r diese Untersuchun-gen ist eine Frage von J¨u rgen Richter-Gebert nach der Existenz eines orientierten Adjoints einer gewissen Instanz (hier der RG-Torus genannt).Ankn¨u pfend an V or-arbeiten von Marion Alfter [2]werden wir hier eine erste hinreichende Bedingung f¨u r die Existenz eines Adjoints im nicht-linearen Fall f¨u r den Rang 4erarbeiten (Theorem 2.3.7):ivSatz 1(Alfter,Hochst¨a ttler [3])Sei ein pseudomodulares Matroid vom Rang4.Dann hat einen Adjoint.Genauer ,sind die Hyperebenen von und ist die Familie der vierelementigen Teilmengen mit 1.(leerer Schnitt)2.(keine Gerade in dreien)3.f¨u r.(windschiefe Schnittpaare)Dann ist die Familie der Basen eines Adjoints von .Dies impliziert zwar die Existenz eines Adjoints des RG-Torus.Aber auch die vol-le Fragestellung von Richter-Gebert nach einem orientierten Adjoint k¨o nnen wir kl¨a ren und zwar mit einer ¨uberraschend einfachen Antwort.Tats¨a chlich nutzt der Beweis der Nicht-Linearit¨a t des RG-Torus die Kommutativit¨a t der Multiplikati-on aus.Die zur Definition eines Duals und eines Adjoints im realisierbaren Fall (”keine Dellen“)notwendige Lineare Algebra l¨a ßt sich aber auch in Schiefk¨o rpern anwenden.Außerdem erf¨u llen die V orzeichen,die man aus einem Untervektor-raum eines V ektorraumes ¨u ber einem geordneten Schiefk¨o rper gewinnt,die Axio-me der orientierten Matroidtheorie.Der RG-Torus ist linear ¨u ber einem geordneten Schiefk¨o rper (die ¨u bliche Repr¨a sentierbarkeitstheorie setzt stets die Kommutati-vit¨a t des koordinatisierenden K¨o rpers voraus)und hat somit nicht nur einen ori-entierten Adjoint,sondern sogar eine unendliche Folge von orientierten Adjoints.Dar¨u berhinaus ist die unorientierte V ersion des RG-Torus genau dann in einem V ektorraum der Dimension enthalten,wenn der koordinatisierende K¨o rper nicht kommutativ ist.Somit bildet die Konfiguration ein projektiv dreidimensio-nales Gegenst¨u ck zum Satz von Pappus.Satz 2(Hochst¨a ttler,Kromberg[34,39])1.Das Dual eines Matroids,das linear ¨u ber einem Schiefk¨o rper ist,ist linear ¨u ber demselben K¨o rper .2.Ein Matroid,das linear ¨u ber einem Schiefk¨o rper ist,hat einen Adjoint,der linear ¨u ber dem gleichen K¨o rper ist.3.Sei ein (nicht notwendig kommutativer)geordneter K¨o rper .Die V orzei-chenvektoren,die von einem Unterraum abgeleitet werden,erf¨u llen die V ektor-Axiome der orientierten Matroidtheorie.v4.Das Dual eines orientierten Matroids,das linear ¨u ber einem Schiefk¨o rper ist,ist linear ¨u ber demselben K¨o rper .5.Ein orientiertes Matroid,das linear ¨u ber einem Schiefk¨o rper ist,hat einen orientierten Adjoint,der linear ¨u ber dem gleichen K¨o rper ist.Ein Beispiel eines orientierten Matroids,das “wirklich”nicht-linear ist und einen Adjoint hat,steht somit noch aus.Wir finden ein solches Beispiel,indem wir den wesentlichen Baustein der von Beth Munson benutzten Konstruktionsidee von La-wrence auf das Non-Desargues-Matroid anwenden und erhalten ein nicht-lineares,zusammenh¨a ngendes Matroid vom Rang 4,das pseudomodular ist.Auch einen orientierten Adjoint k¨o nnen wir in diesem Fall konstruieren.Dazu erweitern wir zun¨a chst einen Adjoint des (ebenen)Non-Desargues-Matroid um eine “redundante weitere Dimension”und kombinieren zwei gegeneinander verschobene Kopien die-ser Konfiguration,die in der Ebene zusammenfallen.V on diesem Arrangement ge-ben wir die Lage der Ecken an.Die Menge der zugeh¨o rigen V orzeichenvektorennennen wir den Squint 1eines orientierten Matroids.Die technischen (im allgemei-nen relativ starken)V oraussetzungen dieses Satzes sind im ebenen Fall trivialer-weise erf¨u llt.Satz 3(Hochst¨a ttler,Kromberg [35,39])Der Squint vondurch erf¨u llt dieKreisaxiome der Theorie der orientierten Matroide.Dar¨u berhinaus zeigen wir,daßkein Adjoint dieser Lawrence-Extension des Non-Desargues-Matroides selber einen Adjoint hat.Da so ein ”Doppeladjoint“stets die Originalkonfigurationenth¨a lt,scheint der Satz von Desargues ”geometrisch wesent-lich“f¨u r die Existenz eines Adjoints zu sein (vgl.hierzu auch [4]).Mit diesem Beispiel beantworten wir eine Frage von G¨u nter Ziegler (vgl.Exercise 7.15(b)in[12]).Eine zentrale Aufgabenstellung in der Theorie der kooperativen Spiele ,ist das Pro-blem der V erteilung des Wertes eines Spieles an die Spieler .Im zweiten Teil der Arbeit untersuchen wir diese Fragestellung am Beispiel dreier kooperativer Spie-le,deren charakteristische Funktion durch kombinatorische Optimierungsproble-me gegeben ist.Alle V erteilungsl¨o sungen,die wir in den drei Arbeiten betrachten,sind verwandt zum Konzept des Core (vgl.z.B.[53])eines Spieles.Eine V erteilung im Core hat die Eigenschaft,daßkeine Koalition (Teilmenge der Spieler)insgesamt wenigererh¨a lt als ihren eigenen Wert in der charakteristischen Funktion.Dies bedeutet,daßkein Anreiz gegeben wird,die große Koalition zu verlassen.Das Problem,die Kosten einen Netzwerkes auf Teilnehmer,die sich an einen Server anschließen,umzulegen,wurde von Claus und Kleitman in[16]1973vor-gestellt und von Bird[9]1976in spieltheoretischen Kontext gestellt.Die charak-teristische Funktion dieses Spieles f¨u r eine Koalition ist definiert als mini-male Kosten eines Netzwerkes,das die Menge an anbindet.Solch ein kosten-minimales Netzwerk erh¨a lt man zum Beispiel,indem man stets die k¨u rzeste Kante w¨a hlt,die zwei bisher noch nicht verbundene Knoten als Endknoten hat.Betrach-tet man diese Prozedur nun beim k¨u rzesten Netzwerk f¨u r,und weist die Kosten solcher Kanten demjenigen ihrer beiden Endknoten zu,der im Netzwerk die gr¨oße-re Distanz zum Knoten hat,so liegt diese V erteilung im Core des Spiels.Aller-dings hat diese L¨o sung einige Nachteile,sie ber¨u cksichtigt zum Beispiel nicht die N¨a he eines Teilnehmers zum Knoten.Eine g¨u nstigere L¨o sung k¨o nnte etwa als Optimum im Core bzgl.einer linearen Zielfunktion definiert werden.Ferner w¨a re es w¨u nschenswert,wenn man Core-Elemente mit einem kurzen Beweis den Teil-nehmern glaubhaft machen k¨o nnte.Da das Problem,ob ein V ektor im Core ist, offensichtlich in co-liegt,ist diese Frage gleichbedeutend damit,ob das Ent-scheidungsproblem der Mitgliedschaft eines Elementes im Core in co-dual charakterisieren l¨aßt“.liegt und somit sich”In unserer Arbeit zeigen wir,daßdas Problem des Enthaltenseins im Core co--vollst¨a ndig ist.Daraus folgt,daßes wohl keine effiziente M¨o glichkeit gibt,¨u ber dem Core zu optimieren und auch eine Dualit¨a tsaussage nicht erwartet werden kann.Satz4(Faigle,Fekete,Hochst¨a ttler,Kern[22])Das folgende Problem ist-vollst¨a ndig:Eingabe:Ein gewichteter Graph und.Frage:Liegt nicht im Core des durch definierten Minimum-Cost-Spanning-Tree-Spiels(MCST-Spiels).Im Gegensatz zum MCST-Spiel ist beim Spiel des Handlungsreisenden(TSP-Spiel), der Core manchmal leer.Hier sollen nicht die Kosten eines Netzwerks verteilt wer-den sondern die einer k¨u rzesten Rundreise.Dieses Spiel wurde in einem allgemei-neren Kontext von Tamir[55,56]vorgestellt und in der hier besprochenen Form von Potters et al.[37]definiert.Da TSP-Probleme im allgemeinen nicht einmal n¨a herungsweise L¨o sungen(mit garantierter G¨u te)gestatten,konzentrieren wir uns auf den Fall,in dem die Kan-tengewichte(Distanzen)des Graphen die Dreiecksungleichung erf¨u llen.Zun¨a chstviigeben wir ein solches ”euklidisches“Beispiel mit 6Spielern und leerem Core an.Nach Kuipers [40]ist dieses Beispiel kleinstm¨o glich.Außerdem schlagen wir ei-ne n¨a herungsweise L¨o sung vor,die effizient berechnet werden kann,die Kosten einer optimalen Tour deckt und bei der von jeder Koalition h¨o chstens 50%mehr verlangt werden,als ihr eigener Spielwert betr¨a gt.Falls die V ermutung ¨u ber diereduzieren.Abschließend zeigen wir,daßletzteres asym-ptotisch bestm¨o glich ist.Satz 5(Faigle,Fekete,Hochst¨a ttler,Kern [20])F¨u r TSP-Spiele mit Dreiecksun-gleichung gibt es V ektoren ,die in Polynomialzeit berechnet werden k¨o nnen und die folgenden Bedingungen erf¨u llen.1.ist eine -approximative Core Allokation f¨u r.3.Falls die ”.Die letzte der drei spieltheoretischen Arbeiten behandelt im Gegensatz zu den an-deren beiden ein Spiel bei dem Gewinne und keine Kosten zu verteilen sind,das Maximum Matching Spiel.Hier ist der Core nur dann nicht leer,wenn das zugeh¨o -rige lineare Programm,das die Blossom-Constraints (see eg.[42])relaxiert,schon eine ganzzahlige Optimall¨o sung hat.Auch hier betrachten wir -approximative L¨o sungen.Wir f¨u hren ein neues spiel-theoretisches Konzept ein,den Nucleon ,der ein multiplikatives Analogon zu dem klassischen Nucleolus von Schmeidler [50]bildet.Anders als dieser ist der Nucle-on nicht “covariant with strategic equivalence”.Dieses Invarianzprinzip l¨a ßt aber quantitative Unterschiede zwischen Spielern außer acht.Unseres Erachtens ist ge-rade bei Gewinnspielen ein L¨o sungsprinzip gefragt,das die Teilnehmer quantitativ abh¨a ngig von Ihrer St¨a rke am Gewinn beteiligt.Zus¨a tzlich hat bei Matching Spielen dieses neue Konzept den V orteil,daßwir die,im wesentlichen eindeutige,L¨o sung im Nucleon –zumindest theoretisch –effizi-ent berechnen k¨o nnen.F¨u r den Nucleolus ist ein effizientes V erfahren nur f¨u r den,wesentlich einfacheren,bipartiten Fall bekannt.Unser Algorithmus benutzt mehrere klassische,polynomielle V erfahren n¨a mlich die primal-duale Methode zur L¨o sung gewichteter Matching Probleme,V erfahren zur L¨o sung linearer Programme und bin¨a re Suche.viiiSatz6(Faigle,Fekete,Hochst¨a ttler,Kern[21])Der Nucleon eines Matching-Spiels auf dem Graphen mit Kantengewichten kann in polynomiel-ler Zeit(abh¨a ngig von und der Gr¨oße von)berechnet werden. Wir hoffen,mit den hier zusammengetragenen Ergebnissen,dem Leser einen Ein-druck von M¨o glichkeiten und Grenzen der Dualit¨a t in der kombinatorischen Op-timierung vermittelt zu haben.ixxChapter 1Introduction”Ich habe meine S¨a tze durch geometrische Anschauung gewonnen ...“Hermann MinkowskiThis thesis consists of two parts.In the first part we discuss aspects of the theory of linear programming duality,polarity and projective duality in a combinatorial context.The second part is devoted to applications of combinatorial optimization and duality to game theory.When duality is mentioned linear programming will spring to the mind of most mathematicians working in Combinatorial Optimization.Nevertheless,this is not the full story.There are several other approaches to duality.Some of them include linear programming duality,others are not fully compatible with it.On the other hand,“the”notion of duality which probably every mathematician knows is duality in linear algebra resp.the duality of projective geometry,a special case of duality in lattice theory.Modelling linear programming duality in a combinatorial setting leads quite nat-urally to the axioms of oriented matroids (see e.g.[7]).It turns out that this com-binatorial model of linear programming and polyhedral theory always represents a geometric configuration which is “close”to a hyperplane arrangement in a well-defined way (Theorem 2.1.5).Furthermore,the main theorems of linear program-ming,strong duality and complementary slackness,hold (Theorem 2.1.17).Nev-ertheless,the Simplex algorithm fails on the general model as there may be “im-proving cycles”.One explanation for this phenomenon is,that in general it is not possible to sweep a “matroid polytope”with a hyperplane,as Euklid’s axiom of parallels fails in gen-12CHAPTER1.INTRODUCTION34CHAPTER1.INTRODUCTION.Finally,we show that no general allocation rule can guar-antee an excess less than56CHAPTER1.INTRODUCTIONChapter2Projective Duality in Matroids and Oriented Matroids2.1Geometric ProgrammingThe objective in linear programming is to minimize a linear function over a poly-hedron,given by a set of inequalities.As the objective function is linear,the set of optimal solutions of such a program is a face of this polyhedron.This enables us to model linear programming as a combinatorial andfinite problem.In this sec-tion we will sketch the corresponding results.For a more detailed treatment of this subject and of oriented matroids at all see[7]or[12].2.1.1From Linear Programming to Signed VectorsGiven an instance of a linear programming problemwe consider the following map(2.1)(2.2)78Projective Duality in Matroids and Oriented Matroids.The latter set we call the support of.The set of all signed vectors on a set is denoted byThe set of feasible points,thus,is given as the preimage of the signed vectors with no negative entry.An objective function maybe be introduced by another coordi-nate but it is not a priori clear how to compare this coordinate wrt.two different feasible vectors.Furthermore,the set of signed vectors derived from such an affine hyperplane arrangement in itself is not completely understood(see[12]4.5).We can make life a lot easier,by homogenizing the problem,i.e.instead of the set of signed vectors derived from2.1we consider the signed vectors which are defined the analogous way from the elements of the vector space im In this set the“affine set of signed vectors”considered before corresponds to the set of signed vectors which have a“”in thefirst coordinate.Definition2.1.2Let be a vector space.The map(2.3)(2.4) whereifififis called forgetting magnitude map.These signed vectors derived from linear hyperplane arrangements behave quite nicely from a combinatorial point of view.Before we introduce the axioms for this structure we will discuss some more geometry and explain what we meant by saying that this model...is“close”to a hyperplane arrangement in a well-defined way.Geometric Programming910Projective Duality in Matroids and Oriented MatroidsGeometric Programming1112Projective Duality in Matroids and Oriented MatroidsGeometric Programming1314Projective Duality in Matroids and Oriented MatroidsGeometric Programming1516Projective Duality in Matroids and Oriented MatroidsEuclideaness1718Projective Duality in Matroids and Oriented MatroidsEuclideaness1920Projective Duality in Matroids and Oriented MatroidsEuclideaness2122Projective Duality in Matroids and Oriented Matroids is an antichain with respect to inclusion.Polarity2324Projective Duality in Matroids and Oriented MatroidsPolarity2526Projective Duality in Matroids and Oriented MatroidsPolarity2728Projective Duality in Matroids and Oriented MatroidsDouble Adjoints and Desargues’Theorem Revisited2930Projective Duality in Matroids and Oriented MatroidsDouble Adjoints and Desargues’Theorem Revisited3132Projective Duality in Matroids and Oriented Matroids.LetRemarks and Open Problems3334Projective Duality in Matroids and Oriented MatroidsChapter3Computational Aspects of Combinatorial Cooperative Games “We think that the procedure of the mathematical theory of games ofstrategy gains definitively in plausibility by the correspondence whichexists between its concepts and those of social organizations.”John von Neumann and Oskar Morgenstern In this chapter we will discuss some complexity aspects of solution concepts in co-operative game theory.The idea of a(transferable utility)cooperative game is that a set of individuals(players)is undertaking some enterprise.Forming coalitions enables them to bundle their strength and increase profit or save costs.Definition3.0.1(von Neumann,Morgenstern[59])A cooperative game is a pair ,where is afinite set of players andis a super-additive,normalized function,i.e.satisfies1.and2..A subset is called a coalition,is called the grand coalition and is the characteristic function.If,i.e.the characteristic function is non-negative,we call a cooperative savings game,if,we will set and–abusing notation–call a cooperative cost game.3536Computational Aspects of Combinatorial Cooperative GamesSolution Concepts3738Computational Aspects of Combinatorial Cooperative Games This,in practice,is infeasible in case of(strictly speaking, this is also infeasible as–violating super-additivity–it is no longer a cooperative game)and contradicts economical behaviour if.。

高考优秀英语作文:因特网的历史History of the Internet一：Nowadays Intemet is very popular all over the world, especially in some big cities. Do you know when the Intemet was first established? Built in 1960s, the Internet was a crude network of a few computers which shared information. If one of the computers broke down, the whole networks would be unable to work, causing continual problems. At first, just the government had access to the Internet, using it for communications among different branches. However, by 1970s the Internet had been used in universities, banks, and hospitals. At the beginning of 1990s computers became affordable for common people and this affordability increased the use of the Internet by people, It is said that each day tens of millions of people log off, making it the most important part of people's life.【参考译文】现在因特网在世界范围内非常流行，特，别是一些大城市更是如此。

神经网络之答禄夫天创作1.单层感知器数据分类输出为0和1解决线性可分的分类模型例1.从待分类的数据中取出一部分数据及其对应的类别作为样本数据,设计并训练一个能对分类数据进行分类的单层感知器神经网络代码:%给定训练样本数据P=[-.4 -.5 .6;.9 0 .1];%给定样本数据所对应的类别，用0和1来暗示两种类别T=[1 1 0];%创建一个有两个输入、样本数据的取值范围都在[-1 1]之间,而且网络只有一个神经元的感知器神经网络net=newp([-1 1;-1 1],1);%设置网络的最大训练次数为20次net.trainParam.epochs=20;%使用训练函数对创建的网络进行训练net=train(net,P,T);%对训练后的网络进行仿真Y=sim(net,P)%计算网络的平均绝对误差，暗示网络错误分类E1=mae(Y-T)%给定测试数据，检测训练好的神经网络的性能Q=[0.6 0.9 -0.1;-0.1 -0.5 0.5];%使用测试数据，对网络进行仿真，仿真输出即为分类的结果Y1=sim(net,Q)%创建一个新的绘图窗口figure;%在坐标中绘制测试数据点，并根据数据所对应的类别用约定的符号画出plotpv(Q,Y1);%在坐标中绘制分类线plotpc(net.iw{1},net.b{1})2.线性神经网络模型线性神经网络类似感知器,但是线性神经网络的激活函数是线性的,而不是硬线转移函数,因此,线性神经网络的输出可以是任意值,而感知器的输出不是0就是1,线性神经网络网络和感知器一样只能解决线性可分的问题.例 2.要求设计一个线性神经网络,寻找给定数据之间的线性关系代码:P=[1.1 -1.3];T=[0.6 1];%创建一个只有一个输出，输入延迟为0，学习速率为0.01的线性神经网络，minmax(P)暗示样本数据的取值范围net=newlin(minmax(P),1,0,0.01);%对创建的神经网络进行初始化，设置权值和阈值的初始值net=init(net);net.trainParam.epochs=500;%设置网络训练后的目标误差为0.0001net.trainParam.goal=0.0001;net=train(net,P,T);y=sim(net,P)%求解网络的均方误差值E=mse(y-T)3.BP神经网络预测能迫近任意非线性函数例3.表2-4为某药品的销售情况，现构建一个如下的BP神经网络对药品的销售进行预测：输入层为三个结点，隐含层结点数为5，隐含层的激活函数为tansig（双曲正切S型传递函数）；输出层结点数为1，输出层的激活函数为logsig（S型的对数函数），并利用此网络对药品的销售量进行预测，预测的方法采取滚动预测方式，即用前三个月的销售量来预测第四个月的销售量，如用1、2、3月的销售量为输入预测第4个月的销售量，用2、3、4月的销售量为输入预测第5个月的销售量。

Dell PowerSwitch S3100 Series © 2022 Dell Inc. or its subsidiaries.The S3100 switch series offers a power-efficient and resilient Gigabit Ethernet (GbE) switching solution with integrated 10GbE uplinks for advanced Layer 3 distribution for offices and campus networks. The S3100 switch series has high-performance capabilities and wire-speed performance utilizing a non-blocking architecture to easily handle unexpected traffic loads. Use dual internal hot-swappable 80PLUS-certified power supplies for high availability and power efficiency. The switches offer simple management and scalability via an 84Gbps (full-duplex) highavailability stacking architecture that allows management of up to 12 switches from a single IP address.Modernize campus network architecturesModernize campus network architectures with apower-efficient and resilient 1/10GbE switching solution with dense Power over Ethernet Plus (PoE+). SelectS3100 models offer 24 or 48 ports of PoE+ to deliver clean power to network devices such as wireless access points (APs), Voice-over-IP (VoIP) handsets, video conferencing systems and security cameras. For greater interoperability in multivendor networks, S3100 series switches offer the latest open-standard protocols and include technology to interface with Cisco protocol PVST+. The S3100 series supports Dell OS9, VLT and network virtualization features such as VRF-lite and support for Dell Embedded Open Automation Framework.Leverage familiar tools and practicesAll S3100 switches include Dell OS9 for easier deployment and greater interoperability. One common command line interface (CLI) using a well-known command language means a faster learning curve for network administrators.Deploy with confidence at any scaleS3100 series switches help create performanceassurance with a data rate up to 260Gbps (full duplex) and a forwarding rate up to 193Mpps. Scale easily with built-in rear stacking ports. Switch stacks of up to 624 ports can be managed from a single screen using the highly-available stacking architecture for high-density aggregation with seamless redundant availability.Hardware, performance and efficiency•Up to 48 line-rate GbE ports of copper or 24 line-rate ports of fiber, two combo ports for fiber/copper flexibili -ty, and two integrated 10GbE SFP+ ports• Up to 48 ports of PoE+ in 1RU without an external power supply• Hot swappable expansion module supporting dual-port SFP+ or dual-port 10GBaseT• Integrated stacking ports with support up to 84Gbps •Up to 624 ports in a 12-unit stack for high-density, high-availability aggregation and distribution in wiring closets/MDFs. Non-stop forwarding and fast failover in stack configurations•Available with dual 80PLUS-certified hot swappable power supplies. Variable speed fan operation helps decrease cooling and power costs•Energy-Efficient Ethernet and lower-power PHYsreduce power to inactive ports and idle links, providing energy savings from the power cord to the port •Dell Fresh Air compliance for operation in environ-ments up to 113°F (45°C) helps reduce cooling costsin temperature constrained deploymentsDELL POWERSWITCH S3100 SERIESHigh-performance managed Ethernet switches designed for non-blocking access2Dell PowerSwitch S3100 Series© 2022 Dell Inc. or its subsidiaries.**Requires C15 plugDeploying, configuring and managing• Tool-less ReadyRails™ significantly reduces rack installation time•Management via an intuitive and familiar CLI, SN -MP-based manage- ment console application (includingDell OpenManage Network Manager), Telnet or serialconnection • Private VLAN support• AAA authorization, TACACS+ accounting and RADIUS support for comprehensive secure access•Authentication tiering allows network administrators to tier port authentication methods such as 802.1x, MAC Authentication Bypass in priority order so that a single port can provide flexible access and security•Achieve high availability and full bandwidth utilization with VLT and support firmware upgrades without taking the network offline•Interfaces with PVST+ protocol for greater flexibility and interoperability in Cisco networks • Advanced Layer 3 IPv4 and IPv6 functionality • Flexible routing options with policy-based routing to route packets based on assigned criteria beyond destination address• Routed Port Monitoring (RPM) covers a Layer 3 domain without costly dedicated network taps•OpenFlow 1.3 provides the ability to separate thecontrol plane from the forwarding plane for deployment in SDN environments*Contact your Dell Technologies representative for a full list of validated storage arrays.3Dell PowerSwitch S3100 Series © 2022 Dell Inc. or its subsidiaries.Physical2 rear stacking ports (21Gbps) supporting up to 84Gbps (full-duplex)2 integrated front 10GbE SFP+ dedicated ports Out-of-band management port (10/100/1000BASE-T)USB (Type A) port for configuration via USB flash driveAuto-negotiation for speed and flow control Auto-MDI/MDIX, port mirroringEnergy-Efficient Ethernet per port settings Redundant variable speed fans Air flow: I/O to power supplyRJ45 console/management port with RS232 signaling (RJ-45 to female DB-9 connector cable included)Dual firmware images on-boardSwitching engine model: Store and forward ChasisSize (1RU): 1.7126in x 17.0866in x 16.0236in (43.5mm x 434.0mm x 407.0mm) (H x W x D)Approximate weight: 13.2277lbs/6kg (S3124 and S3124F), 14.5505lbs/6.6kg (S3124P), 15.2119lbs/6.9kg (S3148P)ReadyRails rack mounting system, no tools requiredEnvironmentalPower supply: 200W (S3124, S3124F and S3148), 715W or 1,100W (S3124P), 1,100W (S3148P)Power supply efficiency: 80% or better in all operating modesMax. thermal output (BTU/hr): 182.55 (S3124), 228.96 (S3124F), 4391.42 (S3124P), 221.11 (S3148), 7319.04 (S3148P)Power consumption max (watts): 52.8 (S3124), 67.1 (S3124F), 1,287 (S3124P), 74.8 (S3148), 2,145 (S3148P)Operating temperature: 32° to 113°F (0° to 45°C)Operating relative humidity: 95%Storage temperature: –40° to 149°F (–40° to 65°C)Storage relative humidity: 85%PerformanceMAC addresses: 56K (80K in L2 scaled mode)Static routes: 16K (IPv4)/8K (IPv6)Dynamic routes: 16K (IPv4)/8K (IPv6) Switch fabric capacity: 212Gbps (S3124, S3124F and S3124P) (full duplex) 260Gbps (S3148 and S3148P)Forwarding rate: 158Mpps (S3124, S3124F and S3124P) 193Mpps (S3148 and S3148P)Link aggregation: 16 links per group, 128 groups Priority queues per port: 8Line-rate Layer 2 switching: All (non-blocking)Line-rate Layer 3 routing: All (non-blocking)Flash memory: 1GPacket buffer memory: 4MB CPU memory: 2GB DDR3Layer 2 VLANs: 4K MSTP: 64 instances VRF-lite: 511 instancesLine-rate Layer 2 switching: All protocols, including IPv4 and IPv6Line-rate Layer 3 routing: IPv4 and IPv6IPv4 host table size: 22K (42K in L3 scaled hosts mode)IPv6 host table size: 16K (both global + Link Local)(32K in L3 scaled hosts mode)IPv4 Multicast table size: 8KLAG load balancing: Based on Layer 2, IPv4 or IPv6 headersIEEE compliance 802.1AB LLDP802.1D Bridging, STP 802.1p L2 Prioritization 802.1Q VLAN T agging 802.1Qbb PFC 802.1Qaz ETS 802.1s MSTP 802.1w RSTP802.1x Network Access Control 802.1x-2010 Port Based Network Access Control802.3ab Gigabit Ethernet (1000BASE-T)802.3ac Frame Extensions for VLAN T agging802.3ad Link Aggregation with LACP 802.1ax Link Aggregation Revision - 2008 and 2011802.3ae 10 Gigabit Ethernet (10GBase-X)802.3af PoE (for S3124P and S3148P)802.3at PoE+ (for S3124P and S3148P)802.3az Energy Efficient Ethernet (EEE)802.3u Fast Ethernet (100Base-TX) on mgmt ports 802.3x Flow Control 802.3z Gigabit Ethernet (1000Base-X) ANSI/TIA-1057 LLDP-MED Force10 PVST+MTU 12,000 bytes RFC and I-D compliance General Internet protocols 768 UDP 793 TCP 854 Telnet 959 FTPGeneral IPv4 protocols 791 IPv4792 ICMP 826 ARP 1027 Proxy ARP 1035 DNS (client)1042 Ethernet Transmission 1305 NTPv31519 CIDR 1542 BOOTP (relay)1812 Requirements for IPv4 Routers 1918 Address Allocation for Private Internets 2474 Diffserv Field in IPv4 and Ipv6 Headers 2596 Assured Forwarding PHB Group 3164 BSD Syslog 3195 Reliable Delivery for Syslog3246Expedited Assured Forwarding4364 VRF-lite (IPv4 VRF with OSPF and BGP)5798VRRPGeneral IPv6 protocols 1981 Path MTU Discovery Features 2460 Internet Protocol, Version 6 (IPv6) Specification 2464 Transmission of IPv6 Packets over Ethernet Networks 2711 IPv6 Router Alert Option 4007 IPv6 Scoped Address Architecture 4213 Basic Transition Mechanisms for IPv6 Hosts and Routers 4291 IPv6 Addressing Architecture 4443 ICMP for IPv64861 Neighbor Discovery for IPv64862 IPv6 Stateless Address Autoconfiguration 5095 Deprecation of Type 0 Routing Headers in IPv6IPv6 Management support (telnet, FTP , TACACS, RADIUS, SSH, NTP)RIP 1058RIPv1 2453 RIPv2OSPF (v2/v3) 1587 NSSA 4552 Authentication/ 2154 OSPF Digital Signatures 2328 OSPFv2 OSPFv3 2370 Opaque LSA 5340 OSPF for IPv6IS-IS 5301 Dynamic hostname exchange mechanism for IS-IS 5302 Domain-wide prefix distribution with two- level IS-IS5303 Three way handshake for IS-IS point- to-point adjacencies 5308 IS-IS for IPv6BGP 1997 Communities 2385 MD5 2545 BGP-4 Multiprotocol Extensions for IPv6 Inter-Domain Routing 2439 Route Flap Damping 2796 Route Reflection 2842 Capabilities 2858 Multiprotocol Extensions 2918 Route Refresh 3065 Confederations 4360 Extended Communities 4893 4-byte ASN 5396 4-byte ASN representations draft-ietf-idr-bgp4-20 BGPv4draft-michaelson-4byte-as-representation-05 4-byte ASN Representation (partial) draft-ietf-idr-add-paths-04.txt ADD PATH Multicast 1112 IGMPv1 2236 IGMPv2 3376 IGMPv3 MSDPdraft-ietf-pim-sm-v2-new-05PIM-SMw4Dell PowerSwitch S3100 Series © 2022 Dell Inc. or its subsidiaries.Network management 1155 SMIv11157 SNMPv11212 Concise MIB Definitions 1215 SNMP Traps 1493 Bridges MIB 1850 OSPFv2 MIB 1901 Community-Based SNMPv22011 IP MIB 2096 IP Forwarding Table MIB 2578 SMIv22579 Textual Conventions for SMIv22580 Conformance Statements for SMIv22618 RADIUS Authentication MIB 2665 Ethernet-Like Interfaces MIB 2674 Extended Bridge MIB 2787 VRRP MIB 2819 RMON MIB (groups 1, 2, 3, 9)2863 Interfaces MIB 3273 RMON High Capacity MIB 3410 SNMPv33411 SNMPv3 Management Framework 3412 Message Processing and Dispatching for the Simple Network Management Protocol (SNMP)3413 SNMP Applications 3414 User-based Security Model (USM) for NMPv33415 VACM for SNMP 3416 SNMPv23417 Transport mappings for SNMP 3418 SNMP MIB 3434 RMON High Capacity Alarm MIB 3584 Coexistence between SNMP v1, v2 and v34022 IP MIB 4087 IP Tunnel MIB 4113 UDP MIB 4133 Entity MIB 4292 MIB for IP 4293 MIB for IPv6 Textual Conventions 4502 RMONv2 (groups 1,2,3,9)5060 PIM MIBANSI/TIA-1057 LLDP-MED MIB Dell_ITA.Rev_1_1 MIBdraft-grant-tacacs-02 TACACS+draft-ietf-idr-bgp4-mib-06 BGP MIBv1IEEE 802.1AB LLDP MIBIEEE 802.1AB LLDP DOT1 MIB IEEE 802.1AB LLDP DOT3 MIB sFlowv5 sFlowv5 MIB (version 1.3)FORCE10-BGP4-V2-MIB Force10 BGP MIB (draft-ietf-idr-bgp4-mibv2-05)FORCE10-IF-EXTENSION-MIB FORCE10-LINKAGG-MIBFORCE10-COPY-CONFIG-MIB FORCE10-PRODUCTS-MIB FORCE10-SS-CHASSIS-MIB FORCE10-SMI FORCE10-TC-MIBFORCE10-TRAP-ALARM-MIBFORCE10-FORWARDINGPLANE-STATS-MIBRegulatory compliance SafetyUL/CSA 60950-1, Second Edition EN 60950-1, Second EditionIEC 60950-1, Second Edition Including All National Deviations and Group Differences EN 60825-1 Safety of Laser Products Part 1: Equipment Classification Requirements and User’s GuideEN 60825-2 Safety of Laser Products Part 2: Safety of Optical Fibre Communication Systems FDA Regulation 21 CFR 1040.10 and 1040.11EmissionsUSA: FCC CFR 47 Part 15, Subpart B:2011, Class AImmunityEN 300 386 V1.4.1:2008 EMC for Network EquipmentEN 55024: 1998 + A1: 2001 + A2: 2003EN 61000-3-2: Harmonic Current Emissions EN 61000-3-3: Voltage Fluctuations and Flicker EN 61000-4-2: ESDEN 61000-4-3: Radiated Immunity EN 61000-4-4: EFT EN 61000-4-5: SurgeEN 61000-4-6: Low Frequency Conducted ImmunityRoHSAll S Series components are EU RoHS compliant.CertificationsAvailable with US Trade Agreements Act (TAA) complianceUSGv6 Host and Router Certified on Dell NetworkingOS 9.7 and greaterIPv6 Ready for both Host and Router DoD UC-APL approved switchFIPS 140-2 Approved Cryptography WarrantyLifetime Limited Hardware Warranty© 2022 Dell Inc. or its subsidiaries. All Rights Reserved. Dell and other trademarks are trademarks of Dell Inc. or its subsidiaries. Other trademarks may be trademarks of their respective owners. February 2022 | V1.9Dell PowerSwitch S3100 Series Spec SheetContact a Dell Technologies ExpertView more resourcesLearn more about Dell Networking solutions Join the conversation with@DellNetworkingIT Lifecycle Services for NetworkingExperts, insights and easeOur highly trained experts, with innovative tools and proven processes, help you transform your IT investments into strategic advantages.Plan & DesignLet us analyze your multivendor environment and deliver a comprehensive report and action plan to build upon the existing network and improve performance.Deploy & IntegrateGet new wired or wireless network technology installed and configured with ProDeploy. Reduce costs, save time, and get up and running fast.EducateEnsure your staff builds the right skills for long-term success. 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1g到5g的发展历程英语作文The Evolution of 1g to 5gThe telecommunication industry has witnessed a remarkable transformation over the past few decades, with the advent of successive generations of wireless technology. From the humble beginnings of 1g to the cutting-edge advancements of 5g, the evolution of mobile communication has been a testament to the ingenuity and innovation of the human mind. Each generation has brought about significant improvements in speed, reliability, and connectivity, ultimately shaping the way we interact with the world around us1g or the first generation of mobile technology was first introduced in the early 1980s. This analog-based system was primarily used for voice communication, offering limited functionality and a low level of security. The network was prone to interference and had a relatively short range, making it less than ideal for modern communication needs. Despite its limitations, 1g paved the way for the development of more advanced cellular networks2g or the second generation of mobile technology emerged in theearly 1990s, ushering in a new era of digital communication. This system introduced features such as text messaging, basic internet access, and improved voice quality. The adoption of digital encoding techniques led to increased capacity, better coverage, and enhanced security. 2g also saw the introduction of global system for mobile communications GSM which became the dominant standard worldwideThe transition from 2g to 3g in the early 2000s marked a significant leap forward in mobile technology. 3g networks offered significantly faster data speeds, enabling the seamless streaming of multimedia content, video calls, and more sophisticated internet-based applications. The introduction of universal mobile telecommunications system UMTS and code division multiple access CDMA2000 standards further expanded the capabilities of 3g networks, paving the way for the widespread use of smartphones and mobile internetThe fourth generation or 4g technology emerged in the late 2000s, providing even faster data speeds, lower latency, and improved spectral efficiency. 4g networks, based on the long-term evolution LTE standard, offered download speeds of up to 100 Mbps, making it possible to stream high-definition video, engage in real-time online gaming, and enjoy a more immersive mobile experience. The adoption of 4g technology ushered in a new era of connectivity,transforming the way we consume and interact with digital contentThe most recent advancement in mobile technology is the fifth generation or 5g, which has been gradually rolling out since the early 2010s. 5g promises to deliver unprecedented speeds, ultra-low latency, and increased network capacity, revolutionizing various industries and enabling a wide range of new applications. With download speeds of up to 10 Gbps, 5g has the potential to support emerging technologies such as autonomous vehicles, remote healthcare, and the internet of things IoT. The introduction of 5g has also coincided with the development of new network architectures, including the implementation of small cells, millimeter wave technology, and software-defined networkingThe evolution from 1g to 5g has been a remarkable journey, marked by continuous innovation and the relentless pursuit of faster, more reliable, and more versatile mobile communication. Each generation has built upon the successes and lessons of its predecessors, pushing the boundaries of what is possible in the realm of wireless technology. As we look to the future, the promise of 6g and beyond beckons, hinting at even greater advancements that will undoubtedly transform the way we live, work, and interact with the world around us. The evolution of mobile communication is a testament to the human spirit of exploration, innovation, and the unending desire to connect and empower individuals across the globe。