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السلام عليكم ورحمه الله وبركاته

اذا ما عليك كلافه اتساعدوني ..:eww:

ابغى اسوى Project عن Computer Networks واحتاج مساعده منكم اذا ما في مانع

ابغى اشويه معلومات عن النت ورك تساعدني في اتمام البروجكت :wep:



اشكركم مقدما ً :eww:

لا تخيبوني

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الصراحــــــــــة ،، ماأعــرف


شوفي باقي الأعضــاء ،، :)

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Networks are connections between groups of computers and associated devices that allow users to transfer information electronically. The local area network shown on the left is representative of the setup used in many offices and companies. Individual computers are called work stations (W.S.), and communicate to each other via cable or telephone line linking to servers. Servers are computers exactly like the W.S., except that they have administrative functions and are devoted entirely to monitoring and controlling W.S. access to part or all of the network and to any shared resources (such as printers). The red line represents the larger network connection between servers, called the backbone; the blue line shows local connections. A modem (modulator/demodulator) allows computers to transfer information across standard telephone lines. Modems convert digital signals into analog signals and back again, making it possible for computers to communicate, or network, across thousands of miles

هناك بعض الصور لا استطيع ارفاقه لعدم امكانية اللصق (بست) هنا إذا كان لديك طريقة لإرفقها الرجاء إخباري .

سوف احاول البحث عن المزيد

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Neural Network, in computer science, highly interconnected network of information-processing elements that mimics the connectivity and functioning of the human brain. Neural networks address problems that are often difficult for traditional computers to solve, such as speech and pattern recognition. They also provide some insight into the way the human brain works. One of the most significant strengths of neural networks is their ability to learn from a limited set of examples.
Neural networks were initially studied by computer and cognitive scientists in the late 1950s and early 1960s in an attempt to model sensory perception in biological organisms. Neural networks have been applied to many problems since they were first introduced, including pattern recognition, handwritten character recognition, speech recognition, financial and economic modeling, and next-generation computing models.
IIHOW A NEURAL NETWORK WORKS Neural networks fall into two categories: artificial neural networks and biological neural networks. Artificial neural networks are modeled on the structure and functioning of biological neural networks. The most familiar biological neural network is the human brain. The human brain is composed of approximately 100 billion nerve cells called neurons that are massively interconnected. Typical neurons in the human brain are connected to on the order of 10,000 other neurons, with some types of neurons having more than 200,000 connections. The extensive number of neurons and their high degree of interconnectedness are part of the reason that the brains of living creatures are capable of making a vast number of calculations in a short amount of time. See alsoNeurophysiology.
ANeurons Biological neurons have a fairly simple large-scale structure, although their operation and small-scale structure is immensely complex. Neurons have three main parts: a central cell body, called the soma, and two different types of branched, treelike structures that extend from the soma, called dendrites and axons. Information from other neurons, in the form of electrical impulses, enters the dendrites at connection points called synapses. The information flows from the dendrites to the soma, where it is processed. The output signal, a train of impulses, is then sent down the axon to the synapses of other neurons.
Artificial neurons, like their biological counterparts, have simple structures and are designed to mimic the function of biological neurons. The main body of an artificial neuron is called a node or unit. Artificial neurons may be physically connected to one another by wires that mimic the connections between biological neurons, if, for instance, the neurons are simple integrated circuits. However, neural networks are usually simulated on traditional computers, in which case the connections between processing nodes are not physical but are instead virtual.
Artificial neurons may be either discrete or continuous. Discrete neurons send an output signal of 1 if the sum of received signals is above a certain critical value called a threshold value, otherwise they send an output signal of 0. Continuous neurons are not restricted to sending output values of only 1s and 0s; instead they send an output value between 1 and 0 depending on the total amount of input that they receive-the stronger the received signal, the stronger the signal sent out from the node and vice-versa. Continuous neurons are the most commonly used in actual artificial neural networks.
BArtificial Neural Network Architecture The architecture of a neural network is the specific arrangement and connections of the neurons that make up the network. One of the most common neural network architectures has three layers. The first layer is called the input layer and is the only layer exposed to external signals. The input layer transmits signals to the neurons in the next layer, which is called a hidden layer. The hidden layer extracts relevant features or patterns from the received signals. Those features or patterns that are considered important are then directed to the output layer, the final layer of the network. Sophisticated neural networks may have several hidden layers, feedback loops, and time-delay elements, which are designed to make the network as efficient as possible in discriminating relevant features or patterns from the input layer.
IIIDIFFERENCES BETWEEN NEURAL NETWORKS AND TRADITIONAL COMPUTERS Neural networks differ greatly from traditional computers (for example personal computers, workstations, mainframes) in both form and function. While neural networks use a large number of simple processors to do their calculations, traditional computers generally use one or a few extremely complex processing units. Neural networks also do not have a centrally located memory, nor are they programmed with a sequence of instructions, as are all traditional computers.
The information processing of a neural network is distributed throughout the network in the form of its processors and connections, while the memory is distributed in the form of the weights given to the various connections. The distribution of both processing capability and memory means that damage to part of the network does not necessarily result in processing dysfunction or information loss. This ability of neural networks to withstand limited damage and continue to function well is one of their greatest strengths.
Neural networks also differ greatly from traditional computers in the way they are programmed. Rather than using programs that are written as a series of instructions, as do all traditional computers, neural networks are "taught" with a limited set of training examples. The network is then able to "learn" from the initial examples to respond to information sets that it has never encountered before. The resulting values of the connection weights can be thought of as a 'program'.
Neural networks are usually simulated on traditional computers. The advantage of this approach is that computers can easily be reprogrammed to change the architecture or learning rule of the simulated neural network. Since the computation in a neural network is massively parallel, the processing speed of a simulated neural network can be increased by using massively parallel computers-computers that link together hundreds or thousands of CPUs in parallel to achieve very high processing speeds (see Supercomputer).
IVNEURAL NETWORK LEARNING In all biological neural networks the connections between particular dendrites and axons may be reinforced or discouraged. For example, connections may become reinforced as more signals are sent down them, and may be discouraged when signals are infrequently sent down them. The reinforcement of certain neural pathways, or dendrite-axon connections, results in a higher likelihood that a signal will be transmitted along that path, further reinforcing the pathway. Paths between neurons that are rarely used slowly atrophy, or decay, making it less likely that signals will be transmitted along them.
The role of connection strengths between neurons in the brain is crucial; scientists believe they determine, to a great extent, the way in which the brain processes the information it takes in through the senses. Neuroscientists studying the structure and function of the brain believe that various patterns of neurons firing can be associated with specific memories. In this theory, the strength of the connections between the relevant neurons determines the strength of the memory. Important information that needs to be remembered may cause the brain to constantly reinforce the pathways between the neurons that form the memory, while relatively unimportant information will not receive the same degree of reinforcement.
AConnection Weights To mimic the way in which biological neurons reinforce certain axon-dendrite pathways, the connections between artificial neurons in a neural network are given adjustable connection weights, or measures of importance. When signals are received and processed by a node, they are multiplied by a weight, added up, and then transformed by a nonlinear function. The effect of the nonlinear function is to cause the sum of the input signals to approach some value, usually +1 or 0. If the signals entering the node add up to a positive number, the node sends an output signal that approaches +1 out along all of its connections, while if the signals add up to a negative value, the node sends a signal that approaches 0. This is similar to a simplified model of a how a biological neuron functions-the larger the input signal, the larger the output signal.
BTraining Sets Computer scientists teach neural networks by presenting them with desired input-output training sets. The input-output training sets are related patterns of data. For instance, a sample training set might consist of ten different photographs for each of ten different faces. The photographs would then be digitally entered into the input layer of the network. The desired output would be for the network to signal one of the neurons in the output layer of the network per face. Beginning with equal, or random, connection weights between the neurons, the photographs are digitally entered into the input layer of the neural network and an output signal is computed and compared to the target output. Small adjustments are then made to the connection weights to reduce the difference between the actual output and the target output. The input-output set is again presented to the network and further adjustments are made to the connection weights because the first few times that the input is entered, the network will usually choose the incorrect output neuron. After repeating the weight-adjustment process many times for all input-output patterns in the training set, the network learns to respond in the desired manner.
A neural network is said to have learned when it can correctly perform the tasks for which it has been trained. Neural networks are able to extract the important features and patterns of a class of training examples and generalize from these to correctly process new input data that they have not encountered before. For a neural network trained to recognize a series of photographs, generalization would be demonstrated if a new photograph presented to the network resulted in the correct output neuron being signaled.
A number of different neural network learning rules, or algorithms, exist and use various techniques to process information. Common arrangements use some sort of system to adjust the connection weights between the neurons automatically. The most widely used scheme for adjusting the connection weights is called error back-propagation, developed independently by American computer scientists Paul Werbos (in 1974), David Parker (in 1984/1985), and David Rumelhart, Ronald Williams, and others (in 1985). The back-propagation learning scheme compares a neural network's calculated output to a target output and calculates an error adjustment for each of the nodes in the network. The neural network adjusts the connection weights according to the error values assigned to each node, beginning with the connections between the last hidden layer and the output layer. After the network has made adjustments to this set of connections, it calculates error values for the next previous layer and makes adjustments. The back-propagation algorithm continues in this way, adjusting all of the connection weights between the hidden layers until it reaches the input layer. At this point it is ready to calculate another output.
VIMPLEMENTATIONS AND FUTURE TECHNOLOGY Neural networks have been applied to many tasks that are easy for humans to accomplish, but difficult for traditional computers. Because neural networks mimic the brain, they have shown much promise in so-called sensory processing tasks such as speech recognition, pattern recognition, and the transcription of hand-written text. In some settings, neural networks can perform as well as humans. Neural-network-based backgammon software, for example, rivals the best human players.
While traditional computers still outperform neural networks in most situations, neural networks are superior in recognizing patterns in extremely large data sets. Furthermore, because neural networks have the ability to learn from a set of examples and generalize this knowledge to new situations, they are excellent for work requiring adaptive control systems. For this reason, the United States National Aeronautics and Space Administration (NASA) has extensively studied neural networks to determine whether they might serve to control future robots sent to explore planetary bodies in our solar system. In this application, robots could be sent to other planets, such as Mars, to carry out significant and detailed exploration autonomously.
An important advantage that neural networks have over traditional computer systems is that they can sustain damage and still function properly. This design characteristic of neural networks makes them very attractive candidates for future aircraft control systems, especially in high performance military jets. Another potential use of neural networks for civilian and military use is in pattern recognition software for radar, sonar, and other remote-sensing devices

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مشكورة صرخه كربلا... ما اتقصرين

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واذا ما عليش كلافه تقدرين تعطيني العنوان مال المواضيع الى طلعتين منهم :wep:

واذا بتحطين اكثر ما بنقول لا :wep::eww:

مشكورة مرة ثانيه :eww:

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IINTRODUCTION Network, in computer science, techniques, physical connections, and computer programs used to link two or more computers. Network users are able to share files, printers, and other resources; send electronic messages; and run programs on other computers.
A network has three layers of components: application software, network software, and network hardware. Application software consists of computer programs that interface with network users and permit the sharing of information, such as files, graphics, and video, and resources, such as printers and disks. One type of application software is called client-server. Client computers send requests for information or requests to use resources to other computers, called servers, that control data and applications. Another type of application software is called peer-to-peer. In a peer-to-peer network, computers send messages and requests directly to one another without a server intermediary.
Network software consists of computer programs that establish protocols, or rules, for computers to talk to one another. These protocols are carried out by sending and receiving formatted instructions of data called packets. Protocols make logical connections between network applications, direct the movement of packets through the physical network, and minimize the possibility of collisions between packets sent at the same time.
Network hardware is made up of the physical components that connect computers. Two important components are the transmission media that carry the computer's signals, typically on wires or fiber-optic cables, and the network adapter, which accesses the physical media that link computers, receives packets from network software, and transmits instructions and requests to other computers. Transmitted information is in the form of binary digits, or bits (1s and 0s), which the computer's electronic circuitry can process.
IINETWORK CONNECTIONS A network has two types of connections: physical connections that let computers directly transmit and receive signals and logical, or virtual, connections that allow computer applications, such as word processors, to exchange information. Physical connections are defined by the medium used to carry the signal, the geometric arrangement of the computers (topology), and the method used to share information. Logical connections are created by network protocols and allow data sharing between applications on different types of computers, such as an Apple Macintosh and an International Business Machines Corporation (IBM) personal computer (PC), in a network. Some logical connections use client-server application software and are primarily for file and printer sharing. The Transmission Control Protocol/Internet Protocol (TCP/IP) suite, originally developed by the United States Department of Defense, is the set of logical connections used by the Internet, the worldwide consortium of computer networks. TCP/IP, based on peer-to-peer application software, creates a connection between any two computers.
AMedia The medium used to transmit information limits the speed of the network, the effective distance between computers, and the network topology. Copper wires and coaxial cable provide transmission speeds of a few thousand bits per second for long distances and about 100 million bits per second (Mbps) for short distances. Optical fibers carry 100 million to 1 billion bits of information per second over long distances.
BTopology Common topologies used to arrange computers in a network are point-to-point, bus, star, and ring. Point-to-point topology is the simplest, consisting of two connected computers. The bus topology is composed of a single link connected to many computers. All computers on this common connection receive all signals transmitted by any attached computer. The star topology connects many computers to a common hub computer. This hub can be passive, repeating any input to all computers similar to the bus topology, or it can be active, selectively switching inputs to specific destination computers. The ring topology uses multiple links to form a circle of computers. Each link carries information in one direction. Information moves around the ring in sequence from its source to its destination (see Computer Architecture).
Local area networks (LANs), which connect computers separated by short distances, such as in an office or a university campus, commonly use bus, star, or ring topologies. Wide area networks (WANs), which connect distant equipment across the country or internationally, often use special leased telephone lines as point-to-point links.
CSharing Information When computers share physical connections to transmit information packets, a set of Media Access Control (MAC) protocols are used to allow information to flow smoothly through the network. An efficient MAC protocol ensures that the transmission medium is not idle if computers have information to transmit. It also prevents collisions due to simultaneous transmission that would waste media capacity. MAC protocols also allow different computers fair access to the medium.
One type of MAC is Ethernet, which is used by bus or star network topologies. An Ethernet-linked computer first checks if the shared medium is in use. If not, the computer transmits. Since two computers can both sense an idle medium and send packets at the same time, transmitting computers continue to monitor the shared connection and stop transmitting information if a collision occurs. Ethernet can transmit information at a rate of 10 Mbps.
Computers also can use Token Ring MAC protocols, which pass a special message called a token through the network. This token gives the computer permission to send a packet of information through the network. If a computer receives the token, it sends a packet, or, if it has no packet to send, it passes the token to the next computer. Since there is only one token in the network, only one computer can transmit information at a time.
IIINETWORK OPERATION AND MANAGEMENT Network management and system administration are critical for a complex system of interconnected computers and resources to remain operating. A network manager is the person or team of people responsible for configuring the network so that it runs efficiently. For example, the network manager might need to connect computers that communicate frequently to reduce interference with other computers. The system administrator is the person or team of people responsible for configuring the computer and its software to use the network. For example, the system administrator may install network software and configure a server's file system so client computers can access shared files.
Networks are subject to hacking, or illegal access, so shared files and resources must be protected. A network intruder could eavesdrop on packets being sent across a network or send fictitious messages. For sensitive information, data encryption (scrambling data using mathematical equations) renders captured packets unreadable to an intruder. Most servers also use authentication schemes to ensure that a request to read or write files or to use resources is from a legitimate client and not from an intruder (see Computer Security).
IVFUTURE TECHNOLOGIES AND TRENDS The wide use of notebook and other portable computers drives advances in wireless networks. Wireless networks use either infrared or radio-frequency transmissions to link these mobile computers to networks. Infrared wireless LANs work only within a room, while wireless LANs based on radio-frequency transmissions can penetrate most walls. Wireless LANS have capacities from less than 1 Mbps to 8 Mbps and operate at distances up to a few hundred meters. Wireless communication for WANS use cellular telephone networks, satellite transmissions, or dedicated equipment to provide regional or global coverage, but they have transmission rates of only 2000 to 19,000 bits per second.
New networks must also meet the growing demand for faster transmission speeds, especially for sound and video applications. One recently developed network, called an Asynchronous Transfer Mode (ATM) network, has speeds of up to 625 Mbps and can be used by either LANS or WANS.
In February 1996 Fujitsu Ltd., Nippon Telephone and Telegraph Corporation, and a team of researchers from AT&T succeeded in transmitting information through an optical fiber at a rate of 1 trillion bits per second-the equivalent of transmitting 300 years of newspapers in a single second. This was accomplished by simultaneously sending different wavelengths of light, each carrying separate information, through the optical fiber. If it can be integrated into a network, this new technology will make it easy, inexpensive, and incredibly fast to send information, such as video and memory-sensitive three-dimensional images.

Contributed By:
Scott F. Midkiff



"Network," Microsoft® Encarta® Encyclopedia 99. © 1993-1998 Microsoft Corporation. All rights reserved.

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REFERENCE

الموضوع الأول :

from :
people in business
first diploma
bahrain traeening instute
BTI

الموضوع الثاني :

Contributed By:

Scott F. Midkiff



الموضوع الثالث:

Contributed By:

Ojvind Bernander


الموضوعين الأول و الثاني من : موسعة إنكارتا 99
ُencart- encyclopedia 99

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و إدا تبغين اي شيء
امري

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