A wireless LAN or WLAN is a wireless local area network, which is the linking of two or more computers without using wires. WLAN utilizes spread-spectrum or OFDM modulation technology based on radio waves to enable communication between devices in a limited area, also known as the basic service set. This gives users the mobility to move around within a broad coverage area and still be connected to the network.
The benefits of wireless LANs include:
Convenience: The wireless nature of such networks allows users to access network resources from nearly any convenient location within their primary networking environment (home or office). With the increasing saturation of laptop-style computers, this is particularly relevant.
Mobility: With the emergence of public wireless networks, users can access the internet even outside their normal work environment. Most chain coffee shops, for example, offer their customers a wireless connection to the internet at little or no cost.
Productivity: Users connected to a wireless network can maintain a nearly constant affiliation with their desired network as they move from place to place.
Deployment: Initial setup of an infrastructure-based wireless network requires little more than a single access point. Wired networks, on the other hand, have the additional cost and complexity of actual physical cables being run to numerous locations (which can even be impossible for hard-to-reach locations within a building).
Expandability: Wireless networks can serve a suddenly-increased number of clients with the existing equipment. In a wired network, additional clients would require additional wiring.
Cost: Wireless networking hardware is at worst a modest increase from wired counterparts. This potentially increased cost is almost always more than outweighed by the savings in cost and labor associated to running physical cables.
Disadvantages:
Wireless LAN technology, with the conveniences and advantages described above, has its own limitations.
Security: Wireless LAN transceivers are designed to serve computers throughout a structure with uninterrupted service using radio frequencies. Because of space and cost, the antennas typically present on wireless networking cards in the end computers are generally relatively poor. In order to properly receive signals using such limited antennas throughout even a modest area, the wireless LAN transceiver utilizes a fairly considerable amount of power. On a wired network, any adversary would first have to overcome the physical limitation of tapping into the actual wires, but this is not an issue with wireless packets. To combat this consideration, wireless networks users usually choose to utilize various encryption technologies available such as Wi-Fi Protected Access (WPA). Some of the older encryption methods, such as WEP are known to have weaknesses that a dedicated adversary can compromise.
Range: The typical range of a common 802.11g network with standard equipment is on the order of tens of meters. While sufficient for a typical home, it will be insufficient in a larger structure. To obtain additional range, repeaters or additional access points will have to be purchased.
Reliability: Like any radio frequency transmission, wireless networking signals are subject to a wide variety of interference, as well as complex propagation effects that are beyond the control of the network administrator. In the case of typical networks, modulation is achieved by complicated forms of phase-shift keying (PSK) or quadrature amplitude modulation (QAM), making interference and propagation effects all the more disturbing. As a result, important network resources such as servers are rarely connected wirelessly.
Speed: The speed on most wireless networks (typically 1-108 Mbit/s) is reasonably slow compared to the slowest common wired networks (100 Mbit/s up to several Gbit/s). There are also performance issues caused by TCP and its built-in congestion avoidance. For most users, however, this observation is irrelevant since the speed bottleneck is not in the wireless routing but rather in the outside network connectivity itself. That is to say, in most environments, a wireless network running at its slowest speed is still faster than the internet connection serving it in the first place.
In summary, WLANs are best used as an addition to a copper-based network in situations where a percentage of users require mobility and/or where it is physically difficult, impossible or extremely expensive to deploy a copper solution.
WLAN technology can also be effectively used as a short haul backbone link between buildings on a single campus or across a road. WLANs are particularly appropriate when it is necessary to set up a small LAN quickly, either as a temporary or permanent solution. Standalone WLANs can also be used for public Internet access.
To know more on the topic:
Monday, January 28, 2008
Wireless LAN or WLAN
Monday, December 3, 2007
Wireless Communication
Wireless communication is the transfer of information over a distance without the use of electrical conductors or "wires".The distances involved may be short (a few meters as in television remote control) or very long (thousands or even millions of kilometers for radio communications). When the context is clear the term is often simply shortened to "wireless". Wireless communications is generally a branch of telecommunications.
Wireless communication may be via:
- Radio frequency communication,
- Microwave communication, for example long-range line-of-sight via highly directional antennas, or short-range communication, or
- Infrared (IR) short-range communication, for example from remote controls or via IRDA
Wireless communication spans the spectrum from 9 KHz to 300 GHz.
Applications may involve point-to-point communication, point-to-multipoint communication, broadcasting , cellular networks and other wireless networks.
Common examples of wireless equipment in use today include:
- Cellular phones and pagers: provide connectivity for portable and mobile applications, both personal and business.
- Global Positioning System (GPS): allows drivers of cars and trucks, captains of boats and ships, and pilots of aircraft to ascertain their location anywhere on earth.
- Cordless computer peripherals: the cordless mouse is a common example; keyboards and printers can also be linked to a computer via wireless.
- Cordless telephone sets: these are limited-range devices, not to be confused with cell phones.
- Satellite television: allows viewers in almost any location to select from hundreds of channels.
Wireless networking is used to meet a variety of needs. Perhaps the most common use is to connect laptop users who travel from location to location. Another common use is for mobile networks that connect via satellite. A wireless transmission method is a logical choice to network a LAN segment that must frequently change locations.
The uses of wireless technology:
- To span a distance beyond the capabilities of typical cabling,
- To avoid obstacles such as physical structures, EMI, or RFI,
- To provide a backup communications link in case of normal network failure,
- To link portable or temporary workstations,
- To overcome situations where normal cabling is difficult or financially impractical, or
- To remotely connect mobile users or networks.
More about Wireless LAN network, Wifi and Bluetooth in the forthcoming posts.
Saturday, November 24, 2007
Fiber Optics
Telecommunication applications are widespread, ranging from global networks to local telephone exchanges to subscribers homes to desktop computers. These involve the transmission of voice, data, or video over distances of less than a meter to hundreds of kilometers. Fiber optics is employed for this purpose. Benefits of Fiber Optics :
Fiber optics has several advantages over traditional metal communications lines:
- Fiber optic cables are much thinner and lighter than metal wires.
- Fiber optic cables have a much greater bandwidth than metal cables. This means that they can carry more data.
- The low attenuation and superior signal integrity found in optical systems allow much longer intervals of signal transmission than metallic-based systems.
- Fiber optic cables are less susceptible than metal cables to interference.
- The voice-grade copper systems longer than a couple of kilometers (1.2 miles) require in-line signal repeaters for satisfactory performance, but optical systems go over 100 kilometers (km), or about 62 miles, with no active or passive processing.
- Data can be transmitted digitally (the natural form for computer data) rather than analogically.
- Unlike metallic-based systems, the dielectric nature of optical fiber makes it impossible to remotely detect the signal being transmitted within the cable. The only way to do so is by actually accessing the optical fiber itself.
The main disadvantage of fiber optics considered was that the cables are expensive to install but as electronics prices fall,Fiber optics is affordable today, and optical cable pricing remains low. As bandwidth demands increase rapidly with technological advances, fiber will continue to play a vital role in the long-term success of telecommunications.
Operational Principle of Fiber optics:Total Internal Reflection:
When a light ray traveling in one material hits a different material and reflects back into the original material without any loss of light, total internal reflection occurs.
Since the core and cladding are constructed from different compositions of glass, theoretically, light entering the core is confined to the boundaries of the core because it reflects back whenever it hits the cladding. For total internal reflection to occur, the index of refraction of the core must be higher than that of the cladding. The index of refraction (IOR) is a way of measuring the speed of light in a material. Light travels fastest in a vacuum, such as outer space. The actual speed of light in a vacuum is 300,000 kilometers per second, or 186,000 miles per second. Index of Refraction is calculated by dividing the speed of light in a vacuum by the speed of light in some other medium.The Index of Refraction of a vacuum by definition has a value of 1.
The Information Transmission Sequence :
As depicted above, information (voice, data, or video) is encoded into electrical signals. At the light source, these electrical signals are converted into light signals.
It is important to note that fiber has the capability to carry either analog or digital signals. Many people believe that fiber can transmit only digital signals due to the on/off binary characteristic of the light source. The intensity of the light and the frequency at which the intensity changes can be used for AM and FM analog transmission.
Once the signals are converted to light, they travel down the fiber until they reach a detector, which changes the light signals back into electrical signals. This area from light source to detector constitutes the passive transmission subsystem; i.e. that part of the system manufactured and sold by Corning Cable Systems.
Finally, the electrical signals are decoded into information in the form of voice, data, or video.
Transmission Modes:
Once light enters an optical fiber, it travels in a stable state called a mode. There can be from one to hundreds of modes depending on the type of fiber. Each mode carries a portion of the light from the input signal.
Every telecommunications fiber falls into one of two categories: single-mode or multimode.
It is impossible to distinguish between single-mode and multimode fiber with the naked eye. There is no difference in outward appearance, only in core size. Both fiber types act as a transmission medium for light, but they operate in different ways, have different characteristics, and serve different applications.
Fiber optic Applications :
Optical fiber is used extensively for transmission of data signals. Private networks are owned by firms such as IBM, Rockwell, Honeywell, banks, universities, Wall Street firms, and more. These firms have a need for secure, reliable systems to transfer computer and monetary information between buildings to the desktop terminal or computer, and around the world. The security inherent in optical fiber systems is a major benefit.
Cable television or community antenna television (CATV) companies also find fiber useful for video services. The high information-carrying capacity, or bandwidth, of fiber makes it the perfect choice for transmitting signals to subscribers.
Finally, one of the fastest growing markets for fiber optics is intelligent transportation systems, smart highways with intelligent traffic lights, automated toll booths, and changeable message signs to give motorists information about delays and emergencies.
Related Sites:
[Diagrams courtesy: Corning Cable Systems]
Wednesday, November 14, 2007
Neural Networks
Neural Network- Biological Inspiration: But to give you some more specific examples; ANN are also used in the following specific paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from faulty software; interpretation of multimeaning Chinese words; undersea mine detection; texture analysis; three-dimensional object recognition; hand-written word recognition; and facial recognition
Neural networks grew out of research in Artificial Intelligence; specifically, attempts to mimic the fault-tolerance and capacity to learn of biological neural systems by modeling the low-level structure of the brain.The brain is principally composed of a very large number (circa 10,000,000,000) of neurons, massively interconnected (with an average of several thousand interconnects per neuron, although this varies enormously).
Each neuron is a specialized cell which can propagate an electrochemical signal. The neuron has a branching input structure (the dendrites), a cell body, and a branching output structure (the axon). The axons of one cell connect to the dendrites of another via a synapse. When a neuron is activated, it fires an electrochemical signal along the axon. This signal crosses the synapses to other neurons, which may in turn fire. A neuron fires only if the total signal received at the cell body from the dendrites exceeds a certain level (the firing threshold).
The strength of the signal received by a neuron (and therefore its chances of firing) critically depends on the efficacy of the synapses. Each synapse actually contains a gap, with neurotransmitter chemicals poised to transmit a signal across the gap.Thus, from a very large number of extremely simple processing units (each performing a weighted sum of its inputs, and then firing a binary signal if the total input exceeds a certain level) the brain manages to perform extremely complex tasks.
Of course, there is a great deal of complexity in the brain which has not been discussed here, but it is interesting that artificial neural networks can achieve some remarkable results using a model not much more complex than this.
Artificial Neural Network:
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.
Why use Neural networks?
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.Other advantages include:
Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.
Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.
Neural networks versus conventional computers:
Neural networks take a different approach to problem solving than that of conventional computers. Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order to solve a problem. Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem. That restricts the problem solving capability of conventional computers to problems that we already understand and know how to solve. But computers would be so much more useful if they could do things that we don't exactly know how to do.
Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements(neurones) working in parallel to solve a specific problem. Neural networks learn by example. They cannot be programmed to perform a specific task. The examples must be selected carefully otherwise useful time is wasted or even worse the network might be functioning incorrectly. The disadvantage is that because the network finds out how to solve the problem by itself, its operation can be unpredictable.
On the other hand, conventional computers use a cognitive approach to problem solving; the way the problem is to solved must be known and stated in small unambiguous instructions. These instructions are then converted to a high level language program and then into machine code that the computer can understand. These machines are totally predictable; if anything goes wrong is due to a software or hardware fault.
Neural networks and conventional algorithmic computers are not in competition but complement each other. There are tasks are more suited to an algorithmic approach like arithmetic operations and tasks that are more suited to neural networks. Even more, a large number of tasks, require systems that use a combination of the two approaches (normally a conventional computer is used to supervise the neural network) in order to perform at maximum efficiency.
Pattern Recognition - an example of ANN:
An important application of neural networks is pattern recognition. Pattern recognition can be implemented by using a feed-forward (figure 1) neural network that has been trained accordingly. During training, the network is trained to associate outputs with input patterns. When the network is used, it identifies the input pattern and tries to output the associated output pattern. The power of neural networks comes to life when a pattern that has no output associated with it, is given as an input. In this case, the network gives the output that corresponds to a taught input pattern that is least different from the given pattern.
Neural Networks in Practice :
Given this description of neural networks and how they work, what real world applications are they suited for? Neural networks have broad applicability to real world business problems. In fact, they have already been successfully applied in many industries.
Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:
Artificial Neural Networks (ANN) are currently a 'hot' research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modelling parts of the human body and recognising diseases from various scans (e.g. cardiograms, CAT scans, ultrasonic scans, etc.).
Neural networks are ideal in recognising diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognise the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. The quantity of examples is not as important as the 'quantity'. The examples need to be selected very carefully if the system is to perform reliably and efficiently.
Business is a diverted field with several general areas of specialisation such as accounting or financial analysis. Almost any neural network application would fit into one business area or financial analysis. There is some potential for using neural networks for business purposes, including resource allocation and scheduling. There is also a strong potential for using neural networks for database mining, that is, searching for patterns implicit within the explicitly stored information in databases. Most of the funded work in this area is classified as proprietary. Thus, it is not possible to report on the full extent of the work going on. Most work is applying neural networks, such as the Hopfield-Tank network for optimization and scheduling.
Neural networks do not perform miracles. But if used sensibly they can produce some amazing results.
Related Topics:
Wednesday, October 31, 2007
Embedded systems
I was just browsing on Embedded systems for real time applications just publishing some basic information I infered.
Role in day-to-day life:
Embedded systems are playing important roles in our lives every day, even though they might not necessarily be visible. Some of the embedded systems we use every day control the menu system on television, the timer in a microwave oven,Traffic Lights,video game console,PDA,Digital Cameras,DVD Player,Cellphone, an MP3 player or any other device with some amount of intelligence built-in. Embedded systems is a rapidly growing industry where growth opportunities are numerous.
What's Embedded system?
An Embedded system is a special-purpose computer system designed to perform one or a few dedicated functions, sometimes with real-time computing constraints. It is usually embedded as part of a complete device including hardware and mechanical parts. In contrast, a general-purpose computer, such as a personal computer, can do many different tasks depending on programming. Since the embedded system is dedicated to specific tasks, design engineers can optimize it, reducing the size and cost of the product, or increasing the reliability and performance.
The software written for embedded systems is often called firmware, and is stored in read-only memory or Flash memory chips rather than a disk drive. It often runs with limited computer hardware resources: small or no keyboard, screen, and little memory.
Because the tasks must solve diverse problems, a language general-purpose enough to solve them all would be difficult to write, analyze, and compile. Instead, a variety of languages have evolved, each best suited to a particular problem domain. For example, a language for signal processing is often more convenient for a particular problem than, say, assembly, but might be poor for control dominated behavior.
Languages used :
The languages used may be hardware, software,dataflow and hybrid languages, each of which excels a certain problems.Hardware languages for hardware description and modeling.Software languages describe sequences of instructions for a processor to execute. Dataflow languages are good for signal processing,and hybrid languages combine ideas from the other three classes.
Related sites: