Fiber Optics

Fiber optics has become a buzzword these days in the field of telecommunication.

Lets explore about it...

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.

What is Fiber Optics?

In its simplest terms, fiber optics is a medium for carrying information from one point to another in the form of light. A technology that uses glass (or plastic) threads (fibers) to transmit data.Unlike the copper form of transmission, fiber optics is not electrical in nature. A fiber optic cable consists of a bundle of glass threads, each of which is capable of transmitting messages modulated onto light waves. The fiber itself is passive and does not contain any active, generative properties.

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:

• Corning Cable Systems
• Working of Fiber optics
• The basics of fiber optic cable
• More on Fiber optics

[Diagrams courtesy: Corning Cable Systems]

Some Fun in Electronics too.............

Found a Funny Invite...thought will post it here :)

You are electronically invited on occasion of the marriage of

Mr. TRANSISTOR BC107

(working as amplifier in "CE" configuration)

The Son of Mr & Mrs. Aluminium

With

Miss. DIODE 2N2222

(working as a rectifier in Electronic Circuits)

The only Daughter of Mr & Mrs. Phosphorous

MUHURTAM Oct 30, 2K8 @ 10-45 Amplitude Modulation

VENUE

At Peizo Electric Palace, Near Wein Bridge,

Nyquist criterion Road-2,Electricity -508085.

Yours inductively

Mr. & Mrs. EDC PDC Near P-N Junction, IC Road, Zener breakdown.

With BEST COMPLIMENTS FROM,

Inductor, Resistor, Capacitor, Transformer Near & Dear

Note: Musical Night by Motors and Generators

Neural Networks

Neural Network- Biological Inspiration:

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:

• Sales forecasting
• Industrial process control
• Customer research
• Data validation
• Risk management
• Target marketing
  

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 in medicine:
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.

Neural Networks in business:
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:

• Fuzzy Logic
• Artificial Intelligence
• Architecture of neural networks

Digital Signal Processing

DSP in day-to-day life:
DSP technology is nowadays commonplace in such devices as mobile phones, multimedia computers, video recorders, CD players, hard disc drive controllers and modems, and has replaced analog circuitry in TV sets and telephones as well. An important application of DSP is in signal compression and decompression. Signal compression is used in digital cellular phones to allow a greater number of calls to be handled simultaneously within each local "cell". DSP signal compression technology allows people not only to talk to one another but also to see one another on their computer screens, using small video cameras mounted on the computer monitors, with only a conventional telephone line linking them together. In audio CD systems, DSP technology is used to perform complex error detection and correction on the raw data as it is read from the CD.

What is DSP?

Digital signal processing ('DSP') is the study of signals in a digital representation and the processing methods of these signals. DSP and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, image processing, signal processing for communications, biomedical signal processing, etc.
Since the goal of DSP is usually to measure or filter continuous real-world analog signals, the first step is usually to convert the signal from an analog to a digital form, by using an analog to digital converter. Often, the required output signal is another analog output signal, which requires a digital to analog converter.

Digital Signal Processors:

The algorithms required for DSP are sometimes performed using specialized computers, which make use of specialized microprocessors called digital signal processors by electronics manufacturers such as Texas Instruments, Analog Devices and Motorola. These process signals in real time and are generally purpose-designed application-specific integrated circuits (ASICs). When flexibility and rapid development are more important than unit costs at high volume, DSP algorithms may also be implemented using field-programmable gate arrays (FPGAs).

DSP Techniques and Algorithms:

Although some of the mathematical theory underlying DSP techniques, such as Fourier and Hilbert Transforms, digital filter design and signal compression, can be fairly complex, the numerical operations required actually to implement these techniques are very simple, consisting mainly of operations that could be done on a cheap four-function calculator. The architecture of a DSP chip is designed to carry out such operations incredibly fast, processing hundreds of millions of samples every second, to provide real-time performance: that is, the ability to process a signal "live" as it is sampled and then output the processed signal, for example to a loudspeaker or video display. All of the practical examples of DSP applications mentioned earlier, such as hard disc drives and mobile phones, demand real-time operation.


Fourier transform (in the form of FFT) is a very commonly used technique for analyzing and filtering digital signals. FFT is not very optimal in many filtering applications because it assumes that the frequency spectrum is not changing over time. There are other digital-signal-processing (DSP) techniques that are more advantageous for filtering a real-world signal. The infinite-impulse-response (IIR) and finite-impulse-response (FIR) filters can be implemented very inexpensively, and they work on continuous stream of data. The wavelet transform is worth investigating new technique for signal analyzing and filtering. Like the FFT, wavelet transform converts time-domain data into the frequency domain. The wavelet transform assumes that the frequency spectrum is changing over time. The result of a wavelet transform is a bit harder to read, but more meaningful for many applications.There are also many other relevant signal processing techniques targeted for different kind of applications.

Related Sites:

• Signal processing
• MATLAB DSP Signal Processing Toolbox
• Fourier transform
• Digital Signal Processors