Electrical Seminar Abstract And Report 10
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15-02-2009, 01:29 PM


Navbelt and Guiecane

Introduction
Recent revolutionary achievements in robotics and bioengineering have given scientists and engineers great opportunities and challenges to serve humanity. This seminar and presentation is about "NAVBELT AND GUIDECANE", which are two computerised devices based on advanced mobile robotic navigation for obstacle avoidance useful for visually impaired people. This is "Bioengineering for people with disabilities".

NavBelt is worn by the user like a belt and is equipped with an array of ultrasonic sensors. It provides acoustic signals via a set of stereo earphones that guide the user around obstacles or displace a virtual acoustic panoramic image of the traveller's surroundings. One limitation of the NavBelt is that it is exceedingly difficult for the user to comprehend the guidance signals in time, to allow fast work.

A newer device, called GuideCane, effectively overcomes this problem. The GuideCane uses the same mobile robotics technology as the NavBelt but is a wheeled device pushed ahead of the user via an attached cane. When the Guide Cane detects an obstacle, it steers around it. The user immediately feels this steering action and can follow the Guide Cane's new path easily without any conscious effort. The mechanical, electrical and software components, user-machine interface and the prototypes of the two devices are described.
Multisensor Fusion and Integration
Multisensor Fusion and Integration

Introduction
Sensor is a device that detects or senses the value or changes of value of the variable being measured. The term sensor some times is used instead of the term detector, primary element or transducer.

The fusion of information from sensors with different physical characteristics, such as light, sound, etc enhances the understanding of our surroundings and provide the basis for planning, decision making, and control of autonomous and intelligent machines.

Sensors Evolution

A sensor is a device that responds to some external stimuli and then provides some useful output. With the concept of input and output, one can begin to understand how sensors play a critical role in both closed and open loops.

One problem is that sensors have not been specified. In other words they tend to respond variety of stimuli applied on it without being able to differentiate one from another. Neverthless, sensors and sensor technology are necessary ingredients in any control type application. Without the feedback from the environment that sensors provide, the system has no data or reference points, and thus no way of understanding what is right or wrong g with its various elements.

Sensors are so important in automated manufacturing particularly in robotics. Automated manufacturing is essentially the procedure of remo0ving human element as possible from the manufacturing process. Sensors in the condition measurement category sense various types of inputs, condition, or properties to help monitor and predict the performance of a machine or system.

Multisensor Fusion And Integration

Multisensor integration is the synergistic use of the information provided by multiple sensory devices to assist in the accomplishment of a task by a system. Multisensor fusion refers to any stage in the integration process where there is an actual combination of different sources of sensory information into one representational format.

Multisensor Integration

The diagram represents multisensor integration as being a composite of basic functions. A group of n sensors provide input to the integration process. In order for the data from each sensor to be used for integration, it must first be effectively modelled. A sensor model represents the uncertainty and error in the data from each sensor and provides a measure of its quality that can be 7used by the subsequent integration functions.
Magneto-optical current transformer technology (MOCT)
Magneto-optical current transformer technology (MOCT)

Introduction
TCP/IP

An accurate electric current transducer is a key component of any power system instrumentation. To measure currents power stations and substations conventionally employ inductive type current transformers with core and windings. For high voltage applications, porcelain insulators and oil-impregnated materials have to be used to produce insulation between the primary bus and the secondary windings. The insulation structure has to be designed carefully to avoid electric field stresses, which could eventually cause insulation breakdown.

The electric current path of the primary bus has to be designed properly to minimize the mechanical forces on the primary conductors for through faults. The reliability of conventional high-voltage current transformers have been questioned because of their violent destructive failures which caused fires and impact damage to adjacent apparatus in the switchyards, electric damage to relays, and power service disruptions.

With short circuit capabilities of power systems getting larger, and the voltage levels going higher the conventional current transformers becomes more and more bulky and costly also the saturation of the iron core under fault current and the low frequency response make it difficult to obtain accurate current signals under power system transient conditions.

In addition to the concerns, with the computer control techniques and digital protection devices being introduced into power systems, the conventional current transformers have caused further difficulties, as they are likely to introduce electro-magnetic interference through the ground loop into the digital systems. This has required the use of an auxiliary current transformer or optical isolator to avoid such problems.

It appears that the newly emerged Magneto-optical current transformer technology provides a solution for many of the above mentioned problems. The MOCT measures the electric current by means of Faraday Effect, which was first observed by Michael Faraday 150 years ago. The Faraday Effect is the phenomenon that the orientation of polarized light rotates under the influence of the magnetic fields and the rotation angle is proportional to the strength of the magnetic field component in the direction of optical path.
Mobile Virtual Reality Service (VRS)
Mobile Virtual Reality Service (VRS)

Introduction
A mobile virtual reality service (VRS) will make the presence and presentation of the sounds and sights of an actual physical environment virtually available everywhere in real time through the use of mobile telecommunication devices and networks. Furthermore, the VRS is the conversion of a physical system into its digital representation in a three-dimension (3D) multimedia format. This paper addresses one aspect of the notion of bringing an actual multimedia environment to its virtual presence everywhere in real time .

An international telecommunication union (ITC) recommendation document, containing ITU's visions on mostly forward-looking and innovative services and network capabilities, addresses the capability needed in a telecommunication system to allow mobile access to real-time sights and sounds of an actual physical environment in the contest and forms of a VRS episode .

Presently, the availability of a VRS is limited to fixed-access phenomena in non-real time , for example , entertainment machines and various simulations equipment. There are also some limited fixed-access and real-time services that require low data transmission rates, such as net meetings. In the latter case, a user can experience a limited real-life environment as opposed to the former case of a non-real-life computer-generated environment. These existing virtual reality services do not allow user control in viewing 3D environments, and they are generally limited to viewing images on a monitor in two dimensions.

The VRS-capable systems, however, will allow rather 3D representations of remote real-life environments. For instance, a passenger in a train or in a car could become a participant in a conference call in a 3D environment or become virtually present among the audience in a concert hall or sports stadium viewing a live concert or event.
Smart Pixel Arrays (SPAs)
Smart Pixel Arrays (SPAs)

Introduction
High speed smart pixel arrays (SPAs) hold great promise as an enabling technology for board-to-board interconnections in digital systems. SPAs may be considered an extension of a class of optoelectronic components that have existed for over a decade, that of optoelectronic integrated circuits (OEICs). The vast majority of development in OEICs has involved the integration of electronic receivers with optical detectors and electronic drivers with optical sources or modulators.

In addition, very little of this development has involved more than a single optical channel. But OEICs have underpinned much of the advancement in serial fiber links. SPAs encompass an extension of these optoelectronic components into arrays in which each element of the array has a signal processing capability. Thus, a SPA may be described as an array of optoelectronic circuits for which each circuit possesses the property of signal processing and, at a minimum, optical input or optical output (most SPAs will have both optical input and output).

The name smart pixel is combination of two ideas, "pixel" is an image processing term denoting a small part, or quantized fragment of an image, the word "smart" is coined from standard electronics and reflects the presence of logic circuits. Together they describe a myriad of devices. These smart pixels can be almost entirely optical in nature, perhaps using the non-linear optical properties of a material to manipulate optical data, or they can be mainly electronic, for instance a photoreceiver coupled with some electronic switching.

Smart pixel arrays for board-to-board optical interconnects may be used for either backplane communications or for distributed board-to-board communications, the latter known as 3-D packaging. The former is seen as the more near-term of the two, employing free-space optical beams connecting SPAs located on the ends of printed circuit boards in place of the current state-of-the-art, multi-level electrical interconnected boards. 3-D systems, on the other hand, are distributed board-to-board optical interconnects, exploiting the third dimension and possibly employing holographic interconnect elements to achieve global connectivity (very difficult with electrical interconnects).
Adaptive Blind Noise Suppression in some Speech Processing Applications

Class-D Amplifiers

Optical Networking and Dense Wavelength Division Multiplexing

Optical Burst Switching

Artificial Intelligence Substation Control

Analog-Digital Hybrid Modulation for Improved Efficiency over Broadband Wireless Systems
Analog-Digital Hybrid Modulation for Improved Efficiency over Broadband Wireless Systems

Introduction
This paper seeks to present ways to eliminate the inherent quantization noise component in digital communications, instead of conventionally making it minimal. It deals with a new concept of signaling called the Signal Code Modulation (SCM) Technique. The primary analog signal is represented by: a sample which is quantized and encoded digitally, and an analog component, which is a function of the quantization component of the digital sample. The advantages of such a system are two sided offering advantages of both analog and digital signaling. The presence of the analog residual allows for the system performance to improve when excess channel SNR is available. The digital component provides increased SNR and makes it possible for coding to be employed to achieve near error-free transmission.

Let us consider the transmission of an analog signal over a band-limited channel. This could be possible by two conventional techniques: analog transmission, and digital transmission, of which the latter uses sampling and quantization principles. Analog Modulation techniques such as Frequency and Phase Modulations provide significant noise immunity as known and provide SNR improvement proportional to the square root of modulation index, and are thus able to trade off bandwidth for SNR.

The SCM Technique : An Analytical Approach

Suppose we are given a bandlimited signal of bandwidth B Hz, which needs to be transmitted over a channel of bandwidth Bc with Gaussian noise of spectral density N0 watts per Hz. Let the transmitter have an average power of P watts. We consider that the signal is sampled at the Nyquist rate of 2B samples per second, to produce a sampled signal x(n).

Next, let the signal be quantized to produce a discrete amplitude signal of M=2b levels. Where b is the no. of bits per sample of the digital symbol D, which is to be encoded. More explicitly, let the values of the 2b levels be, q1, q2, q3, q4¦qM which are distributed over the range [-1, +1], where is the proportionality factor determined relative to the signal. Given a sample x(n) we find the nearest level qi(n). Here, qi(n) is the digital symbol and xa(n)= x(n)-qi(n) is the analog representation. The exact representation of the analog signal is given by x(n)=qi(n)+xa(n). We can accomplish the transmission of this information over the noisy channel by dividing it into two channels: one for analog information and another for digital information. The analog channel bandwidth is Ba= aB, and the digital channel bandwidth being Bd= dB, where Ba+Bd=Bc, the channel bandwidth. Let =Bc/B, be the bandwidth expansion factor, i.e. the ratio of the bandwidth of the channel to the bandwidth of the signal.

Similarly, the variables a and d are the ratios of Ba/B and Bd/B. Here we will assume that a=1 so that d= -1. The total power is also divided amongst the two channels with fraction pa for the analog channel and fraction pd for the digital one, so that pa+pd=1.
Speech Compression - a novel method
Speech Compression - a novel method

Introduction
This paper illustrates a novel method of speech compression and transmission. This method saves the transmission bandwidth required for the speech signal by a considerable amount. This scheme exploits the property of low pass nature of the speech signal. Also this method applies equally well for any signal, which is low pass in nature, speech being the more widely used in Real Time Communication, is highlighted here.

As per this method, the low pass signal (speech) at the transmitter is divided into set of packets, each containing, say N number of samples. Of the N samples per packet, only certain lesser number of samples, say N alone are transmitted. Here is less than unity, so compression is achieved. The N samples per packet are subjected to a N-Point DFT. Since low pass signals alone are considered here, the number of significant values in the set of DFT samples is very limited. Transmitting these significant samples alone would suffice for reliable transmission. The number of samples, which are transmitted, is determined by the parameter .

The parameter is almost independent of the source of the speech signal. In other methods of speech compression, the specific characteristics of the source such as pitch are important for the algorithm to work. An exact reverse process at the receiver reconstructs the samples. At the receiver, the N-point IDFT of the received signal is performed after necessary zero padding. Zero padding is necessary because at the transmitter of the N samples only N samples are transmitted, but at the receiver N samples are again needed to honestly reconstruct the signal.

Hence this method is efficient as only a portion of the total number of samples is transmitted thereby saving the bandwidth. Since the frequency samples are transmitted the phase information has also to be transmitted. Here again by exploiting the property of signals and their spectra that the PHASE INFORMATION CAN BE EMBEDDED WITHIN THE MAGNITUDE SPECTRUM by using simple mathematics without any heavy computations or by increasing the bandwidth.

Also the simulation result of this method shows that smaller the size of the packet the more faithful is the reproduction of received signal that is again an advantage as the computation time is reduced. The reduction in the computation time is due to the fact that the transmitter has to wait until N samples are obtained before starting the transmission. If N is small, the transmitter has to wait for a less duration of time and a smaller value of N achieves a better reconstruction at the receiver.

Thus this scheme provides a more efficient method of speech compression and this scheme is also very easy to implement with the help of available high-speed processors.Transmitting the spectrum of the signal instead of transmitting the original signal is far more efficient. This is because the energy of the speech signal above 4 kHz is negligible; we can very well compute the spectrum of the signal and transmit only the samples that correspond to 4 KHz of the spectrum irrespective of the sampling frequency. By this type of transmission we can save the bandwidth required for transmission considerably. Also it is not necessary that we have to transmit all the samples corresponding to the 4 kHz frequency as it is sufficient to transmit a fraction of the samples without any degradation in the quality.

Since the spectrum is considered in the above method both the magnitude and phase information must be transmitted to reproduce the signal without any error. But this requires twice the actual bandwidth. Exploiting the property of real and even signals can solve this problem. The spectrum of the samples is real and evenliness is artificially introduced such that their spectra are also real and even. Thus by simple mathematics the complete phase information is embedded within the magnitude spectrum and it is needed only to send 'aN' samples instead of '2N'samples of the spectra (Magnitude and phase).

Adopting all these procedures and embedding the phase information in the magnitude spectrum have performed a MATLAB simulation performed to determine the optimum value of 'a' and 'N'. The result of the simulation is also provided
Bluetooth Based Smart Sensor Networks
Bluetooth Based Smart Sensor Networks

Introduction
The communications capability of devices and continuous transparent information routes are indispensable components of future oriented automation concepts. Communication is increasing rapidly in industrial environment even at field level.In any industry the process can be realized through sensors and can be controlled through actuators. The process is monitored on the central control room by getting signals through a pair of wires from each field device in Distributed Control Systems (DCS). With advent in networking concept, the cost of wiring is saved by networking the field devices. But the latest trend is elimination of wires i.e., wireless networks.

Wireless sensor networks - networks of small devices equipped with sensors, microprocessor and wireless communication interfaces.In 1994, Ericsson Mobile communications, the global telecommunication company based in Sweden, initiated a study to investigate, the feasibility of a low power, low cost ratio interface, and to find a way to eliminate cables between devices. Finally, the engineers at the Ericsson named the new wireless technology as "Blue tooth" to honour the 10th century king if Denmark, Harald Blue tooth (940 to 985 A.D).
The goals of blue tooth are unification and harmony as well, specifically enabling different devices to communicate through a commonly accepted standard for wire less connectivity.

BLUE TOOTH

Blue tooth operates in the unlicensed ISM band at 2.4 GHZ frequency band and use frequency hopping spread spectrum technique. A typical Blue tooth device has a range of about 10 meters and can be extended to 100meters. Communication channels supports total bandwidth of 1 Mb / sec. A single connection supports a maximum asymmetric data transfer rate of 721 KBPS maximum of three channels.

BLUE TOOTH - NETWORKS

In bluetooth, a Piconet is a collection of up to 8 devices that frequency hop together. Each Piconet has one master usually a device that initiated establishment of the Piconet, and up to 7 slave devices. Master's Blue tooth address is used for definition of the frequency hopping sequence. Slave devices use the master's clock to synchronize their clocks to be able to hop simultaneously.

A Piconet

When a device wants to establish a Piconet it has to perform inquiry to discover other Blue tooth devices in the range. Inquiry procedure is defined in such a way to ensure that two devices will after some time, visit the same frequency same time when that happens, required information is exchanged and devices can use paging procedure to establish connection.When more than 7 devices needs to communicate, there are two options. The first one is to put one or more devices into the park state. Blue tooth defines three low power modes sniff, hold and park. When a device is in the park mode then it disassociates from and Piconet, but still maintains timing synchronization with it. The master of the Piconet periodically broadcasts beacons (Warning) to invite the slave to rejoin the Piconet or to allow the slave to request to rejoin. The slave can rejoin the Piconet only if there are less than seven slaves already in the Piconet. If not so, the master has to 'park' one of the active slaves first.

All these actions cause delay and for some applications it can be unacceptable for eg: process control applications, that requires immediate response from the command centre (central control room).Scatternet consists of several Piconets connected by devices participating in multiple Piconet. These devices can be slaves in all Piconets or master in one Piconet and slave in other Piconets. Using scatternets higher throughput is available and multi-hop connections between devices in different Piconets are possible. i.e., The unit can communicate in one Piconet at time so they jump from pioneer to another depending upon the channel parameter.
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