Electrical Seminar Abstract And Report 5
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15-02-2009, 01:18 PM

Fuzzy based Washing Machine

Fuzzy Logic has played a pivotal part in this age of rapid technological development .In this paper we have elaborated on the automation process used in a washing machine. This paper has focused on the two subsystems of the washing machine namely the sensor mechanism and the controller unit. It also discuss on the use of singletons for fuzzy sets. This paper also highlights the use of a fuzzy controller to give the correct wash time. The use of fuzzy controller has the advantage of managing time, increasing equipment effiency and diagnosing malfunctions.

Classical feedback control theory has been the basis for the development of simple automatic control systems .It is easily comprehensible principle and relatively simple implementation has been the main reason for its wide applications in industry. Such fixed-gain feedback controllers are insufficient, however to compensate for parameter variations in the plant as well as to adapt to changes in the environment. The need to overcome such problems and to have a controller well-tuned not just for one operating point for a whole range of operating points has motivated the idea of an adaptive controller.

In order to illustrate some basic concepts in fuzzy logic consider a simplified example of a thermostat controlling a heater fan illustrated in fig.1.The room temperature detected through a sensor is input to a controller which outputs a control force to adjust the heater fan speed. A conventional thermostat works like an ON/OFF switch. If we set it at 78F then the heater is activated only when the temperature falls below 75F.When it reaches 81F the heater is turned off .As a result the desired room temperature is either too warm or too hot.

A fuzzy thermostat works in shades of gray where the temperature is treated as a series of overlapping ranges .For example, 78F is 60% warm and 20% hot .The controller is programmed with simple if-then rules that tell the heater fan how fast to run. As a result, when the temperature changes the fan speed will continuously adjust to keep the temperature at desired level. Our first step in designing such a fuzzy controller is to characterize the range of values for the input and output variables of the controller. Then we assign labels such as cool for the temperature and high for the fan speed, and we write a set of simple English-like rules to control the system. Inside the controller all temperature regulating actions will be based on how the current room temperature falls into these ranges and the rules describing the system behavior .The controller's output will vary continuously to adjust the fan speed.

The temperature controller described above can be defined in four simple rules:

If temperature is COLD then fan speed is HIGH
If temperature is COOL then fan speed is MEDIUM
If temperature is WARM then fan speed is LOW
If temperature is HOT then fan speed is ZERO

Here the linguistic variables cool; warm, high, etc. are labels, which refer to the set of overlapping values. These triangular shaped values are called membership functions.
Low Memory Color Image Zero Tree Coding
Low Memory Color Image Zero Tree Coding

This paper presents a zero tree coding method for color images that uses no lists during encoding and decoding,permitting the omission of the lists requirement in Said and Pearlman's Set Partitioning In Hierarchical Trees (SPIHT) algorithm [3]. Without the lists, the memory requirement in a VLSI implementation is reduced significantly. This coding algorithm is also developed to reduce the circuit complexity of an implementation. Our experimental results show only a minor reduction of PSNR values when compared with the PSNR values obtained by the SPIHT codec illustrating well the trade-off between memory requirement and hardware simplicity.

Since being introduced by Shapiro [4], the zerotree wavelet image coding has been a well-recognized image coding method and based on the zerotree theory several coding algorithms have be developed. SPIHT is the most significant algorithm because it demonstrates a very sim-pleand efficient way to code a discrete wavelet transformed (DWT) image. However, a SPIHT codec needs to main-tain three lists during coding and decoding to store the co-ordinates of significance coefficients and subset trees in the sorting order. The three lists become drawbacks for a hard-ware implementation because a large amount of memory is needed to maintain these lists. For color image coding the memory demand increases significantly.

For example, for a 512x512 color image, one single entry of the list needs 18 bits of memory to store the row and column coordinates. Given that the total number of list entries of a single color element is approximately twice the total number of coeffi-cients,the total memory required is 3.375 MBytes 1 and the required memory will increase if the bit rate increases.

This 118(bits)x512(pixels)x512(lines)x3(colors)x2/8bits/1K/1K = 3.375MB high memory requirement makes SPIHT not a cost effective compression algorithm for VLSI implementation.In this paper we present a zerotree coding algorithm for color image coding called Listless Zerotree Coding (LZC). The advantage of LZC over SPIHT is that no lists are needed during coding and decoding. Instead, a color co-efficient only needs a 3-bit flag if the coefficient is in the first wavelet transform level and a 6-bit flag if it is in any other transform level. Consequently, the amount of memory that required by a LZC codec is only a fraction of the amount needed by a SPIHT codec. In common with SPIHT, LZC is a progressive coding algorithm.

The color compo-nents are coded in the sequence of Y tree, V tree, then U tree, and the coding can stop at any point to give a precise bit-rate control.
LZC coding algorithm and SPIHT are quite alike. How-ever, since the usage of lists had been abandoned by LZC, different tree structure and coding procedure were developed for LZC. The tree symbols of LZC zero tree are ex-plained as follow.

_ C(i,j) wavelet coefficient at the coordinate (i,j);
_ O(i,j) set of child coefficients of C(i,j), ie. Coefficients
at coordinates (2i,2j), (2i,2j+1), (2i+1,2j), (2i+1,2j+1);
except at the finest transform level (ie. Level 1);
_ D(i,j) set of descendant coefficients of C(i,j), ie. all offsprings of C(i,j);
_ F C (i,j) significant map of coefficient C(i,j);
_ F D (i,j) significant map of set D(i,j);
_ R(i,j) set of root coefficients at LL band.
_ LZC's zerotree relations adopt Shapiro's zerotree relation .

The positions of significant pixels are encoded by symbol Cand symbol D. The maps used to indicate the significance of C C and D D (ie. storing temporary zerotree structure) arefd map and fcfC map, re-spectively, as shown in Figure 1(b). The size of Fc map is same size as the image. Whereas the size of FD map is only a quarter of the image because coefficients in level 1 do not have any descendants. Therefore, for a 512x512 color image, the total memory required to store zerotree structure is only 120KBytes (2) for all bit rates. Comparing to the 3.375 MBytes memory requirement for SPIHT, memory requirement for LZC has been reduced significantly.
Stealth Fighter
Stealth Fighter


Stealth means 'low observable'. The very basic idea of Stealth Technology in the military is to 'blend' in with the background. The quest for a stealthy plane actually began more than 50 years ago during World War II when RADAR was first used as an early warning system against fleets of bombers. As a result of that quest, the Stealth Technology evolved. Stealth Technology is used in the construction of mobile military systems such as aircrafts and ships to significantly reduce their detection by enemy, primarily by an enemy RADAR. The way most airplane identification works is by constantly bombarding airspace with a RADAR signal.

When a plane flies into the path of the RADAR, a signal bounces back to a sensor that determines the size and location of the plane. Other methods focus on measuring acoustic (sound) disturbances, visual contact, and infrared (heat) signatures. Stealth technologies work by reducing or eliminating these telltale signals. Panels on planes are angled so that radar is scattered and no signal returns. Planes are also covered in a layer of absorbent materials that reduce any other signature the plane might leave. Shape also has a lot to do with the `invisibility' of stealth planes. Extreme aerodynamics keeps air turbulence to a minimum and cut down on flying noise. Special low-noise engines are contained inside the body of the plane. Hot fumes are then capable of being mixed with cool air before leaving the plane. This fools heat sensors on the ground. This also keeps heat seeking missiles from getting any sort of a lock on their targets.

Stealth properties give it the unique ability to penetrate an enemy's most sophisticated defenses and threaten its most valued and heavily defended targets. At a cost of $2 billion each, stealth bombers are not yet available worldwide, but military forces around the world will soon begin to attempt to mimic some of the key features of stealth planes, making the skies much more dangerous.


With the increasing use of early warning detection devices such as radar by militaries around the world in the 1930's the United States began to research and develop aircraft that would be undetectable to radar detection systems. The first documented stealth prototype was built out of two layers of plywood glued together with a core of glue and sawdust. This prototype's surface was coated with charcoal to absorb radar signals from being reflected back to the source, which is how radar detection systems detect items in the air.

Jack Northrop built a flying wing in the 1940's. His plane was the first wave of stealth aircraft that actually flew. The aircraft proved to be highly unstable and hard to fly due to design flaws. The United States initially orders 170 of these aircraft from Northrop but cancelled the order after finding that the plane had stability Flaws. Then in 1964, SR-71 the first Stealth airplane launched. It is well known as 'black bird'. It is a jet black bomber with slanted surfaces. This aircraft was built to fly high and fast to be able to bypass radar by its altitude and speed.


The idea is for the radar antenna to send out a burst of radio energy, which is then reflected back by any object it happens to encounter. The radar antenna measures the time it takes for the reflection to arrive, and with that information can tell how far away the object is. The metal body of an airplane is very good at reflecting radar signals, and this makes it easy to find and track airplanes with radar equipment.

The goal of stealth technology is to make an airplane invisible to radar. There are two different ways to create invisibility: The airplane can be shaped so that any radar signals it reflects are reflected away from the radar equipment. The airplane can be covered in materials that absorb radar signals.
Border Security using Wireless Integrated Network Sensors
Border Security using Wireless Integrated Network Sensors

Wireless Integrated Network Sensors (WINS) now provide a new monitoring and control capability for monitoring the borders of the country. Using this concept we can easily identify a stranger or some terrorists entering the border. The border area is divided into number of nodes. Each node is in contact with each other and with the main node.

The noise produced by the foot-steps of the stranger are collected using the sensor. This sensed signal is then converted into power spectral density and the compared with reference value of our convenience. Accordingly the compared value is processed using a microprocessor, which sends appropriate signals to the main node. Thus the stranger is identified at the main node. A series of interface, signal processing, and communication systems have been implemented in micro power CMOS circuits. A micro power spectrum analyzer has been developed to enable low power operation of the entire WINS system.

Thus WINS require a Microwatt of power. But it is very cheaper when compared to other security systems such as RADAR under use. It is even used for short distance communication less than 1 Km. It produces a less amount of delay. Hence it is reasonably faster. On a global scale, WINS will permit monitoring of land, water, and air resources for environmental monitoring. On a national scale, transportation systems, and borders will be monitored for efficiency, safety, and security.

Wireless Integrated Network Sensors (WINS) combine sensing, signal processing, decision capability, and wireless networking capability in a compact, low power system. Compact geometry and low cost allows WINS to be embedded and distributed at a small fraction of the cost of conventional wireline sensor and actuator systems. On a local, wide-area scale, battlefield situational awareness will provide personnel health monitoring and enhance security and efficiency. Also, on a metropolitan scale, new traffic, security, emergency, and disaster recovery services will be enabled by WINS. On a local, enterprise scale, WINS will create a manufacturing information service for cost and quality control.

The opportunities for WINS depend on the development of scalable, low cost, sensor network architecture. This requires that sensor information be conveyed to the user at low bit rate with low power transceivers. Continuous sensor signal processing must be provided to enable constant monitoring of events in an environment. Distributed signal processing and decision making enable events to be identified at the remote sensor. Thus, information in the form of decisions is conveyed in short message packets. Future applications of distributed embedded processors and sensors will require massive numbers of devices. In this paper we have concentrated in the most important application, Border Security.


Conventional wireless networks are supported by complex protocols that are developed for voice and data transmission for handhelds and mobile terminals. These networks are also developed to support communication over long range (up to 1km or more) with link bit rate over 100kbps. In contrast to conventional wireless networks, the WINS network must support large numbers of sensors in a local area with short range and low average bit rate communication (less than 1kbps).

The network design must consider the requirement to service dense sensor distributions with an emphasis on recovering environment information. Multihop communication yields large power and scalability advantages for WINS networks. Multihop communication, therefore, provides an immediate advance in capability for the WINS narrow Bandwidth devices. However, WINS Multihop Communication networks permit large power reduction and the implementation of dense node distribution. The multihop communication has been shown in the figure 2. The figure 1 represents the general structure of the wireless integrated network sensors (WINS) arrangement.
A Basic Touch-Sensor Screen System
A Basic Touch-Sensor Screen System

The touch-sensor technology is about using our fingers or some other pointer, to view and manipulate information on a screen. On a conventional system, with every mouse click, the operating system registers a mouse event. With a touch-screen system, every time your finger touches the screen, a touch event is registered.


A basic touch-screen system is made up of three components:

1. A touch sensor
2. Controller
3. Software driver

The touch-sensor is a clear panel, which when touched, registers a voltage change that is sent to the controller. The controller processes this signal and passes the touch event data to the PC through a bus interface. The software driver takes this data and translates the touch events into mouse events.

A touch-screen sensor any of the following five mechanics: resistance, capacitance, acoustics, optics and mechanical force.

1. Resistance-based sensors.

A resistant sensor uses a thin, flexible membrane separated from a glass or plastic substance by insulating spacers. Both layers are coated with ITO (Indium-tin-oxide). These metallic coatings meet when a finger or stylus presses against the screen, thus closing an electric circuit.

2. Capacitance-based sensors.

Here voltage is applied to the corners of the screen with electrodes spread uniformly across the field. When a finger touches the screen, it draws current from each current proportionately. The frequency changes are measured to determine the X and Y coordinates of the touch event.

3. Acoustic sensors.

These sensors detect a touch event when a finger touches the screen resulting in absorption of sound energy. Bursts of high frequency (5-MHz) acoustic energy are launched from the edges of the screen. Arrays of reflection at the edges divert the acoustic energy across the screen and redirect the energy to the sensors. Because the speed of sound in glass is constant the energy arrival time identifies its path. A touch causes a dip in the received energy waveform for both axes. The timing of dips indicates the X and Y touch point coordinates.
GSM Security And Encryption

Design of 2-D Filters using a Parallel Processor Architecture

Software-Defined Radio (SDR)

Smart Dust

Adaptive Blind Noise Suppression in some Speech Processing Applications

An Efficient Algorithm for iris pattern Recognition using 2D Gabor Wavelet Transformation in Matlab

An Efficient Algorithm for iris pattern Recognition using 2D Gabor Wavelet Transformation in Matlab

Wavelet analysis have received significant attention because their multi-resolution decomposition allows efficient image analysis. It is widely used for varied applications such as noise reduction, and data compression, etc. In this paper we have introduced and applied the concept of 2 dimensional Gabor wavelet transform to Biometric Iris recognition system. The application of this transform in encoding the iris image for pattern recognition proves to achieve increased accuracy and processing speed compared to other methods. With a strong scientific approach and mathematical background we have developed an algorithm to facilitate the implementation of this method under the platforms of MATLAB

IMAGES - An introduction:

A dictionary defines image as a "reproduction or representation of the form of a person or thing". The inherent association of a human with the visual senses, predisposes one to conceive an image as a stimulus on the retina of the eye, in which case the mechanism of optics govern the image formation resulting in continuos range, multi-tone images.

A digital image can be defined to be a numerical representation of an object or more strictly to be sampled, quantized function of two dimensions which has been generated by optical means, sampled in an equally spaced rectangular grid pattern, and quantized in equal intervals of graylevel. The word is crying out for the simpler access controls to personal authentication systems and it looks like biometrics may be the answer. Instead of carrying bunch of keys, all those access cards or passwords you carry around with you, your body can be used to uniquely identify you. Furthermore, when biometrics measures are applied in combination with other controls, such as access cards or passwords, the reliability of authentication controls takes a giant step forward.


Biometrics is best defined as measurable physiological and/or behavioral characteristics that can be utilized to verify the identity of an indivisual. They include the following:

" Iris scanning
" Facial recognition
" Fingerprint verification
" Hand geometry
" Retinal scanning
" Signature verification
" Voice verification


" Highly protected internal organ of the eye.
" Iris patterns possess a high degree of randomness.
" Variability: 244 degrees of freedom.
" Entropy: 3.2 bits per square millimetre.
" Uniqueness: set by combinatorial complexity.
" Patterns apparently stable throughout life.

IRIS - An introduction:

The iris is a colored ring that surrounds the pupil and contains easily visible yet complex and distinct combinations of corona, pits, filaments, crypts, striations, radial furrows and more. The iris is called the "Living password" because of its unique, random features. It's always with you and can't be stolen or faked. As such it makes an excellent biometrics identifier.
Significance of real-time transport Protocol in VOIP (RTP)
Significance of real-time transport Protocol in VOIP (RTP)

The advent of Voice over IP (VoIP) has given a new dimension to Internet and opened a host of new possibilities and opportunities for both corporate and public network planners. More and more companies are seeing the value of transporting voice over IP networks to reduce telephone and facsimile costs.

Adding voice to packet networks requires an understanding of how to deal with system level challenges such as interoperability, packet loss, delay,density, scalability, and reliability. This is because of the real time constraints that come into picture. But then the basic protocols being used at the network and transport layer have remained unchanged. This calls for the definition of new protocols, which can be used in addition with the existing protocols.

Such a protocol should provide the application using them with enough information to conform to the real-time constraints. This paper discusses the significance of Real-time Transport Protocol (RTP) in VoIP applications.

The actual realisation of the RTP header, packetisation and processing of an RTP packet is discussed section six. Section 7, called 'Realising RTP functionalities', discusses a few problems that occur in a real time environment and how RTP provides information to counter the same. Finally, sample codes that we wrote for realising RTP packetisation, processing and RTP functionalities, written in 'C', for a Linux platform are presented.

Please note that RTP is incomplete without the companion RTP Control Protocol (RTCP), but a detailed description of RTCP is beyond the scope of this paper
Storage Area Networks
Storage Area Networks

A storage area network (SAN) is defined as a set of interconnected devices (for example, disks and tapes) and servers that are connected to a common communication and data transfer infrastructure such as Fibre Channel. The common communication and data transfer mechanism for a given deployment is commonly known as the storage fabric. The purpose of the SAN is to allow multiple servers access to a pool of storage in which any server can potentially access any storage unit. Clearly in this environment, management plays a large role in providing security guarantees (who is authorized to access which devices) and sequencing or serialization guarantees (who can access which devices at what point in time).

SANs evolved to address the increasingly difficult job of managing storage at a time when the storage usage is growing explosively. With devices locally attached to a given server or in the server enclosure itself, performing day-to-day management tasks becomes extremely complex; backing up the data in the datacenter requires complex procedures as the data is distributed amongst the nodes and is accessible only through the server it is attached to. As a given server outgrows its current storage pool, storage specific to that server has to be acquired and attached, even if there are other servers with plenty of storage space available. Other benefits can be gained such as multiple servers can share data (sequentially or in some cases in parallel), backing up devices can be done by transferring data directly from device to device without first transferring it to a backup server.

So why use yet another set of interconnect technologies? A storage area network is a network like any other (for example a LAN infrastructure). A SAN is used to connect many different devices and hosts to provide access to any device from anywhere. Existing storage technologies such as SCSI are tuned to the specific requirements of connecting mass storage devices to host computers. In particular, they are low latency, high bandwidth connections with extremely high data integrity semantics. Network technology, on the other hand, is tuned more to providing application-to-application connectivity in increasingly complex and large-scale environments.

Typical network infrastructures have high connectivity, can route data across many independent network segments, potentially over very large distances (consider the internet), and have many network management and troubleshooting tools.
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