Association of EnC Engineering Students(AEnCES)

AEnCES Office Bearers(2013-2014)

2015-02-28T03:28:00.002-08:00

AEnCES Office Bearers(2013-2014)

President : Lionel D’Souza 7^th A

Vice-President : Poornima D. Pakkala 7^th B

Secretary : Naveen H.G 7^th B

Treasurer : Suchithra 5^th B

Cultural : Melisha Monteiro 7^thA

Pavithra Naik 7^thB

Sports : Jeevan Manohar Vas 7^thA

Technical : Keval 7^thA

Publication : sharanya 7^thB

Ashlin 7^thA

Long distance signal communication from shore to ship using DSBSC

2015-02-28T03:20:00.001-08:00

Long distance signal communication from shore to ship using DSBSC

Abstract— Long distance signal communication using Double Sideband Suppressed Carrier [DSBSC] technique is one of the key areas in wireless communication. This paper contains the analysis, design, development of signal communication for the purpose of shore to ship two way signal communication using the ground wave antennas. The DSBSC travels long distance with low power as compare to commercials AM and also range will can be more than line of sight as in FM (more than 40km). This experiment will also show the radiation of electromagnetic energy allowing antenna sizes to be reasonable and simultaneous transmission of several signals over the same channel.

Introduction: The process of passing any information from one person to the other person with the aid of some medium is termed as communication.

Electronic communication is classified as (1) one-way (simplex) or two-way (full duplex or half duplex) transmissions and (2) Analog or Digital signals. Analog signals are smoothly varying, continuous signals. Digital signals are discrete, two-state (on/off) codes. Electronic signals are often changed from analog to digital and vice versa. This experiment we are using the digital communication, because it flexible, compatible, reliable and accuracy than the Analog modulation. Modulation is a process that causes a shift of the range of frequencies in a signal. It is used to facilitate transmission over a given channel. Communication system in modulation contains 2 types 1) base-band modulation. 2) Carrier modulation. In carrier modulation again subdivided into 1) AM modulation. 2) Angle modulation. There are 2 types in Angle modulation 1) FM modulation 2) PM modulation. Here we have chosen the AM modulation technique, because communication between the shore to boat is very long. For the communication purpose here we are using electromagnetic energy waves. Advantages of modulation are the ease of radiation of electromagnetic energy allowing antenna sizes to be reasonable and simultaneous transmission of several signals over the same channel.Amplitude Modulation: It is the process where, the amplitude of the carrier is varied proportional to that of the message signal.But in the case of AM modulation, transmission of carrier consumes lot of power. Since, only the side bands contain the information about the message, carrier is suppressed. This results in a DSB-SC wave.

DSBSC

DOUBLE SIDE BAND - SUPPRESSED CARRIER (DSB-SC) MODULATION:

General expression:c(t)=[k₁m(t)+C]cos(ω_ct+Φ_c)
Let k1 = 1, C = 0 and c = 0, the modulated carrier signal,therefore: c(t)=m(t)cos ω_c(t)
Consider twoinformation signals like sinusoids, or cosinusoids, cosμt and cosωt. A double sideband suppressed carrier signal, or DSBSC, is defined as their product,DSBSC = Ecos ω_c(t) cosω_m(t)                                                                      ........ 1
            ω_c >> ω_m                                                                                                ........ 2we get below eqn.cosω_c(t) cosω_m(t) = (E/2) cos(ω_c - ω_m)t + (E/2) cos(ω_c + ω_m)t                           ........ 3
Equation 3 shows that the product is represented by two new signals, one on the sumfrequency (ω_c + ω_m), and one on the difference frequency (ω_c - ω_m) - see Figure 1.
Remembering the inequality of eqn. (2) the two new components are located close to the frequency ω rad/s, one just below, and the other just above it. These are referredto as the lower and upper sidebands respectively. These two components were derived from a ‘carrier’ term on ω_c rad/s, and a message on ω_m rad/s. Because there is no term at carrier frequency in the product signal it is described as a double sideband suppressed carrier (DSBSC) signal. Figure 1: spectral components The term ‘carrier’ comes from the context of ‘double sideband amplitude modulation' (commonly abbreviated to just AM).AM is introduced in a later experiment (although, historically, AM preceded DSBSC). The time domain appearance of a DSBSC (eqn. 1). Message notice the waveform of the DSBSC in Figure 2, especially near the times when the message amplitude is zero. The fine detail differs from period to period of the message. This is because the ratio of the two frequencies ω_c and ω_m has been made
Non-integral. Although the message and the carrier are periodic waveforms (sinusoids), the DSBSC itself need not necessarily be periodic.Modulator: The DSBSC modulator is based on the LM1496 balanced modulator IC. The balance circuit consisted by the VR1 is used to control the LM1496 operating in balance state. By adjusting the VR1 properly, this will ensure that the modulator operates in balance state. In short, the major difference between DSB-SC and AM modulated signals is. the DSB-SC modulated signal containing no carrier. To achieve the requirement of suppressing carrier, we should first connect the audio input to ground, and then observe the LM1496 output to ensure no carrier presented by carefully adjusting the VR1. If this is made and then reconnects the audio signal, the DSB-SC modulated signal containing the upper- and lower-sideband signals will be presented at LM1496 output. Carrier voltage level is a very important factor, which affects the carrier suppressing. If the carrier level is too low, it will be not enough to start the carrier amplifier completely; reversely, a too high level will occur carrier feed through. In general, the optimum input range is about 0.2 Vpp to 0.8 Vpp when the carrier frequency is 500 kHz. To identify AM, DSB-SC or SSB modulated signals; we assume a typical audio spectrum shown in Fig. 5-2a. Where f mh is the highest frequency and f ml is the lowest frequency in audio signal. If using this audio signal to modulate the amplitude of sine carrier, we will obtain an AM spectrum Low-pass filter:

    A low-pass filter is an electronic filter that passes low-frequency signals and attenuates (reduces the amplitude of) signals with frequencies higher than the cut-off frequency. The actual amount of attenuation for each frequency varies from filter to filter. It is sometimes called a high-cut filter, or treble cut filter when used in audio applications. A low-pass filter is the opposite of a high-pass filterLow-pass filters exist in many different forms, including electronic circuits (such as a hiss filter used in audio), anti-aliasing filters for conditioning signals prior to analog-to-digital conversion, digital filters for smoothing sets of data, acoustic barriers, blurring of images, and so on. The moving average operation used in fields such as finance is a particular kind of low-pass filter, and can be analyzed with the same signal processing techniques as are used for other low-pass filters. Low-pass filters provide a smoother form of a signal, removing the short-term fluctuations, and leaving the longer-term trend.    Although it is possible to design a wide variety of filters with different levels of gain and different roll off patterns using operational amplifiers, the filter described on this page will give a good sure-fire solution. It offers unity gain and a Butterworth response (the flattest response in band, but not the fastest to achieve ultimate roll off out of band).    Operational amplifier two pole low pass filter Simple sure fire design with Butterworth response and unity gain The calculations for the circuit values are very straightforward for the Butterworth response and unity gain scenario. Critical damping is required for the circuit and the ratio of the resistor and capacitor values determines this. When choosing the values, ensure that the resistor values fall in the region between 10 k ohms and 100 k ohms. This is advisable because the output impedance of the circuit rises with increasing frequency and values outside this region may affect he performance.R1=R2C1=2*C2F=√2/(4*pai*R*C2)

RF power amplifier

A RF amplifier is a device for electrically amplifying the power of an electrical signal, typically, but not exclusively, radio frequency signals.
An RF power amplifier is a type of electronic amplifier used to convert a low-power radio-frequency signal into a larger signal of significant power, typically for driving the antenna of a transmitter. It is usually optimized to have high efficiency, high output Power (P1dB) compression, good return loss on the input and output, good gain, and optimum heat dissipation.
Rf is a single tuned amplifier. Its functions are: - 1.improves selectivity ( i.e. rejection of unwanted signal) , so that it prevents heterodyning which results in interference frequency. 2. Improves image frequency rejection 3. Improves sensitivity ( gain of amplifier ) 4.Improves coupling of receiver with antenna . 5. Improves signal to noise ratio. 6. Reradiation of local oscillator through receiver antenna is prevented

RF Amp Devices

The primary device responsible for the signal enhancement is known as a triode, or a diode with one added part--a control grid. The control grid affects on the electric charge flows through the diode, and by applying small voltage variations in the current passing through, the triode can make large changes in the strength and behaviour of the current.

Of course, this is a very basic description of the process: modern RF amplifiers employ entire circuit boards to make sure the signal is given maximum strength with as little signal distortion as possible. Today, these amplifiers are used no only in radio towers, walkie-talkies, and specialized communication devices, but also in every cell phone and cell phone tower.

Tuned Radio Frequency (TRF) Receiver

The most important part of the circuit is the input stage, where positive feedback is used to achieve good sensitivity and selectivity. The first stage is adjusted so that it is not quite at the point of oscillation. This increases the gain and the selectivity, giving a narrow bandwidth. To achieve this, the potentiometer connected to the drain of the FET must be adjusted very carefully: optimal performance of the receiver depends on its setting. In ideal conditions several strong stations should be obtainable during the day using a 50 cm antenna. At night, several times this number should be obtainable. The frequency range of the receiver runs from 6 MHz to 8 MHz. This range covers the 49 m and the 41 m shortwave bands in which many European stations broadcast. Not bad for such a simple circuit! The circuit employs six transistors. The first stage is a selective amplifier, followed by a transistor detector. Two low-frequency amplifier stages complete the circuit.
Circuit diagram:-

Reciver

Tuned Radio Frequency (TRF) Receiver Circuit DiagramThe final stage is a push-pull arrangement for optimal drive of the low-impedance loudspeaker. This circuit arrangement is sometimes called a ‘1V2 receiver’ (one preamplifier, one detector and two audio-frequency stages). Setting-up is straightforward. Adjust P1 until the point is reached where the circuit starts to oscillate: a whistle will be heard from the loudspeaker. Now back off the potentiometer until the whistle stops. The receiver can now be tuned to a broadcaster. Occasional further adjustment of the potentiometer may be required after the station is tuned in. The receiver operates from a supply voltage of between 5 V and 12 V and uses very little current. A 9 V PP3 (6F22) battery should give a very long life.

Mixer

Mixers are used for frequency conversion and are critical components in modern radio frequency (RF) systems. A mixer converts RF power at one frequency into power at another frequency to make signal processing easier and also inexpensive. A fundamental reason for frequency conversion is to allow amplification of the received signal at a frequency other than the RF, or the audio, frequency. A receiver may require as much as 140 decibels (dB) of gain. It might not be possible to put more than 40 dB of gain into the RF section without risking instability and potential oscillations. Likewise the gain of the audio section might be limited to 60 dB because of parasitic feedback paths, and microphonics. The additional gain needed for a sensitive receiver is normally achieved in an intermediate frequency (IF) section of the receiver. Double-sideband, suppressed-carrier AM is a sum (upper sideband), a difference (lower sideband) and no carrier. We didn’t call its sum and difference outputs upper and lower sidebands earlier in equation 7’s neighborhood, but we’d do so in a transmitting application. In a transmitter, we call a circuit that suppresses the carrier while generating upper and lower sidebands a balanced modulator, and we quantify its carrier suppression, which is always less than infinite. In a receiver, we call such a circuit a balanced mixer, which may be single-balanced (if it lets either its RF signal or its LO [carrier] signal through to its output) or double-balanced (if it suppresses both its input signal and LO/carrier in its output), and we quantify its LO suppression and port-to-port isolation, which are always less than infinite. (Mixers [and amplifiers] thatafford no balance whatsoever are sometimes said to be single-ended.) Sometimes,DSB suppressed-carrier AM is called just DSB. A sine wave at the frequency difference between Signal A and Signal B 2π(fa – fb)t,and a sine wave at the frequency sum of Signal A and Signal B 2π(fa + fb)t. (The products are cosine waves, but since equivalent sine and cosine waves differ only by a phase shift of 90°, both are called sine waves by convention.) This is the basic process by which we translate information into radio form and translate it back again. If we want to transmit a 1-kHz audio tone by radio, we can feed it into one of our mixer’s inputs and feed an RF signal. Modulated carrier is the output of the mixer. Circuit diagram:- IC NJM2552

  Demodulation :

Demodulation is the act of extracting the original information-bearing signal from a modulated carrier wave. A demodulator is an electronic circuit (or computer program in a software defined radio) that is used to recover the information content from the modulated carrier wave.[1]

These terms are traditionally used in connection with radio receivers, but many other systems use many kinds of demodulators. Another common one is in a modem, which is a contraction of the terms modulator/demodulator.

Power amplifier:
    A power amplifier or (informally) amp is an electronic device that increases the power of a signal. It does this by taking energy from a power supply and controlling the output to match the input signal shape but with larger amplitude. In this sense, an amplifier modulates the output of the power supply.
   The term power amplifier is a relative term with respect to the amount of power delivered to the load and/or sourced by the supply circuit. In general a power amplifier is designated as the last amplifier in a transmission chain (the output stage) and is the amplifier stage that typically requires most attention to power efficiency. Efficiency considerations lead to various classes of power amplifier based on the biasing of the output transistors or tubes.

Power amplifier classes:

Power amplifier circuits (output stages) are classified as A, B, AB and C for analog designs, and class D and E for switching designs based on the proportion of each input cycle (conduction angle), during which an amplifying device is passing current. The image of the conduction angle is derived from amplifying a sinusoidal signal. If the device is always on, the conducting angle is 360°. If it is on for only half of each cycle, the angle is 180°. The angle of flow is closely related to the amplifier power efficiency. The various classes are introduced below, followed by a more detailed discussion under their individual headings further down.
In the illustrations below, a bipolar junction transistor is shown as the amplifying device, but the same attributes are found if with MOSFETs or vacuum tubes.

Class AB

Class AB is intermediate between class A and B, the two active elements conduct more than half of the time. Class AB is widely considered a good compromise for audio power amplifiers, since much of the time the music is quiet enough that the signal stays in the "class A" region, where it is amplified with good fidelity, and by definition if passing out of this region, is large enough that the distortion products typical of class B are relatively small. The crossover distortion can be reduced further by using negative feedback.
Reference oscillator :
Antenna: Ground Antenna:
In radio communication, a ground dipole also referred to as an earth dipole antenna, transmission line antenna, and in technical literature as a horizontal electric dipole. A ground dipole consists of two ground electrodes buried in the earth, separated by tens to hundreds of kilometres, linked by overhead transmission lines to a power plant transmitter located between them. Alternating current electricity flows in a giant loop between the electrodes through the ground, radiating ELF waves, so the ground is part of the antenna. To be most effective, ground dipoles must be located over certain types of underground rock formations.
A ground dipole functions as an enormous vertically-oriented loop antenna (see drawing, right). It consists of two widely separated electrodes (G) buried in the ground, connected by overhead transmission cables to a transmitter (P) located between them. The alternating current from the transmitter (I) travels in a loop through one transmission line, kilometres deep into bedrock from one ground electrode to the other, and back through the other transmission line. This creates an alternating magnetic field (H) through the loop, which radiates ELF waves. The axis of the magnetic field produced is horizontal, so it generates vertically polarized waves. The radiation pattern of the antenna is directional, with two lobes (maxima) in the plane of the loop, off the ends of the transmission lines. In the U.S. installations two ground dipoles are used, oriented perpendicular to each other, to allow transmission in all directions.
The amount of power radiated by a loop antenna is proportional to (IA)2, where I is the AC current in the loop and A is the area enclosed, To radiate practical power at ELF frequencies, the loop has to carry a current of hundreds of amperes and enclose an area of at least several square miles. Christofilos found that the lower the electrical conductivity of the underlying rock, the deeper the current will go, and the larger the effective loop area. Radio frequency current will penetrate into the ground to a depth equal to the skin depth of the ground at that frequency, which is inversely proportional to the square root of ground conductivity σ. The ground dipole forms a loop with effective area of A = Lδ / √2, where L is the total length of the transmission lines and δ is the skin depth. So ground dipoles are sited over low conductivity underground rock formations (this contrasts with ordinary radio antennas, which require good earth conductivity for a low resistance ground connection for their transmitters). The two U.S. Navy antennas were located in the Upper Peninsula of Michigan, on the Canadian Shield (Laurentian Shield) formation, which has unusually low conductivity of 2×10−4 siemens/meter resulting in an increase in antenna efficiency of 20 dB.The earth conductivity at the site of the Russian transmitter is even lower.
Theoretical calculation :

Advantages:

Lower power consumption

Disadvantage:

- Complex detection

Applications:

- Analogue TV systems: to transmit colour information
- For transmitting stereo information in FM sound broadcast
at VHF

naveengouda204@gmail.com

2014-11-23T03:22:00.003-08:00

Rules Based Fuzzy Cognitive Mapping: Applications in Education

Naveen H Gouda

Department of electronics and communication, SCEM Mangalore, IEEE

Abstract— Fostering conceptual and cognitive change in learners can be difficult. Students often come to a learning situation with robust, implicit understandings of the material under study. One explanation for the implicit nature of these understandings is a lack of metaknowledge about the knowledge to be acquired. Helping learners create metaknowledge may free paths to conceptual change. This paper proposes the use of fuzzy cognitive maps [FCMs] as a tool for creating metaknowledge and exploring hidden implications of a learner’s understanding. Two specific educational applications of FCMs are explored in detail and recommendations are included for further investigations within educational contexts.

I. Introduction

Fuzzy cognitive mapping [FCM] is a tool for formalizing understandings of conceptual and causal relationships. By combining conceptual mapping tools with fuzzy logic and other techniques originally developed for neural networks, FCMs allow for the representation and formalization of soft knowledge domains [e.g., politics, education]. This paper explores FCM procedures and proposes two methodologies for developing FCMs in educational organization settings. Other potential applications in education are explored and directions for future research are included.

To apply FCMs in education requires a basic understanding of the theoretical foundation of cognitive mapping. This paper presents a brief review of that theoretical foundation, as well as some related research literature. Fuzzy logic allows us to represent truth values on a continuous scale from 0 to 1, providing mathematical methods for representing concepts and causalities that are true to some degree [neither wholly true nor false]. Consequently, the law of the excluded middle does not apply in fuzzy logic. FCMs combine the strengths of cognitive maps with fuzzy logic. By representing human knowledge in a form more representative of natural human language than traditional concept mapping techniques, FCMs ease knowledge engineering and increase knowledge-source concurrence. FCMs can also be modelled on computers, thus allowing for dynamic modelling of cognitive systems.

Two methods for facilitating the creation of FCMs are presented in this paper. Both are in the early stages of testing. The educational field testing includes trials of a method for group knowledge acquisition and for individual knowledge acquisition. In addition, a computer modelling system is presented for the development and analysis of FCMs.

II. CONCEPTUAL CHANGE

It is the processes whereby concepts and relationships between them change over the course of an individual person’s lifetime or over the course of history. Learners’ naive conceptions have been well studied. It is widely accepted within the domain of cognitive psychology that students come to school with some form of conceptualization of the natural world and their place in it. Frequently, however, these conceptions are not scientifically accurate. Instead, these conceptions represent a theory that is useful in everyday experience. Naive theories are based on interaction with the everyday world. A child who repeatedly drops items on the floor is building an implicit theory of gravity. A child who tries to manipulate her parents into taking her for ice cream is building an implicit theory of human behaviour.

Naive understandings display many of the characteristics of implicit knowledge. Implicit knowledge: (a) is characterized by specificity of transfer, (b) is associated with incidental learning conditions, (c) gives rise to a phenomenal sense of intuition, and (d) remains robust in the face of time, psychological disorder and secondary tasks. Naive understandings meet many of the same criteria. They are learned incidentally, they give rise to a sense of ‘‘knowing,’’ and remain robust in the face of time and schooling. These naive understandings can be very difficult to diagnose and change.

Much of the research on naive understandings has been in science education. The difficulty of changing conceptions of the natural world that have been formed over many years is well documented. Situations in which students are unable to process that which they are not expecting create opportunities for implicit learning. For example, when presented with science instruction, students often are looking to confirm what they ‘‘know’’ already, with the result both extremely selective attention and distortion of information provided during instruction. Implicit knowledge concentrates on the process of acquiring the knowledge. Furthermore, it involves learning without awareness. In order to make implicit knowledge available to the learner, some structured task must be available to elicit the knowledge from the learner. Concept, or cognitive, mapping represents a possible tool for developing such a structured environment.

III. Concept Mapping

A concept map is a type of graphic organizer used to help students organize and represent knowledge of a subject. Concept maps begin with a main idea (or concept) and then branch out to show how that main idea can be broken down into specific topics. The current use of concept mapping within education has its roots in research conducted at Cornell that focused on conceptual changes in students over a 12 year

period. This research required a method to compare conceptions over time and between learners. The Cornell researchers developed a system of representing conceptual knowledge graphically: circles for concepts and arrows for the links between them.

A review of the literature provides several definitions of concept maps, also known as cognitive maps. Two factors are common in these definitions: all of the authors reviewed define a cognitive map as a graphical representation. Most include some aspect of subjectivity. A graphical representation is fundamental to the idea of concept mapping. In one of the earliest references, Axelrod developed a system for representing causal relationships in social science domains. The system represented concepts in sociology and political science as nodes in a directed graph. The nodes were connected by arrows that were assigned to represent positive or negative causal relationships. The other common factor in the definition of cognitive mapping is the subjectivity of the map. Irvine describes concept mapping as the individual’s diagrammatic interpretation of ideas. The definition from Park and Kim concisely encapsulates many of these definitions. The cognitive map graphically represents interrelationships among a variety of factors. It is a representation of the perceptions and beliefs of a decision maker or expert about his/her own subjective world, rather than objective reality. Since the development of the graphical system at Cornell, there have been several studies conducted on the efficacy of using concept maps as teaching and learning tools. For example, Jegede, Alaiyemola, and Okebukola report that students in Nigeria who used concept maps documented significantly higher mean scores on an achievement test for the subject matter studied. Other studies have concentrated on the use of concept mapping in teacher education. Novak notes that most science teachers understand science to be a large body of information to be mastered, as opposed to a method for constructing new knowledge. Novak reports that concept mapping plays an important role in facilitating the change of science teachers’ perception of science and the purpose of science education. There are several factors that contribute to the power of cognitive mapping in learning. Johnson, Goldsmith, and Teague describe two values categories of cognitive maps: the stimulus value and the structural advantage. The stimulus value is inherent in the graphical representation. Learners can easily see the global organization of the represented concepts. Graphical representations also allow for the organization of complex domains for learners and designers alike. The network structure of a concept map allows the simultaneous display of all the important relationships. The structural advantage is relative to the assessment of pair-wise ratings. Johnson, Goldsmith, and Teague report that assessing network representations of student understanding resulted in more valid measures than assessing pair-wise comparisons of concepts Thagard proposes a system of conceptual change based on his historical research of scientific revolutions. The system delineates five levels of conceptual change, ranging from the simple to the complex:

1. Addition of concepts.

2. Deletion of concepts.

3. Simple reorganization of concepts in the kind-hierarchy or part-hierarchy which results in new kind-relations and part-relations.

4. Revisionary reorganization of concepts in the hierarchies, in which old kind-relations or part-relations are replaced by different ones.

5. Hierarchy reinterpretation, in which the nature of the kind-relation or part-relation that constitutes a hierarchy changes.

Kind-relations define members of a concept (i.e., a whale is a kind of mammal), while part-relations define the characteristics of a member (i.e. whales have flippers.) Concept maps make visible the potential for conceptual change within a learner. When created correctly and thoroughly, concept mapping is a powerful way for students to reach high levels of cognitive performance. A concept map is also not just a learning tool, but an ideal evaluation tool for educators measuring the growth of and assessing student learning. As students create concept maps, they reiterate ideas using their own words and help identify incorrect ideas and concepts; educators are able to see what students do not understand, providing an accurate, objective way to evaluate areas in which students do not yet grasp concepts fully. Pressley and McCormick’s review of the literature on multiple representations in science revealed a common process for developing concept maps. This process is briefly outlined:

1. Key words and phrases are identified from the reading.

2. Key concepts are ordered from the most general to the most specific.

3. The concepts are then clustered using two criteria. Concepts that interrelate are grouped; concepts are classified with respect to their level of abstraction (i.e. general concepts to specific ones). All of the concepts are then arranged loosely in a two-dimensional array with abstractness defining one dimension and main ideas defining the second dimension.

4. Related concepts are then linked with lines, which are labelled to specify the relationship between concepts.

Thus, a map of the domain is created, with key ideas linked by nodes that describe the type of relationship between them (see Fig.1). The map, however, is static. It represents the crystallized, declarative knowledge of a domain. The map can be used to understand some forms of the conceptual relationships. Cognitive maps represent formal, bivalent (true or false) logical relationships. This is acceptable in ‘‘hard’’

knowledge domains like physics or mathematics where the nature of the knowledge in the domain is usually binary. In most domains, however, the knowledge base is uncertain, or fuzzy. Social studies (e.g., politics, international relations), management science, and the study of art or literature are all ‘‘soft’’ knowledge domains, where uncertainty and degrees of truth are more common. Thus, the true nature of these domains cannot be understood from bivalent, or true-false, concept maps.

Cognitive maps also represent a form of distributed intelligence. It serves the construction and accumulation of spatial knowledge, allowing the "mind's eye" to visualize images in order to reduce cognitive load, enhance recall and learning of information. This type of spatial thinking can also be used as a metaphor for non-spatial tasks, where people performing non-spatial tasks involving memory and imaging use spatial knowledge to aid in processing the task and avoid error. FCM’s are especially applicable to soft knowledge domains. several example FCM’s are given below.

Figure1. Fuzzy cognitive map.

Figure 2. FCM

IV. FUZZY COGNITIVE MAPS

Fuzzy cognitive maps (FCMs) and concept maps have similar applications in education. There are two important differences between traditional concept maps and FCMs: fuzzy logic and feedback. Uncertain or soft knowledge domains can be represented with fuzzy logic. Kosko originally developed FCMs to represent concepts in military science, but his since gone on to demonstrate their usefulness in representing arguments in sociology and political science. Figure 1 illustrates the structure of the FCM. The map consists

of nodes connected by "edges" or links. Nodes represent factors such as "temperature" or "on-ramp congestion." Unlike

Bart Kosko's original FCM design, the software and assignments I present assume the nodes have three potential states (positive, negative, and neutral) instead of two states, since three produces more interesting behaviour. The links between nodes represent promoting/inhibiting relationships. For example, a link between two nodes might represent a relationship like, "as soot in the atmosphere increases then temperature often decreases." Since several nodes may interact, the link weights are "stereotypical" or "fuzzy" in terms of their effects on other nodes. Thus, words like "often" and "typically" and "inhibits" are more accurate than "cause" or "affect". Fuzzy logic is a system for representing uncertainty, or possibility. The formal extension of the original possibility theory created in the 1920s by Lukasiewicz was developed by Zadeh. A generalization of traditional, bivalent, Aristotelian logic, fuzzy logic creates a system for mathematically representing systems with natural linguistic variables (e.g., tall, little). Traditional binary logic requires that a statement must be either true or false. An animal can be a cat or it is not a cat. The world of traditional logic is black or white. This is known as the law of the excluded middle: there is no option between 100% true and 100% false see Fig. 3. The problem with a binary system is that it does not allow for accurate representations of the way humans perceive and represent their world. Formal representations of human knowledge, like those described in the previous section, typically rely on binary logic.

Figure 3. Binary logic representation.

Fuzzy logic ignores the law of the excluded middle and allows for representations that are both true and false.1 Terms like age, height, and intelligence are fuzzy variables; they have no exact definition and allow for degrees of membership. Fuzzy variables, or linguistic variables, have fuzzy values (e.g., young, short, bright).

A fuzzy set is a generalization of an ordinary set by allowing a degree of membership for each element. A membership degree is a real number on [0, 1]. In extreme cases if the degree of membership is 0 the element does not belong, and if 1 the element belongs 100% to the set.

Thus, a person might be considered tall to a 0.9 degree if they were 6’5” tall. Membership value is determined by a function that represents changing membership as a value changes (see Fig. 4)

V. CAUSALITY AND FCMS

Like traditional causal concept maps, FCMs have nodes that represent variable concepts. The links between the concepts are signed police vigilance or theft to represent the nature of the relationship between nodes. Fuzzy logic allows the representation of fuzzy concepts and degree of causality. Feedback allows the user to explore the hidden properties of the map. By creating a formal representation of causality, FCMs can be used to create and explore models of dynamic events and search for causal explanation.

The most important difference between the concept maps which Pressley and McCormick describe and FCMs is the temporal and causal nature of the FCM. FCMs express causality over time and allow for causality effects to fluctuate as input values change. Nonlinear feedback can only be modelled in a time-based system. FCMs are intended to model causality, not merely semantic relationships between concepts. The latter relationships are more appropriately represented with semantic networking tools like Sem Net. By modelling causality over time, FCMs facilitate the exploration of the implications of complex conceptual models, as well as representing them with greater flexibility.

There is evidence to suggest that temporal mapping may improve learning. Lambiotte et report that providing students with maps of procedures or processes, rather than semantic conceptual maps, improved learning; especially for lower ability students. FCMs are a tool for representing a dynamic process and modelling the process in real-time.

Figure 4. Fuzzy and crisps sets of “Tall” people

VI. IMPLEMENTATION

Representing and manipulating FCMs mathematically is not difficult. A given FCM with Cn. concepts can be represented in an N*N matrix. Causality is represented by some nonlinear usually sigmoidal. edge function Ci ,Cj ., which describes the degree to which Ci causes Cj. The edge function occurs over the bipolar interval wy1, 1x, as edges can be inhibitory or excitory. Using the notion of disconcepts; C., the unit interval [0, 1] can be retained. Thus, what results is a matrix with causality between concepts represented by some real number between 1 and 0. A row, i, represents the causality between concept i and all other concepts in the map. No concept is assumed to cause itself, thus the diagonal is zeroed. See Table I for an example of a simple FCM matrix. Traditional cognitive mapping relies on a crisp valued edge function of [1, 0] or [1,-1]. Thus, when an expert creates a concept map, they sign the edges as either positive or negative. FCM representation create a weighted edge function over [1, 0] or [1,-1]. Fuzziness, therefore, allows the developer to capture more fine grain information about the representation. It is possible to ask the expert to assign a real number weight to the edge, but this is difficult and usually unnecessary. Instead, an expert can use linguistic modifiers, which are then converted into fuzzy functions.

Fuzzy inputs can be processed systematically via fuzzy causal algebra. Expert systems development typically involves only one expert due to difficulties with maintaining a tree structure and search limitations. Kosko has developed a mathematical method for combining the FCMs of multiple experts to represent a ‘‘field’’ view. Imagine combining FCMs from educational experts all over the world; creating a giant FCM for generating curriculum and research. FCMs can be combined by representing them as matrices. Each expert’s map is represented as a matrix the size of the total number of unique concepts presented by all of the experts. The matrices are then added together, with the common links naturally achieving more weight. For example, Figure 5 represents an individual’s FCM generated during a pilot test of the group process described later. The corresponding matrix is presented in Table II. The addition of a second participant see the matrix in Table III. is represented in Figure 6.

In this example, both participants used the same concepts, as those were generated in a group session.

Figure5. FCM of the use of distance education for participant #2.

Figure 6. FCM of the use of distance education for participants #2 and #3 .

The resulting additive matrix is found in Table III. Notice that the links representing agreement have larger values, and the areas of disjunction or disagreement have smaller values. If there are k experts, and only one expert includes a given edge, the maximum value for that edge is 1/k. The results can then be normalized averaged. over [0, 1] or [-1, 1].Graphics may be full colour. All colours will be retained on the CDROM. Graphics must not use stipple fill patterns because they may not be reproduced properly. Please use only SOLID FILL colours which contrast well both on screen and on a black-and-white hardcopy, as shown in Fig. 1.

In this system, if two experts perfectly disagree, they cancel each other out i.e., if expert A says that edge is 1 and expert B says that ei is y1, the resulting edge equals 0.. Large sample

sizes tend to produce stable connection strengths.22 Representations of the knowledge of multiple experts has long

been a goal in expert systems development. By creating fuzzy knowledge structures, FCMs finally allow us to achieve this goal. Indeed, if large sample sizes produce stable connection strengths, then the more experts, the better. An initial input activates the matrix. The initial state of the concepts is entered as a fuzzy vector. Inference proceeds by nonlinear spreading activation. The initial activation is allowed to reverberate through the system until it converges on a limit cycle. See Table IV. The limit cycle may be a point solution, a cyclical attractor, or a chaotic strange attractor. In other words, the output may be a steady state (A, A, A, . . . ), a cycle (A, B,C, A, B, C, . . . ), or a chaotic attractor ( A, C, B, D, B, A, D, C, . . . .) Figure 7 presents a graph of the output from one FCM. The transition to the limit cycle is evident as each line straightens and the system converges on the limit cycle. Since FCMs are machine moveable and dynamic, they are also machine tuneable. As the FCM is run through multiple what-if scenarios, adjustments to the nodes and edges can be made to gradually force the map to fit an expected output. Kosko proposes a system of ‘‘adaptive inference through concomitant variation.’’ Instead of using simple Hebbian neural learning algorithms to tune the model.

Hebb's principle can be described as a method of determining how to alter the weights between model neurons. The weight between two neurons increases if the two neurons activate simultaneously—and reduces if they activate separately. Nodes that tend to be either both positive or both negative at the same time have strong positive weights, while those that tend to be opposite have strong negative weights. Kosko uses a differential Hebbian learning law that measures changes in the environmental parameters. A simple Hebbian law would be something like:

where ;Xi: is the activation level of some node i, yXi: is the passive causal decay parameter, Cj: is a sigmoid function, +j Cj ( Xj) eji: is the path-weighted internal feedback, Ii: is the external output.

A differential Hebbian learning law, on the other hand, is represented by:

where ejisthe edge function between concept Ci and Cj Fig. 1.

Figure 7. Graph of an activated matrix converging on a limit cycle.

Simulations show that while the simple Hebbian learning law which correlates activations to output. Produces ‘‘spurious causal conjectures,’’25 the differential Hebbian law causes eji to converge on an exponentially weighted average of correlated change. Using such techniques, FCMs can be gradually improved over time. Nonsalient improvements that would probably have been missed by a human observer mat be added to the map as it becomes more and more accurate.

VII.ADVANTAGES

The previous sections have explored several differences between the concept mapping process described by Pressley and McCormick and the FCM methodologies described in this paper. First, concepts in an FCM are not arrayed according to abstractness or centrality of the idea. The centrality of an idea can be naturally determined after the map has been completed. Centrality becomes a function of the number of links to and from a given node and the weight of those links. The abstractness of an idea can be interpreted as a function of its fuzziness. The more abstract an idea, the more fuzzy subsets it contains. Hierarchical conceptual relationships can be embedded within a FCM node. The node then becomes an embedded FCM within the larger framework. The resulting signal strength from the node is a function of the embedded processing. These features offer several advantages to FCMs over traditional mapping methods. FCMs have these specific advantageous characteristics:

· FCMs capture more information in the relationship between concepts.

· FCMs are dynamic.

· FCMs express hidden relationships.

· FCMs are combinable.

· FCMs are tunable.

FCMs could also facilitate data collection for machine monitoring of student performance. Wallace and Mintzes report that traditional concept mapping techniques are very sensitive to changes in students’ conceptual frameworks. Further research will demonstrate whether this holds for FCMs as well. The FCM framework, however, gives developers the power to begin to compare student FCMs with expert FCMs. Two simple comparisons are measuring the centrality of a given node, and the effect, both direct and indirect, one node has on another. Kosko has developed several methods for categorizing the importance and centrality of nodes within an FCM. A most simple measure is to sum the weights of the edges coming into and leading out of a given node.

VIII. FCMs IN PRACTICE

Potential applications of FCMs are very broad. For the purposes of this paper, we will investigate two categories of potential applications. First, FCMs may be used in an organisational context to promote investigation by participants of their individual, deeply held assumptions, and as a tool for facilitating the adoption of new innovations. Second, FCMs have potential application in intelligent tutoring systems. FCMs in an Organisational Context The power of FCMs to reveal hidden patterns in complex conceptual maps can be exploited to promote institutional learning. Arie De Geuss, former planner for Shell Oil, defines institutional learning as changing shared mental models. Developing shared representations of current and future mental models is a complex task. Strategic decision making environments are complex, unstructured, and not readily quantifiable. Cognitive maps have been used as a decision making tool in international relations, administrative science, management science, and operations research. FCMs can be used to make the mental maps of management teams, and others, visible. When the implicitly held assumptions of the participants are laid bare, the process of exploring and changing mental models may be facilitated. Long-term change comes from building the models and participating in the

process. Planning and decision making can also be facilitated as the models are tuned and adjusted over time. FCMs may not be the ‘‘magic bullet’’ for organizational learning, but the technique definitely shows promise as a tool for investigating organizational paradigms and promoting organizational learning

IX. Conclusions

This paper has discussed the potential usefulness of fuzzy cognitive mapping in educational organization settings. The development of graphical tools to facilitate conceptual change is an important endeavor for educational technologists and facilitators of systemic change. By combining the capability of fuzzy logic to represent soft knowledge domains with dynamic modeling capabilities, the FCM framework has tremendous potential for contribution to the development of useful cognitive tools. FCMs are an extension of earlier concept mapping paradigms, yet they represent a significant advance over earlier, bivalent, static mapping systems. There is much research to be done on the application of FCMs to education and instructional technology. It is heartening to know that there are a growing number of traditional engineering disciplines that are using FCMs to capture and represent expert knowledge resulting in the use of that knowledge in productive and meaningful ways. This paper will hopefully stimulate educational researchers to recognize the unique applicability of fuzzy logic to our field. As our understanding of the complexity of human learning increases, we must embrace new ways of describing, facilitating, and supporting that learning. Fuzzy cognitive maps represent one step in that direction.

References

[1] Kosko B. Fuzzy thinking. Hyperion Press: New York; 1993.

[2] Jason R. Cole, Kay A. Persichitte, University of Northern Colorado,

[3] Kosko B. Fuzzy cognitive maps. Int J Man-Mach Stud 1986;24:65]75.

[4] Gardner H. The unschooled mind: How children learn and how schools should teach.

[5] New York: Harper Collins; 1991.

[6] Holland JH, Holyoak KJ, Nisbett RE, Thagard PR. Induction: Processes of inference,

[7] learning and discovery. Cambridge, MA: MIT Press; 1986.

[8] Thagard P. Conceptual revolutions. Princeton, NJ: Princeton Univ. Press; 1992.

[9] Berry D, Dienes Z. Implicit learning: Theoretical and empirical issues. Earlbaum, East Sussex; 1993.

[10] Novak J. Concept mapping: A useful tool for science education, J Res Sci Teaching 1990;27 10.:937]949.

[11] Axelrod R. Structure of decision. Princeton, NJ: Princeton Univ Press; 1976.

[12] Irvine LMC. Can concept mapping be used to promote meaningful learning in nurse education? J Advanced Nursing 1995;21:1167]1178.

[13] Park K, Kim S. Fuzzy cognitive maps considering time relationships. Int J Human-Comput Stud 1995;42:157]168. . Park K, Kim S. Int J Human-Comput Stud 1995;11:158. . Jegede R, Alaiyemola T,

Call for EOI

2014-09-08T00:47:00.002-07:00

Anybody, who is interested to carry and cotribute is most welcome.

SAHYADRI PREMIER LEAGUE

2011-09-08T08:36:00.000-07:00

AEnCES Placement Desk : Virtual Assistant

2011-08-27T17:42:00.000-07:00

AEnCES Placement Desk

Virtual Assistant

Job Code

VA1

Job Mode

Permanent

Vacancies

Gender M/F

yes/yes

Age

18 - 25 Years

Experience

0 - 1 Years

Required Educational Qualification/s

Education Level	Stream	Subject
Degree	-

Skills

Sound communication, Good computer skills, MS Word, MS Excel, MS Office, MS Powerpoint

Career Level

Fresher / Entry Level

Salary Offered

INR 50000 to 80000 [ Annual Salary]

Job Location/s

City	State	Country
Udupi	Karnataka	India

Short Description

The work profile includes but is not limited to working as Virtual Assistance to our clients in USA.A Virtual Assistant provides services which could be secretarial, administrative, creative, or technical services to small or medium sized businesses externally. They usually use online communication channels, such as phone, e-mail, fax, to deliver their services to clients thus operating as Online Virtual Assistants.

Interested candidates can mail their cvs to mytonse@aences.com

Charges of Rs 2000 per student if enrolled.

AEnCES Placement Desk

2011-08-16T22:55:00.000-07:00

JOB Requirement :

Web Designer

Skills required:

ASP,Silverlight and knowledge of web site development

Salary offered : 5k-6k riyals

Location : Dammam

Indian visas are available.

Interested candidates can send their cvs to mytonse@sahyadri.edu.in

Selected CVS will have to pay Rs 2000 as enrollment fee.

[Event Summary] - AEnCES Staff vs Students Football Match

2011-04-01T07:08:00.000-07:00

AEnCES successfully conducted a STAFF VS STUDENTS football match.The same was latter changed to Vishwanath's Seniors VS TONSE's Juniors

The first two goals was scored by Yunus Tonse for TONSE's Juniors.The first goal for Vishwanath;s Seniors was scored by Ashwath Rao.Oniel Keith smashed a 0 goal the next second.

Vishwanath's Seniors won by 1 goal punched in by Prathapchandra at the last second.The final Score card read 3-2.

Vishwanath's Seniors :

Ashwath Rao (C)

Prathapchandra

Prashant Barla

Sachin Prabhu

Vishwanath S.

Mustaq M.

Ashoka CD

Oniel Keith

Santhosh S.

Hareesh T.

Subash G.

TONSE's Juniors:

Yunus Tonse (C)

Prashant Acharya

Sharath Shetty

Nabeel

Siddharth

Sanath

Suraj

[Photography Club] - Photo Exhibition 1

2011-03-29T09:52:00.000-07:00

Adil Abu Atif , a hobbyist photographer sent some PCB images highlighting in on the particular components .

Must See !

All above images are copyrighted to AEnCES and is the orignal creation of the above mentioned for AEnCES.

[Event Summary] - Aptide

2011-03-29T09:20:00.000-07:00

An Aptitude Test for all ECE students was conducted on .All eagerly participated in the test.The test was conducted for 45 minutes. It consisted of questions based on quatitative aptitude, non-verbal aptitude, logical reasoning and many other aspects. These questions tested the skills and thinking capacity of all the students.The aim was to make the students ready for the campus placement process so that they can pass out the various tests conducted by different companies during aptitude round.The meritious ones were rightly awarded

The test was organised and conducted by the Placement Club of AEnCES.

Mr. Ashwath Rao (Staff Coordinator) and Yunus Tonse(Vice-President) took rounds to supervise the conduction.

[Event Summary] - TECHNICITE

2011-03-29T09:00:00.000-07:00

TECHNISITE: An intradepartmental paper presentation competeition was organized by AEnCES on the 28^th of march 2011.This event was presided over by the head of the department Dr H.M.Ravikumar who addressed the gathering with an insightful talk about the importance of such events that would boost the self confidence levels in students and simultaneously help them understand new topics.

Students of 2^nd 3^rd and 4^th year electronics and communication took part with great zeal and enthusiasm and their presentation were judged by faculty members Mr. Ashwath Rao and Mr Kiran Kumar . Question answer round followed and this made the session more informative.

[Event Summary ] - Blood Donation Drive '11

2011-03-26T01:51:00.000-07:00

AEnCES & BELAKU in coordination with the Health Club of Sahyadri successfully completed the
Blood Donation Drive .

350 registrations with 100 pints of blood collected .

A Blood Donation Drive was conducted by AEnCES in a association with Belaku and

Government Wenlock hospital on 25^th of march in sahayadri college Campus

Dr Suresh Shetty, Chairperson of Belaku and Assistant Professor at KMC,

Dr Preetham Salian Vice Chairperson of Belaku and assistant professor of Orthopedics,

Dr Archit Boloor Member of Belaku an Assistant Professor of Medicine at KMC were Honorary Guests at the event.

The function was presided over by our Dr A.J.Antony , Vice Principal ,SCEM

Dr.Ravikumar H.M , HOD - ECE,SCEM and Prof. Manoj Kumar.

[Event Summary] - AEnCES Staff vs Students Cricket Match

2011-03-18T19:50:00.000-07:00

The event completed succesfully.2 matches were played .Tonse represented the students while Manoj Kumar Sir stood as the playing captain for the other side.First match started with ten overs with the Lects team batting first.

To be continued..

2011-03-18T09:22:00.000-07:00

Legends of Holi are as colourful as Holi itself. There are myriad shades and tones in them from the subtle ones of love and devotion to the darker hues pertaining to demons and their devilish desires.

What is remarkable is the faith of millions of Hindus in these legends. They relive these stories every year and bring to life the incidents which occurred (or might not have occurred) thousands of years ago.

A belief in the legends, devotion to god and a strong belief in the fact that it is the good and the truth that ultimately prevails over the evil pulls the people to religiously follow the set traditions each year.
And, this faith - in god and the ancient traditions is what still binds the people in a spirit of love and harmony in this otherwise divided society

Most Popular Legends of Holi

The Legend of Holika and Prahlad: Legend goes back to the time of demon king Hiranyakashyap who planned to kill his son Prahlad for he worshiped Lord Naarayana...more
The Legend of Radha and Krishna: Lord Krishna as a young child was jealous of his beloved Radha's extremely fair complexion as he himself was very dark. He therefore...more
The Legend of Kaamadeva: The legend narrates the sacrifice of Kaamadeva for the benefit of the world. The god of love shot his arrow on Lord Shiva to bring him... more
The Legend of Dhundhi: There lived an ogress called Dhundhi in the kingdom of Prithu who used to trouble children till they chased her away by playing pranks on her... more
The Legend of Pootana: Lord Krishna's devil uncle, Kansa seeked the help of ogress Pootana to kill the infant Krishna. Pootana treacherously fed Krishna with her poisoned... more

Courtesy :http://www.holifestival.org/legends-of-holi.html

[Seminar Assistance]-Topic List 1

2011-03-01T21:36:00.000-08:00

A HYBRID LARGE VOCABULARY HANDWRITTEN WORD RECOGNITION SYSTEM USING NEURAL NETWORKS WITH HIDDEN MARKOV MODELS

A NEW PARAMETER FOR UWB INDOOR CHANNEL PROFILE IDENTIFICATION

A NORMALIZATION FRAMEWORK FOR MULTIMEDIA DATABASES

A NOVEL VESSEL SEGMENTATION ALGORITHM FOR PATHOLOGICAL RETINA IMAGES BASED ON THE DIVERGENCE OF VECTOR FIELDS

A REAL-TIME ADAPTIVE LEARNING METHOD FOR DRIVER EYE DETECTION

A ZERO VOLTAGE SWITCHING SINGLE-PHASE INVERTER USING HYBRID PWM TECHNIQUE

ACOUSTIC ECHO CANCELLATION

ACTIVE NOISE CANCELLATION WITH A FUZZY ADAPTIVE FILTERED-X ALGORITHM

ADAPTIVE EQUALIZER

ADAPTIVE HISTOGRAM EQUALIZATION

APPLICATION OF SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM FOR IMPROVED BLOOD CELL RECOGNITION

APPROACHES TO SELF-ORGANIZING NEURAL NETWORKS

ARTIFICAL NEURAL NETWORK

AUDIO SIGNAL PROCESSING

AUTOMATIC RECOGNITION OF EXUDATIVE MACULOPATHY USING FUZZY CMEANS CLUSTERING AND NEURAL NETWORKS

BACK PROPAGATION TRAINING AND LOCAL MINIMA

BUTTERWORTH LOWPASS FILTER

CALL ADMISSION CONTROL OPTIMIZATION IN WIMAX NETWORKS

CODE SHIFT KEYING IMPULSE MODULATION FOR UWB COMMUNICATIONS

COLORIZATION USING OPTIMIZATION

COMBINED QRD-M AND DFE DETECTION TECHNIQUE FOR SIMPLE AND EFFICIENT SIGNAL DETECTION IN

MIMO-OFDM SYSTEMS

COMPARATIVE STUDY BETWEEN WAVELET AND CONTOURLET TRANSFORM FEATURES FOR TEXTURAL IMAGE CLASSIFICATION

COMPUTER-AIDED SHAPE ANALYSIS AND CLASSIFICATION OF WELD DEFECTS IN INDUSTRIAL RADIOGRAPHY BASED INVARIANT ATTRIBUTES AND NEURAL NETWORKS

CONTENTION-BASED QOS MAC MECHANISMS FOR VBR VOIP IN IEEE 802.11E WIRELESS LANS

CONTOURLET TRANSFORM FOR TEXTURAL IMAGE CLASSIFICATION

CRYPTOGRAPHY IN COMMUNICATION

DATA CLUSTERING WITH GRAPH THEORY

Data Extraction Mechanism for Mining Association Rule

DESIGN AND ANALYSIS OF BIT INTERLEAVED CODED SPACE-TIME MODULATION

DETECTING HEAD ORIENTATION

DETECTION OF DIGITAL FORGERIES USING AN IMAGE INTERPOLATION FROM DIGITAL IMAGES

DIGITAL IMAGE INTERPOLATION

DOUBLY FED INDUCTION GENERATOR USING BACK-TO-BACK PWM CONVERTERS

DYNAMIC CODE ACQUISITION

ENHANCED LBG ALGORITHM USING NEURAL NETWORKS

ESTIMATION AND DIRECT EQUALIZATION OF DOUBLY SELECTIVE CHANNELS

FINGERPRINTS AUTHENTICATION

FPGA Prototyping of a Digital Camera for Image Security and Authentication

FSK-CDMA NETWORK

GAUSSIAN LOW PASS FILTER

GENETIC ALGORITHMS FOR SIMULATION OPTIMIZATION

GRID POWER QUALITY WITH VARIABLE SPEED WIND TURBINES

HIERARCHICAL CONTOUR MATCHING FOR DENTAL X-RAY RADIOGRAPHS

HISTOGRAM EQUALIZATION

HMM BASED AUTOMATIC LIPREADING

IMAGE COMPRESSION USING WAVELET TRANSFORM

IMAGE COMPRESSION

IMAGE FEATURE EXTRACTION

IMAGE MOSAICING

[Placement Assistance] - Xchanging @ 3rd March

2011-03-01T20:50:00.000-08:00

Xchanging is one of the largest and fastest growing business processors. With a wide range of multinational customers in 42 countries and employing over 8,000 people, we are a truly global company. Our aim is simply to provide business processing services better, cheaper and faster.

Xchanging provides procurement, accounting, human resources and technology services across industries. These include banking, insurance, manufacturing, retail and real estate among others. We combine functional expertise with deep industry domain knowledge to provide industry-specific outcomes for our customers.

Listed on the London Stock Exchange in 2007, the company is in the FTSE250, the index of mid-capitalised companies traded on the London Stock Exchange. Xchanging is also a member of the FTSE4Good index which measures the performance of companies that meet globally recognised corporate responsibility standards.

Specialties

Outsourcing for HR, Procurement, Accounting, IT, Hosting, Claims processing

Salary Range: 1.14-1.44 Lakhs per annum

Eligibility: 55% and above in BE

website www.xchanging.com

[Placement Assistance] - Cruiseline Ship Management

2011-02-18T18:57:00.000-08:00

Cruiseline Ship Management Pvt. Ltd.

Details of ETO ( Electro Technical Officer )

The students will be recruited for ETO & they will get the pre sponsorship letter by the company. They will have to undergo a 4 months training along with STCW (Standard Training Certification & Watch keeping) training to develop the required skills to work on ship & to get the Indian CDC (Continuous Discharge Certificate). This unique sector-specific training focuses on the need of appropriate training in the areas of Electrical & Electronics. After Pre Sea training candidate will join as a Trainee Electrical Officer on board ship.

The flow chart of career path:

Salary:

Onboard Training:- Up to $900/-

After Training:- Up to $2,000/-

Eligibility:

1) B.E. - Electrical/Electronics (2011 pass outs, 55% aggregate throughout)

2) Diploma - Electrical/Electronics (2011 pass outs- 55% aggregate throughout)

Medical Fitness: Medically fit with eyesight 6/6, maximum power permitted (+) 2.5, and no color blindness. All candidates have to pass complete medical examination and mandatory tests prior to joining at the academy. Cruiseline ship management services have panel of qualified Doctors & requirements as per ILO convention 147 Seafarer’s Medical Examination.

Selection Procedure: Cruiseline Ship Management Pvt. Ltd. Undertake written test followed by an interview. The final selection will only takes place after medical check-up through our panel Doctor.

Written Test Pattern : (Duration: 30 min.)

Aptitude : 10 Qs.

Technical : 10 Qs.

G. K. : 5 Qs.

English : 5 Qs.

[Project Club]- Seminar Tips

2011-02-18T18:54:00.000-08:00

Presentation skills can make or break a presentation! Your seminar grade is based on the quality of your presentation, and great presentation skills can make even a project you wish you’d done differently into a first-rate seminar. Below are some tips that will help you avoid common pitfalls and create a sparkling presentation.
Organization

Make the “big picture” clear. In the first few minutes of your talk, your audience–even those that know nothing about your research area–should have a very clear understanding of the big question your research addresses, your specific objectives and hypotheses, how you will accomplish your objectives and the significance of your research. A lengthy introduction before you get to your research question leaves your audience feeling unsure of what they will need to remember.
“Package” each experiment. A rigid intro-methods-results-discussion format is required for a scientific paper, but a presentation needs to be more flexible! In a presentation, your audience can’t flip back to a previous page, so you have to organize your presentation to help them. If you are presenting several experiments, a good format is to give some general background, then introduce the purpose of each experiment, how the experiment was done, the results and their meaning. Once you’ve delivered this “package” to your audience, go on to the next experiment. At the end, summarize the results of each experiment again before wrapping up with general conclusions.

Show me the data! Don’t have one slide where you show the results and then a text slide where you talk about them. The audience wants to see what you’re talking about, so put a little text right on the result slide, or leave out the text completely and just show the results as you talk about them. Spend enough time on the actual graphs, gels, spectra or other data that the audience can clearly see how your results lead to your conclusions. Take the audience through the data step-by-step. Where appropriate, talk about how many times you’ve repeated the experiment or show standard deviations, t-tests or other statistics to provide support for your conclusions.
Keep methods brief. Listeners can easily get lost in a long list of methods. Instead, list only the key steps of the method and tellwhy each step was important. Anyone who wants to know the details can ask.
Give enough background. Anyone in your audience who has taken 200-level courses in your major should be able to understand your entire talk clearly. And anyone in your audience should be able to understand the significance of your research, the hypotheses you were investigating and the take-home message.
Be persuasive. As you design your presentation, ask yourself what you want to convince your audience of. A good scientific presentation should build a case for whatever conclusion you want your audience to believe. Don’t worry about keeping them in suspense–it’s perfectly OK to tell them the conclusion up front, then build up the evidence in support of it piece by piece.
Less is more. Your audience does not have to hear about every experiment you ever did, including the one you totally screwed up. Focus your talk on clearly presenting a limited number of ideas and experiments that really make your point.
Give credit where it’s due. All scientific research builds on and connects with the work of others. Tell how your work fits into the context of what is already known. Show how others’ work leads to or supports your hypotheses, or where your results might disagree with others’. Demonstrate that you have a good grasp of the scientific literature in your field. And of course give appropriate credit! Often, students will put a bibliography slide at the end, but it’s unlikely that the audience will get much out of this, so it might be more useful to put a condensed reference in small type at the bottom of the slide where the information is given–something likeSmith et al., J. Biol. Chem. 53:11417 (2002).

Speaking style

Know your stuff. To engage your audience, you have to be making eye contact and talking directly to them. If you’re reading directly from your slides or relying heavily on your notes, your audience will get bored and think you don’t know your material well. Practice your talk until you know it well, so you won’t stumble or wonder what slide is coming next. Then you can look at your audience when you deliver it. Knowing your material well will also help you get over any nervousness.
But, notes are OK. There’s nothing wrong with having some notes to refer to in case you get stuck, or a list of slides, etc. Just don’t use ‘em as a crutch. It’s a very good idea to write down any details you think you might forget: a chemical structure that someone might ask a question about, or a long chemical name that you might blank on.
Be natural. It’s a seminar, not a campaign speech. The people in the audience are your colleagues, so talk to them as a fellow scientist explaining what you think are important results that need to be shared.
Be enthusiastic. Hey, this is great research you spent all summer on! If you sound bored with it, for sure your audience will be, too. Show them with your voice and manner how excited you are about the work you did.
Make yourself heard. It could be the greatest presentation on earth, but your audience will never know that if they can’t hear you. If you tend to speak quietly, practice with a friend sitting in the back row and have him or her stop you every time you’re not loud enough. Breathing from your diaphragm and pitching your voice a little lower than normal can help.
Know the vocabulary. If you mis-pronounce a key term, your audience automatically assumes you don’t really know your material. Be sure you know how every term is pronounced! And what they mean–someone may ask you.
Practice your talk. Don’t just prepare the slides: prepare yourself. Go through the whole talk and figure out how you’re going to say what you need to say. Where will you need to spend the most time? Where would an example be helpful? In addition to practicing on your own, it is helpful to practice in front of friends and your research mentor.
Dress nicely. A suit and tie or dressy dress isn’t necessary, but cutoffs and a torn shirt don’t make much of an impression. Your dress helps let your audicence know you’re in charge.
Avoid jargon. Scientists use lots of lab slang, but in a formal presentation, you need to be sure you’re using terms precisely and that your audience understands them. Don’t say “I Geneclean-ed the DNA,” say “I purified this DNA fragment from my gel using the Geneclean kit.” Instead of “we PCR’d up the gene,” try “we used PCR to amplify the lacZ gene from the E. coli chromosome.”

Visuals

Keep it simple. Flashy backrounds or fancy animations don’t work well in a formal presentation. Use simple clear fonts and plain backgrounds. Black or dark blue on white, light gray or light tan or else white on dark blue work best. Avoid glaring or clashy colors. Use animation only if needed for emphasis, not for entertainment.
Minimize text. If there are a lot of words on your slide, your audience will spend its time reading them instead of listening to what you’re saying. Plus, you will be tempted to read them instead of making eye contact. Just a few key words will help your audience get the message without distracting them.

Who’s going to read all this text??

Much easier for the audience to grasp!

Use pictures. Wouldn’t it be easier to explain that complicated experiment if you had a diagram? Plus, photos and drawings add life to your presentation. You can take photos (ask to borrow a digital camera), find appropriate images on the Web (try Google image search) or just draw your own–remember, PowerPoint is also a drawing program!
Be sure text is clearly readable even from the back of the room.
Portray results appropriately. No one wants to squint at tiny numbers in a table when a nice, visual graph would show them better. Consider whether a bar graph is appropriate for your data, or whether a line graph is called for. Label all graphs and axes, and add titles to graphs and captions to photos where appropriate. Don’t forget units! Add labels or arrows to a photo or NMR spectrum to help make your point.
Don’t accidentally “squash” photos or graphs. When you resize an illustration, be sure you shrink or stretch both its width and height at the same time. Your audience will notice if you flatten or stretch it by changing its size only in one dimension.

Original image

Squashed

Resized
proportionately

Proofread carefully. Use the spell checker built into PowerPoint, but also read through every slide carefully before the presentation. You don’t want a typographical error to appear in 48-point bold text for your audience to focus on!
Make your visuals look professional. If you need to show a chemical structure, use ISIS Draw. In an Excel graph, change colors, fonts, sizes, backgrounds, etc. to make your graph as clear as possible and remove unnecessary legends, equations, etc. Learn how to insert subscripts, superscripts, greek letters, and so on. Use standard scientific notation: 1.3 x 10⁴ looks a lot better than the computer shorthand 1.3E04. Watch your signficant figures, and don’t let your figures get cluttered with numbers like 147.021341112.

PowerPoint tips

Test your presentation in the seminar room at least the day before you give it! Be sure your images look right, your text shows up, any videos or animations work, your fonts look right, etc. Be sure to go through the actual slides so you don’t get surprised by animated text you didn’t realize was animated, etc.
Keep your file size small. PowerPoint presentations with lots of images get big fast. To keep your file to a reasonable size (so it loads and saves quickly and can be e-mailed if necessary), reduce the size of your images. An image that is 1000 pixels wide at a resolution of 96 dpi will fill a PowerPoint slide (and higher resolutions are not needed unless you need high-quality print-outs of your slides), but most digital cameras give you images that are much, much larger than this. Crop your images to include just what you want to show, and then shrink them to an appropriate sizebefore moving them into PowerPoint. A great freeware program for working with images is IrfanView. Another way to reduce file size is to use File | Save As | Tools | Compress Pictures when you save your presentation. Set the resolution to “Web/Screen” and check both of the options at the bottom.
Use standard fonts. If you use a font that’s on your computer but not the computer you use for your presentation, it won’t display properly. If you must use an unusual font, then use File | Save As | Tools | Save Options | Embed TrueType Fonts to save the font information with the presentation.
Know how to run your show. A little playing with PowerPoint will pay off in making your slide show smooth. Did you know that either the space bar or the left mouse button will advance to the next slide? Did you know that the backspace key goes back one slide? Did you know you can hit the “B” key to temporarily black out the slide (for example, so that you can write on the board or while you’re waiting to be introduced?).
Be sure your movies will play. If you have a movie, video clip or animation inserted into your PowerPoint, keep it in the same folder as the presentation and move the whole folder at once. Otherwise, PowerPoint will lose track of where the movie is and won’t play it. Not all computers (even classroom computers) have exactly the same software installed, and some will play some kinds of movies but not others, so be especially certain to test your presentation on the seminar room computer if you have multimedia.


Courtesy : http://depts.noctrl.edu

[Event Summary] -Suswagatam '11

2011-02-14T22:41:00.000-08:00

[Placement Assistance] - Hewlett Packard

2011-02-08T12:43:00.000-08:00

About HP

Infrastructure Technology Delivery Organization

We provide the highest level of customer satisfaction by tailoring services to meet specific Client requirements along with the use of cutting-edge technologies leading to high levels of accuracy and speed. The Level 1 engineer applies basic foundation of a function's principles, theories and concepts to assignments of limited scope, Utilizes professional concepts and theoretical knowledge acquired through specialized training, education or previous experience, Exercises independent judgment within defined parameters, Develops expertise and practical knowledge of applications within business environment and Acts as team member by providing information, analysis and recommendations in support of team efforts.

· provides end User/Technical user support on

· Platforms like UNIX or WINDOWS Servers , desktops, laptops, handheld devices

· Network problems

· Customized Management reporting and trend analysis

· Technical Service resolution at the First level

Job Responsibilities:

Troubleshooting IT Infrastructure related issues – e.g. Hardware, networking and hosted Operating System /Application administration related issues of our global clients

· Incident management - scripted resolution - follows documented instructions.

· Monitoring and Routing of tickets to other teams at the start of an incident.

· User Management

· Process & Daemon Management

· Checking utilization of File systems

· Coordinating with vendors and other teams.

· Identify reoccurring issues

· He/she must adhere to the Troubleshooting process.

· He/she must escalate calls based on customers demand or on own judgment.

· Documentation of the case, reporting and trend analysis

· Exercises independent judgment within defined parameters.

· Acts as team member by providing information, analysis and recommendations in support of team efforts.

· Willingness to participate in non-standard working-time models (e.g. 24*7 shift rotation, call availability etc...)

· Clear communication skills and ability to work independently

· Fluency in English is a must.

Job Requirements :

o Should be a BE/B.tech from any of these streams : Instrumentation, Electrical, Electronics & Communications

o Should be 2011 pass outs only

o BE – Aggregate 60% & above.

o Open to work on any of the technologies (e.g. Windows support/administration or UNIX support/administration etc.)

o Good communication skills

o Willing to working in Shifts

LIST OF THE CANDIDATES SELECTED FOR INFOSYS ON 01-02-2011

2011-02-05T17:55:00.000-08:00

Congratulations to all our friends and fellow AEnCians to have made it through at

SL. NO.	NAME OF THE STUDENT	BRANCH
1	Chethana Acharya	IS
2	Arpitha C.	IS
3	Deepak B.	IS
4	Sindhu O.	E&C
5	Naveen Patil	E&C
6	Mourya Vikas Harihar	CS
7	Shreya Sathish Pergade	CS
8	Ragavendra Shetty	Mechanical

[Placement Assistance] - Span infotech

2011-02-03T23:54:00.000-08:00

SPAN, an EDB company is an established software services company with a keen accent on Insurance, Logistics and Independent Software Vendors. Our focus is to ease our client into a long-term relationship with a business and execution model that aligns with their culture. SPAN's management team is deeply involved in sustaining relationships through the fulfillment of both measurable and intangible expectations. We believe in being a strong partner and 'Steering Progress. Together'.We provide integrated services across the entire life cycle of software development. The challenge lies in identifying the outsourceability of a specific phase of the project or the entire life cycle itself and then putting down a customer centric method to manage the project

18/2, Vani Vilas Road, Basavanagudi, Bangalore 560 004, India
Ph: +91-80- 40219600
Mob: --
Fax: +91-80- 40219632
Email: naveen@spansystems.com
Official Website
www.spansystems.com/

Job Description:

Company Profile	SPAN is an established Software Services Company with over 15 years of experience. Our clients include Fortune 1000 companies, Independent Software Vendors and start-ups. SPAN’s Offshore Development Center in India is certified for CMM Level 4, ISO 9001:2008, ISO 27001:2005 and is Microsoft Certified Gold Partner. SPAN has a global footprint with offices in the US and India. There are multiple offshore development centers in Bangalore, India. SPAN is an EDB ErgoGroup company. EDB ErgoGroup is the largest Nordic IT Services Company with over 10,000 employees. SPAN has expertise in Custom Applications development, reengineering, Maintenance, testing and support services. We have significant expertise in database, data warehousing and testing. We have strong expertise in R&D and Wireless applications development. SPAN has been ranked Top 20 Best IT Employers in India by a leading Indian IT magazine, two years in a row. SPAN has also been ranked #17 among “Top 20 Global R&D Service Providers” in India by one of the premier offshore advisory firms in India. Recently SPAN is selected in the league of Top 20 IT Services Company by SiliconIndia. We believe in being a strong partner and “Steering Progress. Together.”
No. of Vacancies:	50
Target Degrees & Branches	BE./B.Tech. (EL, CS, E&E, Mechanical, IS, EC)
CTC	CTC Rs. 2,50,000/-
Designation	Trainee Software Engineer
Role & Responsibilities	Development, Testing
Joining Location	Bangalore
Joining Period	Aug 1^st, 2011
Interview Location & Date, Time	Vivekananda College of Engineering & Technology, Puttur 6^th Feb 2011, 9:30 am onwards
Selection Process (No. of Interview Rounds)	Online Test Group Discussion Technical Interview /HR Round
% Cut-off	65%
Any Bond	2 Years

[Placement Assistance] - Capgemini

2011-02-03T05:46:00.000-08:00

About the Company

Capgemini India is a division of Capgemini which is a premier consulting, technology, and outsourcing company, headquartered in Paris, with an employee count of 75,000 and operations in more than 30 countries.

Apart from its USA offices (Chicago, Texas, Michigan, Philadelphia, Virginia, New York etc.), Capgemini has an extensive network of offices, spanning locations such as Canada, Mexico, Brazil, Brunei, China, Australia, Indonesia, Japan, Singapore, Austria, Germany, Hungary, U.K, Italy, Vietnam, Dubai and many more. Capgemini India offices are located in Bangalore, Mumbai, and Kolkata.

In keeping with Capgemini's global services, Capgemini India offers extensive services to its clients in the following sectors:

Business Consultancy
Technology Services
Outsourcing Services
Custom Software Development
Manufacturing
Automotive
Consumer Products, Retail and Distribution
Energy, Utilities, and Chemicals
Financial Services
Healthcare
Life Sciences
Public Services
Telecom, Media, and Entertainment

Capgemini India consulting services
With its business consultancy division, Capgemini India provides clients with growth strategies that bring about improvement in company performance, revenues, and processes. Capgemini India also provides services pertaining to market research and analysis. Capgemini India technology services

Capgemini India is well known for conceptualizing and integrating technological solutions to cater to clients' need for technological innovation in infrastructure and systems architecture. Towards this end, Capgemini has strategic partners such as Microsoft, Cisco, Dell, Oracle, HP, IBM, and SAP in order to deliver the best quality technological support to its clients.

Capgemini India outsourcing services

Capgemini India is stepping up its operations as far as managing clients' IT resources are concerned. The main reason why clients hire Capgemini India outsourcing services is to strike the ideal cost-performance balance. For areas such as business process outsourcing and pricing flexibility, Capgemini has collaborated with global strategic partners.

Capgemini India has successfully worked with clients such as Domino foods, AXA, Cadbury Schweppes, Citibank, and ABN Amro among many others.

Capgemini India prides itself in being driven by values such as honesty, freedom, trust, modesty, team spirit, and fun while catering to the needs of its clients in a dynamic business environment. During its 40 year old history, Capgemini has earned the reputation of being a world leader in business consultancy services. Capgemini India too, in a short span of time, has been able to capture a significant market share and plans to increase its India operations to a large extent.

For more information ,Visit the Wiki page here.

[Placement Assistance] -Tech Mahindra

2011-02-02T21:21:00.000-08:00

About The Company

Tech Mahindra is part of the US $7.1 billion Mahindra Group, in partnership with British Telecommunications plc (BT), one of the world’s leading communications service providers. Focused primarily on the telecommunications industry, Tech Mahindra is a leading global systems integrator and business transformation consulting organization. Tech Mahindra has recently expanded its IT portfolio by acquiring the leading global business and information technology services company, Mahindra Satyam (earlier known as Satyam Computer Services).

Tech Mahindra’s capabilities spread across a broad spectrum, including Business Support Systems (BSS), Operations Support Systems (OSS), Network Design & Engineering, Next Generation Networks, Mobility Solutions, Security consulting and Testing. The solutions portfolio includes Consulting, Application Development & Management, Network Services, Solution Integration, Product Engineering, Infrastructure Managed Services, Remote Infrastructure Management and Business Operations Group(BOG).

Tech Mahindra has a global footprint through operations in more than 25 countries. Assessed at SEI CMMi Level 5, Tech Mahindra's track record for value delivery is supported by over 34, 000 professionals who provide a unique blend of culture, domain expertise and in depth technology skill sets. Its development centers are ISO 9001:2008 & BS7799 certified.

[Placement Assitance]- BIRLASOFT

2011-02-02T10:08:00.000-08:00

BIRLASOFT is a well known name in technology world, part of KC Birla group.

The whole selection process was divided in 3 sections
1. Aptitude Test
2. Group discussion
3. HR + Technical

Aptitude test:
This consisted of 50 questions. 25 of verbal-quant and 25 technical questions.

1. Verbal - Quant: This portion had 15 questions of quant which covered topics such as percentage, ratio, mixture, profit loss AP and GP.
while the verbal was a bit easier with 10 questions.

2. Technical: This was totally a mess. it has all ques from C, specially covering "C Preprocessors and pointers". other than that there were Java questions. but this was totally covering the basics.
250 appeared for this, and 73 cleared it.
the cut off usually lies b/w 35-40.

Group Discussion:
In this round even the HR seemed to be confused, it was not clear to him what a G.D. really is. he made it a gd-cum-debate round. instead of judging the qualities that must be judged in a GD he was just judging the English fluency.
topics given to various batches were :
1. "should mobile be banned"
2. "vision 2020"

HR and Technical
We had the HR round first. but I had it twice, Don't know why !! the first hr round was fine. HR in that round was a lady, who first asked me to introduce myself. then why was I willing to join Birlasoft, speak on a particular topic for a minute.

Then was my second HR. in that things started right, but all of a suden the HR started asking technical questions, such as sorting using pointers, then a puzzle and this round continued for 45 minutes. this new HR was just opposite.

Technical round:
This round was also fine. questions asked were :
1. calloc and malloc
2 arranging two numbers in ascending order without use of third variable
3. macros
4. sort array using pointers
5. oops concept
6. oracle 9i based questions
* all questions based on details in resume.

The package offered was CTC of 2.5 lacks.

About the Company

Birlasoft is a leading provider of information technology services in both onshore and offshore models to Fortune 1000 as well as mid-sized organizations in banking, financial services, insurance, retail, healthcare, manufacturing and independent software vendors sectors.

Birlasoft services include application development, support & maintenance, enterprise application implementation, integration, infrastructure management and quality assurance & testing.

Birlasoft’s robust delivery processes embrace digitized project management methodologies, embedded within proven practices of Six Sigma, SEI CMMi Level 5 on Continuous Representation and secure services framework with BS7799. The Noida centers of the company have been recently assessed at PCMM Level 3 for its HR practices.

Headquartered at Noida, India, Birlasoft has 4,000+ employees across US, UK, Germany, Netherlands, Czech Republic, Malaysia, Australia, Singapore and India.

Birlasoft is part of the global $1.4 billion CK Birla Group which traces its roots back to over 150 years and has diversified interests ranging from automobiles, cement, paper, software etc. to hospitals, schools and colleges as part of its philanthropic work.

For more info ,visit www.birlasoft.com

ALL THE BEST GUYS, HOPE YOU COME OUT WITH FLYING COLORS !!