Department of Electrical Engineering

Speed is a major concern for high density VLSI networks. In this paper the closed form delay model for current mode signalling in VLSI interconnects has been proposed with resistive load termination. RLC interconnect line is modelled using characteristic impedance of transmission line and inductive effect.The inductive effect is dominant at lower technology node is modelled into an equivalent resistance. In this model first order transfer function is designed using finite difference equation, and by applying the boundary conditions at the source and load termination. It has been observed that the dominant pole determines system response and delay in the proposed model. Using CNIA tool (carbon nanotube interconnect analyzer) the interconnect line parameters has been estimated at 45nm technology node. The novel proposed current mode model superiority has been validated for CNT type of material. It superiority factor remains to 66.66% as compared to voltage mode signalling. And current mode dissipates 0.015pJ energy where as VM consume 0.045pJ for a single bit transmission across the interconnect over CNT material. Secondly the damping factor of a lumped RLC circuit is shown to be a useful figure of merit.

In this paper, a new adaptive biased low voltage cascode current mirror with high input/output swing is presented. This advantage is achieved using a self-biasing transistor and compensation resistor. The new structure profits from better input dynamic range and lower supply voltage without frequency response limitation and increasing input impedance. Also, the proposed current mirror is incorporated in folded cascode amplifier in order to enhance its phase-margin. The simulation results in 0.18 μm CMOS technology confirm the theoretical analysis and exhibits 478μA linear input/output current swing and a phase-margin enhancement of 12o for the proposed current mirror and amplifier compared to the
conventional circuits, respectively

This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.

In  this  paper,  extracted  word  features  have  been  used  toward  offline  detection  of  Persian  handwritten
words. This has been done using fourteen features in Persian word structure. In this method after required pre-processing,  numbers  and  location  of  dots  versus
  base  line  are  extracted  then  the  word  becomes  thin
and the feature extraction stage starts. In this stage the words are detected by using extracted features and fuzzy  algorithm  .In  this  paper,  a  practical  case  has  been  investigated,  in  which  we  use  a  group  of  words including names of Iran provinces. The proposed met
hod is faster in comparison to the common algorithm
s due  to  the  fact  that  there  is  no  learning  procedure
  in  the  proposed  method.  According  to  the  collected
database, the presented method is able to detect %8
2.3 of handwritten Persian words

With increasing use of robots instead of human in industrial, medicine and military applications etc. the importance of research on designing and building of robots is increasing. In this paper various methods of the human hand motion simulation has been investigated and we used one of most common method named Data-gloves which extract data from hand and then we simulated hand motion during several processing stages. At first step we designed and built circuits to digitize analog data received from sensors and we sent
them to computer. Then we received extracted data in MATLAB and processed them to simulate bending of
the wrist and fingers joints graphically. In this method we mapped data linearly to 0-90° and rotate points
around relative Coordinate axis in the specific conditions. Results show that we can simulate hand motion in real time with low cost, lowest error and without complex and expensive equipments.

A novel mechanism is proposed for pulse-based logarithmic computation that is scalable across multi-core neuromorphic processors. At the core of the proposed method is an integrate-and-fire margin-propagation (IFMP) unit that facilitates spike-based computation in the logarithmic domain. We first show that a network of three IF neurons implement threshold and rectification operations to compute nonlinear functions. The functions used in the computational algorithms are mapped into log-sum-exp function and
approximated using margin propagation algorithm. Then, we show that a generic pulse neural network is implemented by pipelining and cascading multiple IFMP units. Because all computations are performed in log-domain, multiplication of the pre-synaptic pulse-train by synaptic weights is translated into a simple addition between the two input spike-trains. We demonstrate the scalability of the proposed approach by designing an IFMP-based support vector machine (SVM) for a separable and non separable classification tasks.

Due to the increasing development of IT and communication technologies and the increasing trust in power supply for people, the use of remote terminal units is becoming an important development direction of distribution industry throughout the world. As an important part of the distribution system, the smart distribution requires the terminal unit to realize information interaction and technique of the smart distributed system. The traditional feeder terminal unit cannot meet the requirements and must be replaced by the remote terminal unit. Firstly, the model of the intelligent distribution system is proposed. Secondly,
the model of remote terminal unit is constructed and its structure and function are presented. Finally, the
scheme of the remote terminal unit based fault detection, protection, location, isolation and restorationalgorithms for distribution system with remote terminal unit are presented.

This paper is proposed to show the mutual interaction between of both LFC and the AVR loops. The coupling effects of these two AVR and LFC loops are studied by extending the linear zed AGC system and it include the excitation system also. For a complete system model we have to study the oscillation of LFC
and AVR loops with PID CONTROLLERS. This combined model of LFC and AVR loops is tested on
single-area power system. These results are shown in simulation; and will be reachable in dynamic and
steady state responses [1][2].

In this paper, a new age-group estimation method is proposed based on fuzzy inference system. Using this
method, the facial images could be classified in four groups with more accuracy for classification of marginal ages. The proposed algorithm is divided into 3 stages. At first stage, the images are preprocessed. Then, the image features are extracted using histograms of oriented gradients (HOG) and
local binary patterns (LBP). Finally, by using neural networks and adaptive neuro-fuzzy inference system(ANFIS) the age-groups are classified. Experimental results show that by using this method, the agegroup classification is done with better performance and the accuracy of age estimation is improved.

In any industrial processes, the temperature measurement is an important requirement. Presently thermocouples, pyrometers and contact type sensors etc. devices and techniques are used for temperature measurement of several heat sources. Temperature measurement of furnace, boiler,burner etc. is a very difficult and tedious task also breakage of sensors occurs due to such high temperature so it requires daily maintenance. In this proposed system image processing and neural network technique is used to estimate
the temperature of visible heat sources. System uses camera to take the images of heat source. Thermocouple will be used to measure the actual temperature. Various analytical techniques can be applied to estimate the color temperature correlation. Artificial neural network (ANN) is used to create the database of captured images and measured temperature so that approximate temperature is estimated.

Variable Speed Drives (VSD) due to their high dynamic performances are growing importance in industrial
applications. PWM inverters are mainly responsible for VSDs dynamic operation but they give rise to
bearing currents. Parasitic capacitances present in machine doesn’t create problem at lower frequencies, but at higher carrier frequencies they provide charging path and builds up shaft voltage. When this voltage reaches to a dielectric breakdown voltage of lubricant film, it breaks down, and develops a high discharge bearing current which damage the bearings in a very short time of operation. Multilevel inverter is one of the options to reduce this problem. This paper is an attempt to investigate the effects on CMV, bearing voltage, bearing currents and total harmonic distortion (THD); by diode clamped multilevel (three, five and seven level) and conventional inverter fed induction motor. In this paper phase disposition (PD), sinusoidal pulse width modulation (SPWM) technique is used.

In developing countries a large demand of power will be seen in future. It is essential to maintain power continuity and reliability. Contingencies like fault occurrence and generated power and load demand imbalance causes system frequency instability. Load-shedding is the ultimate solution to restore systemfrequency and ensure availability of electrical power to critical loads in the plant. This paper presents a review of traditional adaptive and computational intelligent load shedding scheme. A comparison of these entire schemes with corresponding advantages and disadvantages is summarized.

In this paper, steady state models of series FACTS devices for power flow studies and the task of that modelling in the study of series FACTS devices for control of power flow was discussed. Series FACTS devices considered in this paper are Thyristor Controlled Series Capacitor (TCSC), Thyristor Controlled
Phase Shifter (TCPS) and Static Synchronous Series Compensator (SSSC). In order to model these series
FACTS devices, a number of power flow study programs were established. The effectiveness of modelling
and convergence was tested with IEEE 14 and 30 bus system with and without series FACTS devices.Newton-Raphson technique is used to solve the nonlinear load flow equations. Power flow study programming was accomplished by using MATLAB. Results are reported and studies are presented to
demonstrate and compare the efficiency of TCPS, TCSC and SSSC.