Scholarly

Analytical Model of Connection Establishment Duration Calculation in Wireless Networks

Year: 2008 Volume: 2 Issue: 9 1967 - 1969 Pages

Authors:
Y. Chaiko

Abstract: It is important to provide possibility of so called “handover" for the mobile subscriber from GSM network to Wi-Fi network and back. To solve specified problem it is necessary to estimate connection time between base station and wireless access point. Difficulty to estimate this parameter is that it doesn-t described in specifications of the standard and, hence, no recommended value is given. In this paper, the analytical model is presented that allows the estimating connection time between base station and IEEE 802.11 access point.

An Efficient Method for Load−Flow Solution of Radial Distribution Networks

Year: 2008 Volume: 2 Issue: 9 1959 - 1966 Pages

Abstract: This paper reports a new and accurate method for load-flow solution of radial distribution networks with minimum data preparation. The node and branch numbering need not to be sequential like other available methods. The proposed method does not need sending-node, receiving-node and branch numbers if these are sequential. The proposed method uses the simple equation to compute the voltage magnitude and has the capability to handle composite load modelling. The proposed method uses the set of nodes of feeder, lateral(s) and sub lateral(s). The effectiveness of the proposed method is compared with other methods using two examples. The detailed load-flow results for different kind of load-modellings are also presented.

Face Recognition Using Morphological Shared-weight Neural Networks

Year: 2008 Volume: 2 Issue: 9 1955 - 1958 Pages

Abstract: We introduce an algorithm based on the morphological shared-weight neural network. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The output is then learned by interacting with the classification process. The feature extraction and classification networks are trained together, allowing the MSNN to simultaneously learn feature extraction and classification for a face. For evaluation, we test for robustness under variations in gray levels and noise while varying the network-s configuration to optimize recognition efficiency and processing time. Results show that the MSNN performs better for grayscale image pattern classification than ordinary neural networks.

Analysis of Bit Error Rate Improvement in MFSK Communication Link

Year: 2008 Volume: 2 Issue: 9 1950 - 1954 Pages

Abstract: Data rate, tolerable bit error rate or frame error rate and range & coverage are the key performance requirement of a communication link. In this paper performance of MFSK link is analyzed in terms of bit error rate, number of errors and total number of data processed. In the communication link model proposed, which is implemented using MATLAB block set, an improvement in BER is observed. Different parameters which effects and enables to keep BER low in M-ary communication system are also identified.

Extraction of Knowledge Complexity in 3G Killer Application Construction for Telecommunications National Strategy

Year: 2008 Volume: 2 Issue: 9 1944 - 1949 Pages

Abstract: We review a knowledge extractor model in constructing 3G Killer Applications. The success of 3G is essential for Government as it became part of Telecommunications National Strategy. The 3G wireless technologies may reach larger area and increase country-s ICT penetration. In order to understand future customers needs, the operators require proper information (knowledge) lying inside. Our work approached future customers as complex system where the complex knowledge may expose regular behavior. The hidden information from 3G future customers is revealed by using fractal-based questionnaires. Afterward, further statistical analysis is used to match the results with operator-s strategic plan. The developments of 3G applications also consider its saturation time and further improvement of the application.

RRNS-Convolutional Concatenated Code for OFDM based Wireless Communication with Direct Analog-to-Residue Converter

Year: 2008 Volume: 2 Issue: 9 1936 - 1943 Pages

Abstract: The modern telecommunication industry demands higher capacity networks with high data rate. Orthogonal frequency division multiplexing (OFDM) is a promising technique for high data rate wireless communications at reasonable complexity in wireless channels. OFDM has been adopted for many types of wireless systems like wireless local area networks such as IEEE 802.11a, and digital audio/video broadcasting (DAB/DVB). The proposed research focuses on a concatenated coding scheme that improve the performance of OFDM based wireless communications. It uses a Redundant Residue Number System (RRNS) code as the outer code and a convolutional code as the inner code. Here, a direct conversion of analog signal to residue domain is done to reduce the conversion complexity using sigma-delta based parallel analog-to-residue converter. The bit error rate (BER) performances of the proposed system under different channel conditions are investigated. These include the effect of additive white Gaussian noise (AWGN), multipath delay spread, peak power clipping and frame start synchronization error. The simulation results show that the proposed RRNS-Convolutional concatenated coding (RCCC) scheme provides significant improvement in the system performance by exploiting the inherent properties of RRNS.

Design of Folded Cascode OTA in Different Regions of Operation through gm/ID Methodology

Year: 2008 Volume: 2 Issue: 9 1930 - 1935 Pages

Abstract: This paper presents an optimized methodology to folded cascode operational transconductance amplifier (OTA) design. The design is done in different regions of operation, weak inversion, strong inversion and moderate inversion using the gm/ID methodology in order to optimize MOS transistor sizing. Using 0.35μm CMOS process, the designed folded cascode OTA achieves a DC gain of 77.5dB and a unity-gain frequency of 430MHz in strong inversion mode. In moderate inversion mode, it has a 92dB DC gain and provides a gain bandwidth product of around 69MHz. The OTA circuit has a DC gain of 75.5dB and unity-gain frequency limited to 19.14MHZ in weak inversion region.

Ray Tracing Technique based 60 GHz Band Propagation Modelling and Influence of People Shadowing

Year: 2008 Volume: 2 Issue: 9 1923 - 1929 Pages

Abstract: The main objectif of this paper is to present a tool that we have developed subject to characterize and modelling indoor radio channel propagation at millimetric wave. The tool is based on the ray tracing technique (RTT). As, in realistic environment we cannot neglect the significant impact of Human Body Shadowing and other objects in motion on indoor 60 GHz propagation channel. Hence, our proposed model allows a simulation of propagation in a dynamic indoor environment. First, we describe a model of human body. Second, RTT with this model is used to simulate the propagation of millimeter waves in the presence of persons in motion. Results of the simulation show that this tool gives results in agreement with those reported in the literature. Specially, the effects of people motion on temporal channel properties.

A Novel Fuzzy Technique for Image Noise Reduction

Year: 2008 Volume: 2 Issue: 9 1917 - 1922 Pages

Abstract: A new fuzzy filter is presented for noise reduction of images corrupted with additive noise. The filter consists of two stages. In the first stage, all the pixels of image are processed for determining noisy pixels. For this, a fuzzy rule based system associates a degree to each pixel. The degree of a pixel is a real number in the range [0,1], which denotes a probability that the pixel is not considered as a noisy pixel. In the second stage, another fuzzy rule based system is employed. It uses the output of the previous fuzzy system to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Experimental results are obtained to show the feasibility of the proposed filter. These results are also compared to other filters by numerical measure and visual inspection.

A Self Adaptive Genetic Based Algorithm for the Identification and Elimination of Bad Data

Year: 2008 Volume: 2 Issue: 9 1911 - 1916 Pages

Abstract: The identification and elimination of bad measurements is one of the basic functions of a robust state estimator as bad data have the effect of corrupting the results of state estimation according to the popular weighted least squares method. However this is a difficult problem to handle especially when dealing with multiple errors from the interactive conforming type. In this paper, a self adaptive genetic based algorithm is proposed. The algorithm utilizes the results of the classical linearized normal residuals approach to tune the genetic operators thus instead of making a randomized search throughout the whole search space it is more likely to be a directed search thus the optimum solution is obtained at very early stages(maximum of 5 generations). The algorithm utilizes the accumulating databases of already computed cases to reduce the computational burden to minimum. Tests are conducted with reference to the standard IEEE test systems. Test results are very promising.

Performance of Soft Handover Algorithm in Varied Propagation Environments

Year: 2008 Volume: 2 Issue: 9 1906 - 1910 Pages

Abstract: CDMA cellular networks support soft handover, which guarantees the continuity of wireless services and enhanced communication quality. Cellular networks support multimedia services under varied propagation environmental conditions. In this paper, we have shown the effect of characteristic parameters of the cellular environments on the soft handover performance. We consider path loss exponent, standard deviation of shadow fading and correlation coefficient of shadow fading as the characteristic parameters of the radio propagation environment. A very useful statistical measure for characterizing the performance of mobile radio system is the probability of outage. It is shown through numerical results that above parameters have decisive effect on the probability of outage and hence the overall performance of the soft handover algorithm.

A Diffusion Least-Mean Square Algorithm for Distributed Estimation over Sensor Networks

Year: 2008 Volume: 2 Issue: 9 1901 - 1905 Pages

Abstract: In this paper we consider the issue of distributed adaptive estimation over sensor networks. To deal with more realistic scenario, different variance for observation noise is assumed for sensors in the network. To solve the problem of different variance of observation noise, the proposed method is divided into two phases: I) Estimating each sensor-s observation noise variance and II) using the estimated variances to obtain the desired parameter. Our proposed algorithm is based on a diffusion least mean square (LMS) implementation with linear combiner model. In the proposed algorithm, the step-size parameter the coefficients of linear combiner are adjusted according to estimated observation noise variances. As the simulation results show, the proposed algorithm considerably improves the diffusion LMS algorithm given in literature.

Thermal Modeling of Dry-Transformers and Estimating Temperature Rise

Year: 2008 Volume: 2 Issue: 9 1898 - 1900 Pages

Authors:
M. Ghareh
L. Sepahi

Abstract: Temperature rise in a transformer depends on variety of parameters such as ambient temperature, output current and type of the core. Considering these parameters, temperature rise estimation is still complicated procedure. In this paper, we present a new model based on simple electrical equivalent circuit. This method avoids the complication associated to accurate estimation and is in very good agreement with practice.

Denoising based on Wavelets and Deblurring via Self-Organizing Map for Synthetic Aperture Radar Images

Year: 2008 Volume: 2 Issue: 9 1888 - 1897 Pages

Authors:
Mario Mastriani

Abstract: This work deals with unsupervised image deblurring. We present a new deblurring procedure on images provided by lowresolution synthetic aperture radar (SAR) or simply by multimedia in presence of multiplicative (speckle) or additive noise, respectively. The method we propose is defined as a two-step process. First, we use an original technique for noise reduction in wavelet domain. Then, the learning of a Kohonen self-organizing map (SOM) is performed directly on the denoised image to take out it the blur. This technique has been successfully applied to real SAR images, and the simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

Design of a Robust Controller for AGC with Combined Intelligence Techniques

Year: 2008 Volume: 2 Issue: 9 1881 - 1887 Pages

Abstract: In this work Artificial Intelligence (AI) techniques like Fuzzy logic, Genetic Algorithms and Particle Swarm Optimization have been used to improve the performance of the Automatic Generation Control (AGC) system. Instead of applying Genetic Algorithms and Particle swarm optimization independently for optimizing the parameters of the conventional AGC with PI controller, an intelligent tuned Fuzzy logic controller (acting as the secondary controller in the AGC system) has been designed. The controller gives an improved dynamic performance for both hydrothermal and thermal-thermal power systems under a variety of operating conditions.

Design of a Permanent Magnet Synchronous Machine for the Hybrid Electric Vehicle

Year: 2008 Volume: 2 Issue: 9 1876 - 1880 Pages

Abstract: Permanent magnet synchronous machines are known as a good candidate for hybrid electric vehicles due to their unique merits. However they have two major drawbacks i.e. high cost and small speed range. In this paper an optimal design of a permanent magnet machine is presented. A reduction of permanent magnet material for a constant torque and an extension in speed and torque ranges are chosen as the optimization aims. For this purpose the analytical model of the permanent magnet synchronous machine is derived and the appropriate design algorithm is devised. The genetic algorithm is then employed to optimize some machine specifications. Finally the finite element method is used to validate the designed machine.

Practical Aspects of Face Recognition

Year: 2008 Volume: 2 Issue: 9 1871 - 1875 Pages

Abstract: Current systems for face recognition techniques often use either SVM or Adaboost techniques for face detection part and use PCA for face recognition part. In this paper, we offer a novel method for not only a powerful face detection system based on Six-segment-filters (SSR) and Adaboost learning algorithms but also for a face recognition system. A new exclusive face detection algorithm has been developed and connected with the recognition algorithm. As a result of it, we obtained an overall high-system performance compared with current systems. The proposed algorithm was tested on CMU, FERET, UNIBE, MIT face databases and significant performance has obtained.

Comparison of Field-Oriented Control and Direct Torque Control for Permanent Magnet Synchronous Motor (PMSM)

Year: 2008 Volume: 2 Issue: 9 1865 - 1870 Pages

Abstract: This paper presents a comparative study on two most popular control strategies for Permanent Magnet Synchronous Motor (PMSM) drives: field-oriented control (FOC) and direct torque control (DTC). The comparison is based on various criteria including basic control characteristics, dynamic performance, and implementation complexity. The study is done by simulation using the Simulink Power System Blockset that allows a complete representation of the power section (inverter and PMSM) and the control system. The simulation and evaluation of both control strategies are performed using actual parameters of Permanent Magnet Synchronous Motor fed by an IGBT PWM inverter.

Keywords:
PMSM
FOC
DTC
hysteresis
PWM.

Evolutionary Techniques Based Combined Artificial Neural Networks for Peak Load Forecasting

Year: 2008 Volume: 2 Issue: 9 1858 - 1864 Pages

Abstract: This paper presents a new approach using Combined Artificial Neural Network (CANN) module for daily peak load forecasting. Five different computational techniques –Constrained method, Unconstrained method, Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) – have been used to identify the CANN module for peak load forecasting. In this paper, a set of neural networks has been trained with different architecture and training parameters. The networks are trained and tested for the actual load data of Chennai city (India). A set of better trained conventional ANNs are selected to develop a CANN module using different algorithms instead of using one best conventional ANN. Obtained results using CANN module confirm its validity.

A Fixed Band Hysteresis Current Controller for Voltage Source AC Chopper

Year: 2008 Volume: 2 Issue: 9 1851 - 1857 Pages

Abstract: Most high-performance ac drives utilize a current controller. The controller switches a voltage source inverter (VSI) such that the motor current follows a set of reference current waveforms. Fixed-band hysteresis (FBH) current control has been widely used for the PWM inverter. We want to apply the same controller for the PWM AC chopper. The aims of the controller is to optimize the harmonic content at both input and output sides, while maintaining acceptable losses in the ac chopper and to control in wide range the fundamental output voltage. Fixed band controller has been simulated and analyzed for a single-phase AC chopper and are easily extended to three-phase systems. Simulation confirmed the advantages and the excellent performance of the modulation method applied for the AC chopper.

International Journal of Electrical, Electronic and Communication Sciences

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