Archives-September 2011

Power electronic devices are non-linear loads that create harmonic distortion and can be susceptible to voltage dips if not adequately protected. The non linear nature of these switching devices causes harmonic current injection into the ac mains; there by polluting the power quality (PQ) at the point of common coupling (PCC).This power quality (PQ) improvement is achieved by using multi-pulse converters in THREE-PHASE ac-dc converters (ADCs).The proposed multi-pulse ac-dc converter is based on autotransformer configurations and passive tuned filters. The proposed ac–dc converter is able to eliminate lower order harmonics in the ac supply current. The resulting supply current is near sinusoidal in shape with low total harmonic distortion and a nearly unity power factor. The proposed multi-pulse ac-dc converter is designed and the simulation model is developed in MATLAB. It improves the power quality at the ac mains and meets IEEE-519 standard requirements at varying loads.

There has been a wide variety of research going on in the field of privacy preservation in data mining. Most of the methods are implemented for static data. But the world is filled with dynamic data which grows rapidly than what we expect. No technique is better than the other ones with respect to all criteria. This paper focus on a methodology that is well suited for incremental data that preserves its privacy while also performing an efficient mining .the method does not require the entire data to be processed again for the insertion of new data. The method uses data summarization technique which is used for both incremental data and providing privacy for such data. We develop the algorithm for making the environment flexible and cost effective.

Selection of feature extraction method is most important factor in achieving high recognition performance in automatic pattern recognition systems. Similarly selection of suitable classifier also plays a very important role for the same. Plenty of feature selection methods and classifiers are existing in computer domain and choice of each of these mainly depends on task in hand. This paper presents an efficient and novel method for recognition of handwritten numerals using bar codes. Handwritten numerals are scan converted to binary images and normalized to a size of 30 x 30 pixels. The features are extracted using barcodes and are classified successfully using the statistical technique.

Variation in load frequency is an index for normal operation of power systems. When load Perturbation takes place anywhere in any area of the system, it will affect the frequency at other areas also. To control load frequency of power systems various controllers are used in different areas, but due to non-linearity's in the system components and alternators, these controllers cannot control the frequency quickly and efficiently. The simple neural networks can alleviate this difficulty. This paper deals with the Artificial Neural Network ( ANN) is applied to self tune the parameters of Proportional-Integral-Derivative(PID) ontroller. The single, Two Area non-reheat system has been considered for simulation of the proposed self tuning ANN based PID controller. In the PID controller parameters are continuously adjusted according to the change in area-control error (ACE). Simulations of the networks are carried out for different load changes 1% and change of 1% in governor time constant and turbine time constant parameters. The proposed method for simulation results are obtained by the other controllers of PI and PID compared highlighting the performance of PID-ANN controller. The simulation works developed by MATLAB- SIMULINK Environment. The simulink results are obtained by qualitatively and quantitatively .The qualitative comparison is used for the Integral Square Error (ISE), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE) is minimized in single and multi area power system. Therefore the Comparison of responses with conventional integral controller(PI) & PID controller show that the neural-network controller (ANN-PID) has quite satisfactory generalization capability, feasibility and reliability, as well as accuracy in both single and multi area power system.

This paper presents an image encryption algorithm using chaos. A more complex version of the chaos called Hyper-chaos is used to improve the security of the procedure. Hyper-chaotic system is used to shuffle the parts of the image. Then the shuffled image is encrypted with the chaos. At the receiving end, decryption is done and then the shuffled parts of the image are put back in their original positions.

We propose a novel method based on artificial neural network (ANN) for beam position calculation and fault diagnosis of beam position monitors (BPM) in electron storage rings. BPM are diagnostic devices used to determine position of stored electron beam in storage rings. BPM commonly uses four button type electrodes as sensors to sense the electric field of electron beam circulating in storage ring. A mathematical polynomial of suitable degree is generally used to compute beam position from the button electrode signals of the BPM when beam excursions are large from the design orbit. The coefficients of the polynomial are derived from the bench calibration data of BPM. In the proposed new method, a neural network predicts the position of electron beam using four electrode signals of a BPM. A feed-forward network with three hidden layers using backpropagation training algorithm has been designed and trained with the bench calibration data of each BPM. The beam position predicted by the network was compared with conventional polynomial method. Neural network based method was tested on the BPM of 2.5 GeV electron synchrotron radiation source named Indus-2 at Raja Ramanna Centre for Advanced Technology, Indore, India. The root mean square (rms) error in neural network predicted beam position in horizontal plane and vertical plane was 24 and 26 microns respectively as compared with 100 and 101 microns with first polynomial and 61 and 66 microns with second polynomial in the central region of ± 5 mm on bench calibration data. The reliability of beam position measurement was assured by another neural network by doing self consistency check. In this paper we present architecture, training of neural network and improvement in beam position measurement in comparison with polynomial method.

This paper presents a novel method for early detection of forming a Tumor i.e. Micro-calcifications in Mammograms. The main objective of this paper is to segment and detect the Micro-calcification (MCCs) from Digital mammograms that helps to provide and support for clinical decision to perform biopsy of the breast. In this paper, there are two aspects. First is to enhance the image by using Mathematical Morphology and denoise by Wavelet Transform and segmentation and detection of Micro-calcifications using Spot detection. Second is to extract texture based features by using Gabor filters from the segmented image and finally classify it by using Probabilistic Neural Network (PNN) classifier. The Mammogram database consist of 154 images, in which 90 images are taken for training the Probabilistic neural network and 64 images are used for testing purpose.

Elliptic curves are algebraic curves that have been studied by many mathematicians since the seventeenth century. In 1985, Neal Koblitz and Victor Miller independently proposed public key cryptosystems using the group of points on an elliptic curve. The elliptic curve cryptosystem (ECC) was thus created. Since then, numerous researchers and developers have spent years researching the strength of ECC and improving techniques for its implementation. Secure applications in smart cards present implementation challenges particular to the platform’s memory, bandwidth, and computation constraints. ECC’s unique properties make it especially well suited to smart card applications. This paper describes the Elliptic Curve Cryptography algorithm and its security over RSA algorithm.

The Data Encryption Standard (DES), a symmetric-key cryptosystem, developed for United States government was intended for use by the general public. It has been officially accepted as a cryptographic standard in United States and other countries. The DES is also known as the Data Encryption Algorithm (DEA) by ANSI and DEA-1 by the ISO. It has been a worldwide standard for 30 years. Many hardware and software system have been designed with the DES. Although it is showing signs of old age, it has hold up remarkably well against years of cryptanalysis and it is still secure against all but possibly the most powerful adversaries. In this paper we begin by describing DES then describe and analyze attacks against DES.

Hash functions, also called message digests and one-way encryption, are algorithms that, in some sense, use no key. Instead, a fixed-length hash value is computed based upon the plaintext that makes it impossible for either the contents or length of the plaintext to be recovered. Hash algorithms are typically used to provide a digital fingerprint of a file's contents, often used to ensure that the file has not been altered by an intruder or virus. Hash functions are also commonly employed by many operating systems to encrypt passwords. Hash functions, then, provide a measure of the integrity of a file. The goal of this paper is to give an overview of the known methods of attack on hash functions.