Showing papers presented at "IEEE India Conference in 2005"

A New Approach to Coin Recognition using Neural Pattern Analysis

Abstract: In business transactions, to enable computers to recognize coins and other different forms of currency has become an essential process. If the computers are able to perform such recognitions, monetary transactions becomes much easier in all forms of trade. Keeping all the necessary factors in mind we have created a system that could easily identify the numeral in the coins. To limit the scope of this problem, our research focuses on recognizing the exact numeral in a 1-rupee, 2-rupee and 5-rupee Indian coin. The proposed system focuses only on the numerals rather than the use of other images presented in the front and rear side of the coin. In the proposed approach coin images are acquired and numeral in the coins are extracted. Unlike other edge detection process, the coin edges are not sharp and gradually become dull by years of usage. Moreover, numeral edges are same as the background pixel value, which increases the complexity of edge detection process. Hence statistical color threshold method is suggested and implemented in the coin recognition process. After finding the Cartesian co-ordinates of numeral in the coins, the sub image of the numeral is extracted from the given coin image. This sub image is used for character recognition process. In this phase, rotation-invariant character recognition is carried out by multi channel Gabor filter and back propagation network methods. The overall collection contains 72 images in which skewed images are acquired in various angles of rotation varying from 30 degrees onwards.

Artificial Intelligence Techniques Applied to Intrusion Detection

Abstract: Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on specific AI approach for intrusion detection. The techniques that are being investigated includes neural networks and fuzzy logic with network profiling, that uses simple data mining techniques to process the network data. The proposed system is a hybrid system that combines anomaly, misuse and host based detection. Simple Fuzzy rules, allow us to construct if-then rules that reflect common ways of describing security attacks. For host based intrusion detection we use neural-networks along with self organizing maps. Suspicious intrusions can be traced back to their original source path and any traffic from that particular source will be redirected back to them in future. Both network traffic and system audit data are used as inputs for both.

Cryptography Using Neural Network

Abstract: The goal of any cryptographic system is the exchange of information among the intended users without any leakage of information to others who may have unauthorized access to it. In 1976, Diffie & Hellmann found that a common secret key could be created over a public channel accessible to any opponent. Since then many public key cryptography have been presented which are based on number theory and they demand large computational power. Moreover the process involved in generating public key is very complex and time consuming. To overcome these disadvantages, the neural networks can be used to generate common secret key. This is the motivation for this present work on interacting neural networks and cryptography[1].In the case of neural cryptography, both the communicating networks receive an identical input vector, generate an output bit and are trained based on the output bit. The dynamics of the two networks and their weight vectors is found to exhibit a novel phenomenon, where the networks synchronize to a state with identical time-dependent weights. This concept of synchronization by mutual learning can be applied to a secret key exchange protocol over a public channel. The generation of secret key over a public channel has been studied and the generated key is used for encrypting and decrypting the given message using DES algorithm which is simulated and synthesized using VHDL

A Morphology Based Approach for Car License Plate Extraction

Abstract: Locating the car license plate in an image or video frame of a car is an important step in car license plate recognition/identification applications. This problem poses many challenges like location of license plate from images taken in poor illumination and bad weather condition; plates that are partly obscured by dirt and images that have low contrast. This paper presents a new morphology based method for license plate extraction from car images. The algorithm uses morphological operations on the preprocessed, edge images of the vehicles. Characteristic features such as license plate width and height, character height and spacing are considered for defining structural elements for morphological operations. Connected component analysis is used to select the band containing license plate from the candidate segmented. The experimental results with a reasonably large set of car images are very encouraging.

Automatic Segmentation and Classification of Diffused Liver Diseases using Wavelet Based Texture Analysis and Neural Network

Abstract: In this paper a computer aided diagnostic system for classifying diffused liver diseases from Computerized Tomography (CT) images using wavelet based texture analysis and neural network is presented. Liver is extracted from CT abdominal images using adaptive threshold and morphological processing. Orthogonal wavelet transform is applied on the liver to get horizontal, vertical and diagonal details. The statistical texture features like Mean, Standard deviation, Contrast, Entropy, Homogeneity and Angular second moment are extracted from these details and hence the eighteen features are used to train the Probabilistic neural network to classify the liver as fatty or cirrhosis. The proposed system is tested for 100 images. It produces an accuracy of 95%. The performance of the proposed system is also evaluated by calculating specificity, sensitivity, positive prediction value and negative prediction value. The performance measures of the above system are compared with the results evaluated by radiologists.

Wavelet Aided SVM Analysis of ECG Signals for Cardiac Abnormality Detection

Abstract: Automatic detection and classification of electrocardiogram (ECG) signals is of great importance for diagnosis of cardiac abnormalities. A method is proposed here to classify different cardiac abnormalities like Cardiomyopathy, Myocardial infarction, Dysrhythmia, Myocardial hypertrophy and Valvular heart disease. Support Vector Machine (SVM) has been used to classify the patterns inherent in the features extracted through Continuous Wavelet Transform (CWT) of different ECG signals. CWT allows a time domain signal to be transformed into time-frequency domain such that frequency characteristics and the location of particular features in a time series may be highlighted simultaneously. Thus it allows accurate extraction of feature from non-stationary signals like ECG. SVM transforms the multi-dimensional feature space into a linearly separable feature space with the help of Kernel function. In the present work, SVM in regression mode has been successfully applied for the classification of cardiac abnormalities with good diagnostic accuracy.

A Framework for Identification of Fuzzy Models through Particle Swarm Optimization Algorithm

Abstract: This paper presents a framework for the identification of fuzzy models from the available input-output data through Particle Swarm Optimization (PSO) algorithm. Like other evolutionary algorithms, PSO is a population-based stochastic algorithm and is a member of the broad category of swarm intelligence techniques based on metaphor of social interaction. The suggested framework has the capability to identify optimized Mamdani and Singleton fuzzy models. For the presentation and validation of the proposed framework, the data from the rapid Nickel-Cadmium (Ni-Cd) battery charger developed by the authors has been used.

Distribution Network Reconfiguration For Loss Reduction Using Ant Colony System Algorithm

Abstract: In Electrical Power System, Network reconfiguration for loss reduction in distribution systems is very important way to save energy. But due to its nature it is inherently a difficult optimization problem. Distribution system reconfiguration for loss reduction is being applied using Ant Colony System Algorithm where in, the behavior of the real ants is developed into a series of steps which find most efficient in the network reconfiguration. Ants of the artificial colony are able to search for the successively shorter feasible routes by using information accumulated in the form of a pheromone trail deposited on the edges of their traveling path. In this work we make use of conventional distribution system load flow algorithm to check the constraints. Power flow constraints, the voltage deviation and the power transferred through the line should be met. A distribution network consisting of 14-bus system with three feeders, 19-branches and 11-load centers from Tamil Nadu Electricity Board (TNEB) is taken as case study. The results outstand positively forming an optimal network.

UML Sequence Diagram Based Testing Using Slicing

Abstract: We present a novel testing methodology to test object oriented software based on UML sequence diagrams. In our approach we use dynamic slicing and generate test cases automatically from UML sequence diagrams. We identify the message guards on sequence diagrams and create dynamic slices with respect to each conditional predicates. We generate the test data with respect to the slice. We have formulated a test adequacy criterion named slice coverage criterion. Test cases that we generate satisfy slice test coverage. Our approach achieves adequate test coverage without unduly increasing the number of test cases. This paper also describes how dynamic slicing is used in testing.

A Comparative Study of Shape Optimization of SRM using Genetic Algorithm and Simulated Annealing

Abstract: This paper describes the Shape Optimization of Switched Reluctance Machine (SRM) using Genetic Algorithm (GA) and Simulated Annealing (SA). To achieve the required performance within a specified space envelope, the physical dimensions of the Switched Reluctance Machine like Stator pole arc, Rotor pole arc, Rotor diameter and Stack length were optimized using GA. The proposed strategy improves the Power Density of the SRM by 11.7% using GA. Similarly using SA, the power density of the machine is increased by 26.94%. The proposed design, in comparison with standard design procedures, highlights improvement in performance with considerable reduction in size. Both the methods GA and SA maximize Flux linkage and Torque per unit rotor volume of the SRM. Even in very high power applications such as Aerospace applications, it is possible to achieve similar optimization using the proposed strategy. The simulation results obtained for a 4 phase, 8/6 pole, lkW, 100V, 25A, 1500 rpm SRM signify the usefulness and effectiveness of the proposed strategy.

Stochastic Multi Objective Short Term Hydrothermal Scheduling Using Particle Swarm Optimization

Abstract: In the multi objective framework particle swarm optimization technique is used to find the generation schedule of a short-range fixed head hydrothermal problem, through the improvement of the selection manner for global and individual extreme. The multi objective problem is formulated considering five objectives (1) cost (2) NOx emission (3) SO 2 emission (4) CO 2 emission (5) variance of generation mismatch with the explicit recognition of statistical uncertainties in the thermal generation cost, NOx, SO 2 and CO 2 emission curves and power demand, which are random variables. Solution to the problem is based on the generation of non-inferior solution, which allows explicit trade off between objective levels. The search for optimal set of multi objective optimization problem is done using PSO. Numerical simulation of two-sample system shows the effectiveness of the proposed algorithm.

Cluster Based Congestion Management in Deregulated Electricity Market Using PSO

Abstract: In this Paper, a Cluster based congestion management has been presented. The Paper is concerned with the Real and Reactive power rescheduling problem in a deregulated market environment. The aim of the proposed work is to minimize deviations from transaction schedules and hence the congestion cost. The TCSC maximizes the power transfer capability between a specific power seller and a power buyer in a network. The TCSC is installed in the most congested lines and analyzed. The method is formulated as a stochastic optimization problem and is solved by Particle Swarm Optimization. The algorithm is illustrated using New England 39-bus system.

Neural Network Based Control of Reduced Rating DSTATCOM

Abstract: This paper presents a DSTATCOM controlled by a neural network based reference current extractor (NNREC) and rating reduction by use of shunt connected capacitor bank for three-phase three-wire system. The working of DSTATCOM with self supported dc bus is used in two different modes; one is, unity power factor (UPF) mode and second is, zero voltage regulation (ZVR) mode. The heart of the control of DSTATCOM is the derivation of reference currents, which decide the switching of three legs of voltage source inverter (VSI) used in DSTATCOM. The NNRCE is used for control of DSTATCOM, which operates on LMS algorithm. The real positive sequence current component of current is extracted using NNRCE without any phase shift. A PI controller is used to regulate the voltage across the capacitor connected at the dc bus of the VSI. In the ac voltage regulation mode, second PI controller is used to maintain the constant voltage at PCC with variation of load. To reduce the rating of DSTATCOM, an ac capacitor bank of almost half rating of the VAR required is connected at the load end. The simulations are carried out in MATLAB environment for unity power factor operation and ac voltage regulation through leading power factor operation. Simulation results verify the effectiveness of the DSTATCOM system to meet the requirements of distribution system

Adaptive Neuro Fuzzy Inference System for Extraction of fECG

Abstract: Fetal ECG (IECG) monitoring enables accurate measurement of fetal cardiac performance including transient or permanent abnormalities of rhythm. Thie fetal signal obtained from the maternail abdomnen is mixed with much interference. The major source of this interference is maternal ECG (mECG). This paper describes the cancellation of mECG in fECG using Adaptive Netiro fuzzy logic technique (ANFIS).

Adaptive Core Based Scalable Multicasting Networks

Abstract: Multicast is efficient way to distribute information from single source to multiple destination or many-to-many in communication networks. Mobile ad-hoc network needs special multicast routing protocol to adapt its characteristics including local broadcast capacity, arbitrary topology change, and bandwidth constraint and power limitation. A multicast routing protocol for mobile Ad-hoc network should find compromise between routing overhead and data transmission efficiency so that it can efficiently use bandwidth and power. For this aim, this paper proposes a new multicast routing protocol called Adaptive Core based Multicast Routing protocol (ACMP) which constructs and maintains a group-shared tree using adaptively selected core only when group traffic exists. ACMP attempts to react more quickly to broken tree edge by detecting link failures during data forwarding. Once a link failure is detected, this protocol uses local route recovery to establish a temporary route and periodical tree refreshing to maintain an optimal multicast tree. The performance of ACMP is evaluated via simulation.

A New Push-Pull Zero Voltage Switching Quasi-Resonant Converter: Topology, Analysis and Experimentation

Abstract: This paper introduces a new buck type push-pull Zero Voltage Switching Quasi Resonant Converter (ZVS-QRC) in continuous conduction mode. Theoretical analysis and design methodology for a 100W, 25 kHz laboratory prototype push-pull ZVS-QRC are discussed in this paper. The simulation and experimental results are also presented. The proposed converter is suitable for aerospace applications.

Decentralized Biased Controllers for Load — Frequency Control of Interconnected Power Systems Considering Governor Dead Band Non-Linearity

Abstract: A new approach for the design of decentralized biased controllers for load-frequency control of interconnected power systems is presented in this paper. In the design of load-frequency control system, it is a usual practice to use simple proportional plus integral (PI) controllers to reduce the area control error (ACE) of the system to zero in order to obtain satisfactory system performances. Generally, the PI controllers are designed based on either Integral Squared Error (ISE) criterion or Maximum Stability Margin (MSM) criterion. In this paper the optimum proportional plus integral biased controllers are designed based on the compromise between the ISE criterion as well as MSM criterion to obtain better transient and steady state responses. These biased controllers are designed and implemented in a two — area interconnected thermal power system with non-reheat turbines considering governor deadband (GDB) non-linearity. Based on the simulation results it is proved that the biased controllers provide better transient as well as steady state response and increased stability margin when compared with the responses obtained using conventional (PI) controllers designed using ISE criterion.

Comparison of Feature Selection Techniques for Detection of Malignant Tumor in Brain Images

Abstract: This paper presents and compares feature selection algorithms for the detection of glioblastoma multiforme in brain images. Texture features are extracted from normal and tumor regions (ROI) using spatial gray level dependence method and wavelet transform. An artificial neural network has been used for classification. A very difficult problem in classification. techniques is the choice of features to distinguish between classes. The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. Principal component analysis, classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features. The classification performance of 97.3% is achieved in GA with optimal features compared to sequential methods and Principal component analysis.

A Genetic Algorithm Based Approach for Static Virtual Topology Design in Optical Networks

Abstract: Optical networks based on WDM technology are potential candidates for future networks. The design of virtual topology using routing and wavelength assignment algorithms is the primary problem of optical network design. The problem being a NP-complete one, researchers have proposed a number of heuristic approaches. In this paper we present a genetic algorithm based approach to formulate and solve the virtual topology design problem in optical networks. The major design issues here are the limited number of available wavelengths on the links, routing of higher traffic requests on single hop lightpaths and a predefined traffic pattern. The results obtained in this approach are compared with the existing approaches. The results clearly indicate the effectiveness of the proposed approach.

Analysis of Batcing Policy in View of User Reneging in VOD System

Abstract: Several multimedia applications use video-on-demand (VOD) as basic technology. In a VOD system the number of concurrent video streams a server can support are limited by available server capacity. Several resource sharing techniques such as batching, patching and caching are employed for efficient use of server capacity. This paper primarily focuses on VOD server that employ batching as the resource sharing technique. While batching uses server and network resources efficiently, it leads to increase in the time of service (waiting time). If the time of service becomes too large, users may renege. It is a challenge for VOD system designers and developers to keep the time of service within the user's reneging tolerance with limited server resources. This study brings out a model to analyze proposed batching policy in view of different user reneging behaviors and gives out the optimum value of batching interval to maximize the average number of uses serviced and minimize reneging probability.

Transmission Line Fault Detection Using Time-Frequency Analysis

Abstract: A new approach for fault detection in power system network using time-frequency analysis is presented in this paper. The S-transform with complex window is used for generating frequency contours(S-contours), which distinguishes the faulted condition from no-fault. Here the fault current data for one cycle back and one cycle from the fault inception is processed through S-transform to generate time-frequency patterns with varying window. The generated time-frequency patterns clearly distinguishes the faulted condition from un-faulted.

Facial Expression Recognition Using Neural Classifier and Fuzzy Mapping

Abstract: Facial expression recognition is one of the most important aspects in recognizing human emotions and is a very interesting problem under the broad area of machine vision. In this paper we propose a method for recognition of facial expressions using a neural network classifier followed by a fuzzy mapping. We have tested our algorithm on JAFFE and AT & T database and we have received encouraging results.

Integrating Genetic Algorithms and Fuzzy c-Means for Anomaly Detection

Abstract: The goal of intrusion detection is to discover unauthorized use of computer systems. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate. We used data set from 1999 KDD intrusion detection contest.

Spectral Analysis of DC-DC Buck Converter with Chaotic Dynamics

Abstract: Chaos, a specific type of nonlinearity is commonly found in nonlinear deterministic systems. The DC-DC converters have been reported as exhibiting a wide range of bifurcation and chaotic behavior under certain conditions. Thus the design objective of Buck converter must include the stable operation of Buck converter at switching frequency and prevention of any bifurcation within the intended operating range. This paper reviews our studies on the observation of nonlinear, ubiquitous chaos in designed DC-DC buck converter whose output voltage is controlled by PWM switching, operating in continuous mode. Subharmonics, ultraharmonics and chaos of buck converter have been investigated and different route to chaos were derived by simulation and confirmed by numerical analysis. The nonperiodic appearance of the converter's response may suggest chaos. But it does not by itself prove that a response is chaotic. Quantitative measures like a distributed frequency spectrum of the system response is required to prove chaos Hence Time frequency analysis is used for investigating the frequency pattern of chaos generated in Buck converter circuit. Power spectrumn and spectrograms are used as principle tools for the qualitative evaluation of behavior of nonlinearity in Buck converter and the results confirm the advantages of this advanced method for better explanation and prediction of chaos.

Improved Genetic Algorithm Approach for Multi-Objective contingency constrained Reactive Power Planning

Abstract: Reactive Power Planning (RPP) is one of the important tasks in the operation and control of power system. This paper presents a Genetic Algorithm (GA) based approach for solving the contingency-constrained reactive power optimization problem. Voltage bus magnitude, transformer tap setting and reactive power generation of capacitor bank are the control variables. A binary-coded GA with tournament selection, two point crossover and bit-wise mutation is used to solve this complex optimization problem. In the proposed algorithm, some modifications are applied to the original GA in order to take into account the discrete nature of transformer tap setting and capacitor bank. The proposed approach has been evaluated with four different objective functions namely, loss minimization, voltage profile improvement, voltage stability enhancement and total cost minimization. Voltage stability level of the system is defined based on the L-index of load buses. The optimal locations of the VAR sources are also identified using the GA based algorithm. The proposed algorithm has been tested on IEEE 30-bus and IEEE 57-bus test systems and successful results have been obtained.

Application of Wavelet and Fourier Transforms for Vibration Analysis of Motor

Abstract: This paper describes the application of signal processing tools for a qualitative analysis of motor vibration subjected to different loading conditions The motor chosen here is a three phase squirrel cage Induction Motor of rating 15 kW Discrete wavelet transform (DWT) has been performed on the raw time-domain vibration signal correspond to the three different loading conditions viz-low, medium and high The frequency selective signals obtained through DWT at three different levels are then passed through a Fast Fourier Transform (FFT) routine to extract relative frequency information between different loading conditions

Digital Fuzzy Logic Controller using VHDL

Abstract: The hardware implementation of fuzzy logic controller(FLC) on FPGA is very important because of the increasing number of fuzzy applications requiring highly parallel and high speed fuzzy processing. This paper describes the hardware implementation of two input, one output fuzzy logic controller using VHDL. The architectural design is tested using Spartan FPGA chip xc2s300e-pq208. The maximum frequency of the clock running the FLC and the total number of gates required for the hardware implementation of fuzzy logic controller are compared for the proportionally increasing number of rules.

Optimal Capacitor Allocation in Radial Distribution Systems under APDRP

Abstract: Optimum location and size of capacitors for a distribution system under APDRP (Accelerated Power Development Programme) is presented. In the present study capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Genetic algorithm is used as an optimization tool, which obtains the optimal values and location of capacitors and minimizes the objective function, which is the power loss in the distribution network under study. A dedicated distribution system load flow is used to calculate power loss and voltage profile of the distribution system. Implementation aspects and important results for a 33 bus, 29 Indian power distribution system and practical 34 bus system have been presented to highlight the working of the algorithm.

Colour Image Segmentation Using Fuzzy Clustering Techniques

Abstract: Segmentation of an image entails the division or separation of the image into regions of similar attribute. The most basic attribute for segmentation of an image is its luminance amplitude for a monochrome image and color components for a color image. Fuzzy clustering is one of the methods used for image segmentation. This paper describes two fuzzy clustering methods to analyze and segment the color space. The clustering algorithms, namely, Fuzzy c means algorithm(FCM) and Possibilistic c means algorithm(PCM) are used for image segmentation. A self estimation algorithm has been developed for determining the number of clusters. The quality of the segmented image is estimated by their Peak Signal to noise ratio(PSNR).

Particle Methods for Risk Sensitive Filtering

Abstract: Risk sensitive filters (RSF) are known to be robust in the presence of uncertainties in the system parameters. Unfortunately these filters only admit closed form expressions for a very limited class of models including finite state-space Markov chains and linear Gaussian models. In this paper, we present an efficient Monte Carlo particle implementation of these filters for non-linear and non-Gaussian state-space models. This non-standard particle algorithm is based on a probabilistic interpretation of the RSF recursion. This algorithm significantly extends the range of applications of risk-sensitive techniques. Simulation results demonstrate the performance of the algorithm.