R. Rajesh

T-S fuzzy model with nonlinear consequence and PDC controller for a class of nonlinear control systems

In this paper a new Takagi-Sugeno (T-S) fuzzy model with nonlinear consequence (TSFMNC) is presented which can approximate a class of smooth nonlinear systems, nonlinear dynamical systems and nonlinear control systems. It is also proved that Takagi-Sugeno fuzzy controller with nonlinear consequence (TSFCNC) can be used to approximate a class of nonlinear state-feedback controllers using the so-called parallel distributed compensation (PDC) method. The inverted pendulum problem has been simulated with TSFCNC and compared with Takagi-Sugeno fuzzy controller with linear consequence (TSFCLC) and the results show that TSFCNC performs better than TSFCLC. A real-life example of dynamic positioning of ship is simulated and the results also show that TSFCNC performs better than TSFCLC.

Network intrusion detection using feature selection and Decision tree classifier

Security of computers and the networks that connect them is increasingly becoming of great significance. Machine learning techniques such as Decision trees have been applied to the field of intrusion detection. Machine learning techniques can learn normal and anomalous patterns from training data and generate classifiers that are used to detect attacks on computer system. In general the input to classifiers is in a high dimension feature space, but not all features are relevant to the classes to be classified. Feature selection is a very important step in classification since the inclusion of irrelevant and redundant features often degrade the performance of classification algorithms both in speed and accuracy. In this paper, we have considered three different approaches for feature selection, Chi square, Information Gain and ReliefF which is based on filter approach. A comparative study of the three approaches is done using decision tree as classifier. The KDDcup 99 data set is used to train and test the decision tree classifiers.

Image Segmentation - A Survey of Soft Computing Approaches

Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Segmentation is an essential step in image processing since it conditions the quality of the resulting interpretation. Lots of approaches have been proposed and a dense literature is available In order to extract as much information as possible from an environment, multicomponent images can be used. In the last decade, multicomponent images segmentation has received a great deal of attention for soft computing applications because it significantly improves the discrimination and the recognition capabilities compared with gray-level image segmentation methods. In this paper, the main aim is to understand the soft computing approach to image segmentation.

A modified ant colony optimization based approach for image edge detection

Ant Colony Optimization (ACO) is used to detect edges in digital images. Such techniques generate a pheromone matrix that represents the edge information at each pixel position on the routes formed by ants dispatched on the image. In this paper a modified ACO-based edge detection is proposed. Ants try to find possible edges by using a heuristic information based on the degree of edginess of each pixel. The proposed ACO-based approach also takes advantage of the fuzzy clustering to determine whether a pixel is edge or not. Experimental results demonstrate superior performance of the proposed approach.

Coherence vector of Oriented Gradients for traffic sign recognition using Neural Networks

This paper makes use of Coherence Vector of Oriented Gradients (CVOG) for traffic sign recognition. Experiments are conducted on German Traffic Sign benchmark dataset. The results on traffic sign recognition using CVOG features with neural network classifier is promising. The results based on the combination of other features gave better recognition rates.

An Improved Association Rule Mining Technique for Xml Data Using Xquery and Apriori Algorithm

The usage of XML data in the World Wide Web and elsewhere as a standard for the exchange of data and to represent semi structured data tends to develop the various tools and techniques to perform various data mining operations on XML documents and XML repositories. In recent years, several encouraging methods have been identified and developed for mining XML data. In this paper, we present an improved framework for mining association rules from XML data using XQUERY and . NET based implementation of Apriori algorithm.

Spectral histogram of oriented gradients (SHOGs) for Tamil language male/female speaker classification

Gender (Male/Female) classification plays a primary vital role to develop a robust Automatic Tamil Speech Recognition (ASR) applications due to the diversity in the vocal tract of speakers. Various features including Formants (F1, F2, F3, F4), Zero Crossings, and Mel-Frequency Cepstral Coefficients (MFCCs) etc. have appeared in the literature especially for speech/signal classification/recognition. Recently Dalal et al. have proposed a feature called as Histogram of Oriented Gradients (HOG) for extracting feature from an image for efficient detection/classification of objects. We extend and apply the HOG for spectrogram of speech signal and hence called as Spectral Histogram of Oriented Gradients (SHOGs). The results of Tamil language male/female speaker classification using SHOGs features shows good improvement in the classification rate when compared to other features. The results of combination of various features with SHOGs are also promissing.

GAVLC: GA with Variable Length Chromosome for the simultaneous design and stability analysis of T-S fuzzy controllers

Most of the design techniques of T-S fuzzy controllers assumes that there exists an approximate T-S model of the system with fixed antecedent parts & rules and uses techniques like GA, LMI, etc for the optimal design of the gain values. This paper presents a novel integrated approach for the design and stability analysis of T-S fuzzy controllers using GA with variable length chromosomes (VLCs) and LMI. This approach helps to find out the optimal parameters of the antecedent parts of the rules along with rule optimization and also to optimize the consequent parts.

On experimenting with pedestrian classification using neural network

Pedestrian classification is addressed by T. Watanabe et al. using SVM with 34704 CoHOG features. This paper addresses the pedestrian classification using neural network with 1344 CoHOG features (feature size is 25 times small) and still achieve comparable results.

A note on fingerprint recognition systems

Among all the techniques developed for personal authentication, fingerprint recognition system is the most visible one due to its wide range of successful applications in many disciplines such as computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services, etc. This paper presents a note on fingerprint recognition systems.