Electrical Engineering

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Shiv Nadar University

The paper presents application of multiple features for word based document image indexing and retrieval. A novel framework to perform Multiple Kernel Learning for indexing using the Kernel based Distance Based Hashing is proposed. The Genetic Algorithm based framework is used for optimization. Two different features representing the structural organization of word shape are defined. The optimal combination of both the features for indexing is learned by performing MKL. The retrieval results for document collection belonging to Devanagari script are presented. © 2010 IEEE.

World University root node

(SoE) School of Engineering

Proceedings - International Conference on Pattern Recognition

Document image retrieval using feature combination in Kernel space

For sequential design processes, the min-max strategy minimizes the worst-case performance cost. This is a game against nature, where the agent attempts to minimize a specified cost criterion, while nature attempts to maximize it. In this paper, we formulate the problem of decision making under uncertainty as a game in which the opponent (nature) is " disinterested" and plays at random, while the agent forces to pick a strategy that maximizes the probability of wining. The potency of proposed worst-case design strategy for games against nature has been established through simulation experiments on inverted-pendulum swing-up. Simulation results show the accelerated learning, and better relative stability of the system. ©2010 IEEE.

Proceedings - 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010

Markov game based control: Worst case design strategies for games against nature

Non-Destructive Testing Conference 2010, NDT 2010

Matched energy comparison of the pulse, lock-in and frequency modulated thermography NDE techniques

Cu-doped Zn1-xCdxS nanoparticles were synthesized by coprecipitation technique in ice bath at 280 K. The band gap energy of Zn 1-xCdxS:Cu nanoparticles can be tuned to a lower energy by increasing the Cd content, indicating the formation of the alloyed nanoparticles. The alloy structure is further supported by the systematic shifting of characteristic x-ray diffraction peaks to lower angles with increase in Cd content. Systematic copper doping induces a red shift in the energy band gap of Zn0.9Cd0.1S:Cu nanoparticles with increase in copper concentration. Cu-doped Zn0.9Cd0.1S nanoparticles were found to have ferromagnetic nature at 5 K whereas undoped particles were found to be diamagnetic. Green luminescence further proves proper doping of Cu into the ZnCdS matrix. It is believed that the green luminescence originates from the recombination between the shallow donor level (sulfur vacancy) and the t2 level of Cu2+. This method provides an inexpensive and simple procedure to produce ternary ZnCdS:Cu nanoparticles with tunable optical properties via changing Cd and/or Cu concentrations. © 2010 American Institute of Physics.

Fulltext

Journal of Applied Physics

Study of composition dependent structural, optical, and magnetic properties of Cu-doped Zn1-xCdxS nanoparticles

The paper presents three novel features for handwritten data based identity recognition. A novel framework for combining the features for identification is presented. The framework combines the features in kernel space in MKL based framework. The application of features individually and in combination is presented for writer recognition and signature verification. The writer recognition results have been presented for Devanagari script input and signature verification results have been presented for open dataset [1]. The experiments have shown encouraging results. © 2010 IEEE.

Proceedings - 12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010

Identity determination with offline handwritten input using multi kernel feature combination

The present work is part of ongoing effort to improve the performance of Gujarati character recognition. In the recent advancement in kernel methods, the novel concept of multiple kernel learning(MKL) has given improved results for many problems. In this paper, we present novel application ofMKL for Gujarati character recognition. We have applied three different feature representations for symbols obtained after zone wise segmentation of Gujarati text. TheMKLbased classification is proposed, where the MKL is used for learning optimal combination of different features for classification. In addition MKL based classification results for different features is also presented. The multiclass classification is performed in Decision DAG framework. The comparison results in 1-Vs-1 framework and using KNN classifier is also presented. The experiments have shown substantial improvement in earlier results. © Copyright 2010 ACM.

ACM International Conference Proceeding Series

Use of MKL as symbol classifier for Gujarati character recognition

Zn1-xCdxS Nanoparticles with varying Cd concentration were synthesized via co-precipitation technique at 280 K. Despite the analogous size, the nanoparticles with different Cd content exhibit composition-dependent absorption resulting from the distinguished band gap. Due to size effect Zn1-xCdxS nanoparticles, self activated PL band shifted to higher energies compared to the absorption band of bulk counterpart. Origin of the PL emission bands are investigated via fluorescence lifetime measurement. Decay time constant is found in nano second regime and is attributed to the spatial confinement of photo generated electron-hole pairs. © 2010 Bentham Science Publishers Ltd.

Current Nanoscience

Optical properties of Zn1-xCdxS nanoparticles in the quantum confinement regime

The basic component of an electromagnetic levitation systems are inductance and resistance of the actuator, input DC link voltage, mass of the payload, air-gap between the pole face of the magnet and ferromagnetic guide-way, controller parameters etc. All these parameters are supposed to get change in real life situation. Electromagnetic levitation system (EMLS) is inherently unstable and strongly nonlinear in nature. In most cases a classical controller has been designed for the linear model around an operating point for maintaining overall closed-loop stability. In this work, a cascade lead compensation control scheme utilizing inner current loop and outer position loop has been designed and implemented for stabilization of a single magnet based single axis levitation system. The prototype has been successfully tested and stable levitation has been demonstrated at the desired operating gap. The performances of the linear controller with the change in different parameters have been studied experimentally. The dynamic performance of the controller has been tested by applying different disturbance inputs (square wave, sinusoidal etc.) with increasing magnitudes and frequencies. The parameters of the designed controller also get varied during experimentation. So a sensitivity study of the controller for an EMLS is also important. © JES 2010.

Journal of Electrical Systems

Performance study of the controller of an attraction type levitation system under parametric change

Multiclass support vector machine (SVM) methods are well studied in recent literature. Comparison studies on UCI/statlog multiclass datasets suggest using one-against-one method for multiclass SVM classification. However, in unilabel (multiclass) text categorization with SVMs, no comparison studies exist with one-against-one and other methods, e.g. one-against-all and several well-known improvements to these approaches. In this paper, we bridge this gap by performing empirical comparison of standard one-against-all and one-against-one, together with three improvements to these standard approaches for unilabel text categorization with SVM as base binary learner. We performed all our experiments on three standard text corpuses using two types of document representation. Outcome of our experiments partly support Rifkin and Klautau's (2004) statement that, for small scale unilabel text categorization tasks, if parameters of the classifiers are well tuned, one-against-all will have better performance than one-against-one and other methods. © 2010 Elsevier B.V. All rights reserved.

Pattern Recognition Letters

A comparison study on multiple binary-class SVM methods for unilabel text categorization

This letter proposes Inverse QR two-dimensional Recursive Least Square (IQR-2D-RLS) adaptive channel estimation for Orthogonal. Frequency Division Multiplexing (OFDM) systems (using Givens Rotations and Householder Transformations). It is more stable numerically than 2D-RLS algorithm. MATLAB simulations show that BER performance of IQR-2D-RLS algorithm is similar to that of 2D-RLS algorithm. Copyright © 2010 The Institute of Electronics, Information and Communication Engineers.