Electrical Engineering

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A critical understanding of self ballasting behavior due to current crowding of avalanche generated carriers in a DeNMOS is developed. Then we study its performance under the gate biased conditions. The impact of flow of holes and electron in the bulk and across the surface - on the snapback-back features has been critically evaluated through variations in the device structure (associated with process parameter) which has also been extensively studied through 2D & 3D TCAD simulations. We demonstrate that after an initial homogeneous triggering (due to bipolar snapback), self heating preferentially activates the 2D array of bipolars in the bulk and subsequently current instability under negative resistance regime (as the bipolar turns on) leads to inhomogeneous triggering in the 3D. © 2011 IEEE.

(SoE) School of Engineering

World University root node

Proceedings of the International Symposium on Power Semiconductor Devices and ICs

Modeling the 3D self ballasting behavior and filamentation under high current stressing in DeNMOS

In this paper, a Hamilton-Jacobi-Bellman (HJB) equation-based optimal control algorithm for robust controller design is proposed for nonlinear systems. The HJB equation is formulated using a suitable nonquadratic term in the performance functional to tackle constraints on the control input. Utilizing the direct method of Lyapunov stability, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort and the maximum bound on system uncertainty. The bounded controller requires the knowledge of the upper bound of system uncertainty. In the proposed algorithm, neural network is used to approximate the solution of HJB equation using least squares method. Proposed algorithm has been applied on the nonlinear system with matched and unmatched type system uncertainties and uncertainties in the input matrix. Necessary theoretical and simulation results are presented to validate proposed algorithm. © 2010 Springer-Verlag London Limited.

Neural Computing and Applications

Bounded robust control of nonlinear systems using neural network-based HJB solution

Zn0.9Cd0.1S:yCo nanoparticles were prepared by a co-precipitation method at low temperature. The obtained products were identified to be of cubic structure without any impurity phase. Cobalt incorporation leads to an increase in the local strain value and a decrease in the lattice constants as measured from XRD. Magnetic measurements showed that cobalt was incorporated in the Zn0.9Cd0.1S lattice as Co2+ and substituted for the Zn site as there was no evidence of the presence of metallic cobalt. Transmission electron microscopy suggests the crystalline nature of nanoparticles, with average particle size of ∼3.5 nm. UV-Vis measurements showed a red shift with respect to undoped nanoparticles in energy band gap with increasing cobalt concentration. Photoluminescence spectra reveal the defect-related emissions. The decay time constant is found to be in the nanosecond regime and is attributed to the spatial confinement of photo generated electron-hole pairs. © 2010 Springer-Verlag.

Applied Physics A: Materials Science and Processing

Optical, structural and magnetic properties of Zn0.9Cd 0.1S:yCo nanoparticles

The main goal of research reported in this paper is to propose a new technique for schemata count of Genetic Algorithms. Till recently, no methods existed for counting the schemata except the manual one that was obviously cursed by the tedium involved. A substantially generalized expression has been developed that directly computes the number of unique (different) schemata in a population, using breakup of similarity bits for non-repeated strings. © 2010 Elsevier B.V. All rights reserved.

Applied Soft Computing Journal

Towards generalized expression for schemata count

This article addresses the problem of adaptive control of nonlinear chemical processes with time-varying dynamics. The Q-learning and policy-iteration algorithms have been considered in the reinforcement learning (RL) framework. The performance of the two algorithms has been tested on highly nonlinear simulated continuous stirred tank reactor (CSTR). Comparison with conventional methods shows that the RL techniques are able to achieve better performance, and robustness against uncertainties. The policy-iteration algorithm is relatively faster in convergence, and better in robustness. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.

Asia-Pacific Journal of Chemical Engineering

Reinforcement learning framework for adaptive control of nonlinear chemical processes

We present an improved version of one-against-all method for multiclass SVM classification based on subset sample selection, named reduced one-against-all, to achieve high performance in large multiclass problems. Reduced one-against-all drastically decreases the computing effort involved in training one-against-all classifiers, without any compromise in classification accuracy. Computational comparisons on publicly available datasets indicate that the proposed method has comparable accuracy to that of conventional one-against-all method, but with an order of magnitude faster. On the largest dataset considered, reduced one-against-all method achieved 50% reduction in computing time over one-against-all method for almost the same classification accuracy. We further investigated reduced one-against-all with linear kernel for multi-label text categorization applications. Computational results demonstrate the effectiveness of the proposed method on both the text corpuses considered. © 2011 Elsevier Ltd. All rights reserved.

Expert Systems with Applications

Reduced one-against-all method for multiclass SVM classification

In this paper, we present an analysis of drain extended n-channel metaloxidesemiconductor (DeNMOS) and study the impact of both substrate and gate biasing on the regenerative avalanche injection phenomenon at the edge of drain contact. We will demonstrate that the flow and distribution of avalanche-generated holes and electrons are significantly impacted by biasing the gate and pumping current through the substrate. Finally, we show that gate bias or drain bias, when individually applied, can only lead to marginal improvement in It2 however, when both the biases are applied simultaneously, it can then optimally improve the failure performance. Subsequently, we compare high current performance of DeNMOS with NMOS or swapped DeNMOS configuration through a simplified 1-D macroscopic model. © 2006 IEEE.

IEEE Transactions on Electron Devices

An insight into ESD behavior of nanometer-scale drain extended NMOS (DeNMOS) devices: Part II (two-dimensional study-biasing &amp; comparison with NMOS)

Bamboo as a substitute to timber, in housing, has occupied the attention of researchers due to concerns of deforestation and the consequent impact on environment. The use of bamboo as a load distributor is well accepted in rural areas. Replacing timber with bamboo as a structural load bearing element could have far reaching consequences for rural housing. This in turn would have implications for greening wastelands and earning carbon credits. However, introduction of innovations is more than a technology issue. The paper deals with a possible approach which could entail acceptability and sustainability of the innovation. Based on field inputs from the State of Jharkhand, the study brings to light that, for better acceptance, it is preferable that minimal changes are introduced in the existing houses, only on those aspects, where constraints and limitations are felt. Thus, going in for a totally new model of innovative housing is not a preferred option. The present paper proposes a participatory approach involving awareness building, through grassroots organisation, supported with skill development to transfer the technology in the rural housing structure.

Journal of Rural Development

Strategising innovations in rural housing using bamboo

In this paper, we propose a novel framework for segmentation of documents with complex layouts. The document segmentation is performed by combination of clustering and conditional random fields (CRF) based modeling. The bottom-up approach for segmentation assigns each pixel to a cluster plane based on color intensity. A CRF based discriminative model is learned to extract the local neighborhood information in different cluster/color planes. The final category assignment is done by a top-level CRF based on the semantic correlation learned across clusters. The proposed framework has been extensively tested on multi-colored document images with text overlapping graphics/image. © 2011 IEEE.

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

A CRF based scheme for overlapping multi-colored text graphics separation

Indexing and retrieval performance over digitized document collection significantly depends on the performance of available Optical Character Recognition (OCR). The paper presents a novel document indexing framework which attends the document digitization errors in the indexing process to improve the overall retrieval accuracy. The proposed indexing framework is based on topic modeling using Latent Dirichlet Allocation (LDA). The OCR's confidence in correctly recognizing a symbol is propagated in topic learning process such that semantic grouping of word examples carefully distinguishes between commonly confusing words. We present a novel application of Lucene with topic modeling for document indexing application. The experimental evaluation of the proposed framework is presented on document collection belonging to Devanagari script. © 2011 IEEE.