(SoE) School of Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

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.

Electrical 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)

We propose a methodology for recognizing actions at a distance by watching the human poses and deriving descriptors that capture the motion patterns of the poses. Human poses often carry a strong visual sense (intended meaning) which describes the related action unambiguously. But identifying the intended meaning of poses is a challenging task because of their variability and such variations in poses lead to visual sense ambiguity. From a large vocabulary of poses (visual words) we prune out ambiguous poses and extract key poses (or key words) using centrality measure of graph connectivity [1]. Under this framework, finding the key poses for a given sense (i.e., action type) amounts to constructing a graph with poses as vertices and then identifying the most "important" vertices in the graph (following centrality theory). The results on four standard activity recognition datasets show the efficacy of our approach when compared to the present state of the art. © 2011 Springer-Verlag Berlin Heidelberg.

Computer Science and Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Modeling sense disambiguation of human pose: Recognizing action at a distance by key poses

In this paper, we propose a graph theoretic technique for recognizing human actions at a distance in a video by modeling the visual senses associated with poses. The proposed methodology follows a bag-of-word approach that starts with a large vocabulary of poses (visual words) and derives a refined and compact codebook of key poses using centrality measure of graph connectivity. We introduce a meaningful threshold on centrality measure that selects key poses for each action type. Our contribution includes a novel pose descriptor based on histogram of oriented optical flow evaluated in a hierarchical fashion on a video frame. This pose descriptor combines both pose information and motion pattern of the human performer into a multidimensional feature vector. We evaluate our methodology on four standard activity-recognition datasets demonstrating the superiority of our method over the state-of-the-art. © 2011 IEEE.

IEEE Transactions on Circuits and Systems for Video Technology

Recognizing human action at a distance in video by key poses

Estimating the criticality of each link in a transportation network is a crucial step for guiding the design and deployment of vulnerability reduction measures. Using exhaustive simulations based on user-equilibrium assignment to evaluate the effect of the failure of each link (or set of links) can be prohibitively time-consuming for reasonably-sized transportation networks. In this paper, we propose an alternative heuristic approach to the estimation the vulnerability of network links employing efficient graph-theoretical algorithms. In particular, our model estimates the cost of single link failure based on the increase in shortest path travel time taking into account the effect of congestion. Results show that the proposed method can reliably be used to estimate the relative effect of each link failure on the system travel time and rank links accordingly. © 2011 IEEE.