Electrical Engineering

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Shiv Nadar University

The tilting phenomenon may lead to a catastrophic failure, such as an interturn fault in transformers, under the action of axial short-circuit electromagnetic forces. In this paper, first, a state-space approach has been used to determine the eigenvalues of various winding configurations used in practice by assuming a small value of tilt angle up to 10°. These eigenvalues decide the critical tilting forces and the natural frequencies of windings. The four cases involving disk, layer, helical windings (all these with strip conductor), and layer winding with continuously transposed cable conductor have been analyzed. Further, initial critical tilt angles are determined for various magnitudes of the axial force in a typical 5-MVA transformer. © 2011 IEEE.

(SoE) School of Engineering

World University root node

IEEE Transactions on Power Delivery

Eigenvalue analysis for investigation of tilting of transformer winding conductors under axial short-circuit forces

We report a systematic study of structural, optical, and magnetic measurements on Zn0.9Cd0.1S:yCo films in the concentration range of 0.005 ≤ y ≤ 0.05 M using pulsed laser deposition technique. Structure, composition analysis, and optical measurements revealed that Cobalt is incorporated into the lattice, as Co2+ substituting Zn 2+ ions, forming a solid solution with cubic structure instead of Cobalt precipitates. Low temperature magnetization measurements reveal a paramagnetic behavior. UV-vis measurements showed a red shift with respect to undoped sample in the energy band gap with increasing Cobalt concentration. Photoluminescence measurements shows ∼ 300 times increase in intensity by Cobalt doping in Zn0.9Cd0.1S matrix. © 2011 Elsevier B.V.

Thin Solid Films

Study of the physical properties of PLD grown cobalt doped nanocrystalline Zn0.9Cd0.1S thin films

In this paper, We extended our previous work of QR-RLS based MIMO(Multiple input Multiple output) channel estimation to Mobile Wimax 802.16m system. Mobile wimax system provides high data rate, also fulfills user's requirement like VOD(Video on demand)at very high vehicle speed and also provides better cell coverage area. Channel estimation is crucial part to achieve this goals especially in fast fading environment. Generally, Mobile Wimax systems uses Preamble and Pilots for channel estimation purpose. In the proposed method both preamble and pilots are jointly used for robust channel estimation. At First, QR-RLS Estimator uses Preamble for coarse channel estimation at start of every frame. Once the coarse channel is estimated, then pilots (scattered throughout time-frequency grid) are jointly used with the coarse channel component to derive the channel fading rate. This fading rate is then used to finely estimate the channel at pilot as well as data subcarrier. Thus robust estimation results without adding any overhead. Jointly estimated channel is then used with QR-LRL based data detection, where hard decision values are calculated. Simulation results are shown under various slow-fast channel fading conditions. Results are compared with pilot based channel estimation with LS(least square) interpolation, which shows that joint coarse-Fine estimation gives better performance. © 2011 IEEE.

GLOBECOM - IEEE Global Telecommunications Conference

A joint QR-LS based coarse-fine channel estimation and QR-LRL detection for mobile WiMAX 802.16m

We have proposed a hybrid SVM based decision tree to speedup SVMs in its testing phase for binary classification tasks. While most existing methods addressed towards this task aim at reducing the number of support vectors, we have focused on reducing the number of test datapoints that need SVM's help in getting classified. The central idea is to approximate the decision boundary of SVM using decision trees. The resulting tree is a hybrid tree in the sense that it has both univariate and multivariate (SVM) nodes. The hybrid tree takes SVM's help only in classifying crucial datapoints lying near decision boundary; remaining less crucial datapoints are classified by fast univariate nodes. The classification accuracy of the hybrid tree is guaranteed by tuning a threshold parameter. Extensive computational comparisons on 19 publicly available datasets indicate that the proposed method achieves significant speedup when compared to SVMs, without any compromise in classification accuracy. © 2010 Elsevier Ltd. ALL rights reserved.

Pattern Recognition

A hybrid SVM based decision tree

This paper presents a MIMO two-way relaying scheme where the transceiver filter at the Relay Station (RS) processes the data using Recursive Least Square (RLS) technique based on Inverse QR (IQR) algorithm. The relaying technique used is Amplify and Forward scheme (AF). The channel is assumed to be frequency selective and the Channel State Information (CSI) is available only at the RS. So the transmit and receive processing are both done at the RS. Orthogonal Frequency Division Multiplexing (OFDM) is used so as to combat the Inter-Symbol Interference (ISI) occurring due to the frequency selectivity of the channel. The performance of the IQR-RLS algorithm is verified by comparing its Mean-Square error performance (MSE) with that of a transceiver filter based on Normalized Least Mean Square (NLMS) algorithm. ©2010 IEEE.

Proceedings - 2010 2nd UK-India-IDRC International Workshop on Cognitive Wireless Systems, UKIWCWS 2010

An IQR-RLS based transceiver filter for MIMO two-way relaying in frequency selective environment

In this paper, we propose a learning based approach for alias minimization of 1-D signals. Given an under-sampled test speech signal and a training set consisting of several speech signals each of which are under-sampled as well as sampled at greater than Nyquist rate, we estimate the non-aliased frequencies for the test signal using the training set. The learning of non-aliased frequencies corresponds to estimating them using a training set. The test signal and each of the under-sampled training set signal are first interpolated to the size of The non-aliased signals. They are then divided into a number of segments and discrete cosine transform (DCT) is computed for each segment. Assuming that the lower frequencies are non-aliased and minimally distorted, we replace the aliased DCT coefficients of the test signal with the best search from the training set. The non-aliased test signal is then re-constructed by taking the inverse DCT. The comparison with the standard interpolation technique in terms of both subjective and quantitative analysis indicates better performance. © 2010 IEEE.

Midwest Symposium on Circuits and Systems

Alias minimization of 1-D signals using DCT based learning

In this paper, A novel method for OFDM-MIMO channel estimation using QR-RLS(Square Root-Recursive Least Square) estimator is presented. Preamble aided channel estimation is performed in time-domain, estimated channel is then used for data detection during data transmission within that frame. The performance results are compared with wiener RLS channel estimator in terms of channel estimator MSE performance. Wiener based Standard-RLS estimator uses correlation matrix inverse for estimation and recursion, Correlation matrix may become singular under low noise/high correlated channels, results in round-off error. On the other hand, Square-Root estimator use QR-factorization of the correlation matrix and thus avoids square matrix inverse results in less computations as well as less roundoff error. The simulation results shows that square root QR-RLS estimator give better results in terms of Estimation error. © 2010 IEEE.

2010 6th Conference on Wireless Advanced, WiAD 2010

A novel training based QR-RLS channel estimator for MIMO OFDM systems

We present a detailed study of low-K/Cu structures under short duration pulse regime and establish a microscopic understanding of breakdown behavior under high current stressing. Random and anomalous behavior of the breakdown characteristics observed under very fast pulsing conditions are explained through electro-thermal instability. A model based on extensive experimental study has been developed to show switching behavior of a conducting path (short failure) due to meltdown of copper. The dumping of critical energy can lead to permanent damage and leads to an open failure. Established breakdown model has been critically linked to the material behavior and extrapolated to understand the TDDB behavior of the low K dielectric. © 2010 IEEE.

IEEE International Reliability Physics Symposium Proceedings

Investigating the electro-thermal origin of breakdown in low-K/Cu dielectrics under short duration over stressed pulsed regime

Drain Extended (DE-NMOS) ESD protection devices exhibit low ESD performance due to poor bipolar action. The purpose of this work is to investigate the role of surface bipolar phenomenon in 90nm DE-NMOS, which triggers failure due to bipolar turn-on, and next study the impact of adding source/drain resistors on their ESD robustness.

Proceedings of the International Symposium on Power Semiconductor Devices and ICs

Improving surface bipolar activity in thin gate oxide DE-NMOS - A critical HV I/O protection element for nano-meter scale technologies

Linear Support Vector Machines (SVMs) have been used successfully to classify text documents into set of concepts. With the increasing number of linear SVM formulations and decomposition algorithms publicly available, this paper performs a study on their efficiency and efficacy for text categorization tasks. Eight publicly available implementations are investigated in terms of Break Even Point (BEP), F1 measure, ROC plots, learning speed and sensitivity to penalty parameter, based on the experimental results on two benchmark text corpuses. The results show that out of the eight implementations, SVM lin and Proximal SVM perform better in terms of consistent performance and reduced training time. However being an extremely simple algorithm with training time independent of the penalty parameter and the category for which training is being done, Proximal SVM is appealing. We further investigated fuzzy proximal SVM on both the text corpuses; it showed improved generalization over proximal SVM. © 2010 IEEE.