Electrical Engineering

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Shiv Nadar University

Today an average transmission line is loaded more heavily than ever before, and this has given rise to a serious problem of voltage instability. The authors present a novel method for power system voltage instability estimation and improvement using ANN. The method is based on the fact that reactive power injections at critical buses of the power system help to steer the system away from a developing voltage collapse. The location and quantum of reactive power support has been computed based on sensitivity. The sensitivity matrix is formulated by cascading the output/input sensitivity of multiplayer perceptron model with that of input features versus reactive power injections. The effectiveness of the proposed method is demonstrated on Ward-Hale 6-bus, IEEE 14-bus, and IEEE 30-bus test systems. The method can be effectively used to make the system secure against voltage collapse condition in system planning and online operation.

World University root node

(SoE) School of Engineering

International Journal of Power and Energy Systems

Sensitivity-based hybrid approach for voltage instability alleviation using ANN

Genomic Signal Processing is an emerging interdisciplinary area. The problem of Identifying Protein Coding Regions in DNA is addressed using signal processing techniques in this paper. DNA can be thought as a string formed from the alphabet set script A = {A, C, G, T}. It is found that, in protein coding regions the symbols have periodicity of 3 [1], which can be used as a cue to identify the protein coding regions using signal processing techniques. This is possible only if the symbol sequences are mapped to numbers. In this paper a new lower dimensional mapping is proposed which reduces the computational complexity by half, producing results nearly equal to those produced by a higher dimensional mapping. ©2007 IEEE.

IEEE Region 10 Annual International Conference, Proceedings/TENCON

2-Simplex mapping for identifying the protein coding regions in DNA

This paper proposes a reinforcement learning (RL)-based game-theoretic formulation for designing robust controllers for nonlinear systems affected by bounded external disturbances and parametric uncertainties. Based on the theory of Markov games, we consider a differential game in which a 'disturbing' agent tries to make worst possible disturbance while a 'control' agent tries to make best control input. The problem is formulated as finding a min-max solution of a value function. We propose an online procedure for learning optimal value function and for calculating a robust control policy. Proposed game-theoretic paradigm has been tested on the control task of a highly nonlinear two-link robot system. We compare the performance of proposed Markov game controller with a standard RL-based robust controller, and an H∞ theory-based robust game controller. For the robot control task, the proposed controller achieved superior robustness to changes in payload mass and external disturbances, over other control schemes. Results also validate the effectiveness of neural networks in extending the Markov game framework to problems with continuous state-action spaces. © 2006 Elsevier B.V. All rights reserved.

Applied Soft Computing Journal

A robust Markov game controller for nonlinear systems

ECS Transactions

Parasitic effects depending on shape of spacer region on FinFETs

Second Breakdown phenomenon (It2) in drain extended NMOS (DENMOS) which is associated with complex triggering of the parasitic BJT is relatively less understood. We present experiments and models to understand the physics of snapback in DENMOS in nanometer scale technologies. Avalanche injection phenomenon at the drain contact has been analyzed for a 90 nm DENMOS transistor under high current stressing. © 2007 IEEE.

Annual Proceedings - Reliability Physics (Symposium)

An insight into the high current ESD behavior of drain extended NMOS (DENMOS) devices in nanometer scale CMOS technologies

This paper highlights the modeling, simulation and detection of deposited scales in boiler tubes by Pulsed Thermography (PT). A novel data analysis method, based on variations of slopes in Temperature-Time profiles approach has been adopted, to estimate subsurface scale thickness and material loss in the tubes. The slopes are obtained for different pixels in the field of view of the captured image sequence during transient heating of the boiler tube. Relative thickness variations due to material loss and the deposited scales of boiler tube have been estimated from the calculated slopes. Experimental results are presented in support.

Proceedings of SPIE - The International Society for Optical Engineering

Thermal non-destructive evaluation of scaling in boiler tubes

The alias-free nature of non uniform sampling schemes can be used to design wideband digitizers. Existing non uniform digitizer designs have a finite alias-free bandwidth because of time quantization of sampling instants. This paper proposes a new design for a non uniform digitizer in which the sampling instants are no longer time quantized. Hence, there is no upper limit on the alias-free bandwidth and the entire analog bandwidth of the digitizer front end can be used for alias-free sampling of a wideband analog waveform. ©2007 IEEE.

Non uniform digitizer for alias-free sampling of wideband analog signals

When performing classification of large set of samples, Neural Trees (NTs) are preferably used. To circumvent the problem of poor generalization of Neural Trees, hybrid Neural Trees have been proposed. Recently hybrid SVM based Neural Tree has been shown to be an effective binary classifier. In this paper, we examine the performance of SVM based Neural Trees relative to the nonlinear SVMs. We observe that nonlinear SVMs are more effective, though at higher computational cost. Our conclusions will provide important guidelines in data mining applications on real world datasets. © 2007 IEEE.

Proceedings - International Conference on Information Technology-New Generations, ITNG 2007

Binary classification by SVM based neural Trees and nonlinear SVMs

In this paper, QR-RLS based IIR adaptive channel TEQ(Time Domain Equalizer) to make the OFDM systems robust for delay spreads exceeding the CP(cyclic prefix) is presented. IIR based TEQ results in much shorter length equalizer compared to FIR based TEQ, at the same time guarantee for global convergence within the training period. The length of zeros of the proposed QR-IIR TEQ is restricted to CP. Givens rotation based QRD updation of TEQ coefficients results in computational advantage. The performance of the proposed method is then compared with conventional LMS and QR-FIR TEQ in terms of convergence, computational complexity and BER. The simulation results show that proposed QR-IIR method gives better results as compared to the aforementioned methods.

2007 3rd International Conference on Wireless Communication and Sensor Networks, WCSN

QR-RLS based adaptive channel shortening IIR-TEQ for OFDM wireless LAN

A new computationally efficient channel shortening technique, to make the OFDM systems robust for delay spreads exceeding the cyclic prefix is presented. The proposed technique gives same BER performance with shorter equalizer. The method is based on pole-zero modeling of the channel impulse response. This gives low computational complexity, both during initialization and data transmission. Moreover no delay search is required as compared with other methods. IEEE 802.11a is considered as underlying OFDM system. ©2007 IEEE.