Motilal Nehru National Institute of Technology Allahabad

About: Motilal Nehru National Institute of Technology Allahabad is a education organization based out in Allahabad, Uttar Pradesh, India. It is known for research contribution in the topics: Control theory & Electric power system. The organization has 2475 authors who have published 5067 publications receiving 61891 citations. The organization is also known as: NIT Allahabad & Motilal Nehru Regional Engineering College.

Analytical Modeling of a Triple Material Double Gate TFET with Hetero-Dielectric Gate Stack

Abstract: In this paper, we propose and develop an analytical model of a Triple material double gate Tunnel Field Effect Transistor (TM-DG TFET) with hetero-dielectric gate oxide stack comprising of SiO2 and HfO2. The two-dimensional Poisson’s equation has been solved using parabolic-approximation method to model the channel potential and electric field. Analytical model of drain current is developed by integrating the band-to-band tunneling generation rate over the channel thickness (tsi) and shortest tunneling path ($L_{\min }$). A Transconductance model is also developed using this drain current model. The proposed TM-DG TFET also provides better result with reference to input-output characteristics, subthreshold swing, ION/IOFF current ratio and ambipolar effect compared to the dual material double gate (DM-DG) TFET. The analytical model has been validated with the numerical data obtained from commercial TCAD software.

Fingerprint Based Male-Female Classification

Abstract: Male-female classification from a fingerprint is an important step in forensic science, anthropological and medical studies to reduce the efforts required for searching a person. The aim of this research is to establish a relationship between gender and the fingerprint using some special features such as ridge density, ridge thickness to valley thickness ratio (RTVTR) and ridge width. Ahmed Badawi et. al. showed that male-female classification can be done correctly upto 88.5% based on white lines count, RTVTR & ridge count using Neural Network as Classifier. We have used RTVTR, ridge width and ridge density for classification and SVM as classifier. We have found male-female can be correctly classified upto 91%.

Neural networks and fuzzy logic-based spark advance control of SI engines

Abstract: Research highlights? A combined neural network and fuzzy logic-based control scheme is designed for SA control. ? The controller is designed to maintain LPP of SI engine close to 160 ATDC. ? The controller works in conjunction with RNN model for cylinder pressure identification. ? The controller gives desired performance and is found to be robust. In SI engines, spark advance (SA) needs to be controlled to get Maximum Brake Torque (MBT) timing. Spark advance can be controlled either by open loop or by closed loop controller. The open loop controller requires extensive testing and calibration of engine, to develop look up tables. In closed loop controller, empirical rules relating variables deduced from cylinder pressure are used. One of such empirical rules is to fix location of peak pressure (LPP) at a desired value of the crank angle. In the present work, a combined neural network and fuzzy logic-based control scheme is designed for SA control to get MBT timing. The fuzzy logic controller is designed to maintain LPP of SI engine close to 16? ATDC. The controller works in conjunction with Recurrent Neural Network model for cylinder pressure identification. LPP is estimated from cylinder pressure curve reconstructed using neural network model and is used as feedback signal to fuzzy logic controller. The simulations have been carried out to test the performance of the combined neural network and fuzzy logic-based control strategy. The simulation results show that the proposed strategy can quite satisfactorily control LPP to its desired value.