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Chowdhury S.R.,Center for and Embedded Systems Technology | Kode S.,Center for Education Technology and Learning Science
Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012 | Year: 2012

The paper reports a novel approach of self teaching Very Large Scale Integration (VLSI) to Computer Science students through the use of a Virtual laboratory. It formalizes the notion of hierarchical design of Integrated Circuits and abstracts the notion of design of integrated circuits based on physical approaches. Using the developed Virtual VLSI Laboratory, a set of about 100 Computer Science students have been trained and the feedback from them indicates that more than 80% of these students could understand the experiments being taught through the Virtual Laboratory and 70% of the students were interested in the field of VLSI Design after doing experiments in the lab. © 2012 IEEE.


Ramchandani V.,Center for and Embedded Systems Technology | Pamarthi K.,National Institute of Technology Rourkela | Chowdhury S.R.,Center for and Embedded Systems Technology
International Journal on Smart Sensing and Intelligent Systems | Year: 2012

The paper proposes comparative study of Field Programmable Gate Array implementation of 2 closely related approaches to track maximum power point of a solar photovoltaic array. The current work uses 2 versions of kalman filter viz. linear kalman filter and unscented kalman filter to track maximum power point. Using either of these approach the maximum power point tracking (MPPT) becomes much faster than using the conventional Perturb & Observe approach specifically in case of sudden weather changes. In this paper comparative analysis of both the algorithms being implemented on FPGA is presented. Experiments have been performed under optimal conditions as well as under cloudy conditions i.e. falling irradiance levels. Using the linear kalman filter the maximum power point of a solar PV array has been tracked with an efficiency of 97.11% while using the unscented kalman filter technique the maximum power point of the same solar PV array is tracked with higher efficiency of 98.3%. However, the maximum power point has been tracked at a much faster rate i.e. 4.5 ms using the linear kalman filter approach as compared to the unscented kalman filter approach which tracks maximum power point at 11 ms which is in turn faster than existing generic Perturb and Observe approach which takes 15ms to track the maximum power point. The system has been implemented on Altera EP2C20F484C7 FPGA board.


Agrawal K.,Center for and Embedded Systems Technology | Chowdhury S.R.,Center for and Embedded Systems Technology
19th International Symposium on VLSI Design and Test, VDAT 2015 - Proceedings | Year: 2015

Image fusion is a technique to combine multiple images from a single sensor or multiple sensors into a single composite image without introducing artifacts. This paper presents a novel implementation of Laplacian pyramid image fusion on field programmable gate array (FPGA). Real time image fusion using pyramid decomposition is achieved by utilizing re-usable memory architecture and parallelisation techniques to give an output in 35ms for an image of resolution 320×256, which provides a speedup of 17 times than the general purpose computer based solution and a speedup of 2.2 times compared to a GPU based implementation. © 2015 IEEE.


Ramasahayam S.,Center for and Embedded Systems Technology | Arora L.,Center for and Embedded Systems Technology | Chowdhury S.R.,Indian Institute of Technology Mandi | Anumukonda M.,Center for and Embedded Systems Technology
Proceedings of the International Conference on Sensing Technology, ICST | Year: 2016

This paper proposes a non invasive blood glucose sensing system using photoplethysmography (PPG). Neural network based adaptive noise cancellation (adaline) is employed to reduce the motion artifact. Also artificial neural network is used to create the predictive model which estimates the glucose levels based on PPG signals. Error in estimating glucose levels came out to be 5.48 mg/dl using ANN on MATLAB. This predictive model created by ANN has been implemented on FPGA. Error in estimating glucose levels by the ANN model implemented on FPGA, came out to be 7.23mg/dl. The results have been validated by performing Clarke error grid analysis. © 2015 IEEE.


Ramasahayam S.,Center for and Embedded Systems Technology | Haindavi K.S.,Center for and Embedded Systems Technology | Kavala B.,Center for and Embedded Systems Technology | Kavala B.,Indian Institute of Technology Guwahati | Chowdhury S.R.,Indian Institute of Technology Guwahati
Proceedings of the International Conference on Sensing Technology, ICST | Year: 2013

This paper presents a unique technique for noninvasive estimation of blood glucose concentration using near infra red spectroscopy. The spectroscopy has been performed at the second overtone of glucose which falls in the near infra red region. The near infra red spectroscopy has been performed using transmission photoplethsymography (PPG). The analog front end system has been implemented to get the PPG signal at the near infra red wavelengths of 1070nm, 950nm, 935nm. The PPG signal that has been obtained is processed and double regression analysis is carried out with the artificial neural network for estimating the glucose levels. The root mean square error of the prediction was 5.84mg/dL. © 2013 IEEE.

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