D
Debraj Chakraborty
Researcher at St. Thomas' College of Engineering and Technology
Publications - 20
Citations - 25
Debraj Chakraborty is an academic researcher from St. Thomas' College of Engineering and Technology. The author has contributed to research in topics: Computer science & Drift velocity. The author has an hindex of 2, co-authored 5 publications receiving 8 citations.
Papers
More filters
Journal ArticleDOI
Si/SiC heterostructure MITATT oscillator for higher-harmonic THz-power generation: theoretical reliability and experimental feasibility studies of quantum modified non-linear classical model
TL;DR: In this paper, the authors have made the simulator realistic by incorporating the temperature dependent carrier ionization rate, saturation drift velocity, mobility and effective mass of the base material-pairs in the analysis.
Journal ArticleDOI
Design and development of an AlGaN/GaN heterostructure nano-ATT oscillator: experimental feasibility studies in THz domain
TL;DR: In this article, a generalised self-consistent, nonlinear Mixed Quantum Drift-Diffusion (MQDD) simulator is developed and used for designing p++-n−-n-n++ type room temperature solid-state source within 0.75-1.1 THz regime.
Journal ArticleDOI
Self-consistent non-linear physics based predictive model for the computation of THz-signal attenuation in fog with varying visibility in tropical climatic zone
Proceedings ArticleDOI
Fast Subspace Identification for Large Input-Output Data
Vatsal Kedia,Debraj Chakraborty +1 more
TL;DR: A fast subspace identification method for estimating LTI state-space models corresponding to large input-output data is proposed, which outperforms the MATLAB-MOESP method in terms of memory cost, flop-count, and computation time.
Journal ArticleDOI
Fast Multivariable Subspace Identification (FMSID) of Combined Deterministic-Stochastic/General LTI Systems for Large Input-Output Data
Vatsal Kedia,Debraj Chakraborty +1 more
TL;DR: In this paper , a fast subspace identification method for estimating combined deterministic-stochastic LTI state-space models corresponding to large input-output data is proposed, which achieves lesser runtime RAM usage, reduced data movement between slow (RAM) and fast memory (processor cache), and introduces a novel fast method to estimate input (B), feedforward (D) and steady state Kalman gain (K) matrices.