V
Vimal Bhatia
Researcher at Indian Institute of Technology Indore
Publications - 351
Citations - 3214
Vimal Bhatia is an academic researcher from Indian Institute of Technology Indore. The author has contributed to research in topics: Computer science & Bit error rate. The author has an hindex of 21, co-authored 302 publications receiving 2053 citations. Previous affiliations of Vimal Bhatia include Netaji Subhas Institute of Technology & Indian Institute of Technology Delhi.
Papers
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Journal ArticleDOI
On ASER performance of higher order QAM schemes in two-way multiple-relay networks under imperfect CSI
TL;DR: An analytical approach is presented to evaluate the performance of a two-way multi-relay system with direct link using a three-phase analogue network coding and opportunistic relay selection scheme and the asymptotic behaviour of ASER expression is analysed to evaluation the system's diversity order.
Proceedings ArticleDOI
Performance Analysis of OTFS Over Mobile Multipath Channels for Visible Light Communication
TL;DR: In this paper, the performance analysis of OTFS over mobile multipath VLC channels is investigated for both RF and millimeter wave communication systems, and it is shown that the performance of VLC based system is limited by dispersive characteristics of the VLC channel which leads to inter-symbol interference (ISI) and inter-carrier-interference (ICI).
Journal ArticleDOI
Collimation testing using deflectometry in conjunction with windowed Fourier transform analysis.
TL;DR: A simple automated procedure for the detection of collimation of an optical beam is demonstrated by incorporating the windowed Fourier fringe analysis technique into a deflectometric setup and provides high resolution, high precision, and good sensitivity.
Journal ArticleDOI
Deep transfer learning based photonics sensor for assessment of seed-quality
Puneet Singh Thakur,Bhavya Tiwari,Abhishek Kumar,Bhavesh Gedam,Vimal Bhatia,Ondrej Krejcar,Michal Dobrovolny,Jamel Nebhen,B.S. Prakash +8 more
TL;DR: In this paper , a laser backscattering and deep transfer learning (TL) based photonics sensor is proposed for automatic identification and classification of high-quality seeds, which can accurately monitor the quality of seeds with higher accuracy.
Journal ArticleDOI
An efficient automated biospeckle indexing strategy using morphological and geo-statistical descriptors
TL;DR: This work proposed an efficient automated biospeckle indexing technique by combining morphological and geo-statistical operators that has high accuracy for all assessed conditions and simultaneous dynamicity assessment of multiple objects from a single stack reduced both computational and experimental overheads considerably.