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Devdeep Shetranjiwala

Publications and source records attributed to Devdeep Shetranjiwala.

2 recordsLinked to original sources

On Naisargik Images of Varshamov-Tenengolts and Helberg Codes

The VT and Helberg codes, both in binary and non-binary forms, stand as elegant solutions for rectifying insertion and deletion errors. In this paper we consider the quaternary versions of these codes. It is well known that many optimal binary non-linear codes like Kerdock and Prepreta can be depicted as Gray images (isometry) of codes defined over $\mathbb{Z}_4$. Thus a natural question arises: Can we find similar maps between quaternary and binary spaces which gives interesting properties when applied to the VT and Helberg codes. We found several such maps called Naisargik (natural) maps and we study the images of quaternary VT and Helberg codes under these maps. Naisargik and inverse Naisargik images gives interesting error-correcting properties for VT and Helberg codes. If two Naisargik images of VT code generates an intersecting one deletion sphere, then the images holds the same weights. A quaternary Helberg code designed to correct $s$ deletions can effectively rectify $s+1$ deletion errors when considering its Naisargik image, and $s$-deletion correcting binary Helberg code can corrects $\lfloor\frac{s}{2}\rfloor$ errors with inverse Naisargik image.

cs.IT

Deep Learning assisted microwave-plasma interaction based technique for plasma density estimation

The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) are based on the accurate estimations of plasma density and plasma temperature. The conventional methods for electron density measurements offer axial and radial profiles for any given linear LTP device. These methods have major disadvantages of operational range (not very wide), cumbersome instrumentation, and complicated data analysis procedures. The article proposes a Deep Learning (DL) assisted microwave-plasma interaction-based non-invasive strategy, which can be used as a new alternative approach to address some of the challenges associated with existing plasma density measurement techniques. The electric field pattern due to microwave scattering from plasma is utilized to estimate the density profile. The proof of concept is tested for a simulated training data set comprising a low-temperature, unmagnetized, collisional plasma. Different types of symmetric (Gaussian-shaped) and asymmetrical density profiles, in the range $10^{16}-10^{19}$ m$^{-3}$, addressing a range of experimental configurations have been considered in our study. Real-life experimental issues such as the presence of noise and the amount of measured data (dense vs sparse) have been taken into consideration while preparing the synthetic training data-sets. The DL-based technique has the capability to determine the electron density profile within the plasma. The performance of the proposed deep learning-based approach has been evaluated using three metrics- SSIM, RMSLE, and MAPE. The obtained results show promising performance in estimating the 2D radial profile of the density for the given linear plasma device and affirms the potential of the proposed ML-based approach in plasma diagnostics.

physics.plasm-ph