scispace - formally typeset
S

Soumya Jana

Researcher at Indian Institute of Technology, Hyderabad

Publications -  141
Citations -  2166

Soumya Jana is an academic researcher from Indian Institute of Technology, Hyderabad. The author has contributed to research in topics: Context (language use) & General relativity. The author has an hindex of 20, co-authored 136 publications receiving 1757 citations. Previous affiliations of Soumya Jana include Microsoft & Physical Research Laboratory.

Papers
More filters
Proceedings ArticleDOI

RF energy harvester-based wake-up receiver

TL;DR: This work presents a WuRx design using an RF energy harvesting circuit (RFHC) that can provide a wake-up range sensitivity around 4 cm/mW at low transmit RF powers, which scales to a long wake- up range at high powers.
Journal ArticleDOI

Smart RF energy harvesting communications: challenges and opportunities

TL;DR: The novel communication techniques that enable and enhance the usefulness ofRFH are identified and the challenges in the actual feasibility of RFH communications, new research directions, and the obstacles to their practical implementation are discussed.
Journal ArticleDOI

Distortion discriminant analysis for audio fingerprinting

TL;DR: In this article, distortion discriminant analysis (DDA) is proposed to map audio data to feature vectors for the classification, retrieval or identification tasks, and the feature extraction operation must be computationally efficient.
Journal ArticleDOI

Influence of scanning area on choroidal vascularity index measurement using optical coherence tomography.

TL;DR: This study investigates the impact of scanning area on CVI measurement using spectral‐domain optical coherence tomography (SD‐OCT) to evaluate the choroidal vasculature.
Journal ArticleDOI

Automated estimation of choroidal thickness distribution and volume based on OCT images of posterior visual section.

TL;DR: This work measures the structural dissimilarity between choroid and sclera by structural similarity (SSIM) index, and hence estimates the COB by thresholding, and achieves smooth COB estimates, mimicking manual delineation, using tensor voting.