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R. Jyothi

dblp:207/9623 · DBLP profile ↗
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4ranked-venue papers
4as first author
3since 2021 · last 2022
0000-0002-8335-6103ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 3 · 3 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless sensing and localization · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization
range-based localization
0.412020
SOLVIT: A Reference-Free Source Localization Technique Using Majorization Minimization · IEEE ACM Trans. Audio Speech Lang. Process. 2020
Wireless sensing and localization
source localization
0.412020
SOLVIT: A Reference-Free Source Localization Technique Using Majorization Minimization · IEEE ACM Trans. Audio Speech Lang. Process. 2020

Methods — techniques the papers use, named apart from their topics

majorization-minimization · 0.4fixed point algorithm · 0.4
YearPublicationVenuePosition
2022 PIANO: A fast parallel iterative algorithm for multinomial and sparse multinomial logistic regression
R. Jyothi, Prabhu Babu
Signal Process.1
2022 TELET: A monotonic algorithm to design large dimensional equiangular tight frames for applications in compressed sensing
R. Jyothi, Prabhu Babu
Signal Process.1
2021 DYANOM - Dykstra's projection based atomic norm solver
R. Jyothi, Prabhu Babu, Z. Tian
Signal Process.1
2020 SOLVIT: A Reference-Free Source Localization Technique Using Majorization Minimization
abstract
We consider the problem of localizing the source using range, and range-difference measurements. Both the problems are non-convex, and non-smooth, and are challenging to solve. In this article, we develop an iterative algorithm - Source Localization Via an Iterative technique (SOLVIT) to localize the source using all the distinct range-difference measurements, i.e., without choosing a reference sensor. SOLVIT is based on the Majorization Minimization approach - in which a novel upper bound is formulated, and minimized to get a closed-form solution at every iteration. We also solve the source localization problem based on range measurements, and rederive the Standard Fixed Point algorithm using the Majorization Minimization approach. By doing so, we show a less intricate way to prove the convergence of the Standard Fixed Point algorithm. Numerical simulations, and experiments in an anechoic chamber confirm that SOLVIT performs better than existing reference-based, and reference-free methods in terms of source positioning accuracy.
R. Jyothi, Prabhu Babu
IEEE ACM Trans. Audio Speech Lang. Process.1