Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Kush Jajal

dblp:390/7671 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0006-8277-9952ORCID · reported

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

Computer networks · 1 · 1 since 2021

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%
Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization
indoor mapping
0.812024
Ubiquitous Indoor Mapping Using Mobile Radio Tomography · IEEE Trans. Mob. Comput. 2024
Wireless sensing and localization › RF imaging
radio tomographic imaging
0.812024
Ubiquitous Indoor Mapping Using Mobile Radio Tomography · IEEE Trans. Mob. Comput. 2024
Wireless sensing and localization
simultaneous localization and mapping
0.812024
Ubiquitous Indoor Mapping Using Mobile Radio Tomography · IEEE Trans. Mob. Comput. 2024
Ubiquitous computing and smart environments
indoor mapping
0.212024
Ubiquitous Indoor Mapping Using Mobile Radio Tomography · IEEE Trans. Mob. Comput. 2024

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

radio tomographic imaging · 1.5SLAM fusion · 1.5
YearPublicationVenuePosition
2024 Ubiquitous Indoor Mapping Using Mobile Radio Tomography
abstract
The demand for real-time and accurate mapping is ubiquitous, particularly in complex indoor settings. While SLAM-based methods are popular, Radio Tomographic Imaging (RTI) offers an essential set of advantages, including mapping inaccessible or enclosed spaces, shorter scanning trajectories, or even identifying material properties of structures on the map. However, existing RTI systems typically depend on pre-deployed, precisely calibrated infrastructure with ample computing power, making it challenging to deploy in a ubiquitous setting. We designUbiqMap, a lightweight RTI-based end-to-end system capable of mapping indoor spaces in real-time, with minimal to zero reliance over pre-deployed infrastructure. We evaluate the performance ofUbiqMapin various scenarios, including two real deployments - a moderately complex residential apartment (800 sq. ft) and a large building foyer area (3000 sq. ft) and a few simulated scenarios. We demonstrate howUbiqMapcan benefit over traditional SLAM-based techniques in specific contexts and advocate the fusion of RTI methods with SLAM to improve future mapping technologies. Overall,UbiqMapimproves the quality of the estimated map by 30%–40% over the state-of-the-art with equivalent resource availability.
Amartya Basu, Ayon Chakraborty, Kush Jajal
IEEE Trans. Mob. Comput.3