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Anurag Ganguli

dblp:98/1165 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2015
0000-0002-0427-4944ORCID · corroborated

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

Software engineering, systems software and programming languages · 1Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 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.

Artificial intelligence
1 paper
Multi-agent systems · 87% Robot navigation and mapping · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.112009
Multirobot Rendezvous With Visibility Sensors in Nonconvex Environments · IEEE Trans. Robotics 2009
Knowledge, reasoning and agents › Multi-agent systems › consensus control
rendezvous
0.112009
Multirobot Rendezvous With Visibility Sensors in Nonconvex Environments · IEEE Trans. Robotics 2009
Robotics › Robot navigation and mapping
mobile robot navigation
0.012009
Multirobot Rendezvous With Visibility Sensors in Nonconvex Environments · IEEE Trans. Robotics 2009

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

proximity graph · 0.1connectivity-preserving constraint · 0.1
YearPublicationVenuePosition
2015 The Case for a Hybrid Approach to Diagnosis: A Railway Switch
Ion Matei, Anurag Ganguli, Tomonori Honda 0001, Johan de Kleer
DX2
2010 Hidden Markov Models for modeling blood pressure data to predict acute hypotension
abstract
The ability to predict episodes of acute hypotension (abnormal drop in arterial blood pressure) would be of immense benefit to the healthcare community, and is therefore a focus of research in both medical and engineering domains. This paper presents the use of Hidden Markov Models to predict the onset of acute hypotension, using blood pressure measurements over time. Our use of HMMs has been motivated by their ability to characterize sequential/temporal trends in a given time signal. This lends the ability to infer the health status based on blood pressure information collected over an interval of time, rather than just instantaneous measurements. We have tested the proposed technique on standard physiological signal datasets available online and have obtained promising results. As part of a bigger project, we see potential in the proposed technique being used in real time health monitoring systems.
Tejaswi Tamminedi, Guy Yosiphon, Anurag Ganguli, Jacob Yadegar
ICASSP4
2009 Multirobot Rendezvous With Visibility Sensors in Nonconvex Environments
abstract
This paper presents a coordination algorithm for mobile autonomous robots. Relying on distributed sensing, the robots achieve rendezvous, i.e., they move to a common location. Each robot is a point mass moving in a simply connected, nonconvex, unknown environment according to an omnidirectional kinematic model. It is equipped with line-of-sight limited-range sensors, i.e., it can measure the relative position of any object (robots or environment boundary) if and only if the object is within a given distance and there are no obstacles in between. The perimeter minimizing algorithm is designed using the notions of robust visibility, connectivity-preserving constraint sets, and proximity graphs. The algorithm provably achieves rendezvous if the interagent sensing graph is connected at any time during the evolution of the group. Simulations illustrate the theoretical results and the performance of the proposed algorithm in asynchronous setups and with measurement errors, control errors, and nonzero robot size. Simulations to illustrate the importance of visibility constraints and comparisons with the optimal centralized algorithm are also included.
Anurag Ganguli, Jorge Cortés 0001, Francesco Bullo
IEEE Trans. Robotics1