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V. Chandra

dblp:50/8908 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · unresolved

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

Applied, interdisciplinary, general and emerging computing · 2 · 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 architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%
Theoretical computer science
1 paper
Automated reasoning and model checking · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Embedded and real-time systems
discrete event systems
0.012001
A discrete event systems modeling formalism based on event occurrence rules and precedences · IEEE Trans. Robotics Autom. 2001

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

event occurrence rules · 0.1automaton derivation · 0.1
YearPublicationVenuePosition
2016 Precipitation rates estimated with GPM's Dual-frequency Radar
abstract
Global Precipitation Measurement (GPM) mission's core satellite has been collecting precipitation data from space for more than a year. This paper reviews the performance of precipitation retrieval algorithm and evaluates the rainfall rates estimated with the Dual-frequency Precipitation Radar (DPR) onboard the GPM's core satellite. Differences between the rain estimates with TRMM's Precipitation Radar (PR) and those with GPM's DPR are examined.
Toshio Iguchi, Shinta Seto, Jun Awaka, Robert Meneghini, Takuji Kubota, V. Chandra, Naofumi Yoshida, Nozomi Kawamoto, Riko Oki
IGARSS6
2001 A discrete event systems modeling formalism based on event occurrence rules and precedences
abstract
The analysis (verification, diagnosis, control, etc.) of discrete event systems requires a correct model of the system and of its specifications. In this paper, we present a new modeling formalism for generating valid models of complex systems. The class of systems this applies to is one which consists of signals that take binary values. The technique presented here makes the task of modeling considerably less cumbersome and less error-prone and is user-friendly. Another advantage of using this modeling formalism is that the size of the system model is polynomial in the number of signals, whereas the number of states in the automata models is exponential in the number of signals. We present automated techniques for deriving an automaton-based model from the model in the proposed formalism. We illustrate the modeling formalism using examples drawn from manufacturing systems and process control.
V. Chandra
IEEE Trans. Robotics Autom.1