S
Sunil M. Shende
Researcher at Rutgers University
Publications - 78
Citations - 1145
Sunil M. Shende is an academic researcher from Rutgers University. The author has contributed to research in topics: Robot & Natural language. The author has an hindex of 19, co-authored 77 publications receiving 1064 citations. Previous affiliations of Sunil M. Shende include University of Pennsylvania & New York University.
Papers
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Journal ArticleDOI
Static Frequency Assignment in Cellular Networks
Lata Narayanan,Sunil M. Shende +1 more
TL;DR: This work describes an efficient algorithm to multicolor optimally any weighted even or odd length cycle representing a cellular network, and demonstrates an approximation algorithm which guarantees that no more than 4/3 times the minimum number of required colors are used.
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An analysis of some common scanning techniques for lossless image coding
TL;DR: This analysis shows that, under certain reasonable assumptions, the raster scan is indeed better than the Hilbert scan, thereby dispelling the popular notion that using a Hilbert scan would always lead to improved performance.
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Distributed Online Frequency Assignment in Cellular Networks
TL;DR: This paper presents the first distributed online algorithms for this problem with proven bounds on their competitive ratios, and shows a series of algorithms that use at each vertex information about increasingly larger neighborhoods of the vertex, and that achieve better competitive ratios.
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Complexity of barrier coverage with relocatable sensors in the plane
Stefan Dobrev,Stephane Durocher,Mohsen Eftekhari,Konstantinos Georgiou,Evangelos Kranakis,Danny Krizanc,Lata Narayanan,Jaroslav Opatrny,Sunil M. Shende,Jorge Urrutia +9 more
TL;DR: This work considers several variations of the problems of covering a set of barriers using sensors that can detect any intruder crossing any of the barriers, and gives an O ( n 3 / 2 ) algorithm for a natural special case of this last problem.
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Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across.
Daniel P. Russo,Judy Strickland,Agnes L. Karmaus,Wenyi Wang,Sunil M. Shende,Thomas Hartung,Lauren M. Aleksunes,Hao Zhu +7 more
TL;DR: The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end points.