N
Nagarajan Kandasamy
Researcher at Drexel University
Publications - 126
Citations - 3236
Nagarajan Kandasamy is an academic researcher from Drexel University. The author has contributed to research in topics: Neuromorphic engineering & Spiking neural network. The author has an hindex of 25, co-authored 121 publications receiving 2919 citations. Previous affiliations of Nagarajan Kandasamy include Vanderbilt University & University of Michigan.
Papers
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Journal ArticleDOI
Power and performance management of virtualized computing environments via lookahead control
TL;DR: This work implements and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme.
Journal ArticleDOI
GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration.
TL;DR: This paper shows how to significantly accelerate cone-beam CT reconstruction and 3D deformable image registration using the stream-processing model, and describes data-parallel designs for the Feldkamp, Davis and Kress reconstruction algorithm, and the demons deformable registration algorithm, suitable for use on a commodity graphics processing unit.
Proceedings ArticleDOI
Power and Performance Management of Virtualized Computing Environments Via Lookahead Control
TL;DR: This work implements and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme.
Journal ArticleDOI
On developing B-spline registration algorithms for multi-core processors
TL;DR: This paper proposes a grid-alignment scheme and associated data structures that greatly reduce the complexity of the registration algorithm, and develops highly data parallel designs for B-spline registration within the stream-processing model, suitable for implementation on multi-core processors such as graphics processing units (GPUs).
Journal ArticleDOI
Transparent recovery from intermittent faults in time-triggered distributed systems
TL;DR: This work introduces the cluster-based failure recovery concept which determines the best placement of slack within the FT schedule so as to minimize the resulting time overhead and provides transparent failure recovery in that a processor recovering from task failures does not disrupt the operation of other processors.