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» Mining Performance Data from Sampled Event Traces
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IPSN
2004
Springer
14 years 1 months ago
Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
SIMUTOOLS
2008
13 years 9 months ago
Opportunistic networking in OMNeT++
This work describes mechanisms for simulating opportunistic and delay-tolerant networks in the OMNeT++ discrete event simulator. The mechanisms allow for simulating open systems o...
Ólafur Ragnar Helgason, Kristján Val...
DEXAW
2006
IEEE
116views Database» more  DEXAW 2006»
14 years 1 months ago
Architecture Evaluation for Distributed Auto-ID Systems
Auto-ID technologies allow capturing the time and location of products in the supply chain for tracking and tracing. This paves the way for a variety of business applications, suc...
Hong Hai Do, Jürgen Anke, Gregor Hackenbroich
MICRO
1997
IEEE
90views Hardware» more  MICRO 1997»
13 years 11 months ago
ProfileMe: Hardware Support for Instruction-Level Profiling on Out-of-Order Processors
Profile data is valuable for identifying performance bottlenecks and guiding optimizations. Periodic sampling of a processor's performance monitoring hardware is an effective...
Jeffrey Dean, James E. Hicks, Carl A. Waldspurger,...
PKDD
2009
Springer
146views Data Mining» more  PKDD 2009»
14 years 7 days ago
Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
Monitoring the variables of real world dynamic systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding prob...
Eva Besada-Portas, Sergey M. Plis, Jesús Ma...