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» Mining Performance Data from Sampled Event Traces
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IPSN
2004
Springer
15 years 9 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
148
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SIMUTOOLS
2008
15 years 5 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»
15 years 10 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»
15 years 7 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»
15 years 8 months 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...