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IPSN
2004
Springer
16 years 9 days 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
PVLDB
2008
160views more  PVLDB 2008»
15 years 6 months ago
BayesStore: managing large, uncertain data repositories with probabilistic graphical models
Several real-world applications need to effectively manage and reason about large amounts of data that are inherently uncertain. For instance, pervasive computing applications mus...
Daisy Zhe Wang, Eirinaios Michelakis, Minos N. Gar...
PASTE
2010
ACM
16 years 9 hour ago
Learning universal probabilistic models for fault localization
Recently there has been significant interest in employing probabilistic techniques for fault localization. Using dynamic dependence information for multiple passing runs, learnin...
Min Feng, Rajiv Gupta
KDD
2004
ACM
161views Data Mining» more  KDD 2004»
16 years 9 days ago
ANN quality diagnostic models for packaging manufacturing: an industrial data mining case study
World steel trade becomes more competitive every day and new high international quality standards and productivity levels can only be achieved by applying the latest computational...
Nicolás de Abajo, Alberto B. Diez, Vanesa L...
CF
2010
ACM
16 years 15 hour ago
Interval-based models for run-time DVFS orchestration in superscalar processors
We develop two simple interval-based models for dynamic superscalar processors. These models allow us to: i) predict with great accuracy performance and power consumption under va...
Georgios Keramidas, Vasileios Spiliopoulos, Stefan...