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» Approximate reduction of dynamic systems
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BMCBI
2007
173views more  BMCBI 2007»
15 years 4 months ago
Predicting state transitions in the transcriptome and metabolome using a linear dynamical system model
Background: Modelling of time series data should not be an approximation of input data profiles, but rather be able to detect and evaluate dynamical changes in the time series dat...
Ryoko Morioka, Shigehiko Kanaya, Masami Y. Hirai, ...
NIPS
1996
15 years 5 months ago
Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems
In cellular telephone systems, an important problem is to dynamically allocate the communication resource channels so as to maximize service in a stochastic caller environment. Th...
Satinder P. Singh, Dimitri P. Bertsekas
LCTRTS
2010
Springer
15 years 11 months ago
Analysis and approximation for bank selection instruction minimization on partitioned memory architecture
A large number of embedded systems include 8-bit microcontrollers for their energy efficiency and low cost. Multi-bank memory architecture is commonly applied in 8-bit microcontr...
Minming Li, Chun Jason Xue, Tiantian Liu, Yingchao...
KDD
2008
ACM
186views Data Mining» more  KDD 2008»
16 years 4 months ago
Cut-and-stitch: efficient parallel learning of linear dynamical systems on smps
Multi-core processors with ever increasing number of cores per chip are becoming prevalent in modern parallel computing. Our goal is to make use of the multi-core as well as multi...
Lei Li, Wenjie Fu, Fan Guo, Todd C. Mowry, Christo...
CORR
2011
Springer
259views Education» more  CORR 2011»
14 years 11 months ago
The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding vs. Dynamic Range
Compressive sensing (CS) exploits the sparsity present in many common signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, ...
Mark A. Davenport, Jason N. Laska, John R. Treichl...