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WSC
1998
15 years 7 months ago
Integrating Neural Networks with Special Purpose Simulation
Traditional methods of dealing with variability in simulation input data are mainly stochastic. This is most often the best method to use if the factors affecting the variation or...
Dany Hajjar, Simaan M. AbouRizk, Kevin Mather
JCP
2008
190views more  JCP 2008»
15 years 6 months ago
Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach
In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonl...
Vishwas R. Puttige, Sreenatha G. Anavatti
TASLP
2010
102views more  TASLP 2010»
15 years 4 months ago
A Modular Physically Based Approach to the Sound Synthesis of Membrane Percussion Instruments
—This paper presents a set of novel physical models for sound synthesis of membrane percussion instruments. First, a model for tension modulation in a struck circular membrane is...
Federico Avanzini, Riccardo Marogna
CORR
2012
Springer
170views Education» more  CORR 2012»
14 years 2 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
161
Voted
DATE
2009
IEEE
113views Hardware» more  DATE 2009»
16 years 1 months ago
System-level process variability analysis and mitigation for 3D MPSoCs
Abstract—While prior research has extensively evaluated the performance advantage of moving from a 2D to a 3D design style, the impact of process parameter variations on 3D desig...
Siddharth Garg, Diana Marculescu