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NIPS
2007
13 years 10 months ago
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the ...
Sebastian Gerwinn, Jakob Macke, Matthias Seeger, M...
TWC
2008
155views more  TWC 2008»
13 years 8 months ago
Distributed sensor network localization using SOCP relaxation
The goal of the sensor network localization problem is to determine positions of all sensor nodes in a network given certain pairwise noisy distance measurements and some anchor no...
Seshan Srirangarajan, Ahmed H. Tewfik, Zhi-Quan Lu...
SECON
2008
IEEE
14 years 2 months ago
On Distributed Optimization Using Peer-to-Peer Communications in Wireless Sensor Networks
—We describe and evaluate a suite of distributed and computationally efficient algorithms for solving a class of convex optimization problems in wireless sensor networks. The pr...
Björn Johansson, Cesare M. Carretti, Mikael J...
AAAI
1990
13 years 9 months ago
Symbolic Probabilistic Inference in Belief Networks
The Symbolic Probabilistic Inference (SPI) Algorithm [D'Ambrosio, 19891 provides an efficient framework for resolving general queries on a belief network. It applies the conc...
Ross D. Shachter, Bruce D'Ambrosio, Brendan Del Fa...
IPSN
2010
Springer
14 years 3 months ago
Distributed estimation of linear acceleration for improved accuracy in wireless inertial motion capture
Motion capture using wireless inertial measurement units (IMUs) has many advantages over other techniques. Achieving accurate tracking with IMUs presents a processing challenge, e...
A. D. Young, M. J. Ling, D. K. Arvind