Sciweavers

87 search results - page 15 / 18
» A Theoretical Comparison of Monte Carlo Radiosity Algorithms
Sort
View
MMAS
2010
Springer
14 years 4 months ago
A Novel Method for Solving Multiscale Elliptic Problems with Randomly Perturbed Data
We propose a method for efficient solution of elliptic problems with multiscale features and randomly perturbed coefficients. We use the multiscale finite element method (MsFEM) as...
Victor Ginting, Axel Målqvist, Michael Presh...
TCOM
2008
84views more  TCOM 2008»
14 years 9 months ago
On the Performance of Closed-Loop Transmit Diversity with Noisy Channel Estimates and Finite-Depth Interleaved Convolutional Cod
Abstract-- In this paper, closed-form expressions for the uncoded bit error probability of closed-loop transmit diversity (CLTD) algorithms with two transmit and one receive antenn...
Jittra Jootar, James R. Zeidler, John G. Proakis
132
Voted
GLOBECOM
2006
IEEE
15 years 3 months ago
Performance Analysis of Distributed Source Coding and Packet Aggregation in Wireless Sensor Networks
— In this paper, we propose a theoretical setup for evaluation of energy efficiency of wireless sensor networks (WSNs) with distributed source coding (DSC) algorithms and packet...
L. Di Paolo, Carlo Fischione, Carlo Graziosi, Fort...
NECO
2007
129views more  NECO 2007»
14 years 9 months ago
Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance
It is well-known that, in unidentifiable models, the Bayes estimation provides much better generalization performance than the maximum likelihood (ML) estimation. However, its ac...
Shinichi Nakajima, Sumio Watanabe
GECCO
2005
Springer
138views Optimization» more  GECCO 2005»
15 years 3 months ago
Promising infeasibility and multiple offspring incorporated to differential evolution for constrained optimization
In this paper, we incorporate a diversity mechanism to the differential evolution algorithm to solve constrained optimization problems without using a penalty function. The aim is...
Efrén Mezura-Montes, Jesús Vel&aacut...