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Contact detection is performed in parallel with orthogonal range queries based on a sparse bucket data structure.
This paper presents a framework for computing knots (including the number and positions) in curve fitting based on a sparse optimization model.
We propose a novel method for relighting the image of outdoor scenes viewed from a fixed camera based on a sparse set of images of the same scene under different illumination.
Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM -type analySPM -typed banalysisa sparse graph methodwhere temporal dynamics at each voxel position are describaseds a sparse conbination of global brain dynamicsparse
In Section 3, we present the proposed CSI feedback approach that is based on a sparse approximation of the channel utilizing an over-complete basis decomposition.
Our proposed schemes exploit the sparsity of the relay selection vector to select the relays by minimizing the mean-square error (MSE) based on a sparse signal recovery theory.
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The numerical results indicate that the algorithms based on a preconditioned sparse matrix technique and on a specially designed algorithm for the particular problem under consideration perform best.
More recently, Kerse et al. proposed a method for direct target localization based on a standard sparse identification technique [18].
This filter is based on an enhanced sparse representation in a transform domain.
Another new approach is based on an approximate sparse maximum likelihood (ASML) problem for estimating the parameters in a multivariate binary distribution.
A difficult aspect for signal subspace decomposition based on such a sparse representation is to define the most appropriate criterion to identify the principal components (called atoms) from the learned dictionary to build the principal subspace.
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