PRIM: An Efficient Preconditioning Iterative Reweighted Least Squares Method for Parallel Brain MRI Reconstruction
Sign Up to like & getrecommendations! Published in 2017 at "Neuroinformatics"
DOI: 10.1007/s12021-017-9354-9
Abstract: The most recent history of parallel Magnetic Resonance Imaging (pMRI) has in large part been devoted to finding ways to reduce acquisition time. While joint total variation (JTV) regularized model has been demonstrated as a… read more here.
Keywords: reweighted least; iterative reweighted; least squares; preconditioning iterative ... See more keywords
A Novel Iterative Reweighted Method for Forest Height Inversion Using Multibaseline PolInSAR Data
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3124094
Abstract: Multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the advanced technologies of forest height inversion, as it provides rich observation information. In this letter, we propose a new iterative reweighted method for multibaseline… read more here.
Keywords: forest height; height inversion; method; iterative reweighted ... See more keywords
Improved Iterative Reweighted Multiuser Detectors for Massive Machine-Type Communications
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2025.3568213
Abstract: Grant-free non-orthogonal multiple access (GF-NOMA) is a promising technique for 6G massive machine-type communications (mMTC) to support massive connectivity and reduce access latency. However, the multi-user detection (MUD) method for single measurement vector (SMV) models… read more here.
Keywords: massive machine; machine type; iterative reweighted; type communications ... See more keywords
Novel Iterative Reweighted ℓ1 Minimization for Sparse Recovery
Sign Up to like & getrecommendations! Published in 2025 at "Mathematics"
DOI: 10.3390/math13081219
Abstract: Data acquisition and high-dimensional signal processing often require the recovery of sparse representations of signals to minimize the resources needed for data collection. ℓp quasi-norm minimization excels in exactly reconstructing sparse signals from fewer measurements,… read more here.
Keywords: novel iterative; iterative reweighted; sparse recovery; recovery ... See more keywords