Results 11 to 20 of about 2,392 (180)
What If Each Voxel Were Measured With a Different Diffusion Protocol? [PDF]
Magn Reson MedABSTRACT Purpose Expansion of diffusion MRI (dMRI) both into the realm of strong gradients and into accessible imaging with portable low‐field devices brings about the challenge of gradient nonlinearities. Spatial variations of the diffusion gradients make diffusion weightings and directions non‐uniform across the field of view, and deform perfect ...
Coelho S +7 more
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Two-conformer equilibrium of maltose-binding protein in the absence of ligand from residual dipolar coupling analysis. [PDF]
Protein SciAbstract Prior analyses found good agreement between numerous residual dipolar couplings (RDCs) measured in the apo‐state of maltose binding protein (MBP) and its X‐ray crystal structure. However, paramagnetic relaxation enhancement (PRE) measurements on the same system reported on the presence of a small population of partially closed states in the ...
Shen Y, Bax A.
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Online Tensor Robust Principal Component Analysis
IEEE Access, 2022 Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In
Mohammad M. Salut, David V. Anderson
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Cross Tensor Approximation Methods for Compression and Dimensionality Reduction
IEEE Access, 2021 Cross Tensor Approximation (CTA) is a generalization of Cross/skeleton matrix and CUR Matrix Approximation (CMA) and is a suitable tool for fast low-rank tensor approximation.
Salman Ahmadi-Asl +6 more
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Adaptive Multilinear SVD for Structured Tensors [PDF]
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006 The higher-order SVD (HOSVD) is a generalization of the SVD to higher-order tensors (ie. arrays with more than two indexes) and plays an important role in various domains. Unfortunately, the computational cost of this decomposition is very high since the basic HOSVD algorithm involves the computation of the SVD of three highly redundant block-Hankel ...
R. Boyer, R. Badeau
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Hot-SVD: higher order t-singular value decomposition for tensors based on tensor–tensor product
Computational and Applied Mathematics, 2022 This paper considers a way of generalizing the t-SVD of third-order tensors (regarded as tubal matrices) to tensors of arbitrary order N (which can be similarly regarded as tubal tensors of order (N-1)). \color{black}Such a generalization is different from the t-SVD for tensors of order greater than three [Martin, Shafer, Larue, SIAM J. Sci.
Ying Wang, Yuning Yang
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Tensor SVD: Statistical and Computational Limits [PDF]
IEEE Transactions on Information Theory, 2018 Typos ...
Anru Zhang, Dong Xia
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Sensors, 2022 A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance.
Lijie Wang +3 more
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Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations [PDF]
Hum Brain MappWe introduce a new framework for accelerated processing of diffusion‐weighted imaging (DWI) data using a machine learning approach to optimize parameter estimation. We demonstrate that this new method, called DBSIpy, significantly improves computational speed and robustness to Rician noise compared to the standard DBSI method, with the improvements ...
Utt K, Blum J, Rim D, Song S.
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An Out of Memory tSVD for Big-Data Factorization
IEEE Access, 2020 Singular value decomposition (SVD) is a matrix factorization method widely used for dimension reduction, data analytics, information retrieval, and unsupervised learning.
Hector Carrillo-Cabada +4 more
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