Results 21 to 30 of about 2,604 (146)

Objects features extraction by singular projections of data tensor to matrices

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
The problem of multidimensional tensor objects features extraction in a manner of matrices is considered. The tensor’ elements Higher Order Singular Value Decomposition (SVD) is presented as the d-SVD which includes SVD of the tensor reshaped as a ...
Yuriy Bunyak, Roman Kvуetnyy, Olga Sofina, Volodymyr Kotsiubynskyi   +3 more
doaj   +1 more source

The use of diffusion-tensor imaging to assess microstructural integrity of white matter of patients with Alzheimer’s disease

Вестник рентгенологии и радиологии, 2019
Objective. To compare diffusion-tensor imaging (DTI) measures in different anatomic regions of the brain in patients with an isolated Alzheimer's disease (AD) and patients with AD and small-vessel disease (SVD).Material and methods.
V. A. Perepelov, V. I. Solodovnikov, V. E. Sinitsyn, E. M. Perepelova, N. N. Koberskaya, V. N. Gridin, N. N. Yakhno   +6 more
doaj   +1 more source

Multi-Directional Tensor Average Rank Regularization for High-Order Tensor Completion

IEEE Access
Recently, the high-order tensor Singular Value Decomposition (t-SVD) and the t-SVD rank has achieved great success in tensor completion. However, the t-SVD rank lacks the flexibility to capture the correlations between different modes of a high-order ...
Zixuan Han, Mingjian Gu, Yong Hu
doaj   +1 more source

Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme

Applied Sciences
Employing low tensor rank decomposition in image inpainting has attracted increasing attention. This study exploited novel tensor arrangement schemes to transform an image (a low-order tensor) to a higher-order tensor without changing the total number of
Shuli Ma, Youchen Fan, Shengliang Fang, Weichao Yang, Li Li   +4 more
doaj   +1 more source

Mechanisms of cognitive impairment in cerebral small vessel disease: multimodal MRI results from the St George's cognition and neuroimaging in stroke (SCANS) study. [PDF]

PLoS ONE, 2013
Cerebral small vessel disease (SVD) is a common cause of vascular cognitive impairment. A number of disease features can be assessed on MRI including lacunar infarcts, T2 lesion volume, brain atrophy, and cerebral microbleeds.
Andrew J Lawrence, Bhavini Patel, Robin G Morris, Andrew D MacKinnon, Philip M Rich, Thomas R Barrick, Hugh S Markus   +6 more
doaj   +1 more source

Serum Neurofilament Light Chain Levels Are Related to Small Vessel Disease Burden [PDF]

Journal of Stroke, 2018
Background and Purpose Neurofilament light chain (NfL) is a blood marker for neuroaxonal damage. We assessed the association between serum NfL and cerebral small vessel disease (SVD), which is highly prevalent in elderly individuals and a major cause of ...
Marco Duering, Marek J. Konieczny, Steffen Tiedt, Ebru Baykara, Anil Man Tuladhar, Esther van Leijsen, Philippe Lyrer, Stefan T. Engelter, Benno Gesierich, Melanie Achmüller, Christian Barro, Ruth Adam, Michael Ewers, Martin Dichgans, Jens Kuhle, Frank-Erik de Leeuw, Nils Peters   +16 more
doaj   +1 more source

Longitudinal changes in rich club organization and cognition in cerebral small vessel disease

NeuroImage: Clinical, 2019
Cerebral small vessel disease (SVD) is considered the most important vascular contributor to the development of cognitive impairment and dementia. There is increasing awareness that SVD exerts its clinical effects by disrupting white matter connections ...
Esther M.C. van Leijsen, Ingeborg W.M. van Uden, Mayra I. Bergkamp, Helena M. van der Holst, David G. Norris, Jurgen A.H.R. Claassen, Roy P.C. Kessels, Frank-Erik de Leeuw, Anil M. Tuladhar   +8 more
doaj   +1 more source

Rank revealing‐based tensor completion using improved generalized tensor multi‐rank minimization

IET Signal Processing, 2021
The authors address the problem of tensor completion from limited samplings. An improved generalized tubal Kronecker decomposition is first proposed to reveal the tensor structure of the targeted data, and the improved generalized tensor tubal‐rank and ...
Wei Z. Sun, Peng Zhang, Bo Zhao
doaj   +1 more source

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source

A randomized block Krylov method for tensor train approximation

Frontiers in Applied Mathematics and Statistics
Tensor train decomposition is a powerful tool to tackle high-dimensional large-scale tensor data and is not suffering from the curse of dimensionality. It relies on performing the singular value decomposition (SVD) of auxiliary unfolding matrices.
Gaohang Yu, Jinhong Feng, Zhongming Chen, Xiaohao Cai, Liqun Qi   +4 more
doaj   +1 more source