Results 81 to 90 of about 110,162 (274)

Transformation of Non-Euclidean Space to Euclidean Space for Efficient Learning of Singular Vectors

IEEE Access, 2020
Singular value decomposition (SVD) is a popular technique to extract essential information by reducing the dimension of a feature set. SVD is able to analyze a vast matrix in spite of a relatively low computational cost.
Seunghyun Lee, Byung Cheol Song
doaj   +1 more source

N-Dimensional Principal Component Analysis [PDF]

, 2010
In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation.
Yu, Hongchuan
core  

Strong‐Gradient Diffusion‐Weighted Imaging of Prostate Cancer Using an Inside‐Out Nonlinear Gradient Coil

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To demonstrate improved image quality and lesion conspicuity in prostate diffusion‐weighted imaging (DWI) using an inside‐out nonlinear gradient coil that provides locally strong gradients (200–500 500 mT/m) at typical prostate positions.
Horace Z. Zhang, Nahla M. H. Elsaid, Terence W. Nixon, Andrew Dewdney, Dana C. Peters, Jeffrey C. Weinreb, Preston C. Sprenkle, R. Todd Constable, Gigi Galiana   +8 more
wiley   +1 more source

Unraveling complexity: Singular value decomposition in complex experimental data analysis

SciPost Physics Core
Analyzing complex experimental data with multiple parameters is challenging. We propose using Singular Value Decomposition (SVD) as an effective solution.
Judith F. Stein, Aviad Frydman, Richard Berkovits
doaj   +1 more source

Robust Optimized Discrete Wavelet Transform-Singular Value Decomposition Based Video Watermarking

Traitement du signal, 2019
Received: 2 September 2019 Accepted: 13 November 2019 The effortless editing, interchanging and replication of multimedia data on the internet is growing exponentially and has created copyright protection uncertainties for content providers.
Swaraja Kuraparthi, Meenakshi Kollati, Padmavathi Kora   +2 more
semanticscholar   +1 more source

Quantitative Comparison of Multi‐Echo Spin Echo and Multi‐Echo Gradient Echo Myelin Water Imaging in a Panel of Mbp Enhancer‐Edited Mouse Lines

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To evaluate the correspondence between myelin water fraction (MWF) estimates derived from multi‐echo spin echo (MESE) and multi‐echo gradient echo (MGRE) imaging in fixed mouse brain tissue, using a panel of myelin basic protein (Mbp) enhancer‐edited mouse lines exhibiting graded hypomyelination.
Vladimir Grouza, Yawen Shi, Sean Goldfarb, Klaudia M. Bednarz, Isabella Gomez, Marius Tuznik, Zhe Wu, Hooman Bagheri, Katherine A. Siminovitch, Alan C. Peterson, Thomas Stroh, David A. Rudko   +11 more
wiley   +1 more source

Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

Shock and Vibration, 2009
Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is ...
Junsheng Cheng, Dejie Yu, Jiashi Tang, Yu Yang   +3 more
doaj   +1 more source

Rosette Cardiac MR Fingerprinting for Simultaneous T1, T2, T2*$$ {\mathrm{T}}_2^{\ast } $$, and Fat Fraction Mapping Using a Multi‐Echo Deep Image Prior Reconstruction

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Quantitative mapping of cardiac tissue properties is used clinically in diagnosis and monitoring of a wide variety of cardiac pathologies. Cardiac Magnetic Resonance Fingerprinting (cMRF) enables rapid and simultaneous quantification of multiple parameters in the myocardium from a single scan.
Evan Cummings, Gastao Cruz, Jacob Richardson, Sydney Kaplan, Jesse Hamilton, Nicole Seiberlich   +5 more
wiley   +1 more source

An Enhanced Incremental SVD Algorithm for Change Point Detection in Dynamic Networks

IEEE Access, 2018
Change point detection is essential to understand the time-evolving structure of dynamic networks. Recent research shows that a latent semantic indexing (LSI)-based algorithm effectively detects the change points of a dynamic network.
Yongsheng Cheng, Jiang Zhu, Xiaokang Lin
doaj   +1 more source

Comparing the power of phylogenetic, trait and network structure information to predict plant–frugivore interactions

Oikos, EarlyView.
Due to the constraints of limited effort and sampling error, observed species interaction networks are an imperfect representation of the ‘true' underlying community. Link prediction methods allow us to construct a potentially more complete representation of a given empirical network by guiding targeted sampling of predicted links, as well as offer ...
Grant Foster, Tad A. Dallas
wiley   +1 more source