Results 81 to 90 of about 710,592 (303)

An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition

Remote Sensing, 2020
The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area.
Yanan You, Rui Wang, Wenli Zhou
doaj   +1 more source

Oligoethylene Glycol Side‐Chain Engineering of Y6‐Derived Nonfullerene Acceptors for Thermally Induced Spontaneous Pseudo‐Planar Heterojunction Organic Photodetectors

Advanced Functional Materials, EarlyView.
OEG‐functionalized Y6‐derived acceptors induce thermally driven vertical phase separation, enabling pseudo‐planar heterojunction organic photodetectors. The resulting PM6:YTEG‐4F and PM6:YTEG‐4Cl organic photodetectors suppress dark current density by approximately 260‐fold and 210‐fold, respectively, while maintaining efficient photoresponse ...
Se Jeong Park, Sang Young Jeong, Yunji Eom, Yelim Kang, Yeonsu Choi, Seunghyun Rhee, Bo Ram Lee, Bong Joo Kang, Nam Joong Jeon, Jae Won Shim, Han Young Woo, Seo‐Jin Ko   +11 more
wiley   +1 more source

Tensor polyadic decomposition for antenna array processing

, 2014
International audienceIn the present framework, a tensor is understood as a multi-way array of complex numbers indexed by three (or more) indices. The decomposition of such tensors into a sum of decomposable (i.e.
Sahnoun, Souleymen, Comon, Pierre
core   +1 more source

FastTWD: A MATLAB package for matrix-free CPU/GPU tensor wheel decomposition

SoftwareX
Tensor wheel (TW) decomposition is an emerging tensor-network format for compact representation of multidimensional data. Owing to its wheel topology, TW connects several important tensor formats, including CP, Tucker, tensor-train, and tensor-ring ...
Rafał Zdunek
doaj   +1 more source

Architecture‐Driven Functional Coupling in Vertically Aligned Nanocomposites

Advanced Functional Materials, EarlyView.
Vertically aligned nanocomposites define a growth‐engineered architecture in which vertical interfaces, strain fields, defect pathways, and phase connectivity are created simultaneously. This review shows how these architectural features couple ferroic, optical, ionic, electrochemical, and device responses, establishing design rules and open challenges
Md Shatil Islam‐Shanto, Javier Zamudio‐García, Abraham Sánchez‐Caballero, David Marrero‐López, Vincenzo Esposito   +4 more
wiley   +1 more source

Tensor-Based Low-Rank and Sparse Prior Information Constraints for Hyperspectral Image Denoising

IEEE Access, 2020
Hyperspectral data have been widely used in various fields due to its rich spectral and spatial information in recent years. Yet, hyperspectral images are always tainted by a variety of mixed noises.
Guxi Wang, Hongwei Han, Emmanuel John M. Carranza, Si Guo, Ke Guo, Keyan Xiao   +5 more
doaj   +1 more source

Full‐Field Damage Monitoring in Architected Lattices Using In situ Electrical Impedance Tomography

Advanced Functional Materials, EarlyView.
In situ electrical impedance tomography (EIT) turns 3D‐printed, CNT‐infused architected lattices into full‐field damage‐imaging systems. Tunable Voronoi‐based geometries act as active sensing architectures, enabling conductivity maps to detect early‐stage damage and localise sequential ligament fracture before catastrophic failure.
Akash Deep, Andrea Samore, Alistair McEwan, Andrew McBride, Shanmugam Kumar   +4 more
wiley   +1 more source

General Tensor Decomposition, Moment Matrices and Applications [PDF]

, 2013
SubmittedInternational audienceThe tensor decomposition addressed in this paper may be seen as a generalisation of Singular Value Decomposition of matrices. We consider general multilinear and multihomogeneous tensors.
Mourrain, Bernard, Pierre Comon, Bernardi, Alessandra, Comon, Pierre, Bernard Mourrain, Brachat, Jerome, Jerome Brachat   +6 more
core   +1 more source

Constrained Cramér-Rao Bound for Higher-Order Singular Value Decomposition

IEEE Open Journal of Signal Processing
Tensor decomposition methods for signal processing applications are an active area of research. Real data are often low-rank, noisy, and come in a higher-order format.
Metin Calis, Massimo Mischi, Alle-Jan van der Veen, Raj Thilak Rajan, Borbala Hunyadi   +4 more
doaj   +1 more source

Tensor decompositions and algorithms, with applications to tensor learning

CoRR, 2021
A new algorithm of the canonical polyadic decomposition (CPD) presented here. It features lower computational complexity and memory usage than the available state of the art implementations. We begin with some examples of CPD applications to real world problems. A short summary of the main contributions in this work follows.
openaire   +3 more sources