Results 81 to 90 of about 50,718 (252)
Iteratively Reweighted Least Squares Algorithm for Sparse Principal Component Analysis with Application to Voting Records [PDF]
Statistika: Statistics and Economy Journal, 2017 Principal component analysis (PCA) is a popular dimensionality reduction and data visualization method. Sparse PCA (SPCA) is its extensively studied and NP-hard-to-solve modifcation. In the past decade, many diferent algorithms were proposed to perform
Tomáš Masák
doaj
An improved nonlocal sparse regularization-based image deblurring via novel similarity criteria
International Journal of Advanced Robotic Systems, 2018 Image deblurring is a challenging problem in image processing, which aims to reconstruct an original high-quality image from its blurred measurement caused by various factors, for example, imperfect focusing caused by the imaging system or different ...
Nannan Wang +3 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.This perspective reframes additive manufacturing for electrical machines as a qualification‐limited materials and architecture design problem. It links process–structure–property–performance relationships to magnetic, conducting, dielectric, and thermal property windows, highlighting where AM can enable segmented magnetic circuits, permanent magnet ...
Dénes Fodor, Loránd Szabó
wiley +1 more source
LFM: An R package for laplace factor model
SoftwareXThe Laplace Factor Model (LFM) is a valuable mathematical tool used in statistics, machine learning, and data analysis. It uses the Laplace distribution to capture data sparsity and uncertainty, effectively handling complex, large-scale data.
Siqi Liu, Guangbao Guo
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Advanced Functional Materials, EarlyView.Shellac, a centuries‐old natural resin, is reimagined as a green material for flexible electronics. When combined with silver nanowires, shellac films deliver transparency, conductivity, and stability against humidity. These results position shellac as a sustainable alternative to synthetic polymers for transparent conductors in next‐generation ...
Rahaf Nafez Hussein +4 more
wiley +1 more source
Electrospun Wood‐Derived Biopolymers as Electrodes in Electrochemical Energy Storage Technologies
Advanced Functional Materials, EarlyView.Electrospinning transforms wood‐derived cellulose and lignin into architecturally defined, binder‐free carbon electrodes with tuneable porosity and functionality. This review shows how fibre design enables decoupled charge and mass transport, enhancing performance across battery systems, while identifying key challenges in spinnability, scalability ...
Michael W. Thielke +3 more
wiley +1 more source
Strong Lead‐Free Bioinspired Piezoceramics for Durable Energy Transducers
Advanced Functional Materials, EarlyView.We fabricated brick‐and‐mortar lead‐free piezoceramics with Bi0.5Na0.5TiO3 monocrystal platelets as bricks and amorphous silica as mortar. The alignment of the bricks and the presence of the amorphous silica led to anisotropic residual stresses. These residual stresses increase the samples toughness, strength and durability while the brick alignment ...
Ruxue Yang +12 more
wiley +1 more source
Advanced Functional Materials, EarlyView.A dynamically actuated reconfigurable topographical surface (DARTS) integrates contact‐mediated bactericidal nanotopography with programmable mechanical actuation to actively disrupt bacterial biofilms. Dynamic surface reconfiguration enhances bacterial killing and suppresses implant‐associated infections in vivo, providing a new strategy for active ...
Mohammad Asadi Tokmedash +4 more
wiley +1 more source
Advanced Functional Materials, EarlyView.The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan +4 more
wiley +1 more source
Sparse Principal Component Analysis with Missing Observations [PDF]
, 2013 In this paper, we study the problem of sparse Principal Component Analysis (PCA) in the high-dimensional setting with missing observations. Our goal is to estimate the first principal component when we only have access to partial observations. Existing estimation techniques are usually derived for fully observed data sets and require a prior knowledge ...
openaire +2 more sources