Results 71 to 80 of about 118,476 (365)
Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani +10 more
wiley +1 more source
Wavelet shrinkage using adaptive structured sparsity constraints [PDF]
, 2015 Structured sparsity approaches have recently received much attention in the statistics, machine learning, and signal processing communities. A common strategy is to exploit or assume prior information about structural dependencies inherent in the data ...
Tomassi, Diego Rodolfo +7 more
core +1 more source
Advanced Engineering Materials, EarlyView.New features on yttria‐stabilized zirconia after exposure at 1500°C: Newly discovered pyramidal structures on an old material. After exposure at 1550°C on the cross section of YSZ new features, namely pyramidal structures are discovered. These structures grow with time, increase in numbers, appear as singularities, are often arranged in strings, and ...
Doris Sebold +2 more
wiley +1 more source
Learning-based Low Light Image Denoising [PDF]
, 2023 openDenoising low-light images is a challenging task due to the high noise level. When the illumination is low, digital cameras increase the ISO (electronic gain) to amplify the brightness of captured data.
ALNAJJAR, ESRAA M B
core
Deep Parameterized Neural Networks for Hyperspectral Image Denoising
, 2023 Sparse representation (SR)-based hyperspectral image (HSI) denoising methods normally average the local denoising results of multiple overlapped cubes to recover the whole HSI.
Jiantao Zhou +8 more
core +1 more source
CoRR, 2021 Discrete flow-based models are a recently proposed class of generative models that learn invertible transformations for discrete random variables. Since they do not require data dequantization and maximize an exact likelihood objective, they can be used in a straight-forward manner for lossless compression.
Alexandra Lindt, Emiel Hoogeboom
openaire +2 more sources
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising
Remote Sensing, 2020 Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods.
Shaobo Li +4 more
doaj +1 more source
Rehaussement du signal de parole voisé par filtrage adaptatif des modes intrinsèques empiriques [PDF]
, 2010 In this paper a new method for voiced speech enhancement combining the Empirical Mode Decomposition (EMD) and the Adaptive Center Weighted Average (ACWA) filter is introduced.
TURKI, Monia +2 more
core +1 more source
Advanced Engineering Materials, EarlyView.Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source