Results 101 to 110 of about 1,046,371 (288)
Hologram Noise Model for Data Augmentation and Deep Learning
SensorsThis paper introduces a noise augmentation technique designed to enhance the robustness of state-of-the-art (SOTA) deep learning models against degraded image quality, a common challenge in long-term recording systems.
Dániel Terbe +3 more
doaj +1 more source
MAGORINO: Magnitude-only fat fraction and R* 2 estimation with Rician noise modeling. [PDF]
Magn Reson Med, 2023 Bray TJP +4 more
europepmc +1 more source
Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge.
Kilian Hett +8 more
wiley +1 more source
Sensor-Physics-Driven Noise Modeling for Low-Light Imaging Using Adversarial Learning
Applied SciencesHigh-fidelity imaging in extreme low light is challenged by ultra-low signal-to-noise ratios. We propose a hybrid noise modeling framework integrating physical priors with generative adversarial networks (GANs). The method simulates photon shot noise via
Peihua Zhao +5 more
doaj +1 more source
Millimeter-wave noise modeling of nanoscale MOSFETs
Dianzi Jishu Yingyong, 2018 Based on the physical structure of 40 nm MOSFETs, this paper establishes a unified MOSFET millimeter-wave noise model to characterize the characteristics of drain-current noise, inducted gate-current noise and cross-correlation noise between them.
Peng Xiaomei, Zhao Aifeng, Wang Jun
doaj +1 more source
Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer. [PDF]
Bioinformatics, 2022 Li H +8 more
europepmc +1 more source
Noise reduction using connectionist models
ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003 Using a back propagation network learning algorithm, a four-layered feed-forward network is trained on learning samples to realize a mapping from the set of noisy signals a set of noise-free signals. Computer experiments were carried out on 12 kHz sampled Japanese speech data, using stationary and nonstationary noise.
Tamura, Sin’ichi, Waibel, Alex
openaire +3 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano +11 more
wiley +1 more source
Jitter noise modeling and its removal using recursive least squares in shape from focus systems. [PDF]
Sci Rep, 2022 Mutahira H, Shin V, Park U, Muhammad MS.
europepmc +1 more source
Aircraft noise synthesis system: Version 4 user instructions [PDF]
A modified version of the Aircraft Noise Synthesis System with improved directivity and tonal content modeling has been developed. The synthesis system is used to provide test stimuli for studies of community annoyance to aircraft flyover noise.
Grandle, Robert E. +2 more
core +1 more source