Results 31 to 40 of about 82,300 (309)

Causal Identification with Additive Noise Models: Quantifying the Effect of Noise [PDF]

CoRR, 2021
In recent years, a lot of research has been conducted within the area of causal inference and causal learning. Many methods have been developed to identify the cause-effect pairs in models and have been successfully applied to observational real-world data to determine the direction of causal relationships. Yet in bivariate situations, causal discovery
KAP, Benjamin, ALEKSANDROVA, Marharyta, ENGEL, Thomas   +2 more
openaire   +2 more sources

Statistical Disclosure Control Methods for Microdata from the Labour Force Survey

Acta Universitatis Lodziensis. Folia Oeconomica, 2020
The aim of this article is to analyse the possibility of applying selected perturbative masking methods of Statistical Disclosure Control to microdata, i.e. unit‑level data from the Labour Force Survey.
Michał Pietrzak
doaj   +1 more source

Convex optimization for additive noise reduction in quantitative complex object wave retrieval using compressive off-axis digital holographic imaging

Journal of Intelligent Systems, 2022
Image denoising is one of the important problems in the research field of computer vision, artificial intelligence, 3D vision, and image processing, where the fundamental aim is to recover the original image features from a noisy contaminated image.
Reddy B. Lokesh, Nelleri Anith
doaj   +1 more source

Noise stability of diversity system for signals PSK and DPSK in multipath channels [PDF]

Омский научный вестник, 2018
We consider noise stability of diversity system for signals PSK and DPSK in multipath channels with additive Gaussian noise. The analysis of noise stability is based in fluctuation of signal distance through multipath.
G. M. Sidelnikov, D. V. Ognev
doaj   +1 more source

Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms

Sensors
Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances.
Minh-Thien Duong, Bao-Tran Nguyen Thi, Seongsoo Lee, Min-Cheol Hong   +3 more
doaj   +1 more source

Additive noise and jitter performance analysis of passive optical interferometers operated at ultrahigh rates

, 2006
In this paper, we theoretically associate the additive noise, the amplitude jitter and the timing jitter at the input and output of passive optical interferometers.
Yiannopoulos, K, Yiannopoulos, Konstantinos, Kanellos, GT, Avramopoulos, Hercules, Pleros, Nikos, Pleros, N, Avramopoulos, H, Kanellos, George Theodore   +7 more
core   +1 more source

Coherence resonance in a noise-driven gene network regulated by small RNA

Theoretical and Applied Mechanics Letters, 2014
Small RNA has recently drawn more and more attention. In this paper, we concentrate on the influence of noises on gene network regulated by small RNA using chemical Langevin equation.
Yanan Zhu, Jianwei Shen, Yong Xu
doaj   +1 more source

Ermakov-Ray-Reid systems with additive noise [PDF]

, 2015
"Using the methods developed by us in Cervantes-López et al. (2014) for multiplicative noises, we present results on the effects of the additive noise on the Ermakov-Lewis invariant.
Rosu Barbus, Haret-Codratian, Ernesto cervantes, Luis Armando Gallegos   +2 more
core   +1 more source

Spatiotemporal and quantitative analyses of phosphoinositides – fluorescent probe—and mass spectrometry‐based approaches

FEBS Letters, EarlyView.
Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho, Shin Morioka, Junko Sasaki, Takehiko Sasaki   +3 more
wiley   +1 more source

A Hybrid Adaptive Fusion Deep Learning Model for Fault Diagnosis of Rotating Machinery Under Noisy Conditions

IEEE Access
Rotating machinery is essential in modern industry. A robust noise-resistant method is proposed to improve diagnostic accuracy under intense noise conditions.
Junyu Ren, Soo Siang Teoh
doaj   +1 more source