Results 211 to 220 of about 1,032,480 (246)

Adaptive geometric-attention network for two-stage lung nodule segmentation and malignancy classification in federated healthcare IoT edge environments. [PDF]

PLoS One
Sufyan M, Qian J, Li J, Imran A, Sabah F, Sarwar R.   +5 more
europepmc   +1 more source

CTMNet: causal trend evolution and adaptive modulation for time series forecasting. [PDF]

Sci Rep
Wang Y, Chen X, Chen J, Liu M.
europepmc   +1 more source

Calibrated Prediction Intervals for Polygenic Scores: Updated Comparisons, Contextual Calibration, and Data Normalization

Xu C, Hou S, Zhou X.
europepmc   +1 more source

DOA Estimation in heteroscedastic noise

Signal Processing, 2019
The paper considers direction of arrival (DOA) estimation from long-term observations in a very noisy environment. The concern is to derive methods obtaining reasonable DOAs at very low SNR. The noise is assumed zero-mean Gaussian and its variance varies in time and space, causing stationary data models to fit poorly over long observation times ...
Geert Leus, Christoph Mecklenbräuker, Peter Gerstoft   +2 more
exaly   +7 more sources

Estimation of heteroscedastic measurement noise variances.

Chemometrics and Intelligent Laboratory Systems, 2007
For any quantitative data interpretation it is crucial to have information about the noise variances. Unfortunately, this information is often unavailable a priori. We propose a procedure to estimate the noise variances starting from the residuals. The method takes two difficulties into account.
Debrauwere, Anouk, Pintelon, Rik, De Ridder, Fedor, Schoukens, Joannes, Baeyens, Willy   +4 more
core   +4 more sources

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Characterization of heteroscedastic measurement noise in the absence of replicates

Analytica Chimica Acta, 2014
A method is described for the characterization of measurement errors with non-uniform variance (heteroscedastic noise) in contiguous signal vectors (e.g., spectra, chromatograms) that does not require the use of replicated measurements. High-pass digital filters based on inverted Blackman windowed sinc smoothing coefficients are employed to provide ...
Peter D. Wentzell
exaly   +3 more sources

Evolving factor analysis in the presence of heteroscedastic noise

Analytica Chimica Acta, 1992
Abstract Evolving factor analysis (EFA) is a promixing method for the analysis of multivariate data with an intrinsic order. When applying EFA for assessment of peak homogeneity in liquid chromatography, one has to be aware of instrumental and experimental difficulties.
O M Kvalheim, H R Keller
exaly   +2 more sources

Inferring Cause and Effect in the Presence of Heteroscedastic Noise [PDF]

, 2022
We study the problem of identifying cause and effect over two univariate continuous variables X and Y from a sample of their joint distribu- tion. Our focus lies on the setting where the variance of the noise may be dependent on the cause. We propose to partition the domain of the cause into multiple segments when the noise in- deed is dependent.
Xu, Sascha, Mian, Osman, Vreeken, Jilles
core   +3 more sources

Estimation of the heteroscedastic noise in large data arrays

Analytica Chimica Acta, 2000
An approach has been developed to estimate the uncertainties in experimental data that follows a heteroscedastic model. The method presented is based on a hypothesis that the size of data is sufficiently large such that the data values over a limited domain have approximately homoscedastic variance.
Philip K. Hopke
exaly   +2 more sources

Estimation of Optimal Fiducial Target Registration Error in the Presence of Heteroscedastic Noise

IEEE Transactions on Medical Imaging, 2010
We study the effect of point dependent (heteroscedastic) and identically distributed anisotropic fiducial localization noise on fiducial target registration error (TRE). We derive an analytic expression, based on the concept of mechanism spatial stiffness, for predicting TRE.
Purang Abolmaesumi
exaly   +4 more sources