Results 191 to 200 of about 7,174 (250)

Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. [PDF]

open access: yesPLoS Comput Biol, 2020
Saxena S   +10 more
europepmc   +1 more source

Finite‐Time Disturbance Observer‐Based Nonlinear Robust Adaptive Control of Hydraulic Manipulator With Parameter Identification

open access: yesInternational Journal of Mechanical System Dynamics, Volume 6, Issue 2, Page 282-298, June 2026.
ABSTRACT Hydraulic manipulators exhibit strong coupling, pronounced nonlinearities, and significant modeling uncertainties, which hinder high‐precision motion control. This paper proposes a finite‐time disturbance observer–based nonlinear robust adaptive control (RAC‐FTDO) framework enhanced by a physically consistent dynamic parameter identification ...
Tianyu Gao   +3 more
wiley   +1 more source

Input Layer Regularization and Automated Regularization Hyperparameter Tuning for Myelin Water Estimation Using Deep Learning

open access: yesNMR in Biomedicine, Volume 39, Issue 6, June 2026.
We propose a novel deep learning algorithm for predicting the myelin water fraction from multiple gradient‐echo or spin‐echo pulse sequences arising in magnetic resonance relaxometry (MRR) measurements of the human brain. Our method incorporates both regularized nonlinear least squares and pure deep learning through a concatenation paradigm known as ...
Mirage Modi   +7 more
wiley   +1 more source

A Preconditioned Majorization‐Minimization Method for ℓ2$$ {\ell}^2 $$‐ℓq$$ {\ell}^q $$ Minimization

open access: yesNumerical Linear Algebra with Applications, Volume 33, Issue 3, June 2026.
ABSTRACT The need to minimize a linear combination of an expression that involves an ℓq$$ {\ell}^q $$‐norm of a linear transformation of the computed solution and the ℓ2$$ {\ell}^2 $$‐norm of the residual error arises in image restoration as well as in statistics.
A. Buccini   +3 more
wiley   +1 more source

Row‐Aware Randomized SVD With Applications

open access: yesNumerical Linear Algebra with Applications, Volume 33, Issue 3, June 2026.
ABSTRACT The randomized singular value decomposition proposed in [28] has certainly become one of the most well‐established randomization‐based algorithms in numerical linear algebra. The key ingredient of the entire procedure is the computation of a subspace which is close to the column space of the target matrix A∈ℝm×n$$ \mathbf{A}\in {\mathbb{R}}^{m\
Davide Palitta, Sascha Portaro
wiley   +1 more source

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