Results 51 to 60 of about 3,866 (190)
Superfast Tikhonov Regularization of Toeplitz Systems [PDF]
IEEE Transactions on Signal Processing, 2014 26 pages, 6 figures, 3 tables, 2 algorithms Submitted to IEEE Transactions on Signal Processing on 25 Feb ...
Christopher K. Turnes +2 more
openaire +2 more sources
InfoScience, EarlyView.Traditional light‐analyzing tools, known as spectrometers, are typically too bulky and expensive to fit into portable electronics like smartphones or wearables. In this study, we developed a microscopic, high‐performance spectrometer using a material called Indium Selenide combined with smart algorithms to accurately analyze light from the visible to ...
Jing Chen +8 more
wiley +1 more source
A stabilized algorithm for multi-dimensional numerical differentiation
Journal of Algorithms & Computational Technology, 2016 We develop a multi-dimensional numerical differentiation method in this paper. To obtain stable numerical derivatives, the Tikhonov regularization method in Hilbert scales is proposed to deal with illposedness of the problem. The penalty term in Tikhonov
Zhenyu Zhao +4 more
doaj +1 more source
Quantitative Assessment of Visible Nigrosome‐1 in Patients with Parkinson's Disease
Movement Disorders Clinical Practice, EarlyView.Abstract Background The nigrosome‐1 (N1) sign on susceptibility‐weighted imaging (SWI) typically disappears in Parkinson's disease (PD), though some patients can show uni−/bilaterally preserved N1. Objective Investigating whether visible nigrosomes in PD patients differ from those of healthy subjects (HC). Methods Forty‐eight PD and 35 HC underwent 3 T‐
Maria Eugenia Caligiuri +10 more
wiley +1 more source
Comparative Systematic Analysis of Gray Matter Biophysical Models on a Public Dataset
Magnetic Resonance in Medicine, EarlyView.ABSTRACT Purpose Biophysical models of diffusion tailored to characterize gray matter (GM) microstructure are gaining traction in the neuroimaging community. NEXI, SMEX, SANDI, and SANDIX represent recent efforts to account for different microstructural features, such as soma contributions and inter‐compartment exchange, in the diffusion MRI (dMRI ...
Santiago Mezzano +6 more
wiley +1 more source
General Tikhonov Regularization with Applications in Geoscience
CSIAM Transactions on Applied Mathematics, 2020 Summary: The article is devoted to a review of the following new elements of the modern theory of solving inverse problems: (a) general theory of Tikhonov's regularization with practical examples is considered; (b) an overview of a-priori and a-posteriori error estimates for solutions of ill-posed problems is presented as well as a general scheme of a ...
Wang, Yanfei +3 more
openaire +3 more sources
The trade-off between regularity and stability in Tikhonov regularization [PDF]
Mathematics of Computation, 1997 Summary: When deriving rates of convergence for the approximations generated by the application of Tikhonov regularization to ill-posed operator equations, assumptions must be made about the nature of the stabilization (i.e., the choice of the seminorm in the Tikhonov regularization) and the regularity of the least squares solutions which one looks for.
M. Thamban Nair +2 more
openaire +1 more source
Magnetic Resonance in Medicine, EarlyView.ABSTRACT Purpose To jointly reconstruct high‐resolution diffusion‐weighted volumes and eliminate slab‐boundary artifacts while preserving fine anatomical detail from undersampled 3D multi‐slab k‐space acquisitions. Methods A bilinear forward model was formulated to describe the 3D multi‐slab acquisition, treating the image volume and slab excitation ...
Reza Ghorbani +4 more
wiley +1 more source
A Multipath Error Reduction Method for BDS Using Tikhonov Regularization with Parameter Optimization
Remote Sensing, 2023 Multipath error is an important factor restricting the relative positioning accuracy of the Beidou Navigation Satellite System (BDS). Because of the complexity of the reflection environment, the mathematical modeling of multipath errors is quite ...
Xinzhong Li +5 more
doaj +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma +4 more
wiley +1 more source