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Search · four archives
18 papers · ranked by Valyu relevance
M. Mosleh E. Abu Samak, A. Ashrif A. Bakar, Muhammad Kashif, Mohd Saiful Dzulkifly Zan + 4 more
'Mohd Saiful Dzulkifly Zan' 'Teen-Hang Meen' 'Shoou-Jinn Chang' 'Stephen D. Prior' 'Artde Donald Kin Tak Lam'] This paper discusses numerical analysis methods for different geometrical features that have limited interval values for typically used sensor wavelengths. Compared with existing Finite Difference Time Domain…
Han Zhou, Shuwang Li, Wenjun Ying
This paper is concerned with the mean curvature flow, which describes the dynamics of a hypersurface whose normal velocity is determined by local mean curvature. We present a Cartesian grid-based method for solving mean curvature flows in two and three space dimensions. The present method embeds a closed hypersurface…
Jifeng Ge, Bastien Vieublé, Juan Zhang
In this article, we introduce a three-precision formulation of the General Alternating-Direction Implicit method (GADI) designed to accelerate the solution of large-scale sparse linear systems Ax = b. GADI is a framework that can represent many existing Alternating-Direction Implicit (ADI) methods. These methods are a…
Arash Sarshar, Steven Roberts, Adrian Sandu
Alternating Directions Implicit (ADI) integration is an operator splitting approach to solve parabolic and elliptic partial differential equations in multiple dimensions based on solving sequentially a set of related one-dimensional equations. Classical ADI methods have order at most two, due to the splitting errors.…
Shan Zhao
A novel Douglas alternating direction implicit (ADI) method is proposed in this work to solve a two-dimensional (2D) heat equation with interfaces. The ADI scheme is a powerful finite difference method for solving parabolic equations, due to its unconditional stability and high efficiency. However, it suffers from a…
Eng Leong Tan
—This paper presents the generalized formulations of fundamental schemes for efficient unconditionally stable implicit finite-difference time-domain (FDTD) methods. The fundamental schemes constitute a family of implicit schemes that feature similar fundamental updating structures, which are in simplest forms with most…
Juan Zhang, Yiyi Luo
The article mainly introduces preprocessing algorithms for solving linear equation systems. This algorithm uses three algorithms as inner iterations, namely RPCG algorithm, ADI algorithm, and Kaczmarz algorithm. Then, it uses BA-GMRES as an outer iteration to solve the linear equation system. These three algorithms can…
Linyuan Wang, Ailong Cai, Hanming Zhang, Bin Yan + 2 more
With the development of compressive sensing theory, image reconstruction from few-view projections has received considerable research attentions in the field of computed tomography (CT). Total-variation- (TV-) based CT image reconstruction has been shown to be experimentally capable of producing accurate…
Ailong Cai, Linyuan Wang, Hanming Zhang, Bin Yan + 4 more
'Xiaoqi Xi' 'Min Guan' 'Jianxin Li'] Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In…
Jingguo Dai, Yeqing Yi, Chengzhi Liu, Xuebo Zhang
In (Dai et al. 2023), the authors proposed a fast algorithm for surface reconstruction that converges rapidly from point cloud data by alternating Anderson extrapolation with implicit progressive iterative approximation (I-PIA). This algorithm employs a fixed step size during iterations to enhance convergence. To…
Solivan A. Valente, Marcelo V. W. Zibetti, Daniel R. Pipa, Joaquim M. Maia + 2 more
'Joaquim M. Maia' 'Fabio K. Schneider' 'Vittorio M. N. Passaro'] Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers…
Hongxing Hao, Wenjie Zhu, Ronghuan Yu, Desheng Liu + 1 more
Inverse synthetic aperture radar (ISAR) technology is widely used in the field of target recognition. This research addresses the image reconstruction error in sparse imaging for bistatic radar systems. In this paper, sparse imaging technology is studied, and a sparse imaging recovery algorithm based on an improved…
Peilin Zhao, Jin-Wei Yang, Tong Zhang, Ping Li
The Alternating Direction Method of Multipliers (ADMM) has been studied for years. The traditional ADMM algorithm needs to compute, at each iteration, an (empirical) expected loss function on all training examples, resulting in a computational complexity proportional to the number of training examples. To reduce the…
Harry T. Mason, Nadine N. Graedel, Karla L. Miller, Mark Chiew
Acceleration methods in fMRI aim to reconstruct high fidelity images from undersampled k-space, allowing fMRI datasets to achieve higher temporal resolution, reduced physiological noise aliasing, and increased statistical degrees of freedom. While low levels of acceleration are typically part of standard fMRI protocols…
Yiyu Wang, Jordan A. Taylor
The influence of explicit strategies on implicit recalibration during visuomotor adaptation has become a central question in motor learning. Because the two systems operate in tandem, explicit strategies could indirectly influence implicit recalibration. However, explicit strategies are not unitary: they may rely on…
Arni Sturluson, Ali Raza, Grant D. McConachie, Daniel Siderius + 2 more
Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A…
Kazunori D Yamada
In the deep learning era, a gradient descent method is the most common method to optimize parameters of neural networks. Among various mathematical optimization methods, a gradient descent method is the most naive method. Although controlling a learning rate of the method is necessary for quick convergence, the…
Ray Zirui Zhang, Christopher E. Miles, Xiaohui Xie, John S. Lowengrub
We propose a new neural network based method for solving inverse problems for partial differential equations (PDEs) by formulating the PDE inverse problem as a bilevel optimization problem. At the upper level, we minimize the data loss with respect to the PDE parameters. At the lower level, we train a neural network to…