Results 31 to 40 of about 44,503 (183)

Conformal phased array with beam forming for airborne satellite communication [PDF]

, 2008
For enhanced communication on board of aircraft novel antenna systems with broadband satellite-based capabilities are required. The installation of such systems on board of aircraft requires the development of a very low-profile aircraft antenna, which ...
Heideman, R.G., Hulzinga, A., Jorna, P., Leinse, A., Meijerink, A., Roeloffzen, C.G.H., Schippers, H., Verpoorte, J., Wintels, M.   +8 more
core   +2 more sources

Application of two-dimensional truncated singular value decomposition in image restoration

Journal of Physics: Conference Series, 2021
Abstract Large solution size and ill-posed problem often exists in image restoration. In order to overcome these problems, the application of two-dimensional truncated singular value decomposition(2-D TSVD) in large-scale image restoration is studied in this paper. The 2-D TSVD overcomes the inherent ill-posed problem and also solves the
Hongyu Zhou, Ting Kou, Xu Song
openaire   +1 more source

Fractional Norm Regularization Using Truncated Singular Value Decomposition

IEEE Access
In a previous work, a solution to the fractional norm regularization (FNR) was discovered in a closed form and an inverse perturbation was adopted as a tool to overcome the ill condition of a matrix whose inverse is required by the fixed-point FNR.
Bamrung Tausiesakul, Krissada Asavaskulkiet   +1 more
doaj   +1 more source

Image Denoising Using Hybrid Singular Value Thresholding Operators

IEEE Access, 2020
Truncated singular value decomposition (TSVD) is a simple and efficient technique for patch-based image denoising, in which a hard thresholding operator is utilized to set some small singular values to zero.
Fan Zhang, Hui Fan, Peiqiang Liu, Jinjiang Li   +3 more
doaj   +1 more source

Modified Truncated Randomized Singular Value Decomposition (MTRSVD) Algorithms for Large Scale Discrete Ill-posed Problems with General-Form Regularization

, 2018
In this paper, we propose new randomization based algorithms for large scale linear discrete ill-posed problems with general-form regularization: ${\min} \|Lx\|$ subject to ${\min} \|Ax - b\|$, where $L$ is a regularization matrix.
Jia, Zhongxiao, Yang, Yanfei
core   +1 more source

A New Hybrid Inversion Method for 2D Nuclear Magnetic Resonance Combining TSVD and Tikhonov Regularization

Journal of Imaging, 2021
This paper is concerned with the reconstruction of relaxation time distributions in Nuclear Magnetic Resonance (NMR) relaxometry. This is a large-scale and ill-posed inverse problem with many potential applications in biology, medicine, chemistry, and ...
Germana Landi, Fabiana Zama, Villiam Bortolotti   +2 more
doaj   +1 more source

A modified truncation singular value decomposition method for solving ill-posed problems

Journal of Algorithms & Computational Technology, 2018
A modified truncated singular value decomposition method for solving ill-posed problems is presented in this paper, in which the solution has a slightly different form.
Zhenyu Zhao, Riguang Lin, Zehong Meng, Guoqiang He, Lei You, Yongxiong Zhou   +5 more
doaj   +1 more source

Comparative study on the applicability of regularization method in ship’s magnetic signature modeling based on monopole array

AIP Advances, 2022
In this paper, we first study the applicability and stability of the Tikhonov regularization method (TRM) and the truncated singular value decomposition method in ship’s magnetic signature inversion modeling.
Baowei He, Shengdao Liu, Jingwen Zong
doaj   +1 more source

Some matrix nearness problems suggested by Tikhonov regularization

, 2016
The numerical solution of linear discrete ill-posed problems typically requires regularization, i.e., replacement of the available ill-conditioned problem by a nearby better conditioned one.
Noschese, Silvia, Reichel, Lothar
core   +1 more source

Spectral analysis of the truncated Hilbert transform with overlap [PDF]

, 2013
We study a restriction of the Hilbert transform as an operator $H_T$ from $L^2(a_2,a_4)$ to $L^2(a_1,a_3)$ for real numbers $a_1 < a_2 < a_3 < a_4$. The operator $H_T$ arises in tomographic reconstruction from limited data, more precisely in the method ...
Al-Aifari, Reema, Katsevich, Alexander
core   +3 more sources