Results 1 to 10 of about 91,565 (275)

Truncated singular value decomposition for through‐the‐wall microwave imaging application [PDF]

IET Microwaves, Antennas & Propagation, 2020
We considered differential through‐the‐wall microwave imaging with different formulations of truncated singular value decomposition (TSVD) method with a non‐anechoic experiment. Previous studies employ TSVD with single transmitting/measuring antenna, while we show how to apply the TSVD in case of a moving linear transmitting/measuring antenna array ...
Semih Doğu, Mehmet Nuri Akıncı, Mehmet Çayören, İbrahim Akduman   +3 more
openaire   +3 more sources

Perturbation expansions and error bounds for the truncated singular value decomposition [PDF]

Linear Algebra and its Applications, 2021
Accepted to Linear Algebra and Its ...
Trung Vu, Evgenia Chunikhina, Raviv Raich   +2 more
openaire   +5 more sources

Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization [PDF]

, 2018
We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach,
Jha, Abhinav K, Rahmim, Arman, Wong, Dean F, Zhu, Yansong   +3 more
core   +3 more sources

3-D Data Interpolation and Denoising by an Adaptive Weighting Rank-Reduction Method Using Multichannel Singular Spectrum Analysis Algorithm

Sensors, 2023
Addressing insufficient and irregular sampling is a difficult challenge in seismic processing and imaging. Recently, rank reduction methods have become popular in seismic processing algorithms for simultaneous denoising and interpolating.
Farzaneh Bayati, Daniel Trad
doaj   +1 more source

Compressed Passive Macromodeling [PDF]

, 2012
This paper presents an approach for the extraction of passive macromodels of large-scale interconnects from their frequency-domain scattering responses. Here, large scale is intended both in terms of number of electrical ports and required dynamic model ...
Grivet-Talocia, S., Olivadese, Salvatore Bernardo   +1 more
core   +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

An Out of Memory tSVD for Big-Data Factorization

IEEE Access, 2020
Singular value decomposition (SVD) is a matrix factorization method widely used for dimension reduction, data analytics, information retrieval, and unsupervised learning.
Hector Carrillo-Cabada, Erik Skau, Gopinath Chennupati, Boian Alexandrov, Hristo Djidjev   +4 more
doaj   +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

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

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