Results 171 to 180 of about 1,596,426 (191)

Fast Sparse Reconstruction of Sound Field Via Bayesian Compressive Sensing

Journal of Vibration and Acoustics, 2019
To overcome the contradiction between the resolution and the measurement cost, various algorithms for reconstructing the sound field with sparse measurement have been developed. However, limited attention is paid to the computation efficiency.
D. Hu, Xin-Yue Liu, Yue Xiao, Yu Fang
semanticscholar   +1 more source

Modified sparse reconstruction imaging of lamb waves for damage quantitative evaluation

NDT & E international, 2019
Lamb wave based sparse reconstruction imaging is promising for damage localization in structural health monitoring (SHM) and nondestructive testing (NDT).
J. Hua, Fei Gao, L. Zeng, Jing Lin
semanticscholar   +1 more source

Sparse reconstruction of ISOMAP representations

Journal of Intelligent & Fuzzy Systems, 2019
Isometric feature mapping (ISOMAP) is one of the classical methods of nonlinear dimensionality reduction (NLDR) and seeks for low dimensional (LD) structure of high dimensional (HD) data. However, the inverse problem of ISOMAP has never been investigated, which recovers the HD sample from the related LD sample, and its application prospect in data ...
Li, Honggui, Trocan, Maria
openaire   +1 more source

Sparse reconstruction imaging of damage for Lamb wave simultaneous excitation system in composite laminates

Measurement, 2019
Most Lamb wave methods utilize transducer array for damage detection and localization. Simultaneous excitation of multiple transmitters is a strategy for efficient data acquisition, in which matched filtering is generally applied for signal separation ...
J. Hua, Zili Wang, Fei Gao, L. Zeng, Jing Lin   +4 more
semanticscholar   +1 more source

DOA Estimation for Sparse Array via Sparse Signal Reconstruction

IEEE Transactions on Aerospace and Electronic Systems, 2013
The problem of direction-of-arrival (DOA) estimation for sparse array is addressed. The perspective that DOA estimation in virtual array response model can be cast as the problem of sparse recovery is introduced. Two methods are proposed, based on different optimization problems, which are solvable using second-order cone (SOC) programming. Without the
Nan Hu, Zhongfu Ye, Xu Xu, Ming Bao
openaire   +1 more source

Sparse modelling and sparse signal reconstruction

Abstract Signal models are central to solving inverse problems, and reconstruction methods either implicitly or explicitly make use of signal models. Assuming the unknown signal of interest lies in a class of signals described by a signal model, we wish to reconstruct the signal with an algorithm that is sample efficient (i.e., only ...
openaire   +1 more source

Fourier diffusion for sparse CT reconstruction

Medical Imaging 2024: Physics of Medical Imaging
Sparse CT reconstruction continues to be an area of interest in a number of novel imaging systems. Many different approaches have been tried including model-based methods, compressed sensing approaches, and most recently deep-learning-based processing.
Anqi, Liu, Grace J, Gang, J Webster, Stayman   +2 more
openaire   +2 more sources

Jointly Sparse Reconstructed Regression Learning

2018
Least squares regression and ridge regression are simple and effective methods for feature selection and classification and many methods based on them are proposed. However, most of these methods have small-class problem, which means that the number of the projection learned by these methods is limited by the number of class.
Dongmei Mo, Zhihui Lai, Heng Kong
openaire   +1 more source

A novel sparse reconstruction method based on multi-objective Artificial Bee Colony algorithm

Signal Processing, 2021
Murat Emre Erkoc, N. Karaboga
semanticscholar   +1 more source

Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

IEEE Journal on Selected Topics in Signal Processing, 2007
Mário A. T. Figueiredo, R. Nowak, S. Wright   +2 more
semanticscholar   +1 more source