A tutorial on sparse signal reconstruction and its applications in signal processing
Sparse signals are characterized by a few nonzero coefficients in one of their transformation
domains. This was the main premise in designing signal compression algorithms …
domains. This was the main premise in designing signal compression algorithms …
Image reconstruction in electrical impedance tomography based on structure-aware sparse Bayesian learning
Electrical impedance tomography (EIT) is developed to investigate the internal conductivity
changes of an object through a series of boundary electrodes, and has become increasingly …
changes of an object through a series of boundary electrodes, and has become increasingly …
Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation with missing observations
In this paper, we address the problem of spectrum estimation of multiple frequency-hopping
(FH) signals in the presence of random missing observations. The signals are analyzed …
(FH) signals in the presence of random missing observations. The signals are analyzed …
Multiple squeezes from adaptive chirplet transform
Time-frequency (TF) analysis method is an effective tool to analyze non-stationary signals.
However, how to generate a clear TF representation for strongly time-varying signals is still a …
However, how to generate a clear TF representation for strongly time-varying signals is still a …
Seismic time-frequency analysis based on time-reassigned synchrosqueezing transform
P Bing, W Liu, Y Liu - IEEE Access, 2021 - ieeexplore.ieee.org
In this paper, we propose a novel seismic time-frequency analysis method via the time-
reassigned synchrosqueezing transform (TSST), in which the time-frequency coefficients are …
reassigned synchrosqueezing transform (TSST), in which the time-frequency coefficients are …
Detection of weak astronomical signals with frequency-hopping interference suppression
This paper addresses the detection of weak astronomical signals that are contaminated by
strong frequency-hopping (FH) interferers and suffer from missing samples. The problem is …
strong frequency-hopping (FH) interferers and suffer from missing samples. The problem is …
On the adversarial robustness of LASSO based feature selection
In this paper, we investigate the adversarial robustness of feature selection based on the
regularized linear regression model, namely LASSO. In the considered model, there is a …
regularized linear regression model, namely LASSO. In the considered model, there is a …
An effective reconstruction algorithm based on modulated wideband converter for wideband spectrum sensing
J He, W Chen, L Jia, T Wang - IEEE Access, 2020 - ieeexplore.ieee.org
In recent years, wideband spectrum sensing combined with sub-Nyquist sampling and
compressed sensing technology in the field of cognitive radio has received widespread …
compressed sensing technology in the field of cognitive radio has received widespread …
Image reconstruction algorithm for electrical impedance tomography based on block sparse Bayesian learning
Electrical impedance tomography (EIT) is a promising agile imaging modality that allows
estimation of the electrical conductivity distribution at the interior of an object from boundary …
estimation of the electrical conductivity distribution at the interior of an object from boundary …
Generalized ridge reconstruction approaches toward more accurate signal estimate
X Zhu, Z Zhang, H Zhang, J Gao, B Li - Circuits, Systems, and Signal …, 2020 - Springer
Ridge reconstruction (RR) method is one of the most commonly used ways for
multicomponent signal reconstruction from time–frequency representations. However, this …
multicomponent signal reconstruction from time–frequency representations. However, this …