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IEEE Signal Processing Letters, ISSN 1070-9908, 12/2016, Volume 23, Issue 12, pp. 1786 - 1790
Journal Article
International Journal of Adaptive Control and Signal Processing, ISSN 0890-6327, 09/2015, Volume 29, Issue 9, pp. 1189 - 1206
Journal Article
Signal Processing, ISSN 0165-1684, 04/2014, Volume 97, pp. 100 - 109
Journal Article
Signal Processing, ISSN 0165-1684, 02/2018, Volume 143, pp. 94 - 105
•The BC-ZA-NLMS and BC-RZA-NLMS algorithms are firstly proposed.•The mean behavior of BC-ZA-NLMS and BC-RZA-NLMS algorithms is analyzed.•We analyze the... 
Normalized least mean square | Zero attractor | Bias compensated | Noisy input | SPARSE SYSTEM-IDENTIFICATION | STOCHASTIC-ANALYSIS | WHITE GAUSSIAN INPUTS | TRANSIENT ANALYSIS | ENGINEERING, ELECTRICAL & ELECTRONIC | ECHO CANCELLATION | LMS | PNLMS ALGORITHM | NLMS ALGORITHM | ASSUMPTION | NOISY INPUTS | Analysis | Algorithms | Usage
Journal Article
IEEE Transactions on Circuits and Systems II: Express Briefs, ISSN 1549-7747, 04/2019, Volume 66, Issue 4, pp. 712 - 716
Journal Article
IEEE Transactions on Circuits and Systems II: Express Briefs, ISSN 1549-7747, 03/2016, Volume 63, Issue 3, pp. 274 - 278
Journal Article
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISSN 1520-6149, 04/2018, Volume 2018-, pp. 4294 - 4298
Group zero-attracting LMS and its reweighted form have been proposed for addressing system identification problems with structural group sparsity in the... 
Simulation | Estimation | Sparse system identification | adaptive parameter adjustment | Mathematical model | Indexes | Matrix decomposition | Covariance matrices | Transient analysis | group sparsity | zero-attracting LMS | Group sparsity | Adaptive parameter adjustment | Zero-attracting LMS
Conference Proceeding
IEEE Access, ISSN 2169-3536, 2018, Volume 6, pp. 56911 - 56923
Journal Article
Signal, Image and Video Processing, ISSN 1863-1703, 3/2017, Volume 11, Issue 3, pp. 533 - 540
Journal Article
Signal Processing, ISSN 0165-1684, 12/2012, Volume 92, Issue 12, pp. 3075 - 3079
A new sparse signal recovery algorithm for multiple-measurement vectors (MMV) problem is proposed in this paper. The sparse representation is iteratively drawn... 
The modulated wideband converter | Sparse recovery | Multiple-measurement vectors | Zero-point attracting projection | Compressed sensing | SIGNAL RECONSTRUCTION | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms
Journal Article
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISSN 1520-6149, 04/2015, Volume 2015-, pp. 604 - 608
This paper proposes an improved variable step-size (VSS) scheme for zero-point attracting projection (ZAP) algorithm. The proposed VSS is proportional to the... 
Least squares approximations | zero-point attracting projection | Current measurement | adaptive filter | Adaptive filters | IIR filters | Variable step-size | Filtering algorithms | sparse system identification | System identification | Dispersion | Algorithms | Tracking | Computer simulation | Conferences | Projection | Speech | Mathematical models | Convergence
Conference Proceeding
2013 Asilomar Conference on Signals, Systems and Computers, ISSN 1058-6393, 11/2013, pp. 1524 - 1528
This paper proposes a new variable step-size (VSS) scheme for the recently introduced zero-point attracting projection (ZAP) algorithm. The proposed variable... 
Least squares approximations | zero-point attracting projection | Current measurement | adaptive filter | Adaptive filters | Signal processing algorithms | Variable step-size | Filtering algorithms | Dispersion | Convergence
Conference Proceeding
IEICE Electronics Express, ISSN 1349-2543, 2012, Volume 9, Issue 4, pp. 314 - 319
Sequential Compressive Sensing, which may be widely used in sensing devices, is a popular topic of recent research. This paper proposes an online recovery... 
Compressive sensing | Online algorithm | Sparse signal recovery | Zero-point attracting projection | Sequential | sparse signal recovery | zero-point attracting projection | sequential | online algorithm | compressive sensing | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
Signal Processing, ISSN 0165-1684, 07/2020, Volume 172, p. 107542
•This paper studies adaptive algorithm robust against impulsive noise.•A weighted l1-norm is incorporated into the cost function based on the absolute of the... 
Model-driven method | Weighted l1-norm | Mean-square deviation | Zero-attraction | Variable step-size | Sign algorithm
Journal Article
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISSN 1520-6149, 04/2015, Volume 2015-, pp. 5485 - 5489
Sparse signal recovery in the static case has been well studied under the framework of Compressive Sensing (CS), while in recent years more attention has also... 
sparse signal recovery | exponential smoothing | dynamic zero-point attracting projection (DZAP) | Time-varying | nonconvex approach | Lead | Predictive models | Prediction algorithms | MATLAB | Dynamic tests | Algorithms | Conferences | Dynamics | Speech | Recovery | Temporal logic | Optimization
Conference Proceeding
IEEE Transactions on Circuits and Systems II: Express Briefs, ISSN 1549-7747, 7/2019, pp. 1 - 1
The reweighted zero-attracting least mean square (RZA-LMS) algorithm has good performance in sparse system identification. However, the convergence performance... 
sparse system identification | impulsive noise | variable step-size | reweighted zero-attracting | M-estimate
Journal Article
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