M. Shams Esfand Abadi, S. Nikbakht,
Volume 7, Issue 2 (June 2011)
Abstract
Two-dimensional (TD) adaptive filtering is a technique that can be applied to many image, and signal processing applications. This paper extends the one-dimensional adaptive filter algorithms to TD structures and the novel TD adaptive filters are established. Based on this extension, the TD variable step-size normalized least mean squares (TD-VSS-NLMS), the TD-VSS affine projection algorithms (TD-VSS-APA), the TD set-membership NLMS (TD-SM-NLMS), the TD-SM-APA, the TD selective partial update NLMS (TD-SPU-NLMS), and the TD-SPU-APA are presented. In TD-VSS adaptive filters, the step-size changes during the adaptation which leads to improve the performance of the algorithms. In TD-SM adaptive filter algorithms, the filter coefficients are not updated at each iteration. Therefore, the computational complexity is reduced. In TD-SPU adaptive algorithms, the filter coefficients are partially updated which reduce the computational complexity. We demonstrate the good performance of the proposed algorithms thorough several simulation results in TD adaptive noise cancellation (TD-ANC) for image restoration. The results are compared with the classical TD adaptive filters such as TD-LMS, TD-NLMS, and TD-APA
M. Shams Esfand Abadi, M.s. Shafiee,
Volume 9, Issue 1 (March 2013)
Abstract
This paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm. The proposed algorithm uses the prior knowledge of the system impulse
response statistics and the optimal step-size vector is obtained by minimizing the mean-square deviation(MSD). In comparison with NSAF, the VSS-NSAF algorithm has faster convergence speed and lower MSD. To reduce the computational complexity of VSSNSAF, the VSS selective partial update NSAF (VSS-SPU-NSAF) is proposed where the filter coefficients are partially updated in each subband at every iteration. We demonstrated the good performance of the proposed algorithms in convergence speed and steady-state MSD for a system identification set-up.
M. Shams Esfand Abadi, H. Mesgarani, S. M. Khademiyan,
Volume 13, Issue 3 (September 2017)
Abstract
The wavelet transform-domain least-mean square (WTDLMS) algorithm uses the self-orthogonalizing technique to improve the convergence performance of LMS. In WTDLMS algorithm, the trade-off between the steady-state error and the convergence rate is obtained by the fixed step-size. In this paper, the WTDLMS adaptive algorithm with variable step-size (VSS) is established. The step-size in each subfilter changes according to the largest decrease in mean square deviation. The simulation results show that the proposed VSS-WTDLMS has faster convergence rate and lower misadjustment than ordinary WTDLMS.
