Steady-State Mean-Square Error Analysis for Non-Negative Least Lncosh Algorithm

2021 ◽  
pp. 1-1
Author(s):  
Zeyang Sun ◽  
Yingsong Li ◽  
Yibing Li ◽  
Tao Jiang ◽  
Wei Sun
Keyword(s):  
Steady State ◽  
Error Analysis ◽  
Mean Square Error ◽  
Mean Square

The Steady-State Mean-Square Error Analysis for Least Mean $p$-Order Algorithm

2009 ◽  
Vol 16 (3) ◽  
pp. 176-179 ◽  
Author(s):  
Bin Lin ◽  
Rongxi He ◽  
Xudong Wang ◽  
Baisuo Wang
Keyword(s):  
Steady State ◽  
Error Analysis ◽  
Mean Square Error ◽  
Mean Square ◽  
Order Algorithm

scholarly journals Adaptive modulation based on steady-state mean square error for underwater acoustic communication

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhiyong Liu ◽  
Zhoumei Tan ◽  
Fan Bai
Keyword(s):  
Steady State ◽  
Mean Square Error ◽  
Adaptive Modulation ◽  
Blind Equalization ◽  
Training Sequence ◽  
Transmission Efficiency ◽  
Underwater Acoustic Communication ◽  
Mean Square ◽  
Modulation Mode ◽  
Simulation Results

AbstractTo improve the transmission efficiency and facilitate the realization of the scheme, an adaptive modulation (AM) scheme based on the steady-state mean square error (SMSE) of blind equalization is proposed. In this scheme, the blind equalization is adopted and no training sequence is required. The adaptive modulation is implemented based on the SMSE of blind equalization. The channel state information doesn’t need to be assumed to know. To better realize the adjustment of modulation mode, the polynomial fitting is used to revise the estimated SNR based on the SMSE. In addition, we also adopted the adjustable tap-length blind equalization detector to obtain the SMSE, which can adaptively adjust the tap-length according to the specific underwater channel profile, and thus achieve better SMSE performance. Simulation results validate the feasibility of the proposed approaches. Simulation results also show the advantages of the proposed scheme against existing counterparts.

Time-Domain Filtering of Two-Dimensional Fluorescence Data

1986 ◽  
Vol 40 (4) ◽  
pp. 542-548 ◽  
Author(s):  
Maria Vicsek ◽  
Sharon L. Neal ◽  
Isiah M. Warner
Keyword(s):  
Error Analysis ◽  
Mean Square Error ◽  
Time Domain ◽  
Moving Average ◽  
Mean Square ◽  
Two Dimensional ◽  
Spline Method ◽  
Dimensional Version ◽  
The Difference ◽  
Polynomial Smoothing

Four time-domain filtering methods are applied to simulated and experimental two-dimensional fluorescence data in order to evaluate their performance. The methods that were evaluated are (1) moving average, (2) Savitsky-Golay polynomial smoothing, (3) Chebyshev filtering, and (4) bicubic spline filtering. The methods are compared with the use of mean square error analysis and the difference in the amplitudes of the filtered noisy and ideal data. The two-dimensional version of the Savitzky-Golay filtering and the spline method produced the best overall results.

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