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Numerous variable step-size normalized least mean-square (VSS-NLMS) algorithms have been derived to solve the dilemma of fast convergence rate or low excess mean-square error in the past two decades. This paper proposes a new, easy to implement, nonparametric VSS-NLMS algorithm that employs the mean-square error and the estimated system noise power to(More)
This paper considers an extended recursive least squares (RLS) adaptive bilinear predictor. It is shown that the extended RLS adaptive bilinear predictor is guaranteed to be stable in the sense that the time average of the squared a-posteriori prediction error signal is bounded whenever the input signal is bounded in the same sense. It also shows that the(More)
In this paper, a novel cryptographic system for color image security using chaotic Amplitude Phase Frequency Model (APFM) nonlinear adaptive filter is proposed. We set nine parameters, simulated time interval, and initial values for APFM nonlinear adaptive filter to generate chaos, and use the chaos property to design a color image encryption algorithm. The(More)
It is known that regularization plays an important part in adaptive filtering. Several time-varying regularized normalized least-mean-square (NLMS) algorithms have been derived in the past decade. This paper proposes a variable regularization control method for the NLMS algorithm that employs the input signal power, the mean-square error and the estimated(More)
Least-mean-square (LMS) and block LMS (BLMS) adaptive filters are generally believed to have similar step-size bounds for convergence. Similarly, convergence analyses of frequency-domain block LMS (FBLMS) adaptive filters have suggested that they have very restrictive convergence bounds. In this letter, we revisit Feuer's work and reveal a much larger(More)