Mariem Kallel Smaoui

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Deserving attention to the optimization of algorithm architecture adequacy for polynomial filtering with good numerical stability and convergence speed performances, we propose in this paper an adaptive filtering scheme based on a non iterative orthogonalization polynomials procedure allowing to enhance the performances of a real-time adaptive system. We(More)
The clipped input LMS (known as CLMS) is a common adaptive algorithm that has a lower complexity than the conventional LMS. Furthermore, the CLMS is appreciated in real-time audio embedded systems as it uses an implicit input normalization, which is necessary for non stationary audio/speech inputs. In this paper -through an exact convergence analysis- done(More)
This paper deals with the benefits of mixing an orthogonalization procedure to the sub-quantization of the input operation, in order to reduce the complexity and to enhance the robustness implementation of optimal polynomial filters. These non linear filters are now generic devices for real-time high speed multimedia applications. We propose an optimal(More)
Polynomial nonlinear system identification suffers from numerical instability related to the ill-conditioning of the involved matrices. Orthogonal methods consist in conditioning the input signal in order to reduce the eigenvalues spread of the correlation matrix. The selection of an appropriate orthogonalization procedure, for robustness improvement,(More)
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