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Silviu Ciochina
Researcher at Politehnica University of Bucharest
Publications - 162
Citations - 2618
Silviu Ciochina is an academic researcher from Politehnica University of Bucharest. The author has contributed to research in topics: Adaptive filter & System identification. The author has an hindex of 23, co-authored 140 publications receiving 2148 citations. Previous affiliations of Silviu Ciochina include Université du Québec.
Papers
More filters
Journal ArticleDOI
Proportionate Adaptive Filters From a Basis Pursuit Perspective
TL;DR: This letter shows that the normalized least-mean-square algorithm and the affine projection algorithm can be decomposed as the sum of two orthogonal vectors, and derives the proportionate NLMS (PNLMS), and many other adaptive filters can be derived following this approach.
Journal ArticleDOI
An overview on optimized NLMS algorithms for acoustic echo cancellation
TL;DR: Several solutions to control the adaptation of the NLMS adaptive filter are presented and these algorithms are “non-parametric” in nature, i.e., they do not require any additional features to control their behavior.
Journal ArticleDOI
Recursive Least-Squares Algorithms for the Identification of Low-Rank Systems
Camelia Elisei-Iliescu,Constantin Paleologu,Jacob Benesty,Cristian Stanciu,Cristian Anghel,Silviu Ciochina +5 more
TL;DR: A variable regularized version of the RLS algorithm is proposed, using the DCD method to reduce the complexity, with improved robustness to double-talk and results indicate the good performance of these algorithms.
Book
A Perspective on Stereophonic Acoustic Echo Cancellation
TL;DR: This paper proposes to redesign the stereophonic acoustic echo scheme as a single-input/single-output system with complex random variables and illustrates the behavior of some basic adaptive algorithms and presents a distortion method which is more suitable for this model.
Journal ArticleDOI
Adaptive filtering for the identification of bilinear forms
TL;DR: Some basic algorithms tailored for the identification of bilinear forms, i.e., least-mean-square (LMS), normalized LMS (NLMS), and recursive-least-squares (RLS) are developed and analyzed.