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Ali H. Sayed
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 766
Citations - 39568
Ali H. Sayed is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Adaptive filter & Optimization problem. The author has an hindex of 81, co-authored 728 publications receiving 36030 citations. Previous affiliations of Ali H. Sayed include Harbin Engineering University & University of California, Los Angeles.
Papers
More filters
Journal ArticleDOI
Revisiting correlation-based functional connectivity and its relationship with structural connectivity
Raphaël Liégeois,Raphaël Liégeois,Augusto Santos,Vincenzo Matta,Dimitri Van De Ville,Dimitri Van De Ville,Ali H. Sayed +6 more
TL;DR: It is found that precision-based FC yields a better match to SC than correlation- based FC when using 5 minutes of functional data or more, and it is shown that the SC-FC match can be used to further interrogate various aspects of brain structure and function.
Book ChapterDOI
Design criteria for uncertain models with structured and unstructured uncertainties
Ali H. Sayed,Vitor H. Nascimento +1 more
TL;DR: This paper introduces and solves a weighted game-type cost criterion for estimation and control purposes that allows for a general class of uncertainties in the model or data.
Journal ArticleDOI
On the learning mechanism of adaptive filters
Vitor H. Nascimento,Ali H. Sayed +1 more
TL;DR: The paper shows that even ensemble-average learning curves of single-tap LMS filters actually exhibit two distinct rates of convergence: one for the initial time instant and another, faster one, for later time instants.
Journal ArticleDOI
A Fast Stable Solver for Nonsymmetric Toeplitz and Quasi-Toeplitz Systems of Linear Equations
TL;DR: A stable and fast solver for nonsymmetric linear systems of equations with shift structured coefficient matrices (e.g., Toeplitz, quasi-Toeplitzer, and product of two Toe Plitz matrices) is derived.
Proceedings ArticleDOI
Diffusion LMS for multitask problems with overlapping hypothesis subspaces
TL;DR: This paper forms an online multitask learning problem where node hypothesis spaces partly overlap, and a cooperative algorithm based on diffusion adaptation is derived.