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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
Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm
TL;DR: The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square type distributed adaptive filters with colored inputs to achieve an acceptable misadjustment performance with lower computational and memory cost.
Journal ArticleDOI
Coherent optical MIMO (COMIMO)
TL;DR: In this article, a multiple-input multiple-output (MIMO) optical link based on coherent optics and its ability to exploit the inherent information capacity of multimode fiber is presented.
Journal ArticleDOI
On the Learning Behavior of Adaptive Networks—Part I: Transient Analysis
Jianshu Chen,Ali H. Sayed +1 more
TL;DR: A detailed transient analysis of the learning behavior of multiagent networks reveals how combination policies influence the learning process of networked agents, and how these policies can steer the convergence point toward any of many possible Pareto optimal solutions.
Journal ArticleDOI
Transient analysis of data-normalized adaptive filters
TL;DR: This paper develops an approach to the transient analysis of adaptive filters with data normalization that characterizes the transient behavior of such filters in terms of a linear time-invariant state-space model based on energy-conservation arguments.
Proceedings ArticleDOI
Distributed Recursive Least-Squares Strategies Over Adaptive Networks
Ali H. Sayed,Cassio G. Lopes +1 more
TL;DR: A distributed least-squares estimation strategy is developed by appealing to collaboration techniques that exploit the space-time structure of the data, achieving an exact recursive solution that is fully distributed.