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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
Proceedings ArticleDOI
Dictionary learning over large distributed models via dual-ADMM strategies
TL;DR: It is demonstrated that the proximal operators utilized in the ADMM algorithm can be characterized in closed-form with linear complexity for certain useful dictionary learning scenarios and it is established that the dual cost function is smooth, strongly-convex, and possesses Lipschitz continuous gradients.
Book ChapterDOI
Mathematical models of cerebral hemodynamics for detection of vasospasm in major cerebral arteries.
TL;DR: Simulations show that Model 2 is capable of providing good estimates for the radius of the MCA, allowing the detection of the vasospasm, and results indicate that arterial radius may be estimated using measurements of ABP, ICP and CBFV, allowingThe detection of vasospasms.
Proceedings Article
Cluster formation over adaptive networks with selfish agents
TL;DR: This work allows the agents to select their partners according to whether they can help them reduce their utility costs and illustrates how the clustering technique enhances the mean-square-error performance of the agents over non-cooperative processing.
Proceedings ArticleDOI
Social Learning Under Inferential Attacks
TL;DR: In this paper, the authors consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis, but the adversaries are unaware of the true hypothesis and behave similarly to the other agents and will manipulate the likelihood functions used in the belief update process.
Proceedings ArticleDOI
Modelling brain cortical connectivity using diffusion adaptation
TL;DR: The method uses the directed transfer function (DTF) technique to estimate combination coefficients to drive the adaptation and learning process and its superior performance is demonstrated relative to solutions that rely on stand-alone electrodes and do not exploit coordination among multiple electrodes.