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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
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Patent
Method and apparatus for resolving multipath components for wireless location finding
Nabil R. Yousef,Ali H. Sayed +1 more
TL;DR: In this article, a method and apparatus that provides an accurate estimate of the time and amplitude of arrival of the first arriving overlapping multipath components (rays) in wireless locating finding systems is presented.
Journal ArticleDOI
Asynchronous Adaptation and Learning Over Networks—Part III: Comparison Analysis
Xiaochuan Zhao,Ali H. Sayed +1 more
TL;DR: In this paper, a detailed mean-square-error analysis of the performance of asynchronous adaptation and learning over networks under a fairly general model for asynchronous events including random topologies, random link failures, random data arrival times and agents turning on and off randomly.
Proceedings ArticleDOI
Distributed detection over adaptive networks based on diffusion estimation schemes
TL;DR: This work establishes the connection between the detection and estimation problems, proposes a distributed detection algorithm, and analyzes the performance of the algorithm in terms of its probabilities of detection and false alarm.
Journal ArticleDOI
A Proximal Diffusion Strategy for Multiagent Optimization With Sparse Affine Constraints
TL;DR: This article develops a proximal primal–dual decentralized strategy for multiagent optimization problems that involve multiple coupled affine constraints, where each constraint may involve only a subset of the agents.
Journal ArticleDOI
Learning Over Multitask Graphs—Part I: Stability Analysis
TL;DR: In this paper, a multi-task optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph is formulated.