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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
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Fundamentals and challenges of optical multiple-input multiple-output multimode fiber links [Topics in Optical Communications]
TL;DR: In this article, MIMO processing is shown to increase the information capacity of communication links linearly as the minimum number of transmitters/receivers increases.
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Adaptive filters with error nonlinearities: mean-square analysis and optimum design
TL;DR: This paper performs stability and steady-state analysis of adaptive filters with error nonlinearities under weaker conditions than what is usually encountered in the literature, and without imposing any restriction on the color or statistics of the input.
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Decentralized Consensus Optimization With Asynchrony and Delays
TL;DR: An asynchronous, decentralized algorithm for consensus optimization that involves both primal and dual variables, uses fixed step-size parameters, and provably converges to the exact solution under a random agent assumption and both bounded and unbounded delay assumptions.
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Distributed Policy Evaluation Under Multiple Behavior Strategies
TL;DR: In this paper, the authors apply diffusion strategies to develop a fully-distributed cooperative reinforcement learning algorithm in which agents in a network communicate only with their immediate neighbors to improve predictions about their environment.
Diffusion strategies for distributed Kalman filtering: formulation and performance analysis
TL;DR: This work derives and analyze the mean and mean-square performance of the proposed algorithms and shows by simulation that they outperform previous solutions to the problem of distributed Kalman filtering.