Ali H. Sayed

Bio: 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.

Dynamic Average Diffusion With Randomized Coordinate Updates

Abstract: This work derives and analyzes an online learning strategy for tracking the average of time-varying distributed signals by relying on randomized coordinate-descent updates. During each iteration, each agent selects or observes a random entry of the observation vector, and different agents may select different entries of their observations before engaging in a consultation step. Careful coordination of the interactions among agents is necessary to avoid bias and ensure convergence. We provide a convergence analysis for the proposed methods, and illustrate the results by means of simulations.

Decentralized clustering for node-variant graph filtering with graph diffusion LMS

Abstract: In this work, we consider the problem of estimating the coefficients of linear shift-invariant FIR graph filters. We assume hybrid node-varying graph filters where the network is decomposed into clusters of nodes and, within each cluster, all nodes have the same filter coefficients to estimate. We assume that there is no prior information on the clusters composition and that the nodes do not know which other nodes share the same estimation task. We are interested in distributed, adaptive, and collaborative solutions. In order to limit the cooperation between clustered agents sharing the same estimation task, we propose an extended diffusion preconditioned LMS strategy allowing the nodes to perform automatic network clustering. Simulation results illustrate the effectiveness of the proposed unsupervised method for clustering nodes into clusters and collaborative estimation.

Adaptive channel estimation for MIMO space-time coded communications

Abstract: We develop adaptive channel estimation techniques for multiple-input multiple-output (MIMO) channels with space-time block-coded (STBC) communications. We describe a unified framework for adaptive channel estimation for multi-user Alamouti STBC transmissions over flat fading channels and over frequency selective fading channels using single-carrier frequency domain (SC-FD) STBC or STBC-OFDM. The structure of the space-time code is exploited to reduce the complexity of block NLMS and RLS receivers.

A generalized Schur-type algorithm for the joint factorization of a structured matrix and its inverse

Abstract: A Schur-type algorithm is presented for the simultaneous triangular factorization of a given (non-degenerate) structured matrix and its inverse The algorithm takes the displacement generator of a Hermitian, strongly regular matrixR as an input, and computes the displacement generator of the inverse matrixR−1 as an output From these generators we can directly deduce theLD−1L* (lower-diagonal-upper) decomposition ofR, and theUD−1U* (upper-diagonallower) decomposition ofR−1 The computational complexity of the algorithm isO(rn2) operations, wheren andr denote the size and the displacement rank ofR, respectively Moreover, this method is especially suited for parallel (systolic array) implementations: usingn processors the algorithm can be carried out inO(n) steps

Exact RLS Laguerre-lattice adaptive filtering

Abstract: This paper solves the problem of designing an exact RLS lattice (or order-recursive) algorithms for adaptive filters that do not involve tapped-delay-line structures. As a special case, an exact RLS Laguerre lattice filter is obtained.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Tensor Decompositions and Applications

Abstract: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Table Of Integrals Series And Products

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