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.

Gramian-Based Adaptive Combination Policies for Diffusion Learning Over Networks

Abstract: This paper presents an adaptive combination strategy for distributed learning over diffusion networks. Since learning relies on the collaborative processing of the stochastic information at the dispersed agents, the overall performance can be improved by designing combination policies that adjust the weights according to the quality of the data. Such policies are important because they would add a new degree of freedom and endow multi-agent systems with the ability to control the flow of information over their edges for enhanced performance. Most adaptive and static policies available in the literature optimize certain performance metrics related to steady-state behavior, to the detriment of transient behavior. In contrast, we develop an adaptive combination rule that aims at optimizing the transient learning performance, while maintaining the enhanced steady-state performance obtained using policies previously developed in the literature.

Normalized LMS Algorithm

Abstract: This chapter contains sections titled: Instantaneous Approximation Computational Cost Power Normalization Least-Perturbation Property

A unified approach to the steady-state analysis of quantized adaptive filtering algorithms

Abstract: The steady-state performance of adaptive filters can significantly vary when they are implemented in finite precision arithmetic, which makes it vital to analyze their performance in a quantized environment. Such analyses can become difficult for adaptive algorithms with nonlinear update equations. This paper develops a new feedback approach to the steady-state analysis of quantized adaptive algorithms that bypasses many of the difficulties encountered in traditional approaches. In so doing, we not only re-derive several earlier results in the literature, but we often do so under weaker assumptions, in a more compact way, and we also obtain new results.

Local and global passivity relations for Gauss-Newton methods in adaptive filtering

Abstract: We provide a time-domain analysis of the robustness and stability performance of Gauss- Newton recursive methods that are often used in identification and control. Several free parameters are included in the filter description while combining the covariance update with the weight-vector update; the exponentially weighted recursive least squares algorithm being an important special case. One of the contributions of this work is to show that by properly selecting the free parameters, the resulting filter can be shown to impose certain bounds on the error quantities, thus resulting in desireable robustness and stability properties. We also show that an intrinsic feedback structure, mapping the noise sequence and the initial weight guess to the a priori estimation errors and the final weight estimate, can be associated with such schemes. The feedback configuration is motivated via energy arguments and is shown to consist of two major blocks: a time-variant lossless (i.e., energy preserving) feedforward path and a time-variant feedback path.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Time-variant structured matrices: an application to instrumental variable methods

Abstract: We derive a recursive algorithm for the time—update of the triangular factors of non-Hermitian time-variant matrices with structure. These are matrices that undergo low-rankmodifications as time progresses, special cases of which often arise in adaptive filtering andinstrumental variable (IV) methods. A natural implementation of the algorithm is via twocoupled triangular arrays of processing elements. We consider, in particular, an IV parameterestimation problem and show how the arrays collapse to a coupled parallelizable solution ofthe identification problem. 1. INTRODUCTION The notion of displacement structure provides a natural framework for the solution of manyproblems in signal processing and mathematics. It represents a powerful and unifying toolfor exploiting structure in numerous applications, as detailed in several recent surveys on thetopic.1'2'3 More recently, we have extended the concept of structured matrices to the time-variant setting4'5'6 and shown that we can, as well, study matrices that undergo low-rankmodifications as time progresses. Special examples often arise in adaptive filtering.7'8'9 Inthis paper, we further extend our earlier results to the non-Hermitian case and exhibit anapplication to instrumental variable

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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