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.

Diffusion Adaptation over Multi-Agent Networks with Wireless Link Impairments

Abstract: We study the performance of diffusion least-mean squares algorithms for distributed parameter estimation in multi-agent networks when nodes exchange information over wireless communication links. Wireless channel impairments, such as fading and path-loss, adversely affect the exchanged data and cause instability and performance degradation if left unattended. To mitigate these effects, we incorporate equalization coefficients into the diffusion combination step and update the combination weights dynamically in the face of randomly changing neighborhoods due to fading conditions. When channel state information (CSI) is unavailable, we determine the equalization factors from pilot-aided channel coefficient estimates. The analysis reveals that by properly monitoring the CSI over the network and choosing sufficiently small adaptation step-sizes, the diffusion strategies are able to deliver satisfactory performance in the presence of fading and path loss.

Array Algorithms for Estimation

Abstract: In this paper we develop array algorithms for filtering. These algorithms can be regarded as the Krein space generalizations of array algorithms, which are currently the preferred method for im- plementing filters. The array algorithms considered include two main families: square-root array algorithms, which are typically numerically more stable than conventional ones, and fast array algorithms which, when the system is time-invariant, typically offer an order of magnitude reduction in the computational effort. Both have the interesting feature that one does not need to explicitly check for the positivity conditions required for the existence of filters, as these conditions are built into the algorithms themselves. However, since square-root algorithms predominantly use -unitary transformations, rather than the unitary transformations required in the case, further investigation is needed to determine the numerical behavior of such algorithms. Index Terms—Array algorithms, estimation, fast algorithms, robustness, .

Tracking behavior of mobile adaptive networks

Abstract: Adaptive networks consist of a collection of nodes with learning abilities that interact with each other locally in order to solve distributed processing and distributed inference problems in real-time Various algorithms and performance analyses have been put forward for such networks, such as the adapt-then-combine (ATC) and combine-then-adapt (CTA) diffusion algorithms, the probabilistic diffusion algorithm, and diffusion with adaptive weights over the links In this paper, we add mobility as another dimension and study the behavior of the network when the nodes move in pursuit/avoidance of a target Mobility leads naturally to an adaptive topology with changing neighborhoods Mobility also imposes physical constraints on the proximity among the nodes and on the velocity and location of the center of the network We develop adaptation algorithms that exhibit self-organization properties and apply them to the modeling of collective behavior in biological systems, such as fish schooling The results help provide an explanation for the agile adjustment of network patterns of fish schools in the presence of predators

Distributed pareto-optimal solutions via diffusion adaptation

Abstract: We consider solving multi-objective optimization problems in a distributed manner over a network of nodes. The problem is equivalent to optimizing a global cost that is the sum of individual components. Diffusion adaptation enables the nodes to cooperate locally through in-network processing in order to approach Pareto-optimality. We analyze the mean-square-error performance of the diffusion strategy and show that, at steady-state, all nodes can be made to approach a Pareto-optimal solution.

A distributed MMSE relay strategy for wireless sensor networks

Abstract: In this paper we propose a multi-sensor relay strategy that achieves path-loss saving and improved power efficiency. In the proposed distributed scheme, the relay sensors do not need to share information about the received signals. An mean-square error design is pursued and the performance is shown to improve as the number of relay sensors (N) increases. Specifically, it is shown that the average power usage per sensor and the total average power drop as O(1/N/sup 2/) and O(1/N), respectively.

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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