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.

Spatio-Temporal Diffusion Strategies for Estimation and Detection Over Networks

Abstract: We present diffusion algorithms for distributed estimation and detection over networks that endow all nodes with both spatial cooperation abilities and temporal processing abilities. Each node in the network is allowed to share information locally with its neighbors; this step amounts to sharing and processing of spatial data. At the same time, each node is allowed to after and process past estimates to improve estimation accuracy through an overall collaborative process. In this manner, the resulting distributed algorithms consist of three stages: adaptation, spatial processing, and temporal processing. Moreover, the order of these three stages can be interchanged leading to a total of six variations. The results indicate that whether temporal processing is performed before or after adaptation, the strategy that performs adaptation before spatial cooperation leads to smaller mean-square error. The additional temporal processing step is useful in combating perturbations due to noise over the communications links. We further describe an application in the context of distributed detection and provide computer simulations to illustrate and support the findings.

Time-variant displacement structure and triangular arrays

Abstract: The authors extend the concept of displacement structure to time-variant matrices and use it to efficiently and recursively propagate the Cholesky factor of such matrices. A natural implementation of the algorithm is via a modular triangular array of processing elements. When the algorithm is applied to solve the normal equations that arise in adaptive least-squares filtering, they get the so-called QR algorithm, with the extra bonus of a parallelizable procedure for determining the weight vector. It is shown that the general algorithm can also be implemented in time-variant lattice form; a specialization of this result yields a time-variant Schur algorithm. >

Bio-inspired swarming for dynamic radio access based on diffusion adaptation

Abstract: The goal of this paper is to study the learning abilities of adaptive networks in the context of cognitive radio networks and to investigate how well they assist in allocating power and communications resources in the frequency domain. The allocation mechanism is based on a social foraging swarm model that lets every node allocate its resources (power/bits) in the frequency regions where the interference is at a minimum while avoiding collisions with other nodes. We employ adaptive diffusion techniques to estimate the interference profile in a cooperative manner and to guide the motion of the swarm individuals in the resource domain. A mean square performance analysis of the proposed strategy is provided and confirmed by simulation results. Numerical examples show that cooperative spectrum sensing improves the performance of the swarm-based resource allocation technique considerably.

LMS is H/sup /spl infin// optimal

Abstract: Shows that the celebrated LMS (least-mean squares) adaptive algorithm is an H/sup /spl infin// optimal filter In other words, the LMS algorithm, which has long been regarded as an approximate least-mean squares solution, is in fact a minimizer of the H/sup /spl infin// error norm In particular, the LMS minimizes the energy gain from the disturbances to the predicted errors, while the normalized LMS minimizes the energy gain from the disturbances to the filtered errors Moreover, since these algorithms are central H/sup /spl infin// filters, they are also risk-sensitive optimal and minimize a certain exponential cost function The authors discuss various implications of these results, and show how they provide theoretical justification for the widely observed excellent robustness properties of the LMS filter >

Adjustment of combination weights over adaptive diffusion networks

Abstract: We show how the convergence time of an adaptive network can be estimated in a distributed manner by the agents. Using this procedure, we propose a distributed mechanism for the nodes to switch from using fixed doubly-stochastic combination weights to adaptive combination weights. By doing so, and by knowing when to switch, the agents are able to enhance their steady-state mean-square-error performance without degrading the rate of convergence during the transient phase of the learning algorithm.

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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