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Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in a distributed manner. In th ...
The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, ...
Bayesian estimation problems involving Gaussian distributions often result in linear estimation techniques. Nevertheless, there are no general statements as to whether the linearity of the Bayesian estimator is restricted to the Gaussian case. The two comm ...
This work shows how to develop distributed versions of block blind estimation techniques that have been proposed before for batch processing. Using diffusion adaptation techniques, data are accumulated at the nodes to form estimates of the auto-correlation ...
We present an extension of cross-recurrence analysis for dual gaze analysis which is suited for complex situations where for instance the objects of interest are not all visible at all times or when stimulus exploration is not homogeneous. The typicical si ...
An active plasma profile control approach for ITER, which is potentially robust by being tolerant to changing and uncertain physics, has been explored in this work, using a technique based on real-time estimation of linearized profile response models. The ...
In this work, we study the mean-square-error performance of a diffusion strategy for continuous-time estimation over networks. We derive differential equations that describe the evolution of the mean and correlation of the weight-error vector, and provide ...
We study the problem of distributed least-squares estimation over ad hoc adaptive networks, where the nodes have a common objective to estimate and track a parameter vector. We consider the case where there is stationary additive colored noise on both the ...
We study distributed least-mean square (LMS) estimation problems over adaptive networks, where nodes cooperatively work to estimate and track common parameters of an unknown system. We consider a scenario where the input and output response signals of the ...
The quality of recyclable and residual municipal solid waste (MSW) is, among other factors, strongly influenced by the seasonal variation in MSW composition. However, a relatively marginal amount of published data on seasonal MSW composition especially in ...
