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In this talk, a distributed stochastic gradient (SG) algorithm is proposed where the estimators are aimed to collectively estimate an unknown time-invariant parameter from a set of noisy measurements ...
This talk considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic s...
This talk considers the distributed estimation problem of a linear stochastic system described by an autoregressive model with exogenous inputs when both the system orders and parameters are unknown. ...
This talk concerns the nonsmooth distributed optimization problem via multi-agent network with the coupled equality and inequality constraints. The considered global optimization problem is to minimiz...
A modified two-scale microwave scattering model (MTSM) was presented to describe the scattering coefficient of natural rough surface in this paper. In the model, the surface roughness was assumed to b...
We describe the procedures used to combine into a uniform velocity solution the observations of more than 80 continuous GPS stations operating in the central Mediterranean in the 1998-2004 time interv...
Many exciting advances in oceanography are occurring because of the data being acquired from newly developed instruments and observing systems. However, the volume and diversity of the data being gene...
We describe a new method for tuning a certain family of low-bandwidth controllers for linear time-invariant and spatially-invariant (LTSI) plants. We consider LTSI controllers with a fixed structure, ...
The general setting we consider involves a process, iteration, or method in which the computation or communication at each step is local, determined by a given graph, and involves some parameters or c...
A study has been conducted in three sub-watersheds to model the spatial distribution of runoff and sediment yield. The basic structure of the model includes generation of runoff using SCS curve number...
We consider a stochastic model for distributed average consensus, which arises in applications such as load balancing for parallel processors, distributed coordination of mobile autonomous agents, and...
Generalized additive models are an effective regression tool, popular in the statistics literature, that provide an automatic extension of traditional linear models to nonlinear systems. We pres...
This paper describes a general purpose method for solving convex optimization problems in a distributed computing environment. In particular, if the problem data includes a large linear operator or ma...
Experiences with three practical meteorological applications with different characteristics are used to highlight the core computer science aspects and applicability of distributed computing to meteor...
The extent and type of watershed cover affects the movement of water in the hydrologic cycle, thus accurate representation of the physical and biological features of the landscape within the watershed...

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