On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks
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In many signal such speech, bio-signals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration such data can be called as Piecewise-Dependent- Data (PDD). In clustering it is frequently needed to m ...
In many signal such speech, bio-signals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration such data can be called as Piecewise-Dependent- Data (PDD). In clustering it is frequently needed to m ...
IDIAP and Ben-Gurion University of the Negev, Israel2002
Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the da ...
Consider a set of correlated sources located at the nodes of a network, and a sink to which the data from all the sources has to arrive. We address the minimization of a separable joint communication cost function given by the product [rate] × [edge ...
It is known that in the absence of distortion, the necessary sampling density for a multiband signal is given by its spectral occupancy. However, in general, the samples have to he acquired nonuniformly. There exist sampling patterns such that reconstructi ...
The Direct Metal Laser Sintering (DMLS) manufacturing technique induces thermal stresses in parts. When such parts are used at elevated temperatures, residual stresses are relaxed and the part can suffer significant distortion. This study presents values o ...
University Texas Austin, Mechanical Engineering Dept, Austin, Tx 00000 Usa1998
Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the da ...
