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Sparse coding and dynamic sampling constitute the keystone of our approach.
However, when control parameters are over- or under-estimated, a dynamic sampling static sampling strategy can lead to poor results.
In this paper we propose an integer linear programming approach to optimize the use of inspection capacity through dynamic sampling.
Then, the obtained results are extended to design the sampling map in three dynamic sampling control situations: event-triggered control, self-triggered control, and state-dependent sampling.
In this paper, a comparison study is designed with a nearest neighboring method searching for adjacent facets, along with the introduction of a dynamic sampling method to investigate the effect of the resolution of a data set on the accuracy of surface normal determination.
The continuous audio signal is sampled at a continuous dynamic sampling rate.
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Comparison was performed in terms of citation proportion of the terms, significant differences among samples detected, dynamic sample profiles, and task perceptions.
However, under the effect of shear stress (dynamic samples), both droplet and cylinder morphologies can be formed depending on the volume ratio.
To achieve this, BlinkDB uses two key ideas: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional stratied samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query's accuracy or response time requirements.
Multiwalled carbon nanotubes (CNTs) filled polymer composite based on polycarbonate (PC) and polyethylene (PE) was fabricated by shear controlled orientation in injection molding (dynamic samples) and conventional injection molding (static samples).
For dynamic samples of HDPE/33EVA blends, droplet, cylinder and co-continuous network (co-continuous in both parallel and perpendicular to flow direction) can be formed under the effect of shear stress.
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