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State of the art.
If something is state of the art, it is the most up-to-date model incorporating the latest and best technology.
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Performance and energy consumption analysis of multi-hop wireless sensor network running state-of-the-art error control technique (ARQ), communicating over weak channels.
In this paper, we address this issue and propose using classification to integrate the state-of-the-art error concealment techniques.
This paper presents the following: Performance and energy consumption analysis of multi-hop wireless sensor network running state-of-the-art error control technique (ARQ), communicating over weak channels.
Combining state-of-the-art error correction and our edge analysis is another subject worthy of investigation.
We compared the performance of Fiona with state-of-the-art genome error correction methods for the respective type of sequencing technology.
To date, prediction of particulate erosion in suspension ows has been unreliable, with the state-of-the-art reporting errors upwards of 40% [Zhang et al., Wear 240 (2000 40 51]].
Combined with a state-of-the-art a posteriori error estimator, we present an adaptive refinement procedure.
Furthermore, experimental results demonstrate that the proposed algorithm outperforms some state-of-the-art reversible data hiding error concealment schemes in improving the perceptual quality.
The resulting system outperforms in terms of peak signal-to-noise ratio (PSNR), a state-of-the-art, fine-tuned equal error protection (EEP) solution by as much as 2 dB.
State-of-the-art methods construct fault estimators with identified state-space models or Markov parameters, without compensating for identification errors.
The proposed approach achieves the state-of-the-art performance in terms of mean absolute error and mean squared error.
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