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Previous coding methods rely for their security on the limitations of computing power.
Qualitative data were analysed using open coding methods.
A description of the coding methods employed is provided.
These notably include sparse coding methods and deep neural networks.
Both point-to-point and distributed source coding methods in CPSs are discussed.
We show that, contrary to prior expectations, different coding methods can have a significant impact on the topology of the resulting trees.
However, the way that such data are coded into characters for phylogenetic analysis has been approached in different ways, without investigating how coding methods may affect the results.
This appendix does not cover audio coding methods such as MPEG (MP3) [5,7,52,6] or Linear Predictive Coding (LPC) [12,42,43].
The visual layers method produced significantly faster search times than the other two coding methods which did not differ significantly from each other.
Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods.
The results show that the proposed technique increases the classification accuracy in comparison with the state of the art coding methods.
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