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Each module performs a certain task only and contributes to the modelling of landscape transformations in a different way.
Whenever the capture module performs a TFC procedure, it starts a timer for the Capture Period (CP timer) and saves the current context information such as the channel number and the operation mode of the NIC.
Additionally, an Image Preprocessing module performs a connected components analysis using pixel-level information to partition each figure into a set of bounding boxes.
The second module performs a reciprocal BLAST on sequences unannotated in the first step to generate annotations from orthologous database sequences (Fig. 1, M2).
The Caption Segmentation module performs a lexical analysis to identify the number of panels from the caption, and generates the corresponding subcaptions.
Finally, the self-test module performs a number of additional software regression tests to confirm that the automated quality checks in the pipeline are functioning correctly.
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The students attend lecture modules, perform a piece of individual research and work on a GDP.
The select_RFE module performs recursive feature elimination in a cross-validation loop to find the optimal number of features.
This module performs, at the encoder side, a rough SI estimation using low-complexity interpolation techniques in order that the overall encoder complexity does not significantly change.
If a certain multi-neuron module performs some stereotypical function it may appear in the nervous system repeatedly.
This module performs data filtering by eliminating bad/absent flag spots and weak signals (lower than "A" cutoff).
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