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Figure 1 shows the components describing the faces to be used for the learning of the CCA-based mapping.
This category comprehends the components describing the actions triggered when the user interacts with different IoT devices.
Principal component analysis (PCA) is a commonly used method to reduce the raw data dimensionality into few principal components describing the maximum possible variation in the data.
The figure shows the components describing the faces to be used for the learning of the CCA-based mapping between frontal and right profile poses.
The figure shows the components describing the faces to be used for the learning of the CCA-based mapping between frontal and left profile poses.
By GEMANOVA the information in the data is compressed down to a few multiplicative components describing the main variation in the data including relevant interaction phenomena.
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Section System components describes the main components used in the implementation of the sensor node.
"Research methodology" thoroughly describes the research methodology while "Framework for developing repair process taxonomy of mechanical components" describes the framework developed for repair process taxonomy of the mechanical components development.
A similar set of components describes the decay measured in the other experimental conditions (Fig. 3).
These components describe the variables X and explain the variables Y.
The reaction terms R (c i ) of the components describe the regulatory interactions based on information from the literature.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com