A support vector machine classification of computational capabilities of 3D map on mobile device for navigation aid
3D maps for mobile devices provide more realistic views of environments and serve as better navigation aids. Previous research studies show differences on how 3D maps effect the acquisition of spatial knowledge. This is attributable to the differences in mobile device computational capabilities....
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
International Association of Online Engineering
2016
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/51422/ http://irep.iium.edu.my/51422/ http://irep.iium.edu.my/51422/ http://irep.iium.edu.my/51422/1/Attached_SVM.pdf http://irep.iium.edu.my/51422/4/51422_A%20support%20vector%20machine%20classification.pdf |
| Summary: | 3D maps for mobile devices provide more realistic
views of environments and serve as better navigation aids.
Previous research studies show differences on how 3D maps
effect the acquisition of spatial knowledge. This is attributable
to the differences in mobile device computational capabilities.
Crucial to this is the time it takes for a 3D map
dataset to be rendered for a complete navigation task. Different
findings suggest different approaches on how to solve
the problem of time required for both in-core (inside mobile)
and out-core (remote) rendering of 3D datasets. Unfortunately,
there have not been sufficient studies regarding the
analytical techniques required to show the impact of computational
resources required to use 3D maps on mobile devices.
This paper uses a Support Vector Machine (SVM) to
analytically classify mobile device computational capabilities
required for 3D maps that are suitable for use as navigation
aids. Fifty different Smart phones were categorized
on the basis of their Graphical Processing Unit (GPU), display
resolution, memory and size. The result of the proposed
classification shows high accuracy. |
|---|