In [8], the TMP (Temporal Movement Patterns)—Tree was proposed to efficiently discover the temporal movement patterns of objects in wireless sensor networks.
Therefore, with laser beam scattering, temporal movement of the water-mercury interface was observed.
In terms of activity alignment, the results showed that faster teams exhibited higher temporal movement coordination.
Point-tracking is a good method to provide the temporal movement of lip.
Motion estimation involves computing the temporal movement between the current frame and the previous frame, in the form of motion vectors.
On the one hand, it is clear from Bergson's earlier works that life is the absolute temporal movement informed by duration and retained in memory.
Moreover, we propose novel location prediction strategies that employ the discovered temporal movement patterns to reduce prediction errors to save energy.
In this paper, we propose a seamless data mining algorithm named STMP-Mine to efficiently discover the temporal movement patterns of objects in sensor networks without predefining the segmenting time unit.
The aim of this work was to use an appropriate proxy material, in this case (Decabromodiphenyl ether (DBDE)), to represent 1 2 μm sized chemical particles, frequently used in agricultural practices, in order to trace their spatial and temporal movement through a soil column.
