Recent research has shown that human motions and positions can be recognized through WiFi signals. The key intuition is that different motions and positions introduce different multi-path distortions in WiFi signals and generate different patterns in the time-series of channel state information (CSI). In this paper, we propose Wi-Motion, a WiFi-based human activities recognition system. Unlike existing systems, Wi-Motion adopts the amplitude and phase information extracted from the CSI sequence to construct the classifiers respectively, and combines the results using a combination strategy based on posterior probability. As the simulation results shows, Wi-Motion can recognize six human activities with the mean accuracy of 98.4%.

No abstract

We demonstrate the concentration-quenching-free properties of the 406 nm emission in NaPrF4 nanoscintillators, which result from the large energy gap between the 1S0 and 1I6 states (ΔE = ∼25 000 cm−1) of Pr3+ ions.

No abstract

No abstract