Contribution to the analysis of the predissociated rovibronic structure of the symmetric isotopomers 16O3 and 18O3 of ozone near 10,400 cm-1: 3A2(A20)

The absorption spectrum of ozone was Recorded at low temperatures (down to −135 °C) by high resolution Fourier transform spectrometry and intra cavity laser absorption spectroscopy (ICLAS) near 10,400 cm−1. A preliminary analysis of the rotational structure of the absorption spectra of 16O3 and 18O3 shows that this spectral region corresponds to a superposition of two different electronic transitions, one with a very broad rotational structure, showing for the first time the asymmetric stretching frequency mode ν3 of the electronic state 3A2, the other formed by a completely diffuse band, probably the 201 band of a new transition due to the triplet electronic state 3B2. Predissociation effects induce large broadening of the rotational lines for the transition centered at 10,473 cm−1 identified as the 302 band of the Image electronic transition. The rotational structure cannot be analyzed directly but instead the band contour method was used to confirm the symmetry of the transition and to estimate the spectroscopic constants for the 16O isotopomer. The origin of the band is at 10,473±3 cm−1 and the value of the 16O3(3A2) antisymmetric stretching frequency mode is equal to 460±2 cm−1. We believe that the diffuse band is due to the 3B2 state and is located at about 10,363±3 cm−1 for 16O3 and 10,354±3 cm−1 for 18O3. The isotopic rules confirm the different results obtained for 18O3 and 16O3

SHREC 2019 Track: Online Gesture Recognition

This paper presents the results of the Eurographics 2019 SHape Retrieval Contest track on online gesture recognition. The goal of this contest was to test state-of-the-art methods that can be used to online detect command gestures from hands' movements tracking on a basic benchmark where simple gestures are performed interleaving them with other actions. Unlike previous contests and benchmarks on trajectory-based gesture recognition, we proposed an online gesture recognition task, not providing pre-segmented gestures, but asking the participants to find gestures within recorded trajectories. The results submitted by the participants show that an online detection and recognition of sets of very simple gestures from 3D trajectories captured with a cheap sensor can be effectively performed. The best methods proposed could be, therefore, directly exploited to design effective gesture-based interfaces to be used in different contexts, from Virtual and Mixed reality applications to the remote control of home devices