Visual odometry with depth-wise separable convolution and quaternion neural networks

Monocular visual odometry is a fundamental problem in computer vision and it was extensively studied in literature. The vast majority of visual odometry algorithms are based on a standard pipeline consisting in feature detection, feature matching, motion estimation and local optimization. Only recently, deep learning approaches have shown cutting-edge performance, replacing the standard pipeline with an end-to-end solution. One of the main advantages of deep learning approaches over the standard methods is the reduced inference time, that is an important requirement for the application of visual odometry in real-time. Less emphasis, however, has been placed on memory requirements and training efficiency. The memory footprint, in particular, is important for real world applications such as robot navigation or autonomous driving, where the devices have limited memory resources. In this paper we tackle both aspects introducing novel architectures based on Depth-Wise Separable Convolutional Neural Network and deep Quaternion Recurrent Convolutional Neural Network. In particular, we obtain equal or better accuracy with respect to the other state-of-the-art methods on the KITTI VO dataset with a reduction of the number of parameters and a speed-up in the inference time

Time-resolved x-ray spectroscopy of optical-field-ionized plasmas

The time-dependent soft X-ray emission of helium and nitrogen plasmas generated by optical-field ionization is reported. The experiments were carried out by focusing pulses of the high-power Ti:sapphire laser of the Lund Institute of Technology (lambda = 796 nm, pulse duration 150 fs, pulse energy 150 mJ) to a 50-mu m diameter spot close to a nozzle, using He and N-2 as target gases. The emission on He+, N4+, and N3+ resonance lines was recorded by means of a flat-field grating spectrometer coupled to an X-ray streak camera. A pronounced difference in the temporal shape of the emission of the Lyman-alpha line of hydrogen-like helium and of the 2p-3d resonance lines of lithium-like and beryllium-like nitrogen was observed. The helium line exhibited an initial spike followed by a slow revival of the emission, whereas the nitrogen lines showed a slow decay after a fast initial rise. These observations are explained with the help of simulations

Total hemoglobin mass, aerobic capacity, and hbb gene in polish road cyclists

The relationship between genes, amount of hemoglobin, and physical performance are still not clearly defined. The aim of this study was to examine the association between-551C/T and intron 2, +16 C/G polymorphisms in the beta hemoglobin (HBB) gene and total hemoglobin mass (tHbmass) and aerobic capacity in endurance athletes. Total hemoglobin mass and aerobic capacity indices, i. e.,VO2max, oxygen uptake at anaerobic threshold (VO2AT), maximal power output (Pmax), and power at anaerobic threshold (PAT) were determined in 89 young road cyclists, female (n = 39) and male (n = 50), who were genotyped for 2 polymorphisms in the HBB gene. The relative values of aerobic capacity indices differed significantly among intron 2, +16 C/G polymorphisms of the HBB gene only in female cyclists; athletes with GG genotype had significantly higher values of V O2max (p = 0.003), VO2AT (p = 0.007), PAT (p = 0.015), and Pmax (p = 0.004) than C carriers. No relationships were found between the C-carrier model (CC + CG vs. GG in the case of intron 2, +16 C/G and CC + CT vs. TT for -551 C/T polymorphisms of the HBB gene) and relative values of tHbmass. Our results demonstrated that the HBB gene could be related to aerobic capacity, but it seems that it does not result from an increase in the amount of hemoglobin in the blood