Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, (doi:10.1109/LGRS.2018.2806940) This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/156827/ Abstract-In this letter we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in our work to improve classification accuracy. We first propose the voted monostatic DCNN method (VMo-DCNN), which trains DCNNs on each receiver node separately, and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN method (Mul-DCNN), which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4 GHz multistatic radar system. Experimental results show that Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node.
Lithium niobate on insulator (LNOI) is an emerging photonic platform with great promises for future optical communications, nonlinear optics and microwave photonics. An important integrated photonic building block, active waveguide amplifiers, however, is still missing in the LNOI platform. Here we report an efficient and compact waveguide amplifier based on erbium-doped LNOI waveguides, realized by a sequence of erbium-doped crystal growth, ion slicing and lithography-based waveguide fabrication. Using a compact 5-mm-long waveguide, we demonstrate on-chip net gain of > 5 dB for 1530-nm signal light with a relatively low pump power of 21 mW at 980 nm. The efficient LNOI waveguide amplifiers could become an important fundamental element in future lithium niobate photonic integrated circuits.
Precursors derived from the hydrolysis of organic or inorganic salts have been widely used to produce ceramic coatings for a broad variety of applications. When applying the liquid precursors to the substrates, it is extremely challenging to control the film uniformity and homogeneity. The rate of solvent evaporation at different locations is different, causing the viscosity variation and flows in the film. There is very limited knowledge about the viscosity change in evaporating ceramic precursors. Therefore, it is crucial to understand the effect of evaporation on viscosity variation in thin films and droplets. We use magnetic rotational spectroscopy to study the time dependence of viscosity in mullite precursors. A correlation between the viscosity change and evaporation kinetics is revealed. This correlation was used to relate the change of viscosity to the concentration of mullite. A master curve relating viscosity to the mullite concentration was constructed and used to propose a possible scenario of the viscosity increase during solvent evaporation.
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