Zinc oxide is a promising wide band gap semiconductor for future optoelectronic devices. Today ZnO bulk single crystals are grown by three different techniques: hydrothermally, from the melt, and by chemical vapor transport. For our studies, the authors employed in addition a simple and low cost vapor phase method which gives us good quality crystals and flexibility in crystal doping. The as-grown needle-shaped single crystals were characterized by resistivity measurements, scanning electron microscopy, electron backscatter diffraction, and low temperature photoluminescence spectroscopy.
Nowadays the mobile phone has become an indispensable tool in the lives of many people. While facilitating people's lives, it also provides criminals with a very important tool for spreading the terrorist video. Traditional manual detection of the terrorist video has the problem of low accuracy and inefficiency. To address the issue, this paper proposes a terrorist video detection system based on Light Gradient Boosting Machine (LightGBM) and Faster Region-based Convolutional Neural Network (Faster R-CNN) for mobile phone forensics system, which is used to quickly detect whether there is a terrorist video in the suspect's mobile phone. The system uses a multi-model method for detection, which includes preliminary detection and deep detection in two stages. Experimental research shows that it can effectively and accurately detect terrorist videos in mobile phones, thereby helping criminal investigation personnel to quickly grasp criminal evidence and provide some clues for the detection of the case.