Relaxation of interfacial stress and improved quality of heteroepitaxial 3C-SiC films on (100)Si deposited by organometallic chemical vapor deposition at 1200 °C

This paper proposes a real-time electroencephalogram (EEG)-based detection method of the potential danger during fatigue driving. To determine driver fatigue in real time, wavelet entropy with a sliding window and pulse coupled neural network (PCNN) were used to process the EEG signals in the visual area (the main information input route). To detect the fatigue danger, the neural mechanism of driver fatigue was analyzed. The functional brain networks were employed to track the fatigue impact on processing capacity of brain. The results show the overall functional connectivity of the subjects is weakened after long time driving tasks. The regularity is summarized as the fatigue convergence phenomenon. Based on the fatigue convergence phenomenon, we combined both the input and global synchronizations of brain together to calculate the residual amount of the information processing capacity of brain to obtain the dangerous points in real time. Finally, the danger detection system of the driver fatigue based on the neural mechanism was validated using accident EEG. The time distributions of the output danger points of the system have a good agreement with those of the real accident points.

Single-crystal tungsten nanobelts with thicknesses from tens to hundreds of nanometers, widths of several micrometers and lengths of tens of micrometers were synthesized using chemical vapor deposition. Surface energy minimization was believed to have played a crucial role in the growth of the synthesized nanobelts enclosed by the low-energy {110} crystal planes of body-centered-cubic structure. The anisotropic growth of the crystallographically equivalent {110} crystal planes could be attributable to the asymmetric concentration distribution of the tungsten atom vapor around the nanobelts during the growth process. The elastic moduli of the synthesized tungsten nanobelts with thicknesses ranging from 65 to 306 nm were accurately measured using a newly developed thermal vibration method. The measured modulus values of the tungsten nanobelts were thickness-dependent. After eliminating the effect of surface oxidization using a core-shell model, the elastic modulus of tungsten nanobelts became constant, which is close to that of the bulk tungsten value of 410 GPa.

By measuring the resonant frequencies of the first two symmetric vibration modes of a circular thin-film diaphragm and solving the Rayleigh-Ritz equation analytically, the residual stress and elastic modulus of the film were determined simultaneously. The results obtained employing this method are in excellent agreement with those obtained numerically in finite element modelling when tested using freestanding circular SiC diaphragms with residual tensile stress. The stress and modulus values are also in reasonably good agreement with those obtained from nanoindentation and wafer curvature measurements, respectively.

Digital holographic microscopy (DHM), a quantitative phase-imaging technology, has been widely used in various applications. Phase-aberration compensation in off-axis DHM is vital to reconstruct topographical images with high precision, especially for microstructures with a small background or a dense phase distribution. We propose a numerical method based on deep learning combined with DHM. First, a convolutional neural network (CNN) recognizes and segments the sample and the background area of the hologram. Zernike polynomial fitting is then executed on the extracted background area. Finally, the whole process of phase-aberration compensation is automatically performed. To obtain a robust and accurate deep-learning model for hologram segmentation, we collected many holograms corresponding to several samples that had different morphological characteristics. The experimental results confirm that the trained CNN can accurately segment the sample from the background area of the hologram, and that this method is applicable and effective in off-axis DHM.