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Based on the Kubo formula, we have studied the electron transport properties of a gapped graphene in the presence of a strong magnetic field. By solving the Dirac equation, we find that the Landau level spectra in two valleys differ from each other in that the n = 0 level in the K valley is located at top of the valence band, whereas it is at the bottom of the conduction band in the K' valley. Thus, in an individual valley, the symmetry between conduction and valence bands is broken by the presence of a magnetic field. By using the self-consistent Born approximation to treat the long range potential scattering, we formulate the diagonal and the Hall conductivities in terms of the Green function. To perform the numerical calculation, we find that a large bandgap can suppress the quantum Hall effect, owing to the enhancement of the bandgap squeezing the spacing between the low-lying Landau levels. On the other hand, if the bandgap is not very large, the odd integer quantum Hall effect experimentally, observed in the gapless graphene, remains in the gapped one. However, such a result does not indicate the half integer quantum Hall effect in an individual valley of the gapped graphene. This is because the heights of the Hall plateaux in either valley can be continuously tuned by the variation of the bandgap. More interestingly, we find that the height of the diagonal conductivity peak corresponding to the n = 0 Landau level is independent of the bandgap if the scattering is not very strong. In the weak scattering limit, we demonstrate analytically that such a peak takes a universal value e(2)/(hpi), regardless of the bandgap.

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

In this work, a hierarchic reflow method is demonstrated for the monolithic micro-fabrication of biomimetic compound eye arrays.