Bias stability is an important performance indicator for MEMS gyroscopes. In this paper, a gyroscope zero rate output (ZRO) model under force-to-rebalance (FTR) closed-loop detection is presented, and the effect of circuit phase delay and various noises on the ZRO is analyzed. Based on the fact that the two feedback forces in the FTR system are insensitive to the phase delay of the sense mode, while they are sensitive to the phase delay of the drive mode, a method for quickly calculating the circuit phase delay is proposed, and an all-pass filter is used to realize one-time automatic compensation for the phase delay in the drive mode. The control system is implemented with an FPGA. The experimental results show that this method can be used to accurately calculate the circuit phase delay and that phase compensation can be used to effectively reduce the effect of quadrature error on ZRO. This technique provides bias instability and angle random walk performances of 0.338° h −1 and 0.061 ° (√h) −1 , respectively. In addition, the sensitivity of ZRO to temperature at 0 °C to 70 °C has reached 0.001°/s/°C.
In order to solve the problem where existing mode-matching methods in microelectromechanical systems (MEMS) vibrating gyroscopes fail to meet real-time and reliability requirements, this paper presents a novel method to accomplish automatic and real-time mode-matching based on phase-shifted 45° additional force demodulation (45° AFD-RM). The phase-shifted 45° additional force signal has the same frequency as the quadrature force signal, but it is phase-shifted by 45° and applied to the sense mode. In addition, two-way phase-shifted 45° demodulations are used at the sense-mode detection output to obtain a phase metric that is independent of the Coriolis force and can reflect the mode-matching state. Then, this phase metric is used as a control variable to adaptively control the tuning voltage, so as to change the sense-mode frequency through the negative stiffness effect and ultimately achieve real-time mode-matching. Simulation and experimental results show that the proposed 45° AFD-RM method can achieve real-time matching. The mode frequency split is controlled within 0.1 Hz, and the gyroscope scale factor, zero-bias instability, and angle random walk are effectively improved.
Ultrasound imaging technology plays an important role to assist doctors in diagnosing thyroid nodules. The tissue structure around the thyroid is very complex, which makes it difficult to segment and extract the ultrasound image of thyroid nodules accurately. For address this problem, this paper proposes a model algorithm for thyroid nodule ultrasound image segmentation using ASPP fusion features. First, spatial pyramid pooling and depthwise separable convolution are combined in order to solve the problem that the size of the mapping feature will change in the process of better capturing the context information. Besides, Atrous Spatial Pyramid Pooling (ASPP) is proposed to achieve the purpose of processing input image channel and spatial information separately. In order to appropriately reduce the dimension and size of feature images, a 1×1 convolution operation is performed before each convolution calculation, and the model size is optimized. In the decoding stage, decoder module appropriately adjusts the feature map with a relatively low resolution previously from decoder module, and sets the output channel number of two convolutions to the same value. All features have the same dimension by adjustment, and features can be fused by element-wise summation. Finally, Dice Similarity Coefficient (DSC), Prevent Match (PM) and Correspondence Patio (CR) are used as evaluation criteria to compare with other model algorithms. The experimental results show that the proposed model can significantly improve the segmentation effect of ultrasound images for thyroid nodules compared with traditional models.
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