Dual-Mass MEMS Gyroscope Structure, Design, and Electrostatic Compensation

Dual-mass MEMS gyroscope is one of the most popular inertial sensors. In this chapter, the structure design and electrostatic compensation technology for dual-mass MEMS gyroscope is introduced. Firstly, a classical dual-mass MEMS gyroscope structure is proposed, how it works as a tuning fork (drive anti-phase mode), and the structure dynamical model together with the monitoring system are presented. Secondly, the imperfect elements during the structure manufacture process are analyzed, and the quadrature error coupling stiffness model for dual-mass structure is proposed. After that, the quadrature error correction system based on coupling stiffness electrostatic compensation method is designed and evaluated. Thirdly, the dual-mass structure sensing mode modal is proposed, and the force rebalancing combs stimulation method is utilized to achieve sensing mode transform function precisely. The bandwidth of sensing open loop is calculated and experimentally proved as 0.54 times with the resonant frequency difference between sensing and drive modes. Then, proportional-integral-phase-leading controller is presented in sensing close loop to expand the bandwidth, and the experiment shows that the bandwidth is improved from 13 to 104 Hz. Finally, the results are concluded and summarized

BEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View Images

Place recognition is a key module for long-term SLAM systems. Current LiDAR-based place recognition methods usually use representations of point clouds such as unordered points or range images. These methods achieve high recall rates of retrieval, but their performance may degrade in the case of view variation or scene changes. In this work, we explore the potential of a different representation in place recognition, i.e. bird's eye view (BEV) images. We observe that the structural contents of BEV images are less influenced by rotations and translations of point clouds. We validate that, without any delicate design, a simple VGGNet trained on BEV images achieves comparable performance with the state-of-the-art place recognition methods in scenes of slight viewpoint changes. For more robust place recognition, we design a rotation-invariant network called BEVPlace. We use group convolution to extract rotation-equivariant local features from the images and NetVLAD for global feature aggregation. In addition, we observe that the distance between BEV features is correlated with the geometry distance of point clouds. Based on the observation, we develop a method to estimate the position of the query cloud, extending the usage of place recognition. The experiments conducted on large-scale public datasets show that our method 1) achieves state-of-the-art performance in terms of recall rates, 2) is robust to view changes, 3) shows strong generalization ability, and 4) can estimate the positions of query point clouds. Source codes are publicly available at https://github.com/zjuluolun/BEVPlace.Comment: Accepted by ICCV 202

Optical Flow Sensor/INS/Magnetometer Integrated Navigation System for MAV in GPS-Denied Environment

The drift of inertial navigation system (INS) will lead to large navigation error when a low-cost INS is used in microaerial vehicles (MAV). To overcome the above problem, an INS/optical flow/magnetometer integrated navigation scheme is proposed for GPS-denied environment in this paper. The scheme, which is based on extended Kalman filter, combines INS and optical flow information to estimate the velocity and position of MAV. The gyro, accelerator, and magnetometer information are fused together to estimate the MAV attitude when the MAV is at static state or uniformly moving state; and the gyro only is used to estimate the MAV attitude when the MAV is accelerating or decelerating. The MAV flight data is used to verify the proposed integrated navigation scheme, and the verification results show that the proposed scheme can effectively reduce the errors of navigation parameters and improve navigation precision

Intrinsic Surface Effects of Tantalum and Titanium on Integrin α5β1/ ERK1/2 Pathway-Mediated Osteogenic Differentiation in Rat Bone Mesenchymal Stromal Cells

Background/Aims: Accumulating evidence demonstrates the superior osteoinductivity of tantalum (Ta) to that of titanium (Ti); however, the mechanisms underlying these differences are unclear. Thus, the objective of the present study was to examine the effects of Ta and Ti surfaces on osteogenesis using rat bone mesenchymal stromal cells (rBMSCs) as a model. Methods: Ta and Ti substrates were polished to a mirror finish to minimize the influences of structural factors, and the intrinsic surface effects of the two materials on the integrin α5β1/mitogen-activated protein kinases 3 and 1 (ERK1/2) cascade-mediated osteogenesis of rBMSCs were evaluated. Alkaline phosphatase (ALP) activity, Alizarin Red staining, real-time polymerase chain reaction, and western blot assays of critical osteogenic markers were conducted to evaluate the effects of the two substrates on cell osteogenesis. Moreover, the role of the integrin α5β1/ERK1/2 pathway on the osteoinductive performance of Ta and Ti was assessed by up- and down-regulation of integrin α5 and β1 with RNA interference, as well as through ERK1/2 inhibition with U0126. Results: Osteogenesis of rBMSCs seeded on the Ta surface was superior to that of cells seeded on the Ti surface in terms of ALP activity, extracellular matrix calcification, and the expression of integrin α5, integrin β1, ERK1/2, Runt-related transcription factor 2, osteocalcin, collagen type I, and ALP at both the mRNA and protein levels. Moreover, down-regulation of integrin α5 or integrin β1, or ERK1/2 inhibition severely impaired the osteoblastic differentiation on the Ta surface. By contrast, over-expression of integrin α5 or integrin β1 improved osteogenesis on the Ti substrates, while subsequent ERK1/2 inhibition abrogated this effect. Conclusion: The integrin α5β1/ERK1/2 pathway plays a crucial role in regulating rBMSCs osteogenic differentiation; thus, the greater ability of a Ta surface to trigger integrin α5β1/ERK1/2 signaling may explain its better osteoinductivity. The different effects of Ta and Ti surfaces on rBMSC osteogenesis are considered to be related to the conductive behaviors between integrin α5β1 and the oxides spontaneously formed on the two metals. These results should facilitate the development of engineering strategies with Ta and Ti surfaces for improved osteogenesis in endosteal implants

Investigations on Inhibitors of Hedgehog Signal Pathway: A Quantitative Structure-Activity Relationship Study

The hedgehog signal pathway is an essential agent in developmental patterning, wherein the local concentration of the Hedgehog morphogens directs cellular differentiation and expansion. Furthermore, the Hedgehog pathway has been implicated in tumor/stromal interaction and cancer stem cell. Nowadays searching novel inhibitors for Hedgehog Signal Pathway is drawing much more attention by biological, chemical and pharmological scientists. In our study, a solid computational model is proposed which incorporates various statistical analysis methods to perform a Quantitative Structure-Activity Relationship (QSAR) study on the inhibitors of Hedgehog signaling. The whole QSAR data contain 93 cyclopamine derivatives as well as their activities against four different cell lines (NCI-H446, BxPC-3, SW1990 and NCI-H157). Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively. From these, demethylation is the best choice for inhibitor structure modifications. Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others

On the issue of transparency and reproducibility in nanomedicine.

Following our call to join in the discussion over the suitability of implementing a reporting checklist for bio-nano papers, the community responds

Improved VMD-ELM Algorithm for MEMS Gyroscope of Temperature Compensation Model Based on CNN-LSTM and PSO-SVM

The micro-electro-mechanical system (MEMS) gyroscope is a micro-mechanical gyroscope with low cost, small volume, and good reliability. The working principle of the MEMS gyroscope, which is achieved through Coriolis, is different from traditional gyroscopes. The MEMS gyroscope has been widely used in the fields of micro-inertia navigation systems, military, automotive, consumer electronics, mobile applications, robots, industrial, medical, and other fields in micro-inertia navigation systems because of its advantages of small volume, good performance, and low price. The material characteristics of the MEMS gyroscope is very significant for its data output, and the temperature determines its accuracy and limits its further application. In order to eliminate the effect of temperature, the MEMS gyroscope needs to be compensated to improve its accuracy. This study proposed an improved variational modal decomposition—extreme learning machine (VMD-ELM) algorithm based on convolutional neural networks—long short-term memory (CNN-LSTM) and particle swarm optimization—support vector machines (PSO-SVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and the gyro output signal with better accuracy is obtained. The VMD algorithm separates the gyro output signal and divides the gyro output signal into low-frequency signals, mid-frequency signals, and high-frequency signals according to the different signal frequencies. Once again, the PSO-SVM model is constructed by the mid-frequency temperature signal to find the temperature error. Finally, the signal is reconstructed through the ELM neural network algorithm, and then, the gyro output signal after noise is obtained. Experimental results show that, by using the improved method, the output of the MEMS gyroscope ranging from −40 to 60 °C reduced, and the temperature drift dramatically declined. For example, the factor of quantization noise (Q) reduced from 1.2419 × 10−4 to 1.0533 × 10−6, the factor of bias instability (B) reduced from 0.0087 to 1.8772 × 10−4, and the factor of random walk of angular velocity (N) reduced from 2.0978 × 10−5 to 1.4985 × 10−6. Furthermore, the output of the MEMS gyroscope ranging from 60 to −40 °C reduced. The factor of Q reduced from 2.9808 × 10−4 to 2.4430 × 10−6, the factor of B reduced from 0.0145 to 7.2426 × 10−4, and the factor of N reduced from 4.5072 × 10−5 to 1.0523 × 10−5. The improved algorithm can be adopted to denoise the output signal of the MEMS gyroscope to improve its accuracy

Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method

This study proposes an improved multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) method based on adaptive Kalman filter (AKF) and grey wolf optimizer-least squares support vector machine (GWO-LSSVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and a gyro output signal is obtained with better accuracy. Firstly, MPE-CEEMDAN is used to decompose the FOG output signal into several intrinsic mode functions (IMFs); then, the IMFs signal is divided into mixed noise, temperature drift, and other noise according to different frequencies. Secondly, the AKF method is used to denoise the mixed noise. Thirdly, in order to denoise the temperature drift, the fiber gyroscope temperature compensation model is established based on GWO-LSSVM, and the signal without temperature drift is obtained. Finally, the processed mixed noise, the processed temperature drift, the processed other noise, and the signal-dominated IMFs are reconstructed to acquire the improved output signal. The experimental results show that, by using the improved method, the output of a fiber optic gyroscope (FOG) ranging from −30 °C to 60 °C decreases, and the temperature drift dramatically declines. The factor of quantization noise (Q) reduces from 6.1269 × 10−3 to 1.0132 × 10−4, the factor of bias instability (B) reduces from 1.53 × 10−2 to 1 × 10−3, and the factor of random walk of angular velocity (N) reduces from 7.8034 × 10−4 to 7.2110 × 10−6. The improved algorithm can be adopted to denoise the output signal of the FOG with higher accuracy

A Hybrid Algorithm for Noise Suppression of MEMS Accelerometer Based on the Improved VMD and TFPF

High-G MEMS accelerometer (HGMA) is a new type of sensor; it has been widely used in high precision measurement and control fields. Inevitably, the accelerometer output signal contains random noise caused by the accelerometer itself, the hardware circuit and other aspects. In order to denoise the HGMA’s output signal to improve the measurement accuracy, the improved VMD and TFPF hybrid denoising algorithm is proposed, which combines variational modal decomposition (VMD) and time-frequency peak filtering (TFPF). Firstly, VMD was optimized by the multi-objective particle swarm optimization (MOPSO), then the best decomposition parameters [kbest, abest] could be obtained, in which the permutation entropy (PE) and fuzzy entropy (FE) were selected for MOPSO as fitness functions. Secondly, the accelerometer voltage output signals were decomposed by the improved VMD, then some intrinsic mode functions (IMFs) were achieved. Thirdly, sample entropy (SE) was introduced to classify those IMFs into information-dominated IMFs or noise-dominated IMFs. Then, the short-window TFPF was selected for denoising information-dominated IMFs, while the long-window TFPF was selected for denoising noise-dominated IMFs, which can make denoising more targeted. After reconstruction, we obtained the accelerometer denoising signal. The denoising results of different denoising algorithms in the time and frequency domains were compared, and SNR and RMSE were taken as denoising indicators. The improved VMD and TFPF denoising method has a smaller signal distortion and stronger denoising ability, so it can be adopted to denoise the output signal of the High-G MEMS accelerometer to improve its accuracy