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This study proposes a fusion algorithm based on forward linear prediction (FLP) and particle swarm optimization–back propagation (PSO-BP) to compensate for the temperature drift. Firstly, the accelerometer signal is broken down into several intrinsic mode functions (IMFs) using variational modal decomposition (VMD); then, according to the FE algorithm, the IMF signal is separated into mixed components, temperature drift, and pure noise. After that, the mixed noise is denoised by FLP, and PSO-BP is employed to create a model for temperature adjustment. Finally, the processed mixed noise and the processed IMFs are rebuilt to obtain the enhanced output signal. To confirm that the suggested strategy works, temperature experiments are conducted. After the output signal is processed by the VMD-FE-FLP-PSO-BP algorithm, the acceleration random walk has been improved by 23%, the zero deviation has been enhanced by 24%, and the temperature coefficient has been enhanced by 92%, compared with the original signal.

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope’s working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time–frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope’s output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time–frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope’s output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10−3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h.

MEMS accelerometers are significantly impacted by temperature and noise, leading to a considerable compromise in their accuracy. In response to this challenge, we propose a parallel denoising and temperature compensation fusion algorithm for MEMS accelerometers based on RLMD-SE-TFPF and GRU-attention. Firstly, we utilize robust local mean decomposition (RLMD) to decompose the output signal of the accelerometer into a series of product function (PF) signals and a residual signal. Secondly, we employ sample entropy (SE) to classify the decomposed signals, categorizing them into noise segments, mixed segments, and temperature drift segments. Next, we utilize the time-frequency peak filtering (TFPF) algorithm with varying window lengths to separately denoise the noise and mixed signal segments, enabling subsequent signal reconstruction and training. Considering the strong inertia of the temperature signal, we innovatively introduce the accelerometer’s output time series as the model input when training the temperature compensation model. We incorporate gated recurrent unit (GRU) and attention modules, proposing a novel GRU-MLP-attention model (GMAN) architecture. Simulation experiments demonstrate the effectiveness of our proposed fusion algorithm. After processing the accelerometer output signal through the RLMD-SE-TFPF denoising algorithm and the GMAN temperature drift compensation model, the acceleration random walk is reduced by 96.11%, with values of 0.23032 g/h/Hz for the original accelerometer output signal and 0.00895695 g/h/Hz for the processed signal.

The integration of micro-electro-mechanical system–inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system’s performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss–Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading’s angular increment, and to fit the integrated navigation system’s dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.

In recent years, rapid advancements in multidisciplinary fields (materials, biology, chemical physics, etc [...]

Micro-electromechanical systems (MEMS) are miniature systems comprising micro-mechanical sensors, actuators, and microelectronic circuits [...]

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.

In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this dataset. Combining the improved EWT algorithm with SEResNeXt-50 reduces the impact of white noise in the signal on the identification task and significantly improves the accuracy of fault identification. The EWT algorithm is a wavelet analysis algorithm with adaptive wavelet analysis, which can significantly reduce the impact of boundary effects, and has a good effect on decomposition of signal segments with short length, but a reconstruction method is needed to effectively separate the noise signal and effective signal, and so this paper uses multiscale permutation entropy for calculation. For the reason that the neural network has a better ability to characterize high-dimensional signals, the one-dimensional signal is reconstructed into a two-dimensional image signal and the signal features are extracted. Then, the constructed image signals are fed into the SEResNeXt-50 network, and the characterization ability of the model is further improved in the network with the addition of the Squeeze-and-Excitation module. Finally, the proposed model is applied to the FMVMG fault dataset and compared with other models. In terms of recognition accuracy, the proposed method improves about 30.25% over the BP neural network and about 1.85% over ResNeXt-50, proving the effectiveness of the proposed method.

MEMS gyroscopes are one of the core components of inertial navigation systems. The maintenance of high reliability is critical for ensuring the stable operation of the gyroscope. Considering the production cost of gyroscopes and the inconvenience of obtaining a fault dataset, in this study, a self-feedback development framework is proposed, in which a dualmass MEMS gyroscope fault diagnosis platform is designed based on MATLAB/Simulink simulation, data feature extraction, and classification prediction algorithm and real data feedback verification. The platform integrates the dualmass MEMS gyroscope Simulink structure model and the measurement and control system, and reserves various algorithm interfaces for users to independently program, which can effectively identify and classify seven kinds of signals of the gyroscope: normal, bias, blocking, drift, multiplicity, cycle and internal fault. After feature extraction, six algorithms, ELM, SVM, KNN, NB, NN, and DTA, were respectively used for classification prediction. The ELM and SVM algorithms had the best effect, and the accuracy of the test set was up to 92.86%. Finally, the ELM algorithm is used to verify the actual drift fault dataset, and all of them are successfully identified.

This paper presents an improved empirical modal decomposition (EMD) method to eliminate the influence of the external environment, accurately compensate for the temperature drift of MEMS gyroscopes, and improve their accuracy. This new fusion algorithm combines empirical mode decomposition (EMD), a radial basis function neural network (RBF NN), a genetic algorithm (GA), and a Kalman filter (KF). First, the working principle of a newly designed four-mass vibration MEMS gyroscope (FMVMG) structure is given. The specific dimensions of the FMVMG are also given through calculation. Second, finite element analysis is carried out. The simulation results show that the FMVMG has two working modes: a driving mode and a sensing mode. The resonant frequency of the driving mode is 30,740 Hz, and the resonant frequency of the sensing mode is 30,886 Hz. The frequency separation between the two modes is 146 Hz. Moreover, a temperature experiment is performed to record the output value of the FMVMG, and the proposed fusion algorithm is used to analyse and optimise the output value of the FMVMG. The processing results show that the EMD-based RBF NN+GA+KF fusion algorithm can compensate for the temperature drift of the FMVMG effectively. The final result indicates that the random walk is reduced from 99.608°/h/Hz1/2 to 0.967814°/h/Hz1/2, and the bias stability is decreased from 34.66°/h to 3.589°/h. This result shows that the algorithm has strong adaptability to temperature changes, and its performance is significantly better than that of an RBF NN and EMD in compensating for the FMVMG temperature drift and eliminating the effect of temperature changes.

Irreversible marginal bone loss can hinder recovery around dental implants. Insufficient alveolar osteogenesis and stress concentration during chewing contribute to marginal bone resorption and can result in implant failure. A biomaterial with a micropore-channel structure was developed using 3D printing technology. This design facilitated bony ingrowth and provided similar mechanical stimulation at the implant neck during mastication to a natural tooth. The micropore channels provided a guiding structure for bone mesenchymal stem cell proliferation and differentiation without the need for growth factors. Specifically, this was achieved through mechanical transduction by F-actin remodeling and the activation of Yes-associated protein (YAP). The implants were verified in a canine dental implant surgery model, which demonstrated the promising use of biomaterial-based dental implants in future clinical applications.

Because the dual-mass MEMS gyroscope’s output is greatly influenced by temperature, which can lead to errors that cannot be ignored. To solve this problem, a novel compensation method is proposed: a parallel processing algorithm, which integrates the Permutation entropy (PE), Local Characteristic-scale Decomposition (LCD) and Adaptive network-based fuzzy inference system (ANFIS). Firstly, LCD is used to decompose the output which contains temperature noises and drifts into a trend component and several intrinsic scale components (ISC), according to autocorrelation and complexity, three different categories will be obtained by PE: pure noise output, mixed output, and drift output. The different processes are as follows, the noise output is discarded, the mixed output is filtered by SG (Savitzky-Golay filter), then dual ANFIS is applied. Since the drift output completely reflects the temperature characteristics, the degree of non-linearity is high, the ANFIS with complex rules is used for processing. And the mixed output is composed of intermediate layer modes, containing a relatively small amount of temperature characteristics, simple rule ANFIS is adopted for processing. Finally, the signal is reconstructed. After that, the temperature error experiment is carried out, the result shows the method can effectively eliminate the error and compensate for the drift, it has a fast convergence speed and good effect, and has the advantage of good compensation efficiency.

A novel fusion algorithm is proposed based on Improved Adaptive Wavelet Threshold De-noising (IAWTD), C-means Support Vector Machine (CSVM) and Ensemble Empirical Mode Decomposition (EEMD) method to eliminate the humidity drift of MEMS gyroscope. Firstly, the IAWTD method is employed to decrease the humidity drift component in MEMS gyroscope output signal. Then, the humidity drift compensation model is established: the input elements are the relative humidity, the change rate of relative humidity and the humidity drift, and the output is the compensated MEMS gyroscope output signal by EEMD method. In order to verify the compensation effect of the fusion algorithm, the gyroscope outputs are collected and analyzed with the relative humidity ranged from 40% to 90% based on the temperature varying from 20°C to 60°C. The results show that the IAWTD-CSVM-EEMD method significantly reduces the influence of relative humidity drift on the gyroscope output, according to the quantitative analysis of Allan variance, the quantization noise of the gyroscope output decreases by 87.78%, 96.37%, 97.77%, 99.17% and 92.62% respectively under the relative humidity ranging from 40% to 90%, as the temperature rose from 20 °C to 60 °C at intervals of 10 °C. In addition, the bias stability decreases by 96.9%, 99.41%, 99.1%, 99.46%, and 99.78% respectively and the angle random walk decreases by 88.16%, 96.54%, 98.16%, 94.43%, and 92.05% respectively at different temperatures. It is worth mentioning that, to further verify the applicability of the fusion algorithm, a group of comparative experiments are added to consider the influence of temperature changes on the gyroscope output under different relative humidity. The experimental results show that the quantization noise, bias stability and angle random walk of the MEMS gyroscope are significantly reduced compared with the original output after processing by IAWTD-CSVM-EEMD. Therefore, the method proposed in this paper is beneficial to reduce the humidity drift in the MEMS gyroscope output.

It is extremely challenging to apply an Inertial Navigation System (INS) to a gun-launched rolling projectile effectively. Overcoming the negative effects produced by high angular rolling and overloading requires innovative solutions. This paper introduces a unique inertial system that differs from a traditional gimballed INS and a strapdown INS. A Partial Strapdown Platform (PSP) was developed based on the principle of a pendulum to mitigate the adverse effects of projectile rolling motion. The anti-overloading performance of the PSP was improved by using a pair of specially designed alloy steel head-on hemispheres. Force condition analysis was conducted on the projectile and the PSP’s inner cylinder to establish the mechanical dynamic model. Computer simulations and turntable experiments were performed to verify the stabilization function of the PSP. The pitch angle was determined to be associated with the stability of the PSP based on simulations and experiments. Gun firing tests were performed to verify the overload-burdening and stabilization functions of the PSP.

Cell-selective biomaterials, i.e. favoring the functions of gingival cells and simultaneously reducing the adhesion of pathogenic bacteria, is highly desired in dental implants. However, due to the same mechanisms shared in attachment by mammalian and microbial cells, it is hard to increase the functions of the former while acting against the latter. Herein, a cell-selectivity was established on titanium oxide coatings by combining a plasma immersion ion implantation & deposition (PIII) and a UV pre-illumination. The titanium oxide coatings were produced on pure titanium substrates by using a plasma electrolytic oxidation technique and then doped of hafnium (Hf) by using a cathodic arc sourced PIII. The Hf-doped titanium oxide coatings (Hf-PIII groups) were capable of delaying the recombination of UV-illumination generated electron-hole pairs, which decreased the adhesion of bacterial cells at the later period (cultured for 24 h). Titanium oxide coating doped of 4.87 at.% hafnium (Hf) gained the biggest decrease (decreased 52.9% compared to its UV negative counterparts) in bacterial adhesion among the UV positive (UV+) groups. This study demonstrates that coupling Hf-doping and UV pre-illumination is promising for improving the cell-selective performance of titanium-based dental implants. Keywords: Titanium, Surface modification, Ultraviolet, Bacteria, Hafnium

A closed-loop controlling system for Micro Electro Mechanical System (MEMS) gyroscope sense mode is investigated in this paper, and the controller is designed to achieve low error, wide bandwidth and low noise capability. The gyroscope monitoring system includes four independent closed-loop, and is simulated by Simulink soft to prove the speedability and stability of the sensing closed-loop. The gyroscope monitoring system is realized through 3 analog circuit boards, and is tested through temperature controlled turntable. The bias stability, angular random walking value and bias temperature coefficients improved from 2.168 °/h, 0.155°/√h and 10.59 °/h/°C to 0.415 °/h, 0.0414°/√h and 3.59°/h/°C. And the bandwidth value is improved from 13Hz to 104Hz. Meanwhile, scale factor nonlinearity, asymmetry, repeatability and temperature coefficient parameters are enhanced from 660 ppm, 430ppm, 403ppm and 180 ppm/°C to 59.3ppm, 62.4ppm, 50.4ppm and 28.7 ppm/°C respectively.

Capacitive accelerometers have been widely used in the fields of mobile phones, automobiles, seismic monitoring and others because of its high sensitivity, good repeatability and high precision. This type of accelerometer usually has a low range. The sensitive structure of the sensor is too vulnerable to damage in high impact environments, so it basically has no ability to detect smaller signals after a relatively high acceleration. This paper presents a capacitive accelerometer which employs a four-terminal fixed structure. Acceleration is outputted by a differential capacitance formed between the mass and the upper and lower glass plates. With consideration of better anti-overload capability and small signal detection capability, structure optimization and anti-overload protection such as chamfer and protection measures have been carried out. According to the simulation results of the optimized structure by finite element analysis software, it has a maximum stress of 62 Mpa when a 20,000g shock is load, the maximum displacement within effective range (100g) is 13.7μm, and it also has a first-order frequency response of 7.477 kHz. And then, the process flow is designed and the device is fabricated. Static capacitance test and flip test are utilized to verify its static performance. The anti-overload capability of the device is tested and verified by Marshall Hammer impact experiments.

Bionic polarization navigation has a broad variety of application in diverse fields for high reliability and strong robustness to interference, fundamental to which is the use of a polarization compass based on polarized light cues. Nevertheless, dramatical reduction of the orientation accuracy resulted from the noise in a measured angle of polarization (AoP) and the tilted angles of a polarization compass during operation gives imperative influence on navigation precision. Herein, we investigate how to improve the navigation accuracy effectively by the proposed comprehensive heading error processing technique for a polarization compass, where a novel denoising scheme is designed to eliminate the noise in AoP images directly by integrating the strength of iterative variance-stabilizing transformation (IVST) and adaptive soft interval thresholding (SIT) so as to compensate the following tilt-induced error accurately. Subsequently, a promising compensation approach inspired by efficient extreme learning machine (EELM) is introduced to correct the tilt-induced error caused by realistic execution. The AoP image denoising advance and the tilt-induced error modeling advance combine to produce remarkable performance gains on the heading error. Experimental results and comparisons with prior arts reveal that the proposed comprehensive heading error processing technique is highly appealing in terms of improving the orientation accuracy for a polarization compass with superiority to state-of-the-art alternatives.

Previous studies demonstrated that the antibacterial action of molybdenum disulfide (MoS2) is highly dependent on the light-activated potential of reactive oxygen species (ROS) generation; here vertically aligned molybdenum disulfide nanosheets (MoS2, with or without doping of iron) were coated on titanium (Ti) substrates via a one-step hydrothermal reaction, and their excellent activity against both Escherichia coli (E. coli, ATCC 25922) and Staphylococcus aureus (S. aureus, ATCC 25923) was evidenced under dark conditions. Release of MoO42− and generation of MoS2-inspired ROS were found to be the key factors answering for the bactericidal property of the nanosheets. What’s more, iron-doping in terms of FeMoO4 could boost the antibacterial efficacy further, which stems from quick release of Fe2+ and generation of ROS by Fenton-like reactions. Accordingly, it is believed that the dark antibacterial efficacy of the vertically aligned MoS2 nanosheets is guaranteed by uniting ion release and ROS generation. This study provides new insight into design and application of MoS2 coatings for disinfection of biomedical devices, where light is hard to be served. Keywords: Molybdenum disulfide, Antibacterial activity, Iron, Reactive oxygen species, Titanium

Magnesium-based metals are promising for biomedical use for its biodegradation characteristic. However, its rapid degradation in physiological environment impedes its application. Oxides coating is a widely-used form of surface modification to improve the corrosion resistance of biomaterials. In this study, we systematically compared two oxides coatings, steam oxidation (SO) and micro-arc oxidation (MAO), in controlling degradation and improving the biocompatibility of pure magnesium in vitro and in vivo. The addition of both coatings was found to reduce the degradation rate of magnesium in vitro at the early stage, but the corrosion behavior became severe with longer immersion time in all the groups. Unfortunately, all the coated and uncoated materials were found undermining the adhesion and proliferation of MC3T3-E1 cells in vitro, even at the first day of culture. In terms of in-vivo circumstance, the Mg-MAO group showed satisfying biocompatibility compared with pure Mg and Mg-SO, as is evidenced by radiographic results and histological analysis. Little corrosion was found, and more newly formed bone was observed in the animal model. These data suggest that the characteristic of in-vivo circumstance have considerable impacts on the degradation and bone integration process. Keywords: Micro-arc oxidation, Magnesium, Biocompatibility, Surface modification

This paper presents a new type of three-axis gyroscope. The gyroscope comprises two independent parts, which are nested to further reduce the structure volume. The capacitive drive was adopted. The motion equation, capacitance design, and spring design of a three-axis gyroscope were introduced, and the corresponding formulas were derived. Furthermore, the X/Y driving frequency of the gyroscope was 5954.8 Hz, the Y-axis detection frequency was 5774.5 Hz, and the X-axis detection frequency was 6030.5 Hz, as determined by the finite element simulation method. The Z-axis driving frequency was 10,728 Hz, and the Z-axis sensing frequency was 10,725 Hz. The MEMS gyroscope’s Z-axis driving mode and the sensing mode’s frequency were slightly mismatched, so the gyroscope demonstrated a larger bandwidth and higher Z-axis mechanical sensitivity. In addition, the structure also has good Z-axis impact resistance. The transient impact simulation of the gyroscope structure showed that the maximum stress of the sensitive structure under the impact of 10,000 g@5 ms was 300.18 Mpa. The gyroscope was produced by etching silicon wafers in DRIE mode to obtain a high aspect ratio structure, tightly connected to the glass substrate by silicon/glass anode bonding technology.

Periprosthetic infection is one of the trickiest clinical problems, which often leads to disastrous consequences. The emergence of tantalum and its derivatives provides novel ideas and effective methods to solve this problem and has attracted great attention. However, tantalum was reported to have different anti-infective effects in vivo and in vitro, and the inherent antibacterial capability of tantalum is still controversial, which may restrict its development as an antibacterial material to some extent. In this study, the polished tantalum was selected as the experimental object, the implant-related tibia osteomyelitis model was first established to observe whether it has an anti-infective effect in vivo compared to titanium, and the early studies found that the tantalum had a lower infectious state in the implant-related tibia osteomyelitis model in vivo than titanium. However, further in vitro studies found that the polished tantalum was not superior to the titanium against bacterial adhesion and antibacterial efficacy. In addition, we focus on the state of interaction between cells, bacteria and materials to restore the internal environment as realistically as possible. We found that the adhesion of fibroblasts to tantalum was faster and better than that of titanium. Moreover, what is more, interesting is that, in the early period, bacteria were more likely to adhere to cells that had already attached to the surface of tantalum than to the bare surface of it, and over time, the cells eventually fell off the biomaterials and took away more bacteria in tantalum, making it possible for tantalum to reduce the probability of infection in the body through this mechanism. Moreover, these results also explained the phenomenon of the “race for the surface” from a completely different perspective. This study provides a new idea for further exploring the relationship between bacteria and host tissue cells on the implant surface and a meaningful clue for optimizing the preparation of antibacterial implants in the future.

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.

In this paper, the structure and working principle of four-mass vibration MEMS gyroscope (FMVMG) are introduced, and the working modes of FMVMG are simulated and analyzed. On the basis of this, an improved noise reduction method based on interval local mean decomposition (ILMD) and parabolic tracking time-frequency peak filtering (PTTFPF) is proposed. PTTFPF can resample the signal along a parabolic path and select the optimal filtering trajectory, but there is still a contradiction, choosing a short window length may lead to good signal amplitude retention, but the random noise reduction effect is not good, while choosing a long window length may lead to serious amplitude attenuation, but the random noise reduction effect is better. In order to achieve a better balance between effective signal amplitude preservation and random noise reduction, the ILMD method was used to improve PTTFPF. First, the original signal was decomposed into product functions (PFs) by local mean decomposition (LMD) method, and the sample entropy (SE) of each PF was calculated. The PFs are divided into three different components. Then, short window PTTFPF is used for useful PF and long window PTTFPF is used for mixed PF, noise PF is directly removed. Then the final signal is reconstructed. Finally, the denoised useful PF and mixed PF are reconstructed to obtain the final signal. The proposed ILMD-PTTFPF algorithm was verified by temperature experiments. The results show that the denoising performance of the ILMD-PTTFPF algorithm is better than that of traditional wavelet threshold denoising and Kalman filtering.

The pyramid Lucas-Kanade (LK) optical flow algorithm has been widely used in velocity measurement applications. However, these applications are limited by some shortcomings of the algorithm, such as its slow calculation speed and susceptibility to illumination changes. To solve these problems, a data fusion scheme based on the scale-invariant feature transform (SIFT) and optical flow is proposed to alleviate the dependence of the optical flow on the illumination conditions. In addition, an improved cubature Kalman filter (CKF) based on multi-rate residual correction (CKF-MRC) is proposed to solve the problem of inconsistency between the sampling frequencies of the SIFT and the optical flow, and takes full advantage of the high sampling frequency of SIFT. The experimental results demonstrate that the proposed CKF-MRC method can effectively improve the accuracy of velocity measurement under variable illumination conditions with a high sampling frequency.

In a bid to solve the gyroscope temperature drift problem, a parallel denoising model based on PE-ITD and SA-ELM has been proposed in this paper, wherein, Intrinsic time-scale decomposition (ITD) is an effective signal decomposition algorithm, Permutation entropy (PE) is a entropy to accurately determine the complexity of the signal, Extreme Learning machine (ELM) is a machine learning algorithm for predicting, and Simulated Annealing(SA) for finding the optimal parameter set. First, ITD is employed to decompose the output signal of the gyroscope and the PE can distinguish the decomposition results into noise-only component, mixed component and trend component. Secondly, the mixed component is filtered by Forward liner prediction (FLP), the denoised signal can be obtained after processing. Finally, the trend component is compensated by SA-ELM, through reconstruction, the compensation result can be obtained. As is shown in experimental results, the parallel model proposed in this paper is able to effectively eliminate the temperature error compared with the traditional serial model. Compared with the EMD analysis method, the ITD is superior to the EMD analysis method in terms of computational efficiency and instantaneous information. The proposed SA-ELM algorithm takes simulated annealing algorithm to find hidden layer neurons' optimal number adaptively, which can improve the model's accuracy and generalization. This paper demonstrates the superiority of this method.

This paper presents a sense mode closed-loop method for dual-mass micro-electro-mechanical system (MEMS) gyroscope based on the bipole temperature compensation method. A pair of conjugate poles are investigated as the bottle neck of the sense closed-loop system of MEMS gyroscope, and the bipole temperature compensation proportional controller (BTCPC) is employed to realize the closed-loop: a pair of additional conjugate zeros are utilized to generate bipoles with poles. Since poles changes with temperature, thermal resistance is utilized in BTCPC to make zeros variable with temperatures. The BTCPC is designed very carefully to make the system have enough bandwidth and better performance. The overall gyroscope model is established and simulated either in the time domain or the frequency domain, and the results verify that the sense closed-loop works rapidly and steadily. The system is realized on PCBs and is tested on the turntable in temperature oven. The experimental results show that the bias stability, angular random walking, bias temperature coefficient, and the bandwidth values of sense open-loop and closed-loop are 2.168 °/h, 0.155 °/%/h, 9.534 °/h/°C, 13 Hz, and 2.168 °/h, 0.140 °/%/h (five tests average value), 5.991 °/h°C, 61 Hz, respectively.

High-G MEMS accelerometer (HGMA) is widely used in the aerospace field and the precise control of missiles. Therefore, its calibration accuracy is critical to sensor performance and the overall control system. In order to decrease the influence of noise on the HGMA output signal, a hybrid denoising algorithm which is based on the Time-frequency peak filtering (TFPF), Local mean decomposition (LMD) and Sample entropy (SE) has been proposed in this article. For the problem that the TFPF algorithm is limited in the choice of window length, LMD and SE are used to distinguish components, which can improve the TFPF algorithm effectively. It provides a better balance between noise reduction and signal fidelity. Firstly, the noise-containing signal can be decomposed by LMD to obtain PFs. Secondly, calculate the sample entropy values of each PFs, then divide the signal into mixed component, useful component and noise component according to the similarity of sample entropy. Thirdly, the mixed component can use long-window TFPF to reduce noise, the short-window TFPF can reduce the noise for the useful component, and the noise component can be wiped off directly. Finally, the useful component and the mixed component are both reconstructed to form the final denoised signal. Experiments have showed that this method can not only remove noise (the noise of static signal is reduced by 91.76%, the signal-noise ratio of dynamic signal has increased to 17.6), but also retain the details of frequency and amplitude (the shock peak amplitude error is 0.062% and the vibration amplitude error is 0.04%). Therefore, this method can reduce the noise of the High-G MEMS accelerometer signal with maintaining the characteristics of the original signal, thereby greatly improves the performance of the accelerometer, making it widely used.

The uses of implantable medical devices are safer and more common since sterilization methods and techniques were established a century ago; however, device-associated infections (DAIs) are still frequent and becoming a leading complication as the number of medical device implantations keeps increasing. This urges the world to develop instructive prevention and treatment strategies for DAIs, boosting the studies on the design of antibacterial surfaces. Every year, studies associated with DAIs yield thousands of publications, which here are categorized into four groups, i.e., antibacterial surfaces with long-term efficacy, cell-selective capability, tailored responsiveness, and immune-instructive actions. These innovations are promising in advancing the solution to DAIs; whereas most of these are normally quite preliminary “proof of concept” studies lacking exact clinical scopes. To help identify the flaws of our current antibacterial designs, clinical features of DAIs are highlighted. These include unpredictable onset, site-specific incidence, and possibly involving multiple and resistant pathogenic strains. The key point we delivered is antibacterial designs should meet the specific requirements of the primary functions defined by the “intended use” of an implantable medical device. This review intends to help comprehend the complex relationship between the device, pathogens, and the host, and figure out future directions for improving the quality of antibacterial designs and promoting clinical translations.

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.

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.

Recently, the High-G MEMS accelerometer (HGMA) has been used in navigation, mechanical property detection, consumer electronics, and other fields widely. As the core component of a measuring system, it is very crucial to enhance the calibration accuracy of the accelerometer. In order to remove the noises in the accelerometer output signals to enhance its calibration accuracy, a combined denoising method which combines variational mode decomposition (VMD) with permutation entropy (PE) and wavelet threshold is given in this article. For the sake of overcoming the defect of signal distortion caused by the traditional denoising methods, this joint denoising method combines the good decomposition characteristics of VMD and the good denoising ability of wavelet threshold and introduces PE as a judgment criterion to achieve a good balance between denoising effect and signal fidelity. The combination of PE and VMD not only avoids the phenomenon of mode aliasing but also improves the ability to identify the noise components, which makes the wavelet threshold denoising more specific. Firstly, some intrinsic mode functions (IMFs) are obtained by using VMD to decompose the complex signal containing noise which is outputted from the accelerometer. Secondly, the IMF components can be divided into noise IMF components, mixed IMF components, and useful IMF components by PE algorithm. Thirdly, the noise IMF components can be discarded directly, and then the mixed IMF components can be denoised by wavelet threshold to obtain the noiseless IMF components; in addition, the useful IMF components need to be retained. Finally, the final denoising signal can be obtained by reconstructing the IMF components which have been denoised by the wavelet threshold and the useful IMF components retained before denoising. The experimental results prove that the combined denoising algorithm combines the merits of VMD, PE, and wavelet threshold, and this new algorithm has a good performance in the calibration denoising of accelerometer. Compared with the serious signal distortion caused by using only EMD or wavelet threshold, this method not only has a good denoising effect (the noises in the static part are eliminated by 99.97% and the SNR of the dynamic part is raised to 18.56) but also can maintain a good signal fidelity (the error of shock peak amplitude is 3.4%, the error of vibration peak amplitude is 0.4%, and the correlation coefficient between the denoising signals and dynamic part is as high as 0.982).

To solve the problem of micro-electro-mechanical system (MEMS) gyroscope noise, this paper presents a variational mode decomposition (VMD) method based on crow search algorithm. First, the signal was decomposed by variational mode decomposition for optimization of crow search algorithm (CSA-VMD) method. The parameters required by the VMD method (penalty parameter α and decomposition number K) are given by the crow search algorithm, and then the signal is decomposed into the superposition of multiple subsignals, called intrinsic mode functions (IMFs). The sample entropy (SE) corresponding to each IMF is then obtained. By calculating the sample entropy, the noise signal can be divided into pure noise part, mixing part, and temperature drift part. Second, Savitzky–Golay smoothing denoising (SG) is used to filter the mixed noise signal to eliminate the influence of noise. Third, for the filtering of the drift part, the least square support vector machine optimized by the crow search algorithm (CSA-LSSVM) was used to filter, so as to reduce the effect of temperature drift. Finally, the processed signal is reconstructed to achieve the goal of denoising. Through the results, it can be found that the optimized VMD and LSSVM using CSA algorithm can achieve more effective denoising. After using the method proposed in this paper, the angular random walk value is 1.1175 ∗ 10−4°/h/√Hz, and the bias stability is 0.0017°/h. Compared with the original signal, the two signals are optimized by 98.1% and 98.2%, respectively. It can be seen from the experimental results that the proposed CSA-VMD method, SG method, and CSA-LSSVM method can effectively eliminate noise effects.

This article suggested two methods to compensate for the temperature drift of the micro-electro-mechanical system gyroscopes, which are support vector machine method and C-means support vector machine. The output of X axis which was ranged from −40°C to 60°C based on the micro-electro-mechanical system gyroscope is reduced and analyzed in this article. The results showed the correctness of the two methods. The final results indicate that when the temperature is ranged from −40°C to 60°C, the factor of B is reduced from 0.424 ( ° h ) to 0.02194 ( ° h ) , and when the temperature is ranged from 60°C to −40°C, the factor of B is reduced from 0.1056 ( ° h ) to 0.0329 ( ° h ) , and the temperature drift trend and noise characteristics are improved clearly.

Inertial guidance system plays a very important role in guided artillery. Micro electro mechanical system (MEMS) gyroscope, as the core component of inertial guidance system, has high resistance to overload and restricts the application of inertial guidance system in high overload environment directly. First of all, the high overload environment in the gun chamber is modeled and quantified, and the mechanism of high overload failure of the MEMS gyro structure is summarized. Secondly, based on the previous public research results from different institutes at home and abroad, the anti-high overload MEMS gyroscopes with different monitoring methods, different structural forms, different structural materials and different working principles are introduced from the perspective of anti-overload characteristics of MEMS gyroscopes impact resistance. Finally, the related reports and papers are summarized, and it is pointed out that the anti-high overload MEMS gyroscope should be designed from the aspects of drive-detection mode, reasonable structure of energy-absorbing and energy-dissipating structure, working principle, new structural material and multi-level system buffer methods to improve the anti-high overload ablility.

To eliminate the noise and temperature drift in an Micro-Electro-Mechanical Systems (MEMS) gyroscope’s output signal for improving measurement accuracy, a parallel processing model based on Multi-objective particle swarm optimization based on variational modal decomposition-time-frequency peak filter (MOVMD–TFPF) and Beetle antennae search algorithm- Elman neural network (BAS–Elman NN) is established. Firstly, variational mode decomposition (VMD) is optimized by multi-objective particle swarm optimization (MOPSO); then, the best decomposition parameters [kbest,abest] can be obtained. Secondly, the gyroscope output signals are decomposed by VMD optimized by MOPSO (MOVMD); then, the intrinsic mode functions (IMFs) obtained after decomposition are classified into a noise segment, mixed segment, and drift segment by sample entropy (SE). According to the idea of a parallel model, the noise segment can be discarded directly, the mixed segment is denoised by time-frequency peak filtering (TFPF), and the drift segment is compensated at the same time. In the compensation part, the beetle antennae search algorithm (BAS) is adopted to optimize the network parameters of the Elman neural network (Elman NN). Subsequently, the double-input/single-output temperature compensation model based on the BAS-Elman NN is established to compensate the drift segment, and these processed segments are reconstructed to form the final gyroscope output signal. Experimental results demonstrate the superiority of this parallel processing model; the angle random walk of the compensated gyroscope output is decreased from 0.531076 to 5.22502 × 10−3°/h/√Hz, and its bias stability is decreased from 32.7364°/h to 0.140403°/h, respectively.

A hemispherical resonator consists of a hemispherical shell and the surrounding circular electrodes. The asymmetry of a hemispherical shell has influence on the vibrating mode and quality factor. The gap distance from shell to electrode is critical for the capacitance and sensitivity of a hemispherical resonator. To realize a symmetric shell and a small gap, a kind of micro-hemispherical resonator (μHR) structure including sandwich-shaped stacks and eave-shaped electrodes has been developed using a glassblowing process. The blowing process could bring favorable surface roughness and symmetry. The locations of the hemispherical shell and surrounding electrodes can be precisely controlled by the designs of sandwich-shaped stacks and eave-shaped electrodes, making it feasible to realize uniform and small gaps. In addition, electrical insulation between the hemispherical shell and eave-shaped electrodes can be guaranteed owing to eave-shaped structure. The fabrication process and results are demonstrated in detail. Furthermore, an estimation method of shell thickness in a nondestructive manner is proposed, with deviation below 5%. Taking asymmetry, surface roughness, and gap into consideration, these results preliminarily indicate this structure with a hemispherical shell and surrounding eave-shaped electrodes is promising in hemispherical resonator applications.

Editor's Note: this Article has been retracted; the Retraction Note is available at https://doi.org/10.1038/s41598-021-88664-z .

Abstract In recent years, magnesium (Mg) alloys show a promising application in clinic as degradable biomaterials. Nevertheless, the poor corrosion resistance of Mg alloys is the main obstacle to their clinical application. Here we successfully seal the pores of plasma electrolytic oxidation (PEO) coating on AZ31 with Mg-Al layered double hydroxide (LDH) via hydrothermal treatment. PEO/LDH composite coating possess a two layer structure, an inner layer made up of PEO coating (~5 μm) and an outer layer of Mg-Al LDH (~2 μm). Electrochemical and hydrogen evolution tests suggest preferable corrosion resistance of the PEO/LDH coating. Cytotoxicity, cell adhesion, live/dead staining and proliferation data of rat bone marrow stem cells (rBMSCs) demonstrate that PEO/LDH coating remarkably enhance the cytocompatibility of the substrate, indicating a potential application in orthopedic surgeries. In addition, hemolysis rate (HR) test shows that the HR value of PEO/LDH coating is 1.10 ± 0.47%, fulfilling the request of clinical application. More importantly, the structure of Mg-Al LDH on the top of PEO coating shows excellent drug delivery ability.

In recent years, High-G MEMS accelerometers have been widely used in aviation, medicine, and other fields. So it is extremely important to improve the accuracy and performance of High-G MEMS accelerometers. For this purpose, we propose a fusion algorithm that combines EMD, wavelet thresholding, and temperature compensation to process measurement data from a High-G MEMS accelerometer. In the fusion algorithm, the original accelerometer signal is first decomposed by EMD to obtain the intrinsic mode function (IMF). Then, sample entropy (SE) is used to divide the IMF components into three segments. The noise segment is directly omitted, wavelet thresholding is performed on the mixing segment, and a GA-BP performs temperature compensation on the drift segment. Finally, signal reconstruction is implemented. Later, a comparative analysis is carried out on the results from four models: EMD, wavelet thresholding, EMD + wavelet thresholding, and EMD + wavelet thresholding + temperature compensation. The experimental data show that the acceleration random walk change from 1712.66 g/h/Hz0.5 to 79.15 g/h/Hz0.5 and the zero-deviation stability change from 49275 g/h to 774.7 g/h. This indicates that the fusion algorithm (EMD + wavelet thresholding + temperature compensation) not only effectively suppresses the noise of high-frequency components but also compensates for temperature drift in the accelerometer.

High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.

For the sake of decreasing the effects of noise and temperature error on the measurement accuracy of micro-electro-mechanical system (MEMS) gyroscopes, a denoising and temperature drift compensation parallel model method based on wavelet transform and forward linear prediction (WFLP) and support vector regression based on the cuckoo search algorithm (CS-SVR) is proposed in this paper. First, variational mode decomposition (VMD) is proposed in this paper, which is aimed at dividing the output signal of the gyroscope into intrinsic mode functions (IMFs); then, the IMFs are classified into three features—drift, mixed, and pure noise features—by the sample entropy (SE) value. Second, a wavelet transform and forward linear prediction (WFLP) are combined to remove the noise from the mixed features. Meanwhile, the drift feature is compensated by support vector regression based on the cuckoo search algorithm (CS-SVR). Finally, through reconstruction, the final signal is obtained. Experimental results demonstrate that the VMD-SE-WFLP-CS-SVR method proposed in this paper can decrease noise and compensate the temperature error effectively (angular random walking value is optimized from 1.667°/√h to 0.0667°/√h and the bias stability is reduced from 30°/h to 4°/h). In terms of denoising, the performance of the WFLP algorithm is superior to the wavelet threshold and FLP, as it combines their advantages; furthermore, in terms of temperature compensation, the proposed CS-SVR algorithm uses the cuckoo search algorithm to find the optimal parameters of SVR, improving the accuracy of the model.

This paper proposed three methods to compensate the temperature energy influence drift of the MEMS vibration gyroscope, including radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), and RBF NN based on GA with Kalman filter (KF). Three-axis MEMS vibration gyroscope (Gyro X, Gyro Y, and Gyro Z) output data are compensated and analyzed in this paper. The experimental results proved the correctness of these three methods, and MEMS vibration gyroscope temperature energy influence drift is compensated effectively. The results indicate that, after RBF NN-GA-KF method compensation, the bias instability of Gyros X, Y, and Z improves from 139°/h, 154°/h, and 178°/h to 2.9°/h, 3.9°/h, and 1.6°/h, respectively. And the angle random walk of Gyros X, Y, and Z was improved from 3.03°/h1/2, 4.55°/h1/2, and 5.89°/h1/2 to 1.58°/h1/2, 2.58°/h1/2, and 0.71°/h1/2, respectively, and the drift trend and noise characteristic are optimized obviously.

This study presents a new MEMS vibrating ring gyroscope (VRG), which is driven by electrostatic force and detected by capacitance. A novel ring resonator with eight S-shaped symmetrical supporting springs is developed based on the advantageous characteristics of a thin-shell vibrating gyroscope. The capacitance electrodes, including the drive electrodes, the sense electrodes and the mode control electrodes, are designed according to the vibration characteristics of the ring resonator and the shape of the supporting springs. In addition, the operational principle of these electrode capacitors and the electrostatic force of the drive electrodes are discussed in detail. The gyroscope with high aspect-ratio structures is manufactured through an efficient fabrication process. Finally, the performance characteristics of the fabricated VRG are tested, and the experimentally obtained the zero-bias instability is about 0.0167°/s and angle random walk (ARW) is about 0.1363°/s1/2 at room temperature. Experimental results show that the VRG has a simple structure and relatively better performance characteristics for low and medium angular velocity measurements.

The effects of calcium (Ca) or strontium (Sr) on host osteogenesis and immune responses have been investigated separately. In clinical practice, these two elements may both be present around an orthopedic device, but their potential synergistic effects on osteogenesis and the immune response have not been explored to date. In this work, we investigated the immunomodulatory effects of Ca and Sr co-doped titanium oxides on osteogenesis in vitro using the mouse macrophage cell line RAW 264.7 alone and in co-culture with mouse bone mesenchymal stem cells (BMSCs), and in vivo using a mouse air-pouch model. Coatings containing Ca and Sr at different concentration ratios were fabricated on titanium substrates using micro-arc oxidation and electrochemical treatment. The in vitro and in vivo results demonstrated that the Ca and Sr concentration ratio has a marked influence on macrophage polarization. The coating with a Ca/Sr ratio of 2:1 was superior to those with other Ca and/or Sr concentrations in terms of modulating M2 polarization, which enhanced osteogenic differentiation of mouse BMSCs in co-culture. These findings suggest that the osteoimmunomodulatory effect of a titanium-oxide coating can be enhanced by modulating the concentration ratio of its components.

Action and identification problems are the challenges that visually impaired people often encounter in their lives. The high price of existing commercial intelligent auxiliary equipment has placed enormous economic pressure on most visually impaired people in developing countries. In order to solve this problem, this paper proposes a smart wearable system that performs image recognition. The system adopts the method of cloud and local cooperative processing. The cloud server mainly performs image processing, while the local unit only uploads images and feedback results. Therefore, the processor of the system does not need to use expensive high-performance hardware, and the cost is greatly reduced. Moreover, the algorithm running on the cloud server can also guarantee the speed and accuracy of recognition. In addition, we have changed the traditional video continuous scanning strategy to a mechanism for capturing points of interest, reducing power consumption. The attitude correction mechanism based on error codes of the system can effectively help the visually impaired to cope with the common living scenes, and the multiple priority feedbacks and arbitration mechanism will ensure real-time feedback of the system. The proposed smart wearable device has been tested in the actual scene, which proves to be helpful to the visually impaired people. It helps them find the right people and objects and read text.

Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.

In this paper, we present a radial basis function (RBF) and cubature Kalman filter (CKF) based enhanced fusion strategy for vision and inertial integrated attitude measurement for sampling frequency discrepancy and divergence. First, the multi-frequency problem of the integrated system and the reason for attitude divergence are analyzed. Second, the filter equation and attitude differential equation are constructed to calculate attitudes separately in time series when visual and inertial data are available or when there are only inertial data. Third, attitude errors between inertial and vision are sent to the input layer of RBF for training. After this, through the activation function of the hidden layer, the errors are transferred to the output layer for weighting the sums, and the training model is established. To overcome the problem of divergence inherent in a multi-frequency system, the well-trained RBF, which can output the attitude errors, is utilized to compensate the attitudes calculated by pure inertial data. Finally, semi-physical simulation experiments under different scenarios are performed to validate the effectiveness and superiority of the proposed scheme in accurate attitude measurements and enhanced anti-divergence capability.

In this paper, a novel temperature compensation method for a dual-mass MEMS gyroscope is proposed based on drive mode vibration characteristic compensation using a temperature variable resistor. Firstly, the drive and sense modes of the gyroscope re analyzed and investigated, and it is found that the scale factor is proportional to the drive mode amplitude controlling reference voltage. Then, the scale factor temperature compensation method is proposed, and a temperature variable resistor is utilized to compensate the drive amplitude working point and make it change with temperature. In addition, the temperature compensation circuit is designed and simulated. After that, the temperature bias drift is compensated in a modular output. The experimental results show that scale factor and bias variation during the temperature range from −40 °C to 60 °C decrease from 3.680% to 1.577% and 3.880% to 1.913%, respectively. In addition, the bias value improves from 103.395 °/s to 22.478 °/s (optimized 78.26%). The bias stability and angular rate walking parameter are also optimized to 45.97% and 16.08%, respectively, which verify the method proposed in this paper.

Animals have certain cognitive competence about the environment so they can correct their navigation errors. Inspired by the excellent navigational behavior of animals, this paper proposes a brain-like navigation scheme to improve the accuracy and intelligence of Micro-Electro-Mechanical System based Inertial Navigation Systems (MEMS-INS). The proposed scheme employs vision to acquire external perception information as an absolute reference to correct the position errors of INS, which is established by analyzing the navigation and error correction mechanism of rat brains. In addition, to improve the place matching speed and precision of the system for visual scene recognition, this paper presents a novel place recognition algorithm that combines image scanline intensity (SI) and grid-based motion statistics (GMS) together which is named the SI-GMS algorithm. The proposed SI-GMS algorithm can effectively reduce the influence of uncertain environment factors on the recognition results, such as pedestrians and vehicles. It solves the problem that the matching result will occasionally go wrong when simply using the scanline intensity (SI) algorithm, or the slow matching speed when simply using grid-based motion statistics (GMS) algorithm. Finally, an outdoor Unmanned Aerial Vehicle (UAV) flight test is carried out. Based on the reference information from the high-precision GPS device, the results illustrate the effectiveness of the scheme in error correction of INS and the algorithm in place recognition.