MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine.

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.

A Temperature Compensation Approach for Micro-Electro-Mechanical Systems Accelerometer Based on Gated Recurrent Unit-Attention and Robust Local Mean Decomposition-Sample Entropy-Time-Frequency Peak Filtering.

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.

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach.

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.

State-of-the-Art Functional Biomaterials in China.

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

Nighttime bionic compass based on a short-wave infrared polarization sensing system.

Sky-bionic polar co-ordinate navigation is an effective means of providing navigational information in the absence of a priori information. Polar co-ordinate navigation during clear daytime conditions has been studied, but there has been a lack of research of it at night due to problems with noise. Therefore, in this paper, a short-wave infrared polarimetric sensor system is designed, which is capable of acquiring atmospheric polarimetric information in low illumination environments at night, compared with traditional visible band sensors. Additionally, based on the statistics of polarization angle information, an algorithm for removing noise and starlight is proposed to solve the influence of starlight and noise on the polarization information at night. After many outdoor experiments, we found that the method can output the heading angle stably and accurately, and its standard deviation is controlled to be 0.42° in a clear night.

Editorial for the Special Issue on Micro-Electromechanical System Inertial Devices.

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

Multispectral silicon-based photodetector with stacked PN junctions.

A multispectral silicon-based photodetector structure with stacked PN junctions is proposed in this study. The substrate layer of the proposed photodetector consists of four vertically stacked PN junction structures that contain four photodiodes. The designed structure achieves quantum efficiency of up to 70% and a response time of 5.1 × 10-8 s. The proposed photodetector has a simple structure, and the vertically stacked PN junction structure not only reduces the phenomenon of color aliasing, but also achieves multispectral absorption over the range from ultraviolet to visible light with high response speeds, which provides an effective way to perform high-quality imaging.

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 Fault Diagnosis Method of Four-Mass Vibration MEMS Gyroscope Based on ResNeXt-50 with Attention Mechanism and Improved EWT Algorithm.

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.

A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform.

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.

Temperature Drift Compensation for Four-Mass Vibration MEMS Gyroscope Based on EMD and Hybrid Filtering Fusion Method.

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.

Design and Fabrication of a Novel Wheel-Ring Triaxial Gyroscope.

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.

Why Is Tantalum Less Susceptible to Bacterial Infection?

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.

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.

A Noise Reduction Method for Four-Mass Vibration MEMS Gyroscope Based on ILMD and PTTFPF.

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.

High-performance subwavelength polarizer using "sandwich" structured substrates.

A subwavelength polarizer based on "sandwich" structured substrates is proposed in this study. The proposed subwavelength polarizer consists of three layers of subwavelength aluminum wires and dielectric substrate. The designed structure achieves an extinction ratio (ER) greater than 90 dB in a 400-800 nm visible wavelength region, achieving a maximum ER of 135 dB at 750 nm. Our results demonstrate significant improvements over the conventional single- and double-grid polarizers in terms of an ER and spectral range coverage. The proposed subwavelength polarizer in this paper has great potential in polarimetric imaging, liquid crystal display, and other optical fields.

Antibacterial Designs for Implantable Medical Devices: Evolutions and Challenges.

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.

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.

The antimicrobial effect of calcium-doped titanium is activated by fibrinogen adsorption.

Directly targeting bacterial cells is the present paradigm for designing antimicrobial biomaterial surfaces and minimizing device-associated infections (DAIs); however, such pathways may create problems in tissue integration because materials that are toxic to bacteria can also be harmful to mammalian cells. Herein, we report an unexpected antimicrobial effect of calcium-doped titanium, which itself has no apparent killing effect on the growth of pathogenic bacteria (Pseudomonas aeruginosa, Pa, ATCC 27853) while presenting strong inhibition efficiency on bacterial colonization after fibrinogen adsorption onto the material. Fine X-ray photoelectron spectroscopy and Fourier-transform infrared spectroscopy analyses reported calcium-dependent shifts of the binding energy in nitrogen and oxygen involved groups and wavenumbers in the amide I and II bands of the adsorbent fibrinogen, demonstrating that locally delivered calcium can react with the carboxy-terminal regions of the Aα chains and influence their interaction with the N-termini of the Bß chains in fibrinogen. These reactions facilitate the exposure of the antimicrobial motifs of the protein, indicating the reason for the surprising antimicrobial efficacy of calcium-doped titanium. Since protein adsorption is an immediate intrinsic step during the implantation surgery, this finding may shift the present paradigm on the design of implantable antibacterial biomaterial surfaces.

A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope.

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.