Search Results (737)

Microelectromechanical systems (MEMS) gyroscopes are now standard rate sensors in inertial navigation, automotive electronics, industrial automation, and medical instrumentation because they are inexpensive, compact, and readily integrated. Their accuracy, however, degrades with temperature: damping and quadrature coupling change, and readout-electronics behavior shifts, producing temperature-dependent zero-rate-output drift, elevated random noise, and poorer long-term stability. Hardware- and structure-based temperature compensation address part of the problem but carry cost and generality penalties, which has moved recent work toward data-driven software-based temperature-drift compensation. This review focuses on the fusion algorithms that have come to dominate that literature, organized as a four-stage pipeline: signal decomposition, learning-based drift modeling, adaptive filtering, and signal reconstruction. We examine how optimizer-tuned variational mode decomposition and improved empirical-mode-decomposition variants separate temperature-related components from noise; how deep temporal networks and optimizer-coupled learners model the nonlinear, time-lagged drift; and how adaptive Kalman variants and time-frequency filtering reconstruct a stable output. We close by identifying four open problems that recur across the recent gyroscope-specific work—cross-device generalization, temperature hysteresis, embedded real-time deployment, and physics-informed lightweight modeling. Full article

The potential to precisely control both the linear and nonlinear index of refraction through optical manipulation of the atomic states has recently pushed warm alkali vapors to the forefront of research in the field of quantum sensors, quantum memories, and quantum fluids of light. Rubidium (Rb) vapor in centimeter-scale glass cells or millimeter-scale micro-electro-mechanical system (MEMS) cells has proven to be a very promising platform for these applications, yet only a handful of research works have been dedicated to the investigation of the (non)linear refractive index of Rb vapor. We present results of theoretical calculations of the (non)linear refractive index of warm Rb vapor, based on the optical Bloch equations for 6-level Rb atoms interacting with a probe laser. They are compared to the experimental results obtained using an interferometric technique, showing very good quantitative agreement. A Kerr nonlinear refractive index $n_2$ of up to $-1.2\times {10}^{-4}$ cm²/W is obtained. Python scripts for all theoretical calculations presented in this work are provided, including the refractive index calculation, that can readily be used in practical implementations for simulating the (non)linear refractive index of Rb vapor including the effects of Doppler broadening, transit time broadening, pressure broadening, saturation, optical pumping, and spin-exchange collisions. Full article

The aviation industry is adopting liquid hydrogen (LH₂) for sustainable flight, requiring robust safety systems. This work is an example of adaptation of a Micro-Electro-Mechanical Systems (MEMS)-based structural health monitoring (SHM) system for LH₂ tanks, developed in the H2ELIOS project. It uses a multisensor approach that combines MEMS sensors to monitor vibration and acceleration, shape memory alloy (SMA) strain sensors for measuring tank expansion, and hydrogen leakage sensors to prevent false alarms. This SHM technology detects cracks and delamination of material and coating, enabling predictive maintenance via digital twins and ensuring structural integrity. Full article

Earthquake-induced landslides represent a critical threat to transportation infrastructure in tectonically active mountainous regions, particularly in tropical volcanic settings where weak, highly weathered geomaterials dominate. This study develops an integrated framework that directly links physically based seismic threshold modelling with real-time landslide monitoring and operational early warning. The approach is demonstrated in the Cugenang area of Cianjur Regency, West Java, Indonesia, which was severely impacted by the moment magnitude (Mw) 5.6 earthquake in 2022. Slopes composed of highly weathered pyroclastic deposits [Plasticity Index (PI) = 54–68%; porosity > 60%] exhibit low shear strength and high sensitivity to seismic loading. Limit equilibrium analysis using the Morgenstern–Price method that combines the influence of seismic loading and groundwater conditions suggests that a horizontal seismic coefficient (kh) of approximately 0.06, corresponding to a Peak Ground Acceleration (PGA) of about 0.12 gravitational acceleration (g), is a critical threshold for initial landsliding. This comparatively low threshold challenges commonly reported values and demonstrates that slope failure in tropical volcanic terrains can occur under moderate ground shaking, reinforcing the need for site-specific hazard characterisation. The derived thresholds are operationalised within a multi-sensor early warning system integrating Micro-Electro-Mechanical Systems (MEMS) accelerometers and inclinometer measurements. Three hazard levels—Normal (<0.06 g), Alert (0.06–0.12 g), and Emergency (≥0.12 g)are combined with deformation thresholds [<10 milimeter (mm), 10–30 mm, >30 mm] to capture progressive failure processes and minimise false alarms. By coupling geotechnical modelling and real-time monitoring, this study provides a transferable and scalable framework for enhancing infrastructure resilience in landslide-prone regions. Full article

As the core sensor of inertial measurement units, the reliability of Micro-Electro-Mechanical Systems (MEMS) gyroscopes is critical for long-term navigation and motion control applications. To bridge the mechanism-data gap in MEMS multi-mechanism degradation modeling, this paper proposes a physics-informed dual-indicator reliability assessment framework based on Wiener processes. Two degradation indicators under consideration are frequency-related degradation caused by stiffness degradation and Q-factor degradation caused by damping degradation, for which corresponding physics-embedded stochastic degradation models are formulated. The two indicators are normalized and fused through a generalized weighted limit state function, where failure is defined as gyroscope-level performance failure. Closed-form reliability expressions are derived for linear limit states, while Monte Carlo simulation is used for nonlinear cases. Reduced-order multiphysics simulation cases, including a double-ended fixed beam and a cantilevered MEMS mass block, are used to demonstrate the mechanism-to-indicator-to-reliability modeling procedure. The results show that the proposed dual-indicator framework provides more balanced reliability assessment than single-indicator analysis under the simulation setting. The proposed method offers an alternative mechanism-informed approach for reliability analysis and lifetime prediction of other MEMS devices. Full article

Characteristic gas-based detection technology can facilitate the warning of lithium-ion battery thermal runaway with a high accuracy at an early stage. Microelectromechanical system (MEMS) metal–oxide–semiconductor (MOS) gas sensors have advantages of a low cost, a high accuracy, and low power consumption; therefore, they are ideal candidates for the lithium-ion battery thermal-runaway warning. MEMS MOS gas sensors are composed of a micro-hotplate and gas-sensitive materials. The micro-hotplate component strongly influences the device’s mechanical and thermal properties. Initially, we used COMSOL to optimize the micro-hotplate component. Then, we fabricated the device based on the optimal micro-hotplate. Next, gas-sensitive materials made of ZnO and ZnO-Au were deposited on the micro-hotplate by radio-frequency magnetic sputtering. The self-made and commercial MEMS MOS sensors were integrated to form an electronic nose. The as-made electronic nose can classify hydrogen, ethylene, acetylene, methane, carbon monoxide, and ethanol with a maximum accuracy of 99.4% using gas response data acquired over only 20 s. The reported work can provide a solution for an early and accurate lithium-ion battery thermal runaway warning. Full article

Permanent-magnet spherical motors (PMSpMs) possess three-degree-of-freedom omnidirectional motion characteristics, and rotor attitude estimation (RAE) is essential for closed-loop control. This article proposes a visual RAE method for spherical motors using a Kalman filter and geometric constraint Transformer (GK-TransT). An RAE system was equipped with a monocular area scan camera with a visual feature component (VFC) mounted on the bottom of the rotor. In the proposed GK-TransT algorithm, the Kalman filter is used to enhance the robustness and accuracy of the TransT tracker. To verify the algorithm, a tracking comparison was conducted among the GK-TransT, original TransT, KCF, and CSRT algorithms. The results indicate that the tracking precisions of the proposed GK-TransT algorithm for the main and auxiliary feature points reach 90.9% and 94.4%, respectively, with an average processing speed of 61.23 FPS and a single-frame latency of 16.33 ms. Considering the tracking precision, real-time performance, and robustness under occlusion and motion blur conditions, the GK-TransT algorithm is more applicable for the RAE of the PMSpM. In addition, an RAE test bench was developed, and the GK-TransT-based method and a micro-electro-mechanical system (MEMS) sensor were compared. The physical ground truth of a hydraulic rotary table was used as the benchmark. The comparison results indicate that the GK-TransT-based method achieves a higher accuracy than the MEMS method. Finally, the practicability of the proposed method is proved. Full article

In early-warning scenarios for thermal runaway in energy storage batteries, carbon monoxide (CO) may interfere with hydrogen detection and reduce the reliability of signal interpretation. To mitigate CO cross-interference under representative mixed-gas conditions and improve sensing stability, a fiber-optic microelectromechanical systems (MEMS) hydrogen sensor based on a Pd–Cu alloy-sensitive layer was developed. The sensor employs a single-cantilever structure and a reflective Fabry–Pérot (F–P) interferometer for optical readout. Comparative experiments were carried out using sensors coated with pure Pd and Pd–Cu-sensitive layers under pure H₂, CO background interference, and temperature-fluctuation conditions. The Pd–Cu sensor exhibited a good linear response over 0–500 ppm H₂, with a sensitivity of 0.0845 nm/ppm. Under a mixed atmosphere of 200 ppm H₂ and 500 ppm CO, the Pd–Cu sensor measured 198 ppm, whereas the pure Pd sensor measured 176 ppm, corresponding to relative errors of approximately 1% and 12%, respectively. In addition, the Pd–Cu sensor showed faster response/recovery behavior and better output stability after temperature compensation. These results indicate that, under the investigated conditions, the selected Pd–Cu-sensitive layer can effectively reduce CO-induced interference and improve the accuracy and stability of fiber-optic MEMS hydrogen sensing, supporting its feasibility for representative early-warning-related monitoring scenarios in energy storage batteries. Full article

Soil moisture monitoring is critical to agricultural production and water resource management, yet existing sensors often exhibit limitations in accuracy, cost-effectiveness, and long-term stability. This study aimed to develop a high-performance capacitive soil moisture sensor to address these issues. We selected eco-friendly lead-free K₂CuBr₃ as the hygroscopic material and optimized the interdigital electrode (IE) structure via theoretical analysis and finite element simulation. The sensors were fabricated using micro-electro-mechanical system (MEMS) technology, and their performance was systematically evaluated with real soil samples. The results demonstrated that K₂CuBr₃ substantially enhanced the sensor sensitivity. The optimal sensor exhibited a measurement error of approximately 5% over the soil moisture range of 0–42.5%, a relative standard deviation (RSD) of less than 2%, and good stability. This low-cost, lead-free MEMS capacitive sensor, based on K₂CuBr₃, offers high accuracy and excellent stability. It resolves key drawbacks of traditional sensors and offers a reliable solution for in situ real-time soil moisture monitoring, with broad prospects in smart agriculture. Full article

Data collected by sensors plays a critical role in system decision-making. Microphone arrays enable distance measurement and fault localization, which is particularly critical in the radiation environments of nuclear facilities. Acoustic localization based on microphone arrays can effectively fulfill this requirement. This study experimentally evaluates the Total Ionizing Dose (TID) effects of ⁶⁰Co γ-ray radiation on commercial MEMS (micro-electro-mechanical systems) silicon microphones. Five identical microphone units were simultaneously irradiated at a dose rate of 0.0342 Gy(Si)/s while continuously monitoring operating current and spectral response. Experimental results show that the commercial MEMS silicon microphones exhibit an average TID failure threshold of 932.6 ± 62.8 Gy(Si), with a 95% confidence interval of [875.5, 989.7] Gy(Si). Three degradation/failure levels are clearly defined: channel degradation, channel failure, and full system failure. Radiation exposure causes a progressive increase in operating current (up to 6.7 times the initial value), severe spectral distortion, and ultimately complete loss of localization function. This indicated that standard commercial MEMS silicon microphones possess a certain degree of tolerance to TID radiation. Subsequently, an annealing test was performed. However, Post-irradiation annealing restored the operating current but not the acoustic performance, indicating irreversible radiation-induced damage. Full article

To address the demand for underwater acoustic detection with hydrostatic pressure resistance, this paper proposes a fiber-optic Fabry–Perot (F-P) underwater acoustic sensor based on micro-electromechanical system (MEMS) technology. According to the F-P interference principle, the diaphragm deforms under acoustic pressure, inducing variations in the F-P cavity length which modulate the interference spectrum and enable the measurement of underwater acoustic signals. A sensing diaphragm with a composite structure consisting of a central boss and a micro-hole array is designed, which improves the optical signal quality while reducing the influence of the pressure difference between the inner and outer surfaces of the diaphragm on sensor operation. MEMS fabrication, computer numerical control (CNC) machining, and laser fusion splicing technologies are employed to achieve batch fabrication of the sensing units and adhesive-free integration of the sensor. Experimental results show that the proposed sensor exhibits a flat frequency response within ±1.5 dB over the range of 1 kHz to 10 kHz, with an average signal-to-noise ratio (SNR) of 86.35 dB. The sensitivity reaches −181.79 dB re 1 rad/μPa at 10 kHz, with a maximum nonlinearity of 0.48% F.S., a repeatability error of 0.15% F.S. and a dynamic range of 100.83 dB. The proposed sensor features miniaturization, high consistency, hydrostatic pressure self-balancing capability, and immunity to electromagnetic interference, providing a solid foundation for hydrostatic-pressure-resistant underwater acoustic measurements in deep-sea environments. Full article

This work presents wirelessly interrogated microelectromechanical system (MEMS) capacitive sensors for continuous intraocular pressure (IOP) monitoring. The sensor uses a passive inductor–capacitor (LC) tank circuit comprising a fixed, on-chip spiral inductor and a pressure-sensitive, variable-gap capacitor with parallel-plate membrane electrodes and side anchors. The membrane is designed with dimensions of 500 µm × 500 µm × 2 µm and a capacitive transducer gap of 2.5 µm. Applied pressure deflects the top membrane, producing a corresponding capacitance variation that changes the frequency and phase response of the LC tank circuit, enabling real-time and continuous IOP monitoring over a target detection range of 0–50 mmHg and beyond. Mutual inductive coupling between the sensor and the external readout coil is investigated as a reliable readout mechanism. Full article

In complex marine environments, achieving low-cost, highly reliable, and continuous navigation is crucial for the intelligent and autonomous operation of unmanned surface vehicles (USVs). Currently, the integrated Global Navigation Satellite System and Strapdown Inertial Navigation System (GNSS/SINS) serves as the primary navigation architecture for USVs. While the cost of high-performance GNSS receivers has steadily decreased, high-precision SINS remains prohibitively expensive. Consequently, micro-electromechanical system (MEMS)-based SINS has emerged as a preferred alternative due to its favorable balance of cost, power consumption, and size. However, significant inertial sensor errors make it difficult to maintain high-precision positioning during GNSS outages. To address this limitation, the single-axis rotational inertial navigation system (SRINS) has been introduced. Nevertheless, constrained by the single-axis mechanical structure and complex sea state disturbances, the system still struggles to effectively modulate random errors and azimuth gyroscope drift, rendering it insufficient for highly demanding navigation tasks. To overcome these bottlenecks, this article systematically reviews four core technologies: (1) Comprehensive denoising and temperature drift compensation techniques for MEMS gyroscopes; (2) rapid moving-base initial alignment models under high sea state disturbances; (3) fast online calibration methods for azimuth gyroscope drift; and (4) adaptive and robust GNSS/SINS integration architectures capable of accommodating high-dynamic conditions and non-Gaussian interference. Finally, this article discusses the engineering conflict between deploying high-precision algorithms and the limited onboard computational capacity of USVs. It concludes by highlighting a highly promising navigation paradigm for future research: the integration of factor graph optimization with physics-informed deep learning. Full article

Acoustic emission (AE) monitoring in metal additive manufacturing (AM) requires compact sensors capable of high-frequency operation, broad bandwidth, and high sensitivity. However, increasing structural stiffness to achieve high resonance frequencies typically reduces electromechanical sensitivity. This work presents a finite element study of a coupled-resonator capacitive micromachined ultrasonic transducer (CMUT) designed to address this trade-off. The proposed architecture integrates three mechanically coupled silicon membranes with a stepped capacitive cavity that increases capacitance while preserving structural stiffness, enabling enhanced sensitivity without compromising high-frequency operation. COMSOL Multiphysics simulations were used to evaluate modal characteristics and frequency response under DC pre-stressed conditions. Modal coupling produced closely spaced resonances that broadened the effective bandwidth, while the stepped cavity significantly increased voltage output through improved electromechanical coupling. Compared to a single-resonator flat-cavity design, the coupled stepped-cavity configuration demonstrated nearly a threefold enhancement in output voltage while maintaining operation near 100 kHz. Additionally, adjusting the central resonator length enabled controlled frequency tuning for scalable array implementation. These results establish a proof of concept for a high-frequency, high-sensitivity micro-electro-mechanical systems (MEMS) CMUT architecture suitable for distributed AE monitoring in advanced manufacturing environments. Full article

A miniaturized (70 × 7.7 cm) multi-parameter synchronous observation system was developed for in situ ocean turbulence measurement, integrating micro-electromechanical system (MEMS)-based two-dimensional (2D) turbulence, pressure, temperature, conductivity, and attitude sensors. Field tests conducted at a depth of 1800 m in the northern South China Sea validated the system’s accuracy through comparisons with standard CTD (Conductivity, Temperature, and Depth) sensors, dual-probe consistency analysis, and Nasmyth spectrum fitting. The system precisely captured thermoclines, internal waves, and turbulent shear fluctuations at a depth of approximately 125 m, revealing enhanced turbulence near the thermocline due to intensified shear effects. With high spatiotemporal synchronization and reliability, the system provides an effective solution for studying multiscale ocean turbulence and associated dynamic processes. Full article