Search Results (1,443)

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

This paper presents a Micro-Electro-Mechanical Systems digital micromirror device (MEMS-DMD)-enabled ghost imaging (GI) framework for high-resolution imaging under scattering conditions. Unlike conventional ghost imaging systems that rely on fixed illumination patterns, the proposed approach exploits the high-speed programmability of a DMD to implement adaptive illumination strategies, enabling dynamic selection of informative patterns during data acquisition. This hardware-enabled pattern selection strategy improves sampling efficiency and reconstruction stability under the modeled fog conditions considered here. A hybrid convolutional neural network–generative adversarial network (CNN–GAN) model is employed as an inversion tool to reconstruct high-quality images from compressed bucket measurements. The proposed system achieves substantial improvements in reconstruction quality, with 23–40% gains in PSNR and 18–26% in SSIM compared to traditional ghost imaging methods, while reducing the number of required measurements by up to 60%. Additional performance gains are achieved through adaptive pattern selection enabled by the MEMS-DMD. The results demonstrate that integrating programmable MEMS hardware with learning-based reconstruction provides an effective solution for imaging under scattering conditions, with potential applications in remote sensing, environmental monitoring, and surveillance. Full article

Invasive fungal respiratory infections (IFRIs) remain a major cause of morbidity and mortality among immunocompromised patients, yet diagnosis continues to be hindered by nonspecific clinical features, limited sample accessibility, and the poor sensitivity or specificity of conventional tests. Microfluidic and microelectromechanical systems (MEMS)-based biosensing platforms have emerged as promising alternatives, enabling rapid, minimally invasive, and highly specific detection of fungal pathogens and host responses. Microfluidic nucleic acid and antigen assays allow on-chip amplification and immunodetection with reduced sample volumes and turnaround times, while CRISPR-enhanced systems further improve analytical sensitivity. Parallel advances in host response profiling—including transcriptomic, proteomic, and cytokine-based signatures—have demonstrated feasibility for integration into lab-on-a-chip platforms. MEMS-based technologies extend this potential by facilitating real-time analysis of exhaled volatile organic compounds, mechanical biosensing of fungal DNA and antigens, and in situ monitoring of device-associated biofilms. Translational studies highlight potential applications across intensive care, hematology–oncology, and transplant settings, as well as in outpatient monitoring of high-risk populations. However, several challenges remain, including limited multicenter validation, matrix-related biofouling effects, and a lack of standardization in fungal biomarker panels. Future directions include AI-driven interpretation of multianalyte data, multiplexed integration of host and pathogen markers, and development of fully cartridge-based systems for near-patient deployment. Collectively, these innovations may shift fungal diagnostics toward earlier, more precise, and patient-tailored interventions, improving outcomes in vulnerable populations. 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

Biosensors have a recognition element that detects a bioanalyte as well as a transducer that transfers the measured physicochemical properties into an electric signal, which is amplified, processed, and depicted on a user interface and usually stored in a data storage system. Such biosensors can be used in a broad range of applications, from personalized medicine to drug discovery, and from food safety to plant disease diagnosis. Portable biosensors are often based on microfluidic systems or micro-electromechanical systems (MEMS), measuring physical or chemical parameters. In spite of their importance for diverse applications, there are still several limits regarding the portability of biosensors, which is often necessary. Besides the required miniaturization of the components and the limited lifetime of some biological reagents, sample preparation and handling can be problematic. This review gives an overview of recent biosensor research, concentrating on optical measurements, and shows the possibilities and limits of the biosensors developed during the last few years. Full article

Subgrade collapse threatens coastal infrastructure under harsh environments, where deterioration accelerates deformation and failure risk. Accurate prediction is essential, yet traditional monitoring suffers from low informatization and delayed response. Thus, this paper presents a Micro-Electro-Mechanical Systems (MEMS)-based intelligent perception-driven method for subgrade collapse deformation prediction to improve the level of intelligence in subgrade collapse monitoring and prediction. Firstly, a hierarchical prediction framework is established based on subgrade deformation monitoring scenarios, consisting of an intelligent perception layer, a collapse deformation prediction layer, and a functional application layer, with the functions of each layer systematically defined. Secondly, two key technologies involved in the proposed framework, including MEMS data cleaning and time-series feature extraction, as well as the deformation prediction model, are identified and corresponding solutions are developed. Finally, a linear sliding rail experiment and a subgrade collapse model test are conducted to validate the feasibility and effectiveness of the proposed method. The results indicated that effective MEMS data cleaning was achieved through Leave-One-Out Encoding (LOOE) encoding, missing value imputation, and normalization. Accurate time-series feature representation was obtained by combining seismic parameter extraction with a sliding window strategy. The improved the improved Long Short-Term Memory–Back Propagation (LSTM-BP) model model achieved accurate prediction of collapse displacement, with an accuracy of 95.56%. The proposed MEMS-based intelligent perception method accurately captured the evolution trend and spatial heterogeneity of subgrade collapse deformation, and the results can be used to support and guide early warning of subgrade collapse, providing technical support for the safety and durability management of coastal and offshore infrastructure under harsh environmental conditions. Full article

As a core component in electronic circuits, the size of inductors is crucial for the thin-film integration and miniaturization of circuits. Although various miniaturized inductors have been fabricated by using integrated circuit technology, their low current-carrying capacity and small inductance values cannot meet current application requirements. Therefore, this paper designs an inductor chip based on a double-layer parallel (DLP) array microcoil structure. Experimental verification demonstrates that the developed DLP inductor exhibits a far superior rated energy storage capability per unit area compared to other single-layer inductors, along with excellent thermal performance. Meanwhile, the 4 × 3 DLP array can withstand a maximum DC current of 4.25 A. This structural innovation provides a meaningful thermal–electromagnetic co-design reference solution for highly reliable integrated power modules. 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

Capacitive micromechanical ultrasonic transducers (CMUTs), as microelectromechanical systems (MEMS) devices, have broad application prospects in ultrasonic imaging and sensing. This study investigates the influence of surface acoustic waves (SAWs) using periodically arranged CMUTs as the fundamental unit cells. We first utilize finite element analysis (FEA) to calculate and analyze the band structure and bandgap characteristics of phononic crystals under infinite periodic conditions. Subsequently, for finite periodic structures in practical applications, acoustic transmission spectra were further simulated using FEA to verify the bandgap characteristics of the structure for SAWs. Accordingly, this paper leverages a deep learning framework based on a multilayer perceptron (MLP) architecture to achieve the inverse design and optimization of CMUT geometric parameters, tailored to specific target bandgap requirements. The results demonstrate that this approach can efficiently and accurately determine the optimal structural configurations, offering a robust and novel technical paradigm for the precise control of SAWs using CMUT-based periodic arrays. Full article

Lead zirconate titanate (PZT) is a widely used material for applications in microsensors, actuators, and transducers. Due to its high piezoelectric coefficient, large dielectric constant, and strong polarization capability near the morphotropic phase boundary (Zr/Ti ≈ 52/48), it is considered one of the most attractive materials for micro-electromechanical systems (MEMS). These advantageous material properties strongly depend on the PZT layer’s microstructure and crystallinity, which are primarily determined by the choice of seed layer, deposition conditions, and the post-deposition annealing treatment that promotes the formation of the PZT’s perovskite phase. In this contribution, sputter-deposited PZT thin films were crystallized by conventional furnace annealing (CFA) to evaluate the effect of heating/cooling rates (1 °C·min⁻¹–7 °C·min⁻¹) within a temperature range of 450 °C to 700 °C on structural, electrical, and ferroelectric properties, with consideration of the seed layer preparation. We characterized the materials’ properties by X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), and measurements of the ferroelectric hysteresis, capacitance, and leakage current. All samples annealed at temperatures of at least 500 °C fully crystallized into the perovskite phase, independently of the heating/cooling rate. The best ferroelectric performance was achieved at 550 °C with a 1 °C·min⁻¹ heating/cooling rate, yielding a saturation polarization of 82.8 µC·cm⁻² and a remnant polarization of 36.9 µC·cm⁻² under a maximum applied field of 300 kV·cm⁻¹. 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