Search Results (1,573)

Surface collisions at Pyrex walls limit the spin coherence in nuclear magnetic resonance gyroscopes (NMRG) vapor cells, while the cavity–stem junction introduces geometry dependent exchange that perturbs the transverse spin relaxation time $T_2$ of ¹²⁹Xe atoms. We combine $T_2$ measurements with Monte Carlo simulations of confined diffusion and surface collisions to decompose the relaxation of Xe atoms and derive a cavity–stem geometry correction for wall relaxation. A structural coupling factor (SCF) is introduced to compress stem length and aperture diameter into a dimensionless metric for diffusion-limited mixing, enabling prediction of the transverse relaxation rate versus geometry. Across eight simulated configurations, the model yields $R^2=0.982$ and agrees with experiments within 7–$9\%$, comparable to the measurement uncertainty ($\pm 0.015{\mathrm{s}}^{-1}$). Using the validated framework, geometry optimization reduces the relaxation rate from $0.225$ to $0.131{\mathrm{s}}^{-1}$ (a $41.8\%$ improvement). This Pyrex surface-collisional analysis provides an in-situ, $T_2$-based route to compare effective surface depolarization across fabrication and surface-treatment protocols while accounting for cavity–stem coupling. Full article

Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems. Full article

Reliable modeling of pressure-broadened spectra is essential for maintaining physical consistency in alkali vapor-cell diagnostics. In this work, we investigate the low-power absorption spectra of isotopically enriched ⁸⁷Rb vapor cells at the D₁ and D₂ transitions, systematically comparing three fitting algorithms: single Lorentzian, hyperfine-resolved Voigt, and the doublet Lorentzian approximation. Experiments were performed across optical intensities from 40 nW to 10 mW, buffer-gas pressures from 200 Torr to 520 Torr, and temperatures between 370 K and 390 K. It is shown that when the pressure broadening dominates over Doppler broadening and the optical intensity remains below the saturation regime, the reduced doublet Lorentzian model achieves a fitting accuracy of R² > 0.996. Under the pressure conditions of 350 Torr, the fitting error for both the doublet and Voigt approximations remains below 0.3% for the D₂ line and below 0.1% for the D₁ line. At the pressures of 520 Torr and under elevated optical intensities, the spectrum evolves toward a single-peaked profile, rendering a single Lorentzian model sufficient. The quantitative applicability boundary of the doublet approximation in ⁸⁷Rb vapor cells is established, defining the operational regime where hyperfine-resolved modeling can be reduced under collision-dominated conditions in NMRG systems. Full article

To reduce random errors effectively and improve measurement precision in MEMS gyroscopes, we establish a dual-layer noise suppression method named EMD-SSA-VMD. The algorithm is grounded in empirical mode decomposition (EMD) and variational mode decomposition (VMD), incorporating the sparrow search algorithm (SSA) and entropy theory. The process starts by breaking down the signal into a series of intrinsic mode functions (IMFs) and a residual via EMD. By calculating the power spectral entropy (PSE) of IMFs, we can sort the signal components into three categories: noise signals, mixed signals, and effective signals. The mixed signals then undergo VMD processing, where SSA optimizes the key decomposition parameters. The sample entropy (SE) of the IMFs from VMD is computed to distinguish between actual signal components and noise. Finally, we combine all valuable signals to reconstruct the denoising signal. MATLAB(R2024b) simulation results show that this algorithm improves both the Signal-to-Noise Ratio (SNR) and the Root Mean Square Error (RMSE), demonstrating a more efficient removal of noise. Experiments on actual gyroscope data confirm these improvements, yielding higher SNR and a waveform that closely matches the original signal. This proves the algorithm’s practical value in engineering applications. Full article

This work presents a high-performance piezoelectric MEMS yaw gyroscope fabricated on a single-crystal silicon platform, which achieves a quality factor of 75 k—the highest reported to date among silicon-based piezoelectric gyroscopes. The device employs a wide annular resonator that operates at 132 kHz in the in-plane wineglass mode. To maximize transduction efficiency, we develop an analytical model that relates output charge to the area-integrated in-plane stress under modal deformation, and we use this model to guide parametric optimization of the annular width. The resulting geometry simultaneously enhances the mechanical quality factor and the piezoelectric coupling. A back-etching fabrication process is used to eliminate front-side release holes, thereby preserving structural continuity and suppressing thermoelastic damping. In open-loop rate mode operation with a native frequency split of 28 Hz, the gyroscope demonstrates an angle random walk of 0.34°/√h and a bias instability of 8.19°/h. These performance metrics are comparable to those of state-of-the-art lead zirconate titanate (PZT)-based annular gyroscopes, while the use of lead-free aluminum nitride as the transduction material ensures compliance with RoHS environmental regulations. Full article

Falls represent a significant global public health issue, particularly among adults over the age of 60. This comprehensive review aims to provide an in-depth examination of current fall detection and prevention technologies. The study categorizes fall detection methods into pre-fall prediction and post-fall detection, using both wearable and unobtrusive sensors. Wearable technologies, such as accelerometers, gyroscopes, and electromyography (EMG) sensors, are explored for their efficacy in real-time fall prediction and detection. Unobtrusive methods, including camera-based systems, LiDAR, radar, ultrasonic sensors, and depth sensors, are evaluated for their ability to monitor falls without intruding on users’ daily activities. The integration of these technologies into healthcare settings is also discussed, with an emphasis on the importance of immediate response to fall events. By analyzing the operational principles, technological advancements, and practical applications of these systems, promising directions for future research and innovation in fall detection and prevention are identified. The findings highlight the need for multifaceted approaches combining various sensor technologies to enhance fall detection accuracy and response times, ultimately improving patient safety and quality of life. Full article

To address the issue of low accuracy and stability in the gyroscope components of the micro-inertial-measurement-unit (MIMU) core units, which limits their application in high-precision scenarios, this paper designs a real-time data acquisition system for array MIMU based on FPGA and ARM. This system establishes a complete data chain encompassing raw data acquisition, real-time processing, multi-source information fusion, data storage, and communication with a host computer. It has been successfully applied to a 100-m pipeline position coordinate measurement scenario. The paper begins by discussing the overall system design, including both hardware circuit and software code development. Attitude update algorithms and measurement accuracy evaluation metrics are also introduced. System functionality is validated through static tests and practical pipeline measurements. Experimental results demonstrate that the system improves the accuracy of a single micro-electro-mechanical system (MEMS) gyroscope by a factor of 7.4 to 7.7. It also enables precise calculation of the pipeline position coordinates over the 100 m distance, achieving a horizontal positioning error of less than 0.0774 m and an elevation positioning error of less than 0.0351 m. These results fully confirm the significant effectiveness of the array design in mitigating gyroscope random errors, providing a reliable technical solution for pipeline measurement. Full article

The experimentation of state observers for the reconstruction of the angular velocity of the links of a flexible industrial manipulator is investigated in this paper, in the presence of unmodeled or uncertain parts. Considering only one axis moving at a time, a study is done to understand how faithfully the dynamics of the machine can be reconstructed using simple single axis models, extending them to take into account the multi-variable dynamics of the system and trying to reconstruct the action of non-linear friction as well. The goal is to show how a good estimate of the interactions between the links can be obtained, with the final aim of including it into a control architecture. Various models of different complexities have been tested with both the asymptotic Luenberger observer and the steady-state Kalman filter. The presence of friction is taken into account by a feedforward compensation or by the addition of a disturbance observer synthesized as a pole placement regulator. First, the observers are tested in simulation, then on real data from a Comau Racer 7-1.0 robot. To evaluate the quality of the reconstruction, a virtual sensor obtained from the identification of the manipulator is used, and then a final test is carried out using a real Xsens gyroscope. An accurate analysis of the achieved results is provided, devoting a particular attention to the trade-off between model complexity, estimate accuracy and computational burden in view of a possible future insertion into the control architecture of an industrial robot. Full article

Condition monitoring of railway track surfaces is crucial for ensuring the safety, operational efficiency, and effective maintenance of railway systems. This work presents a data-driven modelling and an experimental methodology for identifying and classifying contaminants on railway tracks using vibration analysis and artificial intelligence techniques. In this study, the railway dynamics were physically simulated using a 1:20 scaled test rig, where the rails were treated with various contaminants (oil, water, and sand), and the resulting vehicle vibrations were recorded by on-board accelerometers and gyroscopes. To construct the predictive model, a hybrid architecture was designed integrating Short-Time Fourier Transform (STFT) for time-frequency feature extraction and a multi-channel Convolutional Neural Network (CNN) for pattern recognition. Initial results indicate that accelerometer data, particularly from longitudinal and lateral vibrations, are more effective than gyroscope data for classifying certain contaminants. To enhance classification robustness, this work introduces a multi-channel CNN that simultaneously processes the most informative signals, leading to a significant improvement in detection accuracy across all tested contaminants. This study validates the effectiveness of the proposed methodology as a robust and reliable solution for contaminant detection, while also confirming the utility of the scaled testbed as a valuable platform for future research in railway dynamics. Full article

The high-speed rotor electric drive system in control moment gyroscopes (CMGs) is essential for precise spacecraft attitude control. Rigorous testing of this system is critical for ensuring reliability and longevity throughout orbital missions. However, conventional test bench methods exhibit numerous limitations. In contrast, the electric motor emulator (EME) provides a flexible and efficient alternative for power-level testing of the CMG high-speed rotor brushless DC motor drive system. To address the challenges of trapezoidal back-electromotive force (back-EMF) emulation and insufficient square-wave current tracking accuracy in existing brushless DC motor emulator (BLDCME) implementations, this paper proposes a sliding time window-based dynamic current compensation control (STW-DCCC) strategy for the CMG high-speed rotor BLDCME. First, based on the VSC single-conversion-circuit topology, the BLDCME basic control strategy based on the motor port current and the current change rate is implemented to achieve a tracking control of the square-wave current and emulation of the trapezoidal back-EMF. Building upon this foundation, a sliding time window-based RMS current compensation optimization strategy for the BLDCME is designed to provide dynamic compensation for system disturbances and thereby enhance the tracking accuracy of the square-wave current. Furthermore, by incorporating fault information, the proposed STW-DCCC strategy can also emulate the resistance unbalance fault of the brushless DC motor. Finally, through experiments, a comparative analysis is conducted between the basic control strategy and the proposed STW-DCCC strategy under normal operating conditions, parameter mismatch operating conditions, and resistance unbalance fault conditions, thereby validating the effectiveness of the proposed method. Full article

Addressing the scientific problem that the profile modification design of tapered roller bearings primarily focuses on contact stress and fatigue life while neglecting its impact on wear evolution, this paper, based on Hertzian contact theory and the Archard wear theory, and considering centrifugal force, gyroscopic effect, and the complex contact state between rollers and raceways, constructed a comprehensive analysis framework integrating a quasi-static model for profiled rollers and a wear depth calculation model. This framework is novel in that it systematically couples roller profile modification parameters with raceway wear evolution under both pure axial and combined radial–axial loads. The validity and effectiveness of the proposed model were verified by comparing the results of the quasi-static model with load distribution data from existing literature and through measurements conducted on a specially designed bearing wear test platform. The main findings are as follows: (1) When the logarithmic modification parameter f₁ increases from 0.7 μm to 3.6 μm, the maximum wear depth of the inner raceway increases by 133% under pure axial load and 144% under combined load, while that of the outer raceway increases by 142% under pure axial load and expands from 0.1–0.2 μm to 0.23–0.52 μm under combined load. (2) Combined load induces significant asymmetric wear on the outer raceway, and the difference between the two wear peaks increases from 0.13 μm to 0.35 μm as f₁ rises from 0.7 μm to 3.6 μm. (3) The wear peak shifts toward the midpoint of the roller generatrix with increasing modification amount. These results provide important guidance for the wear-oriented optimization design of tapered roller bearings. Full article

In the rubidium-87 atomic fountain clock, the laser collimating and beam-expanding telescope plays a key role in atomic cooling and manipulation, as well as in realizing the cold-atom fountain. To address the bulkiness of conventional laser collimating and beam-expanding telescopes, which limits system integration and miniaturization, we design and implement a compact laser collimating and beam-expanding telescope. The design employs a Galilean beam-expanding optical path to shorten the optical path length. Combined with optical modeling and optimization, this approach reduces the mechanical length of the telescope by approximately 50%. We present the mechanical structure of a five-degree-of-freedom (5-DOF) adjustment mechanism for the light source and the associated optical elements and specify the corresponding tolerance ranges to ensure their precise alignment and mounting. Based on this 5-DOF adjustment mechanism, we further propose a method for tuning the output beam characteristics, enabling precise and reproducible control of the emitted beam. The experimental results demonstrate that, after adjustment, the divergence angle of the output beam is better than 0.25 mrad, the coaxiality is better than 0.3 mrad, the centroid offset relative to the mechanical axis is less than 0.1 mm, and the output beam diameter is approximately 35 mm. Furthermore, long-term monitoring over 45 days verified the system’s robustness, maintaining fractional power fluctuations within ±1.2% without manual realignment. Compared with the original telescope, all of these beam characteristics are significantly improved. The proposed telescope therefore has broad application prospects in integrated atomic fountain clocks, atomic gravimeters, and cold-atom interferometric gyroscopes. Full article

In white-light-driven resonant fiber optic gyroscopes (W-RFOG), modulation and demodulation techniques are essential for signal detection. Currently, in phase modulation and synchronous demodulation, the selection of relevant modulation waveforms and parameters is mostly based on experience, lacking systematic theoretical guidance. This study systematically evaluates the impact of modulation waveforms and parameters on gyroscope performance. We compare sine and triangular waveforms, analyzing their effects on the slope of the demodulation curve and overall performance across varying modulation frequencies. Quantitative results show that sine wave modulation yields a 185% higher sensitivity coefficient near the zero-crossing point than triangular wave modulation. Furthermore, W-RFOG systems using sine wave modulation achieve a 52.6% reduction in angular random walk (ARW) and a 19.4% reduction in bias instability (BI). These findings confirm the superior effectiveness of sine wave modulation in enhancing detection sensitivity coefficient and system accuracy in W-RFOG, which holds significance for modulation waveform selection and parameter optimization in W-RFOG systems. Full article

Owing to the low cost, small size, and convenience for installation, 2D LiDAR has been widely used in mobile robots for simultaneous positioning and mapping (SLAM). However, traditional 2D LiDAR SLAM methods have low robustness and accuracy in LiDAR-degenerated environments. To improve the robustness of the SLAM method in such environments, an innovative SLAM method is developed, which mainly includes two parts, i.e., the front-end positioning and the back-end optimization. Specifically, in the front-end part, the AKF (adaptive Kalman filter) method is applied to estimate the pose of the mobile robot, zero bias of acceleration and gyroscope, lever arm length, and the mounting angle. The adaptive factor of the AKF can dynamically adjust the variance of the process and measurement noises based on the residual. In the back-end part, a particle filter (PF) is employed to optimize the pose estimation and build the map, where the pose domain constraint from the output of the front-end is introduced in the PF to avoid mismatch and enhance positioning accuracy. To verify the performance of the method, a series of experiments is carried out in four typical environments. The experimental results show that the positioning precision has been improved by about 61.3–97.9%, 35.7–99.0%, and 43.8–93.0% compared to the Karto SLAM, Hector SLAM, and Cartographer, respectively. Full article

Hollow-core microstructured optical fibres exhibit excellent properties, such as a low loss, tuneable high birefringence, and low nonlinearity, finding extensive applications across communications, industry, agriculture, medicine, military, and sensing technologies. This paper designs two types of asymmetric hollow-core photonic bandgap fibres featuring a high birefringence and low confinement loss. Both feature a cladding structure of rounded hexagonal honeycomb lattice, while the core structures comprise elliptical hollow cores and rounded rhombic hollow cores, respectively. By adjusting the radius of the cladding air holes and the core structure parameters, this study aims to maximise the birefringence coefficient and minimise the confinement loss. The control variable method is employed to optimise the parameters of two fibres. The simulation results indicate that, at a wavelength of 1.55 μm, the birefringence coefficient of the rhombic core, after parameter optimisation, reaches 1.4 × 10⁻⁴, with the confinement loss achieving 4.4 × 10⁻³ dB/km. Its bending loss remains at the order of 10⁻³ dB/km, indicating that this fibre maintains an exceptionally high transmission efficiency even when wound with a small curvature radius (such as within the resonant cavity of a compact fibre optic gyroscope). The elliptical core’s birefringence coefficient also reaches 3 × 10⁻⁴, with the confinement loss achieving 1.9 × 10⁻¹ dB/km. Specifically, this paper employs bismuth tellurite glass as the substrate material to simulate the performance of elliptical cores. Within a specific refractive index range, the elliptical-core fibre with a bismuth tellurite glass substrate exhibits a confinement loss comparable to quartz glass, whilst its birefringence coefficient reaches as high as 5.8 × 10⁻⁴. Therefore, the hollow-core photonic bandgap fibres designed in this thesis provide valuable reference and innovative significance, both in terms of the performance of two asymmetric core structures and in the exploration of polarisation-maintaining hollow-core photonic bandgap fibres on novel material substrates. Full article