Sensors最新文献

Currently, most wave observation equipment is used for fixed-point measurements, and there is a relative scarcity of ship-borne real-time wave measurement devices, which limits comprehensive and three-dimensional monitoring of wave characteristics. This paper introduces the Wave Acquisition Stereo System (WASS) and describes the design and construction of a ship-borne stereoscopic vision experimental apparatus. Sea trials were conducted to evaluate the system's ship-borne wave-measurement performance and to quantify the effect of deployment parameters on accuracy. The results indicate that the device reliably retrieves wave parameters; compared with concurrent buoy observations, the error in significant wave height did not exceed 0.14 m. Research confirms that deployment parameters have a significant impact on measurement outcomes: sampling frequency directly affects the accuracy of wave-parameter estimation; a higher sampling rate (10 Hz) improves the reliability of the calculated results. The baseline-to-height ratio has an optimal range (0.1-0.3), and values outside this interval reduce measurement accuracy. Under a fixed geometric configuration, the observation field exhibits a band-shaped low-error zone aligned with the baseline direction.

This work proposes new architecture, supported by analytical modelling and computer-aided design (CAD) simulations, for a highly sensitive monolayer graphene-gated AlGaN/GaN HEMT terahertz (THz) detector operating at room temperature (RT). The monolayer graphene gate acts as a surface plasmon absorber for the incident THz radiation. The carrier density perturbation caused by incident THz energy on the monolayer graphene surface is then capacitively coupled to the two-dimensional electron gas (2DEG) channel of the HEMT structure underneath. The channel is partially depleted for increased mobility and nonlinearity with potential asymmetry across the channel for consistent photogeneration. The Drude absorption of THz radiation initiates intraband transitions in monolayer graphene, thereby reducing phonon losses. These reduced phonon losses enable RT THz detection. Based on our simulations, the proposed detector architecture can generate a responsivity of 2.12 × 10⁶ V/W at 1 THz with a broadband bandwidth of 2 THz.

Accurate identification of road roughness is pivotal for optimizing vehicle suspension control and enhancing passenger comfort. However, existing data-driven methods often struggle to balance classification accuracy with the strict computational constraints of real-time onboard monitoring. To address this challenge, this paper proposes a lightweight and robust road roughness classification framework utilizing a single sprung mass accelerometer. First, to overcome the scarcity of labeled real-world data and the limitations of linear models, a high-fidelity co-simulation platform combining CarSim and Simulink is established. This platform generates physically consistent vibration datasets covering ISO A-F roughness levels, effectively capturing nonlinear suspension dynamics. Second, we introduce DB-MLP, a novel Dual-Branch Multi-Layer Perceptron architecture. In contrast to computationally intensive Transformer or RNN-based models, DB-MLP employs a dual-branch strategy with multi-resolution temporal projection to efficiently capture multi-scale dependencies, and integrates dual-domain (time and position-wise) feature transformation blocks for robust feature extraction. Experimental results demonstrate that DB-MLP achieves a superior accuracy of 98.5% with only 0.58 million parameters. Compared to leading baselines such as TimeMixer and InceptionTime, our model reduces inference latency by approximately 20 times (0.007 ms/sample) while maintaining competitive performance on the specific road classification task. This study provides a cost-effective, high-precision solution suitable for real-time deployment on embedded vehicle systems.

Near-field scanning microwave microscopy (NSMM) offers the ability to probe local electromagnetic properties beyond the classical Abbe diffraction limit, but achieving high resolution over practical scan areas remains challenging. In this work, we introduce a novel three-dimensional (3D) NSMM probe consisting of a split-ring resonator (SRR) coupled to a microstrip line and loaded with vertically extended metallic bars. The 3D loading enhances electric-field localization in the sensing region by introducing field singularities. Full-wave numerical simulations are used to extract the field-spread function (FSF) of the probe and to quantify how probe geometry, stand-off distance, and bar dimensions control the FSF and its spatial-frequency (k-space) content. An imaging model is then developed in which the NSMM image is represented as a convolution between the object and FSF in one and two dimensions. This framework demonstrates that progressively localized FSFs, obtained through 3D loading and resonator miniaturization, systematically improve image fidelity and preserve higher spatial frequencies. The probe is fabricated using printed circuit board technology (PCB) with vertically attached metallic bars, and its performance is validated by imaging a dielectric slab containing a cylindrical air-filled void. The measured line profiles and two-dimensional images are in good agreement in general characteristics with the convolution-based model, confirming that the proposed 3D SRR-based probe operates as a spatial filter whose engineered near-field distribution governs the achievable resolution in NSMM imaging.

Understanding how the brain adapts to repeated food-related cues provides insight into attentional and motivational mechanisms that influence eating behaviour. Previous studies using event-related potentials (ERPs) have shown that food cues, particularly high-calorie stimuli, elicit sustained neural responses with repeated exposure. The present study extends this line of inquiry by examining the oscillatory dynamics of within-session habituation using time-frequency analysis of electroencephalographic (EEG) data from 24 healthy adult participants. Repeated presentations of the same high-calorie, low-calorie, and non-food images were shown, and changes in power across the delta, theta, alpha, beta, and gamma bands were analysed using cluster-based permutation testing. The results revealed a significant habituation effect for the non-food image within the theta band at frontal scalp electrode clusters between 110-330 ms, characterised by a progressive reduction in power over time. In contrast, both high and low-calorie food cues maintained more stable oscillatory activity, indicating sustained attentional engagement. Participant-level analyses further suggested that changes in attentional engagement followed a graded pattern rather than clear categorical differences across stimulus types. These findings suggest that neural habituation is modulated by stimulus salience, with high-calorie food images resisting adaptation through persistent theta-band synchronisation at frontal scalp electrodes. Integrating these oscillatory results with prior time-domain evidence highlights a multi-stage attentional process: an early sensory filtering phase reflected in parietal ERPs and a sustained regulatory phase indexed by theta-band activity recorded at frontal scalp electrodes. This study provides novel evidence that time-frequency analysis captures complementary aspects of attentional adaptation that are not visible in traditional ERP measures, offering a richer understanding of how the brain maintains attention to appetitive visual stimuli.

This study investigates the link between changes in physical activity (PA) measured by wearable accelerometers and the worsening of knee osteoarthritis (KOA) symptoms over two years. Using data from 782 participants in the Osteoarthritis Initiative accelerometer sub-study, PA was tracked with hip-worn ActiGraphs. Participants were classified as "worsening" if their Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score increased by >10 points and as "stable" otherwise. PA was categorized into daily counts and minutes spent in various intensity levels, and analyzed in 3 h intervals across the day. Of the participants, 123 (15.7%) experienced worsening symptoms. At baseline, both groups had similar characteristics aside from slower sit-to-stand times in the worsening group. Over two years, the worsening group had a greater decline in total daily activity counts (-18% vs. -10%) and more significant reductions during late afternoon and evening (15:00-21:00; -21% vs. -6%). This group also showed a notable decrease in gait speed, longer sit-to-stand times, and a trend towards greater medial joint space narrowing. These findings suggest that larger declines in PA, especially in activities in the late afternoon and evening, are associated with worsening KOA symptoms, although causality cannot be established.

In contemporary educational spaces, circulation spaces such as corridors and stairwells are central to students' daily experience, yet their capacity to serve as therapeutic environments remains underexplored. This study quantitatively evaluated the physiological and neurocognitive impacts of Biophilic Design (BD) in these circulation spaces. Thirty university students experienced immersive virtual scenarios of corridors and stairwells that integrated four BD elements-weather & view, plants & landscape, material & texture, and forms & shapes-while prefrontal cortex (PFC) activity and stress responses were simultaneously captured using functional Near-Infrared Spectroscopy (fNIRS) and Galvanic Skin Response (GSR). Results showed that BD conditions produced significantly greater stress reduction, reflected in lower GSR, compared with non-BD conditions. fNIRS analyses further indicated enhanced PFC activation, with spatially differentiated patterns that varied by circulation space type and by specific BD elements. Collectively, these findings offer empirical neurophysiological evidence that applying BD to educational circulation spaces can mitigate stress and foster psychological stability, thereby providing a robust basis for evidence-based strategies to create healthier, cognitively supportive learning environments.

Pansharpening is a fundamental image fusion technique used to enhance the spatial resolution of remote sensing imagery; however, it inevitably introduces spectral distortions that compromise the reliability of downstream analyses. Existing no-reference (NR) quality assessment methods often fail to exclusively isolate these spectral errors from spatial artifacts or lack sensitivity to specific radiometric inconsistencies. To address this gap, this paper proposes a novel No-Reference Multivariate Gaussian-based Spectral Distortion Index (MVG-SDI) specifically designed for pansharpened images. The methodology extracts a hybrid feature set, combining First Digit Distribution (FDD) features derived from Benford's Law in the hyperspherical color space (HCS) and Color Moment (CM) features. These features are then used to fit Multivariate Gaussian (MVG) models to both the original multispectral and fused images, with spectral distortion quantified via the Mahalanobis distance between their statistical parameters. Experiments on the NBU dataset showed that the MVG-SDI correlates more strongly with standard full-reference benchmarks (such as SAM and CC) than existing NR methods like QNR. Tests with simulated distortions confirmed that the proposed index remains stable and accurate even when facing specific spectral degradations like hue shifts or saturation changes.

Precise foot contact detection (FCD) is essential for accurate biomechanical analysis in sport performance, injury prevention, and rehabilitation. This study developed and validated an inertial measurement units (IMUs)-based algorithm for FCD during sports movements. Thirty-four healthy athletes (22.8 ± 4.1 years old) performed 90° changes of direction and sprints with deceleration. Data were collected via a force platform (AMTI, 1000 Hz) and a full-body IMU suit (MTw Awinda, Movella, 60 Hz). Two IMU-based algorithms relying on pelvis vertical velocity (PVV) and resultant foot acceleration (RFA), respectively, were tested to detect initial contact (IC) and toe-off (TO). Force platform data served as the gold standard for comparison. Agreement was quantified through median offset and interquartile range (IQR); the influence of task, sex, leg, speed, and acceleration was investigated. The PVV algorithm showed higher offset than RFA for IC detection (16.7 ms vs. 10.2 ms) with comparable IQR and a substantially higher offset for TO (102.8 ms vs. 20.4 ms). Minimal influence of co-factors emerged (variance < 10%). Results were sensibly improved by combining PVV and RFA, for both IC (5.6 [70.4] ms) and TO (20.4 [78.7] ms). This algorithm offers a robust, portable alternative to force platforms, enabling accurate footstep detection and analysis of complex, sports movements in real-world environments, enhancing the ecological validity of sport assessments.

In the development of multi-vehicle cooperative hardware-in-the-loop (HIL) simulation platforms based on machine vision, accurate vehicle pose estimation is crucial for achieving efficient cooperative control. However, monocular vision systems inevitably suffer from limited fields of view and insufficient image resolution during target detection, making it difficult to meet the requirements of large-scale, multi-target real-time perception. To address these challenges, this paper proposes an engineering-oriented multi-camera cooperative vision detection method, designed to maximize processing efficiency and real-time performance while maintaining detection accuracy. The proposed approach first projects the imaging results from multiple cameras onto a unified physical plane. By precomputing and caching the image stitching parameters, the method enables fast and parallelized image mosaicking. Experimental results demonstrate that, under typical vehicle speeds and driving angles, the stitched images achieve a 93.41% identification code recognition rate and a 99.08% recognition accuracy. Moreover, with high-resolution image (1440 × 960) inputs, the system can stably output 30 frames per second of stitched image streams, fully satisfying the dual requirements of detection precision and real-time processing for engineering applications.