Search Results (315)

The electrical resistivity and acoustic properties of marine sediments are essential for understanding their physical and mechanical behavior. Over recent decades, significant advancements have been made in both in situ and laboratory measurement techniques, alongside theoretical models, to establish correlations between these geophysical parameters and sediment properties such as porosity, saturation, and consolidation degree. However, a comprehensive comparison of the advantages, limitations, and applicability of different measurement methods remains underexplored, particularly in complex scenarios such as gas hydrate-bearing sediments. This review provides an in-depth synthesis of recent developments in in situ and laboratory testing technologies for assessing the resistivity and acoustic characteristics of marine sediments. Special emphasis is placed on the latest advances in acoustic measurements during gas hydrate formation and decomposition. The review highlights key challenges, including (1) limited vertical resolution in in situ resistivity measurements due to probe geometry; (2) errors arising from electrode polarization and poor soil–electrode contact; and (3) discrepancies in theoretical models linking geophysical parameters to sediment properties. To address these challenges, future research directions are proposed, focusing on optimizing electrode array designs for high-resolution resistivity measurements and developing non-destructive acoustic techniques for deep-sea sediments. This work offers a critical reference for marine geophysics and offshore engineering researchers, aiding the selection and development of testing technologies for effective marine sediment characterization. Full article

A new type of transportation vehicle, the maglev car, is gaining attention in the automotive and maglev industries due to its potential to meet personalized urban mobility and future travel needs. To optimize the chassis layout of maglev cars, this paper proposes a compact powertrain integrating electrodynamic suspension with in-wheel motor technology, in which a permanent magnet electrodynamic in-wheel motor (PMEIM) enables integrated propulsion and levitation. First, the PMEIM external magnetic field distribution is characterized by analytical and finite element (FEM) approaches, revealing the magnetic field distortion of the contactless powertrain. Subsequently, the steady-state electromagnetic force is modeled and the operating states of the PMEIM powertrain are calculated and determined. Next, the PMEIM electromagnetic design is conducted, and its electromagnetic structure rationality is verified through magnetic circuit and parametric analysis. Finally, an equivalent prototype is constructed, and the non-contact electromagnetic forces of the PMEIM are measured in bench testing. Results indicate that the PMEIM powertrain performs propulsion and levitation functions, demonstrating 14.2 N propulsion force and 45.8 N levitation force under the rated condition, with a levitation–weight ratio of 2.52, which hold promise as a compact and flexible drivetrain solution for maglev cars. Full article

Electromagnetic ultrasonic testing technology, owing to its couplant-free, high-temperature-resistant, and non-contact characteristics, exhibits unique advantages for thickness measurement in harsh industrial environments. However, its accuracy is fundamentally limited by inherent constraints in signal bandwidth and low signal-to-noise ratio. To address these challenges, this work proposes an electromagnetic ultrasonic thickness measurement method that integrates Adaptive Denoising with Bayesian Vector Autoregressive (AD-BVAR) spectral extrapolation. The approach employs Particle Swarm Optimization (PSO) and automatically determines the optimal parameters for Variational Mode Decomposition (VMD), followed by integration with Singular Value Decomposition (SVD) to achieve the adaptive denoising of signals. Subsequently, the BVAR model incorporating prior constraints performs robust extrapolation of the effective frequency band spectrum, ultimately achieving high measurement accuracy signal reconstruction. The experimental results demonstrate that on step blocks with thicknesses of 3 mm and 12.5 mm, the proposed method achieved significantly reduced error rates of 0.267% and 0.240%, respectively. This performance markedly surpasses that of the conventional Autoregressive (AR) method, which yielded errors of 0.767% and 0.560% under identical conditions, while maintaining stable performance across different thicknesses. Full article

To address inefficiencies, pig stress from traditional manual body dimension measurement, and environmental interference in existing automated technologies, this study designed and validated a non-contact measurement system for Jiangquan black pigs based on dual-view (top + side) depth vision (Intel RealSense D455). Key dimensions (body length/width/height, chest depth) were accurately extracted via depth map calibration, dynamic scaling, and U-Net segmentation. Chest girth was estimated using the Ramanujan ellipse perimeter model (MAE = 4.15 cm, R² = 0.908) and integrated as the core parameter for body weight prediction in an empirical formula. This experimental dataset comprises 30 pigs sourced from a single farm, with body weights falling within a limited range (30–100 kg). All dimensions achieved R² > 0.9, with top-view body width performing best (R² = 0.9424, MAE = 1.9 cm). Body weight prediction yielded R² = 0.957 and MAE = 5.1 kg. The system completes measurements in 24 ± 4 s with low hardware costs and stress-free operation, making it suitable for precision breeding in small-to-medium pig farms. Full article

Terahertz (THz) waves, situated between the infrared and microwave regions, possess distinctive properties such as non-contact, high penetration, and high resolution. These properties render them highly advantageous for non-destructive thickness measurement of multilayer structural materials. In comparison with conventional ultrasound or X-ray techniques, THz thickness measurement has the capacity to acquire thickness data for multilayer structures without compromising the integrity of the specimen and is characterized by its environmental sustainability. The extant THz thickness measurement techniques principally encompass time-domain spectroscopy, frequency-domain spectroscopy, and model-based inversion and deep learning methods. A variety of methodologies have been demonstrated to possess complementary advantages in addressing subwavelength-scale thin layers, overlapping multilayer interfaces, and complex environmental interferences. These methodologies render them suitable for a range of measurement scenarios and precision requirements. A wide range of technologies related to this field have been applied in various disciplines, including aerospace thermal barrier coating inspection, semiconductor process monitoring, automotive coating quality assessment, and oil film thickness monitoring. The ongoing enhancement in system integration and continuous algorithm optimization has led to significant advancements in THz thickness measurement, propelling it towards high resolution, real-time performance, and intelligence. This development offers a wide range of engineering applications with considerable potential for future growth and innovation. Full article

Remote photoplethysmography (rPPG) is a non-contact technology that estimates physiological signals, such as Heart Rate (HR), by capturing subtle skin color changes caused by periodic blood volume variations using only a standard RGB camera. While cost-effective and convenient, it suffers from a fundamental limitation: performance degrades severely in dynamic environments due to susceptibility to noise, such as abrupt illumination changes or motion blur. This study presents a deep learning framework that combines two structural modifications to ensure robustness in dynamic environments, specifically modeling movement noise and illumination change noise. The proposed framework structurally cancels global disturbances, such as illumination changes or global motion, through a dual-branch pipeline that encodes the face and background in parallel after Video Color Magnification (VCM) and then performs differencing. Subsequently, it utilizes a structure that injects a Temporal Shift Module (TSM) into the Spatio-Temporal Feature Extraction (SSFE) block to preserve long- and short-term temporal correlations and smooth noise, even amidst short and irregular movements. We measured MAE, RMSE, and correlation on the standard dataset UBFC-rPPG under four noise conditions: clean, illumination change noise, Movement Noise, Both Noise and the real-world in-vehicle dataset MR-NIRP (Stationary and Driving). Experimental results showed that the proposed method achieved consistent error reduction and correlation improvement compared to the VS-Net baseline in the illumination change noise-only and combined noise environments (UBFC-rPPG) and in the high-noise driving scenario (MR-NIRP). It maintained competitive performance in motion-only noise. Conversely, a modest performance disadvantage was observed under clean conditions (UBFC) and quasi-clean stationary conditions (MR-NIRP), interpreted as a design trade-off focused on global noise cancellation and temporal smoothing. Ablation studies demonstrated that the dual-branch pipeline is the primary contributor under illumination change noise, while TSM is the key contributor under movement noise, and that the combination of both elements achieves optimal robustness in the most complex scenarios. Full article

Peach firmness is a critical quality attribute, yet conventional destructive measurement methods are unsuitable for batch detection in industrial settings. This study investigated a noncontact method for firmness assessment across multiple peach cultivars based on acoustic vibration technology. Three peach cultivars were mechanically excited via a controlled air jet, and the resulting acoustic vibration responses were captured noninvasively using a laser Doppler vibrometer. The frequency-domain acoustic vibration spectra were used as input for firmness prediction models developed using partial least squares regression (PLSR), support vector regression (SVR), and a one-dimensional convolutional neural network (ISNet-1D) that incorporated Inception and squeeze-and-excitation modules. Comparative analysis demonstrated that the ISNet-1D substantially outperformed the conventional linear and nonlinear methods on an independent test set, achieving superior predictive accuracy, with a coefficient of determination ( $R_P^2)$ of 0.8069, a root mean square error (RMSEP) of 0.9206 N/mm, and a residual prediction deviation ( ${RPD}_P)$ of 2.2879. The good performance of the ISNet-1D can be attributed to the integration of multi-scale convolutional filters with a channel-wise attention mechanism. This integration allows the network to adaptively prioritize discriminative spectral features, thereby enhancing its prediction accuracy. A hierarchical transfer learning strategy was proposed to improve model generalizability, offering a practical and cost-effective means to adapt to diverse cultivars. In summary, the combination of noncontact acoustic vibration and deep learning presents a robust, accurate, and nondestructive methodology for assessing peach firmness, demonstrating considerable potential for cross-cultivar application in industrial sorting and quality control. Full article

Optical metrology and perception technologies employ light as an information carrier to enable non-contact, high-precision measurement of geometry, dynamics, and material properties. They are widely deployed in industrial and consumer domains, from nanoscale defect inspection in semiconductor manufacturing to environmental perception in autonomous driving and spatial tracking in AR/VR. However, existing reviews often treat individual modalities—such as interferometry, imaging, or spectroscopy—in isolation, overlooking the increasing cross-domain integration in emerging systems. This review proposes a hierarchical taxonomy encompassing four core systems: interferometry, imaging, spectroscopy, and hybrid/advanced methods. It introduces a “theory–application–innovation” framework to unify fundamental principles, application scenarios, and evolutionary trends, revealing synergies across modalities. By mapping technological progress to industrial and societal needs, including AI-driven optimization and quantum-enhanced sensing, this work provides a structured, evolving knowledge base. The framework supports both cross-disciplinary understanding and strategic decision-making, offering researchers and engineers a consolidated reference for navigating the rapidly expanding frontiers of optical metrology and perception. Full article

Piezoelectric materials are fundamental elements in modern science and technology due to their unique ability to convert mechanical and electrical energy bidirectionally. They are widely employed in sensors, actuators, and energy-harvesting systems. In this work, we investigate the behavior of commercial lead zirconate titanate (PZT) sensors under frequency-mode excitation using a combined approach of impedance spectroscopy and optical interferometry. The impedance spectra reveal distinct resonance–antiresonance features that strongly depend on geometry, while interferometric measurements capture dynamic strain fields through fringe displacement analysis. The strongest deformation occurs near the first kilohertz resonance, directly correlated with the impedance phase, enabling the extraction of an effective piezoelectric constant (~40 pC/N). Moving beyond the linear regime, laser-induced excitation demonstrates optically driven activation of piezoelectric modes, with a frequency-dependent response and nonlinear scaling with optical power, characteristic of coupled pyroelectric–piezoelectric effects. These findings introduce a frequency-mode approach that combines impedance spectroscopy and optical interferometry to simultaneously probe electrical and mechanical responses in a single setup, enabling non-contact, frequency-selective sensing without surface modification or complex optical alignment. Although focused on macroscale ceramic PZTs, the non-contact measurement and activation strategies presented here offer scalable tools for informing the design and analysis of piezoelectric behavior in micro- and nanoscale systems. Such frequency-resolved, optical-access approaches are particularly valuable in the development of next-generation nanosensors, MEMS/NEMS devices, and optoelectronic interfaces where direct electrical probing is challenging or invasive. Full article

Advanced manufacturing places stringent demands on measurement technologies, requiring ultra-high precision, non-contact operation, high throughput, and real-time adaptability. Optical metrology, with its distinct advantages, has become a key enabler in this context. This paper reviews optical metrology techniques from the perspective of precision manufacturing applications, emphasizing precision positioning and surface topography measurement while noting the limitations of traditional contact-based methods. For positioning, interferometers, optical encoders, and time-of-flight methods enable accurate linear and angular measurements. For surface characterization, techniques such as interferometry, structured light profilometry, and confocal microscopy provide reliable evaluation across scales, from large structures to micro- and nano-scale features. By integrating these approaches, optical metrology is shown to play a central role in bridging macroscopic and nano-scale characterization, supporting both structural assessment and process optimization. This review highlights its essential contribution to advanced manufacturing, and offers a concise reference for future progress in high-precision and intelligent production. Full article

Background and objectives: Numerous studies and meta-analyses have established the efficacy of telemonitoring for blood pressure and other components of metabolic syndrome in improving disease management. Nevertheless, the adoption of telemonitoring technologies is often hindered by personal, technological, and systemic barriers. In Slovakia, where patient–physician contact rates are high, there is limited research on patients’ perspectives regarding telemedicine adoption for cardiovascular risk management. The objective of this study was to examine patients’ perspectives on and perceived obstacles to the use of telemonitoring for arterial hypertension and dyslipidemia in Slovakia. Methods: This cross-sectional, questionnaire-based survey targeted a cohort of 18,053 patients. The survey instrument was designed to gather data on several key areas: patient demographic characteristics, blood pressure measurement habits, the utilization of smart technologies, perceived benefits and barriers to telemonitoring, and patients’ knowledge of their lipid profiles and cardiovascular risk factors. Statistical analysis included chi-square tests, ANOVA, and effect size calculations with 95% confidence intervals (CI). Results: A total of 1787 patient responses (9.9%) were collected. Among the respondents, 67.4% (n = 1204) had arterial hypertension, while 7.9% (n = 95) were on non-pharmacological therapy. Only 21.2% (n = 255) of hypertensive patients measured their blood pressure daily, with a significantly higher proportion of men than women (28.6% vs. 12.7%, p = 0.011, Cohen’s d = 0.42). The most frequent users of blood pressure monitoring were in the 31–45 age group (p = 0.001, η² = 0.08). A total of 19.4% (n = 347) of respondents used wearable devices, and 6.3% (n = 113) used blood pressure monitors connected to an application. Smart technology use was significantly more common in the 31–45 age group (p = 0.01, Cramer’s V = 0.15). Moderate interest in telemedicine was expressed by 69.8% (n = 1247) of respondents, though only 27.4% (n = 490) showed strong interest. The majority of patients (73.8%, n = 1319) did not know their LDL-C levels, and 45.7% (n = 817) of those who did had elevated levels. Conclusions: The findings suggest that while interest in telemedicine methods for the management of arterial hypertension and dyslipidemia exists among Slovak patients, it is more moderate than initially assumed. Importantly, expressed willingness to participate in a study should not be directly equated with readiness to adopt new technologies in daily practice. Successful integration of telemonitoring into the Slovak healthcare system will therefore require not only patient engagement but also active support from healthcare providers to overcome practical and motivational barriers. These findings highlight the need for targeted implementation strategies that address the specific barriers identified in the Central and Eastern European healthcare context. Full article

The diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters. Traditional, single-dimensional two-dimensional imaging methods often struggle to balance precision with computational efficiency. This paper addresses the limitations of the YOLOv11n-seg model in the instance segmentation of floral disk fine structures by proposing the MBLA-YOLO instance segmentation model, achieving both lightweight efficiency and high accuracy. Building upon this foundation, a non-contact measurement method is proposed that combines an improved model with three-dimensional point cloud analysis to precisely extract key structural parameters of the flower head. First, image annotation is employed to eliminate interference from petals and sepals, whilst instance segmentation models are used to delineate the target region; The segmentation results for the disc surface (front) and edges (sides) are then mapped onto the three-dimensional point cloud space. Target regions are extracted, and following processing, separate models are constructed for the disc surface and edges. Finally, with regard to the differences between the surface and edge structures, targeted methods are employed for their respective calculations. Whilst maintaining lightweight characteristics, the proposed MBLA-YOLO model achieves simultaneous improvements in accuracy and efficiency compared to the baseline YOLOv11n-seg. The introduced CKMB backbone module enhances feature modelling capabilities for complex structural details, whilst the LADH detection head improves small object recognition and boundary segmentation accuracy. Specifically, the CKMB module integrates MBConv and channel attention to strengthen multi-scale feature extraction and representation, while the LADH module adopts a tri-branch design for classification, regression, and IoU prediction, structurally improving detection precision and boundary recognition. This research not only demonstrates superior accuracy and robustness but also significantly reduces computational overhead, thereby achieving an excellent balance between model efficiency and measurement precision. This method avoids the need for three-dimensional reconstruction of the entire plant and multi-view point cloud registration, thereby reducing data redundancy and computational resource expenditure. Full article

Gear defect detection directly affects the operational reliability of critical equipment in fields such as automotive and aerospace. Gear defect detection technology based on machine vision, leveraging the advantages of non-contact measurement, high efficiency, and cost-effectiveness, has become a key support for quality control in intelligent manufacturing. However, it still faces challenges including difficulties in semantic alignment of multimodal data, the imbalance between real-time detection requirements and computational resources, and poor model generalization in few-shot scenarios. This paper takes the paradigm evolution of gear defect detection technology as the main line, systematically reviews its development from traditional image processing to deep learning, and focuses on the innovative application of intelligent algorithms. A research framework of “technical bottleneck-breakthrough path-application verification” is constructed: for the problem of multimodal fusion, the cross-modal feature alignment mechanism based on Transformer network is deeply analyzed, clarifying its technical path of realizing joint embedding of visual and vibration signals by establishing global correlation mapping; for resource constraints, the performance of lightweight models such as MobileNet and ShuffleNet is quantitatively compared, verifying that these models reduce Parameters by 40–60% while maintaining the mean Average Precision essentially unchanged; for small-sample scenarios, few-shot generation models based on contrastive learning are systematically organized, confirming that their accuracy in the 10-shot scenario can reach 90% of that of fully supervised models, thus enhancing generalization ability. Future research can focus on the collaboration between few-shot generation and physical simulation, edge-cloud dynamic scheduling, defect evolution modeling driven by multiphysics fields, and standardization of explainable artificial intelligence. It aims to construct a gear detection system with autonomous perception capabilities, promoting the development of industrial quality inspection toward high-precision, high-robustness, and low-cost intelligence. Full article

Effective detection of acoustic signals plays a crucial role in numerous fields, including industrial equipment fault prediction and environmental monitoring. Acoustic sensing technology, owing to its substantial information carrying capacity and non-contact measurement advantages, has garnered widespread attention in relevant applications. However, the effective detection of weak target acoustic signals amidst strong noise interference remains a critical challenge in this field. The core bottleneck lies in the difficulty of traditional detection methods to simultaneously achieve both high sensitivity and high directionality. To address this limitation, this work proposes a line-defect phononic crystal (PnC) structure that enables directional enhancement and detection of weak target signals under intense spatial noise interference by coupling defect state localization characteristics with anisotropy mechanisms. Through theoretical derivation and finite element numerical simulation, the directional enhancement properties of this structure were systematically validated. Furthermore, numerical simulations were conducted to validate the detection of weak harmonic signals and weak bearing fault signals under strong spatial noise interference. The results demonstrate that this line-defect phononic crystal (PnC) structure exhibits high feasibility and outstanding performance in detecting weak acoustic signals. This work provides novel insights for developing new acoustic detection methods combining high sensitivity with high directivity, showcasing unique advantages and broad application prospects in acoustic signal sensing, enhancement, and localization. Full article

Transmission line icing poses a significant natural disaster threat to power grid security. This paper systematically reviews recent advances in the understanding of icing mechanisms, intelligent detection, and prevention technologies, while providing perspectives on future development directions. In mechanistic research, although a multi-physics coupling framework has been established, characterization of dynamic evolution over complex terrain and coupled physical mechanisms remains inadequate. Detection technology is undergoing a paradigm shift from traditional contact measurements to non-contact intelligent perception. Visual systems based on UAVs and fixed platforms have achieved breakthroughs in ice recognition and thickness retrieval, yet their performance remains constrained by image quality, data scale, and edge computing capabilities. Anti-/de-icing technologies have evolved into an integrated system combining active intervention and passive defense: DC de-icing (particularly MMC-based topologies) has become the mainstream active solution for high-voltage lines due to its high efficiency and low energy consumption; superhydrophobic coatings, photothermal functional coatings, and expanded-diameter conductors show promising potential but face challenges in durability, environmental adaptability, and costs. Future development relies on the deep integration of mechanistic research, intelligent perception, and active prevention technologies. Through multidisciplinary innovation, key technologies such as digital twins, photo-electro-thermal collaborative response systems, and intelligent self-healing materials will be advanced, with the ultimate goal of comprehensively enhancing power grid resilience under extreme climate conditions. Full article