Search Results (916)

The quality change process of table grapes during cold chain logistics is complex and highly susceptible to vibration-induced damage. Traditional monitoring techniques not only consume significant human and material resources but also cause destructive effects on the fruit structure of table grapes, making them difficult to apply in practical scenarios. Based on this, this paper focuses on table grapes in cold chain business processes and designs a flexible wireless vibration sensor for monitoring the quality of table grapes during cold chain transportation. The hardware component of the system fabricates a flexible wireless vibration sensing for monitoring the quality of the table grape cold chain. In contrast, the software component develops corresponding data acquisition and processing functionalities. Using Summer Black table grapes purchased from Tianjin Hongqi Agricultural Market as the research subject, correlation and quality monitoring models for the cold chain process of table grapes were constructed. After Z-score standardization, the prediction results based on the MLR model achieved R² values all greater than 0.87 and RPD values all exceeding 2.7. Comparisons with other regression models demonstrated its optimal fitting performance for monitoring the quality of the cold chain for table grapes. This achieves non-destructive and high-precision data acquisition and processing during the cold chain process of table grapes, wirelessly transmitting results to terminal devices for real-time visual monitoring. Full article

Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects’ micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end. Full article

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data measurements were collected from a cultivator shank in the northeast of Kansas using the AVDAQ system. The test field soils were Reading silt loam and Eudora–Bismarck Grove silt loams. The relationship between shank vibrations, soil moisture (measured by a Hydrosense II soil–water sensor), and soil compaction (measured by a cone penetrometer) was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. Overall, the results demonstrate that combining vibration data with soil moisture data as model inputs enables accurate estimation of soil compaction, providing a foundation for future in situ soil sensing, reduced tillage intensity, and more sustainable cultivation practices. Full article

Multi-core fiber (MCF) has drawn increasing attention for its potential application in distributed acoustic sensing (DAS) due to the compact optical structure of integrating several fiber cores in the same cladding, which indicates an intrinsic space-division-multiplexed (SDM) capability in a single piece of fiber. In this paper, a dual-channel DAS integrated with MCF is presented, of which the equivalent self-noise characteristic is analyzed. The equivalent self-noise of the system can be effectively suppressed by signal superposition with the phase matching method. Considering that the noise correlation among the cores is not zero, the signal-to-noise (SNR) gain after signal superposition is less than the theoretical value. The dual-channel DAS system is set up by a piece of 2 km long seven-core MCF, in which the dual-sensing channels are constructed by a four-core series and three-core series, respectively. The total noise correlation coefficient of the seven cores is 11.28, while the equivalent self-noise of the system can be suppressed by 6.32 dB with signal superposition. An equivalent self-noise suppression method based on a linear delay phase matching scheme is proposed for noise decorrelation in the DAS MCF system. After noise decorrelation, the suppression of the equivalent self-noise of the system can reach the theoretical value of 8.45 dB with a time delay of 1 ms, indicating a noise correlation among the seven cores of almost zero. The feasibility of the equivalent self-noise suppression method for the DAS system is verified for both single-frequency and broadband signals, which is of great significance for the detection of weak vibration signals based on a DAS system. Full article

In this study, we report the fabrication and multi-technique characterization of pure and rare-earth (RE)-doped ZnO thin films using nanostructured microclusters synthesized via electrospinning followed by calcination. Lanthanum (La), erbium (Er), and samarium (Sm) were each incorporated at five concentrations (0.1–5 at.%) into ZnO, and the resulting powders were drop-cast as thin films on glass substrates. This approach enables the transfer of pre-engineered nanoscale morphologies into the final thin-film architecture. The morphological analysis by scanning electron microscopy (SEM) revealed a predominance of spherical nanoparticles and nanorods, with distinct variations in size and aspect ratio depending on dopant type and concentration. X-ray diffraction (XRD) and Rietveld analysis confirmed the wurtzite ZnO structure with increasing evidence of secondary phase formation at high dopant levels (e.g., Er₂O₃, Sm₂O₃, and La(OH)₃). Raman spectroscopy showed peak shifts, broadening, and defect-related vibrational modes induced by RE incorporation, in agreement with the lattice strain and crystallinity variations observed in XRD. Elemental mapping (EDX) confirmed uniform dopant distribution. Optical transmittance exceeded 70% for all films, with Tauc analysis revealing slight bandgap narrowing (Eg = 2.93–2.97 eV) compared to pure ZnO. This study demonstrates that rare-earth doping via electrospun nanocluster precursors is a viable route to engineer ZnO thin films with tunable structural and optical properties. Despite current limitations in film-substrate adhesion, the method offers a promising pathway for future transparent optoelectronic, sensing, or UV detection applications, where further interface engineering could unlock their full potential. Full article

Event-based cameras, as a revolutionary class of dynamic vision sensors, offer transformative advantages for capturing transient mechanical phenomena through their asynchronous, per-pixel brightness change detection mechanism. These neuromorphic sensors excel in challenging industrial environments with their microsecond-level temporal resolution, ultra-low power requirements, and exceptional dynamic range that significantly outperform conventional imaging systems. In this way, the event-based camera provides a promising tool for machine vibration sensing and fault diagnosis. However, the dynamic vision data from the event-based cameras have a complex structure, which cannot be directly processed by the mainstream methods. This paper proposes a dynamic vision-based non-contact machine fault diagnosis method. The Eagle Vision Transformer (EViT) architecture is proposed, which incorporates biologically plausible computational mechanisms through its innovative Bi-Fovea Self-Attention and Bi-Fovea Feedforward Network designs. The proposed method introduces an original computational framework that effectively processes asynchronous event streams while preserving their inherent temporal precision and dynamic response characteristics. The proposed methodology demonstrates exceptional fault diagnosis performance across diverse operational scenarios through its unique combination of multi-scale spatiotemporal feature analysis, adaptive learning capabilities, and transparent decision pathways. The effectiveness of the proposed method is extensively validated by the practical condition monitoring data of rotating machines. By successfully bridging cutting-edge bio-inspired vision processing with practical industrial monitoring requirements, this work creates a new paradigm for dynamic vision-based non-contact machinery fault diagnosis that addresses critical limitations of conventional approaches. The proposed method provides new insights for predictive maintenance applications in smart manufacturing environments. Full article

This study develops a ship propeller diagnostic system to address the issue of insufficient ship maintenance capacity and enhance operational efficiency. It uses the Remaining Useful Life (RUL) prediction technology to establish a sensing platform for ship propellers to capture vibration signals during ship operations. The Diagnosis and RUL Prediction Model is designed to assess bearing aging status and the RUL of the propeller. The synchronized signal reshaping technology is employed in the Diagnosis and RUL Prediction Model to process the original vibration signals as input to the model. The vibration signals obtained are used to analyze the temporal and spectral energy of propeller bearings. Exponential functions are used to generate the health index as model outputs. Model inputs and outputs are simultaneously input into a Long Short-Term Memory (LSTM) model for training, culminating as the Diagnosis and RUL Prediction Model. Compared to Recurrent Neural Network and Support Vector Regression models used in previous studies, the Diagnosis and RUL Prediction Model developed in this study achieves a Mean Squared Error (MSE) of 0.018 and a Mean Absolute Error (MAE) of 0.039, demonstrating outstanding performance in prediction results and computational efficiency. This study integrates the Diagnosis and RUL Prediction Model, bearing aging experimental data, and real-world vibration measurements to develop the diagnosis and RUL prediction system for ship propellers. Experiments with ship propellers show that when the bearing of the propeller enters the worn stage, this diagnostic system for ship propellers can accurately determine the current status of the bearing and its remaining useful life. This study offers a practical solution to insufficient ship maintenance capacity and contributes to improving the operational efficiency of ships. Full article

This study experimentally investigates the wake-induced vibration (WIV) behavior of a bio-inspired harbor seal whisker model subjected to upstream propeller-generated unsteady flows. Vibration amplitudes, frequencies, and wake–whisker interactions were systematically evaluated under various flow conditions. The test matrix included propeller rotational speed N_p = 0~5000 r/min, propeller diameter D_p = 60~100 mm, incoming flow velocity U_∞ = 0~0.2 m/s, and separation distance between the whisker model and the propeller L/D = 10~30 (D = 16 mm, diameter of the whisker model). Results show that inline (IL) and crossflow (CF) vibration amplitudes increase significantly with propeller speed and decrease with increasing separation distance. Under combined inflow and wake excitation, non-monotonic trends emerge. Frequency analysis reveals transitions from periodic to subharmonic and broadband responses, depending on wake structure and coherence. A non-dimensional surface fit using L/D and the advance ratio (J = U_∞/(N_pD_p)) yielded predictive equations for RMS responses with good accuracy. Phase trajectory analysis further distinguishes stable oscillations from chaotic-like dynamics, highlighting changes in system stability. These findings offer new insight into WIV mechanisms and provide a foundation for biomimetic flow sensing and underwater tracking applications. Full article

With accelerating urbanization and the exponential growth in vehicle populations, high-precision traffic monitoring has become indispensable for intelligent transportation systems (ITSs). Conventional sensing technologies—including inductive loops, cameras, and radar—suffer from inherent limitations: restrictive spatial coverage, prohibitive installation costs, and vulnerability to adverse weather. Distributed Acoustic Sensing (DAS), leveraging Rayleigh backscattering to convert standard optical fibers into kilometer-scale, real-time vibration sensor networks, presents a transformative alternative. This paper provides a comprehensive review of the principles and system architecture of DAS for roadway traffic monitoring, with a focus on signal processing techniques, feature extraction methods, and recent advances in vehicle detection, classification, and speed/flow estimation. Special attention is given to the integration of deep learning approaches, which enhance noise suppression and feature recognition under complex, multi-lane traffic conditions. Real-world deployment cases on highways, urban roads, and bridges are analyzed to demonstrate DAS’s practical value. Finally, this paper delineates emerging research trends and technical hurdles, providing actionable insights for the scalable implementation of DAS-enhanced ITS infrastructures. Full article

A new series of luminescent Zn(II) complexes based on mono- and bis-imidazo[1,5-a]pyridine ligands was synthesized to investigate the correlation between structural modifications and photophysical behaviour. Systematic variations in substituent groups, coordination geometry, and π-conjugation extent enabled precise tuning of absorption and emission properties. Spectroscopic analysis revealed that Zn(II) coordination enhances molecular rigidity and induces a conformational change in the ligands, resulting in improved quantum yields (up to 37%) and significant blue shifts in emission. Notably, in bis-ligand systems, each imidazo[1,5-a]pyridine unit retains its distinct emissive signature upon complexation, demonstrating their optical and electronic independence. This modular behaviour confirms that individual emissive centres can be predictably manipulated without mutual interference, offering a powerful design strategy for multichromophoric materials. Structural, vibrational, and mass spectrometric characterizations further corroborate the stability and coordination patterns of the synthesized complexes. These insights lay the groundwork for engineering efficient and tunable Zn(II)-based luminophores for applications in optoelectronics, sensing, and bioimaging. Full article

This study focuses on a comprehensive analysis of the common methods for road infrastructure monitoring, as well as the perspective of various fiber-optic sensor (FOS) realization solutions in road monitoring applications. Fiber-optic sensors are a topical technology that ensures multiple advantages such as passive nature, immunity to electromagnetic interference, multiplexing capabilities, high sensitivity, and spatial resolution, as well as remote operation and multiple physical parameter monitoring, hence offering embedment potential within the road pavement structure for needed smart road solutions. The main key factors that affect FOS-based road monitoring scenarios and configurations are analyzed within this review. One such factor is technology used for optical sensing—fiber Bragg grating (FBG), Brillouin, Rayleigh, or Raman-based sensing. A descriptive comparison is made comparing typical sensitivity, spatial resolution, measurement distance, and applications. Technological approaches for monitoring physical parameters, such as strain, temperature, vibration, humidity, and pressure, as a means of assessing road infrastructure integrity and smart application integration, are also evaluated. Another critical aspect concerns spatial positioning, focusing on the point, quasi-distributed, and distributed methodologies. Lastly, the main topical FOS-based application areas are discussed, analyzed, and evaluated. Full article

This review investigates how process parameters and material choices influence the mechanical performance of parts produced by material extrusion additive manufacturing, with a particular focus on Material Extrusion (ME). Through a systematic bibliometric analysis of literature between 2015 and 2025, the study identifies key factors affecting mechanical strength, anisotropy, and structural reliability, including printing temperature, speed, orientation, layer thickness, and interlayer bonding. Emphasis is placed on emerging techniques such as 4D printing, fiber-reinforced composites, and novel monitoring methods like real-time vibration sensing and thermal imaging, which offer promising pathways to improve part performance and process stability. Three research questions guide the analysis: (1) how printing parameters affect micro- to macrostructure and failure behavior, (2) how optimization strategies enhance part quality, and (3) how material and process selection aligns with functional requirements. The review highlights both advances and persistent limitations in process control, material compatibility, and anisotropic strength. It concludes with a call for further integration of predictive modeling, hybrid material systems, and closed-loop process monitoring to unlock the full potential of additive manufacturing in high-performance engineering applications. Full article

The electric power equipment industry is rapidly advancing toward “informationization,” with the swift progression of intelligent sensing technology serving as a key driving force behind this transformation, thereby triggering significant changes in global electric power equipment. In this process, intelligent sensing has created an urgent demand for high-performance integrated power systems that feature compact size, lightweight design, long operational life, high reliability, high energy density, and low cost. However, the performance metrics of traditional power supplies have increasingly failed to meet the requirements of modern intelligent sensing, thereby significantly hindering the advancement of intelligent power equipment. Energy harvesting technology, characterized by its long operational lifespan, compact size, environmental sustainability, and self-sufficient operation, is capable of capturing renewable energy from ambient power sources and converting it into electrical energy to supply power to sensors. Due to these advantages, it has garnered significant attention in the field of power sensing. This paper presents a comprehensive review of the current state of development of energy harvesting technologies within the power environment. It outlines recent advancements in magnetic field energy harvesting, electric field energy harvesting, vibration energy harvesting, wind energy harvesting, and solar energy harvesting. Furthermore, it explores the integration of multiple physical mechanisms and hybrid energy sources aimed at enhancing self-powered applications in this domain. A comparative analysis of the advantages and limitations associated with each technology is also provided. Additionally, the paper discusses potential future directions for the development of energy harvesting technologies in the power environment. Full article

One of the major challenges in diagnosing various diseases, including neurological and neurodegenerative disorders, as well as carcinogenesis, is detecting unlabeled neurotransmitters. Surface-enhanced Raman spectroscopy (SERS) and surface-enhanced infrared spectroscopy (SEIRA) are promising methods for neurotransmitter biosensing and bioimaging. These methods are unique in that they are non-destructive and can identify molecular fingerprints. In this study, these methods were used to detect the following potent bradykinin (BK) antagonists: [D-Arg⁰,Hyp³,Thi⁵,D-Tic⁷,Oic⁸]BK, [D-Arg⁰,Hyp³,Thi⁵,D-Phe⁷,Thi⁸]BK, [D-Arg⁰,Hyp³,Igl⁵,D-Phe(5F)⁷,Oic⁸]BK, and [D-Arg⁰,Hyp³,Igl⁵,D-Igl⁷,Oic⁸]BK. The peptides were immobilized on a sensor surface consisting of silver (AgNPs) and gold (AuNPs) nanoparticles. These sensors have uniform particle sizes and small size distributions. Thanks to fast synthesis, easy handling, and reproducible results, these sensors enable routine testing. The vibrational structure of these peptides could not be determined using classical vibrational methods (Raman and IR) or surface-enhanced methods (SERS and SEIRA). This work presents the results of that research. Additionally, the SEIRA spectrum for BK or its analogs has not yet been published. This study presents research using SERS and SEIRA that shows that AgNP and AuNP sensors can detect the peptides under investigation. SERS is a more selective method than SEIRA because it allows for the differentiation of peptides based on the enhancement of certain bands in the SERS spectra. Furthermore, each peptide uniquely interacts with AuNPs, whereas all peptides bind to AgNPs via the C-terminus in different orientations. Consequently, the AuNP sensor is more selective than the AgNP sensor. Some bands were selected as markers for the sensing of specific peptides. Full article

This article presents the results of an experimental study focused on evaluating the potential to harvest electrical energy from vertical vibrations affecting a child car seat installed on an ISOFIX base with a support leg during real driving conditions. The objective was to measure vibration levels in the seat structure and assess the feasibility of converting this mechanical energy into electrical power. The study involved two child seat models, each tested under loads of 9 kg and 15 kg, while driving over smooth asphalt, damaged asphalt, and speed bumps. Acceleration data were collected at three key structural locations: the seat surface, the ISOFIX base, and the support leg. These measurements served as the basis for estimating the mechanical energy available and the resulting electrical output. Findings show that in poor road conditions, the system can generate enough energy to power a 10 µW sensor for more than 42 days. The results confirm the feasibility of using vibration energy harvesting to supply smart safety features such as presence detection, temperature monitoring, or posture sensing in child seats, without the need for batteries or a connection to the vehicle’s electrical system. Full article