Search Results (32)

In high-voltage transmission lines, insulators subjected to prolonged electromechanical stress are prone to zero-value defects, leading to insulation failure and posing significant risks to power grid reliability. The conventional detection method of spark gap is vulnerable to environmental interference, while the emerging electric field distribution-based techniques require complex instrumentation, limiting its applications in scenes of complex structures and atop tower climbing. To address these challenges, this study proposes an electroluminescent sensing strategy for zero-value insulator identification based on the electroluminescence of ZnS:Cu. Based on the stimulation of electrical stress, real-time monitoring of the health status of insulators was achieved by applying the composite of epoxy and ZnS:Cu onto the connection area between the insulator steel cap and the shed. Experimental results demonstrate that healthy insulators exhibit characteristic luminescence, whereas zero-value insulators show no luminescence due to a reduced drop in electrical potential. Compared with conventional detection methods requiring access of electric signals, such non-contact optical detection method offers high fault-recognition accuracy and real-time response capability within milliseconds. This work establishes a novel intelligent sensing paradigm for visualized condition monitoring of electrical equipment, demonstrating significant potential for fault diagnosis in advanced power systems. Full article

Electrospun nanofiber-based triboelectric nanogenerators (TENGs) have emerged as a highly promising class of self-powered sensors for a broad range of applications, particularly in intelligent sensing technologies. By combining the advantages of electrospinning and triboelectric nanogenerators, these sensors offer superior characteristics such as high sensitivity, mechanical flexibility, lightweight structure, and biocompatibility, enabling their integration into wearable electronics and biomedical interfaces. This review presents a comprehensive overview of recent progress in electrospun nanofiber-based TENGs, covering their working principles, operating modes, and material composition. Both pure polymer and composite nanofibers are discussed, along with various electrospinning techniques that enable control over morphology and performance at the nanoscale. We explore their practical implementations in both contact-type and non-contact-type sensing, such as human–machine interaction, physiological signal monitoring, gesture recognition, and voice detection. These applications demonstrate the potential of TENGs to enable intelligent, low-power, and real-time sensing systems. Furthermore, this paper points out critical challenges and future directions, including durability under long-term operation, scalable and cost-effective fabrication, and seamless integration with wireless communication and artificial intelligence technologies. With ongoing advancements in nanomaterials, fabrication techniques, and system-level integration, electrospun nanofiber-based TENGs are expected to play a pivotal role in shaping the next generation of self-powered, intelligent sensing platforms across diverse fields such as healthcare, environmental monitoring, robotics, and smart wearable systems. Full article

The transition to renewable energy requires sustainable solar manufacturing through optimized Production–Usage–Recycling (PUR) cycles, where electromagnetic (EM) sensing offers non-destructive monitoring solutions. This review categorizes EM methods into low- (<100 MHz) and medium-frequency (100 MHz–10 GHz) techniques for material evaluation, defect detection, and performance optimization throughout the solar lifecycle. During production, eddy current testing and impedance spectroscopy improve quality control while reducing waste. In operational phases, RFID-based monitoring enables continuous performance tracking and early fault detection of photovoltaic panels. For recycling, electrodynamic separation efficiently recovers materials, supporting circular economies. The analysis demonstrates the unique advantages of EM techniques in non-contact evaluation, real-time monitoring, and material-specific characterization, addressing critical sustainability challenges in photovoltaic systems. By examining capabilities and limitations, we highlight EM monitoring’s transformative potential for sustainable manufacturing, from production quality assurance to end-of-life material recovery. The frequency-based framework provides manufacturers with physics-guided solutions that enhance efficiency while minimizing environmental impact. This comprehensive assessment establishes EM technologies as vital tools for advancing solar energy systems, offering practical monitoring approaches that align with global sustainability goals. The review identifies current challenges and future opportunities in implementing these techniques, emphasizing their role in facilitating the renewable energy transition through improved resource efficiency and lifecycle management. Full article

Bridges are fundamental components of transportation infrastructure, facilitating the efficient movement of people and goods. However, the conservation of heritage bridges introduces additional challenges, encompassing environmental, social, cultural, and economic dimensions of sustainability. This study investigates risk mitigation strategies for a heritage-listed, 120-year-old reinforced concrete bridge in Australia—one of the nation’s earliest examples of reinforced concrete construction, which remains operational today. The structure faces multiple risks, including passage of overweight vehicles, environmental degradation, progressive crack development due to traffic loading, and potential foundation scouring from an adjacent stream. Due to the heritage status and associated legal constraints, only non-invasive testing methods were employed. Ambient vibration testing was conducted to identify the bridge’s dynamic characteristics under normal traffic conditions, complemented by non-contact displacement monitoring using laser distance sensors. A digital twin structural model was subsequently developed and validated against field data. This model enabled the execution of various “what-if” simulations, including passage of overweight vehicles and loss of foundation due to scouring, providing quantitative assessments of potential risk scenarios. Drawing on insights gained from the case study, the article proposes a six-phase Incident Response Framework tailored for heritage bridge management. This comprehensive framework incorporates remote sensing technologies for incident detection, digital twin-based structural assessment, damage containment and mitigation protocols, recovery planning, and documentation to prevent recurrence—thus supporting the long-term preservation and functionality of heritage bridge assets. Full article

Sleep posture detection is a potentially important component of sleep quality assessment and health monitoring. Accurate identification of sleep postures can offer valuable insights into an individual’s sleep patterns, comfort levels, and potential health risks. For example, improper sleep postures may lead to musculoskeletal issues, respiratory disturbances, and even worsen conditions like sleep apnea. Additionally, for long-term bedridden patients, continuous monitoring of sleep postures is essential to prevent pressure ulcers and other complications. Traditional methods for sleep posture detection have several limitations: wearable sensors can disrupt natural sleep and cause discomfort, camera-based systems raise privacy concerns and are sensitive to environmental conditions, and pressure-sensing mats are often complex and costly. To address these issues, we have developed a low-cost non-contact sleeping posture detection system. Our system features eight optimally distributed triaxial accelerometers, providing a comfortable and non-contact front-end data acquisition unit. For sleep posture classification, we employ an improved density peak clustering algorithm that incorporates the K-nearest neighbor mechanism. Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. Experimental results demonstrate that the PCSN can accurately distinguish six sleep postures: prone, supine, left log, left fetus, right log, and right fetus. The average accuracy is 98.42%, outperforming most state-of-the-art deep learning models. The PCSN achieves the highest scores across all metrics: 98.64% precision, 98.18% recall, and 98.10% F1 score. The proposed system shows considerable promise in various applications, including sleep studies and the prevention of diseases like pressure ulcers and sleep apnea. Full article

In this study, we propose a novel approach for dynamic micro-vibration measurement based on an interferometric system utilizing a fractional optical vortex (FOV) beam as the reference and a Gaussian beam as the measurement path. The reflected Gaussian beam encodes the vibration information of the target, which is extracted by analyzing the rotational behavior of the petal-like interference pattern formed through coaxial interference with the FOV beam. When the topological charge (TC) of the FOV beam is less than or equal to one, a single-petal structure is generated, significantly reducing the complexity of angular tracking compared to traditional multi-petals OAM-based methods. Moreover, using a Gaussian beam as the measurement path mitigates spatial distortions during propagation, enhancing the overall robustness and accuracy. We systematically investigate the effects of TC, CCD frame rate, and interference contrast on measurement performance. Experimental results demonstrate that the proposed method achieves high angular resolution with a minimum angle deviation of 18.2 nm under optimal TC conditions. The system exhibits strong tolerance to environmental disturbances, making it well-suited for applications requiring non-contact, nanometer-scale vibration sensing, such as structural health monitoring, precision metrology, and advanced optical diagnostics. Full article

This paper presents a method for estimating arousal and emotional valence levels using non-contact environmental sensing, addressing challenges such as discomfort from long-term device wear and privacy concerns associated with facial image analysis. We employed environmental data to develop machine learning models, including Random Forest, Gradient Boosting Decision Trees, and the deep learning model CNN-LSTM, and evaluated their accuracy in estimating emotional states. The results indicate that decision tree-based methods, particularly Random Forest, are highly effective for estimating emotional states from environmental data. Full article

Perception plays a pivotal role in both biological and technological interactions with the environment. Recent advancements in whisker sensors, drawing inspiration from nature’s tactile systems, have ushered in a new era of versatile and highly sensitive sensing technology. Whisker sensors, which mimic the tactile hairs of mammals, offer both high sensitivity and multifunctionality. They excel in capturing fine-grained environmental data, detecting various stimuli with precision, and finding applications in diverse domains. This review explores the integration of whisker sensors in potential marine applications. Categorized into six types, these sensors are invaluable for tasks such as marine structure monitoring, measurement instruments, tactile perception in marine robots, and non-contact sensing in the marine environment. Challenges and potential solutions are examined, along with the prospects of whisker sensors in the field of marine science and engineering. In an era that demands adaptable sensing solutions, whisker sensors emerge as pivotal components, enabling machines and devices to perceive and respond to external stimuli with heightened sensitivity and versatility. Their application in the marine domain holds substantial promise, propelling advancements in the realms of marine science and engineering. Full article

Acoustic sensing systems play a critical role in identifying and determining weak sound sources in various fields. In many fault warning and environmental monitoring processes, sound-based sensing techniques are highly valued for their information-rich and non-contact advantages. However, noise signals from the environment reduce the signal-to-noise ratio (SNR) of conventional acoustic sensing systems. Therefore, we proposed novel nonlinear gradient-coiling metamaterials (NGCMs) to sense weak effective signals from complex environments using the strong wave compression effect coupled with the equivalent medium mechanism. Theoretical derivations and finite element simulations of NGCMs were executed to verify the properties of the designed metamaterials. Compared with nonlinear gradient acoustic metamaterials (Nonlinear-GAMs) without coiling structures, NGCMs exhibit far superior performance in terms of acoustic enhancement, and the structures capture lower frequencies and possess a wider angle acoustic response. Additionally, experiments were constructed and conducted using set Gaussian pulse and harmonic acoustic signals as emission sources to simulate real application scenarios. It is unanimously shown that NGCMs have unique advantages and broad application prospects in the application of weak acoustic signal sensing, enhancement and localization. Full article

Biomaterials offer great potential for enhancing the performance of humidity sensors, which play a critical role in controlling moisture levels across different applications. By utilizing environmentally friendly, sustainable, and cost-effective biomaterials, we can improve the manufacturing process of these sensors while reducing our environmental impact. In this study, we present a high-performance humidity sensor that utilizes purple sweet potato peel (PSPP) as both the substrate and sensing layer. The PSPP is chosen for its polar hydrophilic functional groups, as well as its environmentally friendly nature, sustainability, and cost-effectiveness. Remarkably, this humidity sensor does not require an external substrate. It exhibits a wide detection range of 0 to 85% relative humidity at various operating frequencies (100 Hz, 1 kHz, and 10 kHz) in ambient temperature, demonstrating its effectiveness in responding to different humidity levels. The sensor achieves a high sensitivity value of 183.23 pF/%RH and minimal hysteresis of only 5% at 10 kHz under ambient conditions. It also boasts rapid response and recovery times of 1 and 2 s, respectively, making it suitable for use in high-end electronic devices. Moreover, the sensor’s applications extend beyond environmental monitoring. It has proven effective in monitoring mouth and nasal breathing, indicating its potential for respiratory monitoring and noncontact proximity response. These findings suggest that sweet potato peel material holds great promise as a highly stable, non-toxic, biodegradable, cost-effective, and environmentally friendly option for various domains, including healthcare monitoring. Full article

Temperature is an important environmental factor for long-span bridges because it induces thermal loads on structural components that cause considerable displacements, stresses, and structural damage. Hence, it is critical to acquire up-to-date information on the status, sustainability, and serviceability of long-span bridges under daily and seasonal temperature fluctuations. This paper intends to investigate the effects of temperature variability on structural displacements obtained from remote sensing and represent their relationship using supervised regression models. In contrast to other studies in this field, one of the contributions of this paper is to leverage hybrid sensing as a combination of contact and non-contact sensors for measuring temperature data and structural responses. Apart from temperature, other unmeasured environmental and operational conditions may affect structural displacements of long-span bridges separately or simultaneously. For this issue, this paper incorporates a correlation analysis between the measured predictor (temperature) and response (displacement) data using a linear correlation measure, the Pearson correlation coefficient, as well as nonlinear correlation measures, namely the Spearman and Kendall correlation coefficients and the maximal information criterion, to determine whether the measured environmental factor is dominant or other unmeasured conditions affect structural responses. Finally, three supervised regression techniques based on a linear regression model, Gaussian process regression, and support vector regression are considered to model the relationship between temperature and structural displacements and to conduct the prediction process. Temperature and limited displacement data related to three long-span bridges are used to demonstrate the results of this research. The aim of this research is to assess and realize whether contact-based sensors installed in a bridge structure for measuring environmental and/or operational factors are sufficient or if it is necessary to consider further sensors and investigations. Full article

In recent years, the applications of lidars for remote sensing of the environment have been expanding and deepening. Among them, continuous-wave (CW) range-resolved (RR) S-lidars (S comes from Scheimpflug) have proven to be a new and promising class of non-contact and non-perturbing laser sensors. They use low-power CW diode lasers, an unconventional depth-of-field extension technique and the latest advances in nanophotonic technologies to realize compact and cost-effective remote sensors. The purpose of this paper is to propose a generalized methodology to justify the selection of a set of non-energetic S-lidar parameters for a wide range of applications and distance scales, from a bench-top test bed to a 10-km path. To set the desired far and near borders of operating range by adjusting the optical transceiver, it was shown how to properly select the lens plane and image plane tilt angles, as well as the focal length, the lidar base, etc. For a generalized analysis of characteristic relations between S-lidar parameters, we introduced several dimensionless factors and criteria applicable to different range scales, including an S-lidar-specific magnification factor, angular function, dynamic range, “one and a half” condition, range-domain quality factor, etc. It made possible to show how to reasonably select named and dependent non-energetic parameters, adapting them to specific applications. Finally, we turned to the synthesis task by demonstrating ways to achieve a compromise between a wide dynamic range and high range resolution requirements. The results of the conducted analysis and synthesis allow increasing the validity of design solutions for further promotion of S-lidars for environmental remote sensing and their better adaptation to a broad spectrum of specific applications and range scales. Full article

Given the continuous improvement in the capabilities of road vehicles to detect obstacles, the road friction coefficient is closely related to vehicular braking control, thus the detection of road surface conditions (RSC), and the level is crucial for driving safety. Non-contact technology for RSC sensing is becoming the main technological and research hotspot for RSC detection because of its fast, non-destructive, efficient, and portable characteristics and attributes. This study started with mapping the relationship between friction coefficients and RSC based on the requirement for autonomous driving. We then compared and analysed the main methods and research application status of non-contact detection schemes. In particular, the use of infrared spectroscopy is expected to be the most approachable technology path to practicality in the field of autonomous driving RSC detection owing to its high accuracy and environmental adaptability properties. We systematically analysed the technical challenges in the practical application of infrared spectroscopy road surface detection, studied the causes, and discussed feasible solutions. Finally, the application prospects and development trends of RSC detection in the fields of automatic driving and exploration robotics are presented and discussed. Full article

Psoriasis is considered a multifactorial and heterogeneous systemic disease with many underlying pathologic mechanisms having been elucidated; however, the pathomechanism is far from entirely known. This opinion article will demonstrate the potential relevance of the somatosensory Piezo2 microinjury-induced quad-phasic non-contact injury model in psoriasis through a multidisciplinary approach. The primary injury is suggested to be on the Piezo2-containing somatosensory afferent terminals in the Merkel cell–neurite complex, with the concomitant impairment of glutamate vesicular release machinery in Merkel cells. Part of the theory is that the Merkel cell–neurite complex contributes to proprioception; hence, to the stretch of the skin. Piezo2 channelopathy could result in the imbalanced control of Piezo1 on keratinocytes in a clustered manner, leading to dysregulated keratinocyte proliferation and differentiation. Furthermore, the author proposes the role of mtHsp70 leakage from damaged mitochondria through somatosensory terminals in the initiation of autoimmune and autoinflammatory processes in psoriasis. The secondary phase is harsher epidermal tissue damage due to the primary impaired proprioception. The third injury phase refers to re-injury and sensitization with the derailment of healing to a state when part of the wound healing is permanently kept alive due to genetical predisposition and environmental risk factors. Finally, the quadric damage phase is associated with the aging process and associated inflammaging. In summary, this opinion piece postulates that the primary microinjury of our “sixth sense”, or the Piezo2 channelopathy of the somatosensory terminals contributing to proprioception, could be the principal gateway to pathology due to the encroachment of our preprogrammed genetic encoding. Full article

As an important source of lithium and rare earth elements (REE) and other critical elements, pegmatites are of great strategic economic interest for present and future technological development. Identifying new pegmatite deposits is a strategy adopted by the European Union (EU) to decrease its import dependence on non-European countries for these raw materials. It is in this context that the GREENPEG project was established, an EU project whose main objective is to identify new deposits of pegmatites in Europe in an environmentally friendly way. Remote sensing is a non-contact exploration tool that allows for identifying areas of interest for exploration at the early stage of exploration campaigns. Several RS methods have been developed to identify Li-Cs-Ta (LCT) pegmatites, but in this study, a new methodology was developed to detect Nb-Y-F (NYF) pegmatites in the Tysfjord area in Norway. This methodology is based on spectral analysis to select bands of the Sentinel 2 satellite and adapt RS methods, such as Band Ratios and Principal Component Analysis (PCA), to be used as input in the Random Forest (RF) and other tree-based ensemble algorithms to improve the classification accuracy. The results obtained are encouraging, and the algorithm was able to successfully identify the pegmatite areas already known and new locations of interest for exploration were also defined. Full article