Search Results (100)

This study presents a hybrid sensor placement methodology that combines criterion-based candidate selection with advanced optimization algorithms. Four established selection criteria—modal kinetic energy (MKE), modal strain energy (MSE), modal assurance criterion (MAC) sensitivity, and mutual information (MI)—are used to evaluate DOF sensitivity and generate candidate pools. These are followed by one of four optimization algorithms—greedy, genetic algorithm (GA), particle swarm optimization (PSO), or simulated annealing (SA)—to identify the optimal subset of sensor locations. A key feature of the proposed approach is the incorporation of constraint dynamics using the Udwadia–Kalaba (U–K) generalized inverse formulation, which enables the accurate expansion of structural responses from sparse sensor data. The framework assumes a noise-free environment during the initial sensor design phase, but robustness is verified through extensive Monte Carlo simulations under multiple noise levels in a numerical experiment. This combined methodology offers an effective and flexible solution for data-driven sensor deployment in structural health monitoring. To clarify the rationale for using the Udwadia–Kalaba (U–K) generalized inverse, we note that unlike conventional pseudo-inverses, the U–K method incorporates physical constraints derived from partial mode shapes. This allows a more accurate and physically consistent reconstruction of unmeasured responses, particularly under sparse sensing. To clarify the benefit of using the U–K generalized inverse over conventional pseudo-inverses, we emphasize that the U–K method allows the incorporation of physical constraints derived from partial mode shapes directly into the reconstruction process. This leads to a constrained dynamic solution that not only reflects the known structural behavior but also improves numerical conditioning, particularly in underdetermined or ill-posed cases. Unlike conventional Moore–Penrose pseudo-inverses, which yield purely algebraic solutions without physical insight, the U–K formulation ensures that reconstructed responses adhere to dynamic compatibility, thereby reducing artifacts caused by sparse measurements or noise. Compared to unconstrained least-squares solutions, the U–K approach improves stability and interpretability in practical SHM scenarios. Full article

This paper proposes a high-sensitivity U-shaped optical fiber sensor based on indium tin oxide (ITO) for surface plasmon resonance (SPR) sensing. Finite element simulations reveal that introducing ITO enhances the surface electric field strength by 1.15× compared to conventional designs, directly boosting sensitivity. The U-shaped structure optimizes evanescent wave–metal film interaction, further improving performance. In an external refractive index (RI) range of 1.334–1.374 RIU, the sensor achieves a sensitivity of 4333 nm/RIU (1.85× higher than traditional fiber sensors) and a figure of merit (FOM) of 21.7 RIU⁻¹ (1.68× improvement). Repeatability tests show a low relative standard deviation (RSD) of 0.4236% for RI measurements, with a maximum error of 0.00018 RIU, confirming excellent stability. The ITO coating’s strong adhesion ensures long-term reliability. With its simple structure, ease of fabrication, and superior sensitivity/FOM, this SPR sensor is well-suited for high-precision biochemical detection in intelligent sensing systems. Full article

In recent years, China has been promoting aquaculture, but extensive water pollution caused by production activities and climate changes has resulted in losses exceeding 4.6 × 10⁷ kg of aquatic products. Widespread water pollution from production activities is a key issue that needs to be addressed in the aquaculture industry. Therefore, dynamic monitoring of water quality and fish-specific solutions are critical to the growth of fry. Here, a low-cost, small, and real-time monitorable bionic robotic fish based on YOLO-PWSL (PConv, Wise-ShapeIoU, and LGFB) is proposed to achieve intelligent control of aquaculture. The bionic robotic fish incorporates a caudal fin for propulsion and adaptive buoyancy control for precise depth regulation. It is equipped with various types of sensors and wireless transmission equipment, which enables managers to monitor water parameters in real time. It is also equipped with YOLO-PWSL, an improved underwater fish identification model based on YOLOv5s. YOLO-PWSL integrates three key enhancements. In fact, we designed a multilevel attention fusion block (LGFB) that enhances perception in complex scenarios, to optimize the accuracy of the detected frames, the Wise-ShapeIoU loss function was used, and in order to reduce the parameters and FLOPs of the model, a lightweight convolution method called PConv was introduced. The experimental results show that it exhibits excellent performance compared with the original model: the mAP@0.5 (mean average precision at the 0.5 IoU threshold) of the improved model reached 96.1%, the number of parameters and the amount of calculation were reduced by 1.8 M and 3.1 G, respectively, and the detected leakage was effectively reduced. In the future, the system will facilitate the monitoring of water quality and fish species and their behavior, thereby improving the efficiency of aquaculture. Full article

Sensors are devices that can detect changes in the external environment and convert them into signals. They are widely used in fields like industrial automation, smart homes, medical devices, automotive electronics, and the Internet of Things (IoT), enabling real-time data collection to enhance system intelligence and efficiency. With advancements in technology, sensors are evolving toward miniaturization, high sensitivity, and multifunctional integration. This paper employs the Direct Sampling Method (DSM) and neural networks to reconstruct the shape of perfect electric conductors from the sensed electromagnetic field. Transverse electric (TE) electromagnetic waves are transmitted to illuminate the conductor. The scattered fields in the x- and y-directions are measured by sensors and used in the method of moments for forward scattering calculations, followed by the DSM for initial shape reconstruction. The preliminary shape data obtained from the DSM are then fed into a U-net for further training. Since the training parameters of deep learning significantly affect the reconstruction results, extensive tests are conducted to determine optimal parameters. Finally, the trained neural network model is used to reconstruct TE images based on the scattered fields in the x- and y-directions. Owing to the intrinsic strong nonlinearity in TE waves, different regularization factors are applied to improve imaging quality and reduce reconstruction errors after integrating the neural network. Numerical results show that compared to using the DSM alone, combining the DSM with a neural network enables the generation of high-resolution images with enhanced efficiency and superior generalization capability. In addition, the error rate has decreased to below 15%. Full article

Cracks are common defects in metallic components, the presence of which can significantly affect service life and operational stability. Sensors based on electromagnetic resonators have relatively high sensitivity; however, they are limited in size, which restricts their coverage and makes large-area monitoring unattainable. The uneven internal field distribution within the resonator is a critical factor contributing to sensitivity variation at different locations. In this study, a vertical U-shaped ring structure is excited using a microstrip line. This allows the sensor to achieve large-area monitoring while maintaining sensitivity. The shift in resonance frequency is investigated and extracted as a characteristic feature for crack identification. The sensitivity of the measurement is 0.95 GHz/mm² for depth and 0.685 GHz/mm² for width. The proposed sensor can be used to detect potential cracks in metal structures. Full article

Metabotropic glutamate receptor 5 (mGlu₅) plays a fundamental role in synaptic plasticity, potentially serving as a therapeutic target for various neurodevelopmental and psychiatric disorders. Previously, we have shown that mGlu₅ can also signal from intracellular membranes in the cortex, hippocampus, and striatum. Using cytoplasmic Ca²⁺ indicators, we showed that activated cell surface mGlu₅ induced a transient Ca²⁺ increase, whereas the activation of intracellular mGlu₅ mediated a sustained Ca²⁺ elevation in striatal neurons. Here, we used the newly designed ER-targeted Ca²⁺ sensor, ER-GCaMP6-150, as a robust, specific approach to directly monitor mGlu₅-mediated changes in ER Ca²⁺ itself. Using this sensor, we found that the activation of cell surface mGlu₅ led to small declines in ER Ca²⁺, whereas the activation of ER-localized mGlu₅ resulted in rapid, more pronounced changes. The latter could be blocked by the Gq inhibitor FR9000359, the PLC inhibitor U73122, as well as IP₃ and ryanodine receptor blockers. These data demonstrate that like cell surface and nuclear mGlu₅, ER-localized receptors play a pivotal role in generating and shaping intracellular Ca²⁺ signals. Full article

The flexibility and rapid mobility of drones make them ideal for Internet of Things (IoT) applications, such as traffic control and data collection. Therefore, the autonomous navigation of 5G drones in unknown and dynamic environments has become a major research topic. Current methods rely on sensors to perceive the environment to plan the path from the start point to the target and to avoid obstacles; however, their limited field of view prevents them from moving in all directions and detecting and avoiding obstacles. This article proposes the deep learning (DL)-based autonomous navigation of 5G drones. This proposal uses sensors capable of perceiving the entire environment surrounding the drone and fuses sensor data to detect and avoid obstacles, plan a path, and move in all directions. We trained a convolution neural network (CNN) using a novel dataset we created for drone ascent and passing over obstacles, which achieved 99% accuracy. We also trained artificial neural networks (ANNs) to control drones and achieved a 100% accuracy. Experiments in the Gazebo environment demonstrated the efficiency of sensor fusion, and our proposal was the only one that perceived the entire environment, particularly above the drone. Furthermore, it excelled at detecting U-shaped obstacles and enabling drones to emerge from them. Full article

In this study of constructing gross primary production (GPP) based on solar-induced chlorophyll fluorescence (SIF) and analyzing its spatial–temporal characteristics and influencing factors, numerous challenges are encountered, especially in arid regions with fragile ecologies. Coupling SIF with other factors to construct the GPP and elucidating the influencing mechanisms of environmental factors could offer a novel theoretical method for the comprehensive analysis of GPP in arid regions. Therefore, we used the GPP station data from three different ecosystems (grasslands, farmlands, and desert vegetation) as well as the station and satellite data of environmental factors (including photosynthetically active radiation (PAR), a vapor pressure deficit (VPD), the air temperature (Tair), soil temperature (Tsoil), and soil moisture content (SWC)), and combined these with the TROPOMI SIF (RTSIF, generated through the reconstruction of SIF from the Sentinel-5P sensor), whose spatiotemporal precision was improved, the mechanistic light reaction model (MLR model), and different weather conditions. Then, we explored the spatiotemporal characteristics of GPP and its driving factors in local areas of Xinjiang. The results indicated that the intra-annual variation of GPP showed an inverted “U” shape, with the peak from June to July. The spatial attributes were positively correlated with vegetation coverage and sun radiation. Moreover, inverting GPP referred to the process of estimating the GPP of an ecosystem through models and remote sensing data. Based on the MLR model and RTSIF, the inverted GPP could capture more than 80% of the GPP changes in the three ecosystems. Furthermore, in farmland areas, PAR, VPD, Tair, and Tsoil jointly dominate GPP under sunny, cloudy, and overcast conditions. In grassland areas, PAR was the main influencing factor of GPP under all weather conditions. In desert vegetation areas, the dominant influencing factor of GPP was PAR on sunny days, VPD and Tair on cloudy days, and Tair on overcast days. Regarding the spatial correlation, the high spatial correlation between PAR, VPD, Tair, Tsoil, and GPP was observed in regions with dense vegetation coverage and low radiation. Similarly, the strong spatial correlation between SWC and GPP was found in irrigated farmland areas. The characteristics of a low spatial correlation between GPP and environmental factors were the opposite. In addition, it was worth noting that the impact of various environmental factors on GPP in farmland areas was comprehensively expressed based on a linear pattern. However, in grassland and desert vegetation areas, the impact of VPD on GPP was expressed based on a linear pattern, while the impact of other factors was more accurately represented through a non-linear pattern. This study demonstrated that SIF data combined with the MLR model effectively estimated GPP and revealed its spatial patterns and driving factors. These findings may serve as a foundation for developing targeted carbon reduction strategies in arid regions, contributing to improved regional carbon management. Full article

To address the challenge of measuring the plate wall offset at the U-shaped groove positions after assembling large cylindrical shell arc segments, this paper proposes a measurement method based on multi-line-structured light vision sensors. The sensor is designed and calibrated to collect U-shaped groove workpiece images containing multiple laser stripes. The central points of the laser stripes are extracted and matched to their corresponding light plane equations to obtain local point cloud data of the measured positions. Subsequently, point cloud data from the plate wall regions on both sides of the groove are separated, and the plate wall offset is calculated using the local distance computation method between planes in space. The experimental results demonstrate that, when measuring a standard sphere with a diameter of 30 mm from multiple angles, the measurement uncertainty is ±0.015 mm within a 95% confidence interval. Within a measurement range of 155 mm × 220 mm × 80 mm, using articulated arm measurements as reference values, the plate wall offset measurement uncertainty of the multi-line-structured light vision sensor is ±0.013 mm within a 95% confidence interval, showing close agreement with reference values. Full article

Fiber optic sensors have gained popularity over the last few decades. This is due to their numerous advantages, such as good metrological parameters, biocompatibility and resistance to magnetic and electric fields and environmental pollution. However, those built from glass fiber have one main disadvantage—they are fragile, meaning they can be easily damaged, even by the presence of vibration. Due to the great progress made by material research recently, it is possible to build such a sensor with polymer fibers instead. Although those fibers have worse transmission parameters compared to telecommunication fibers, they provide the possibility to realize flexible fiber optic sensors. Taking into consideration other advantages of such fibers, including biocompatibility, electromagnetic resistance and even, biodegradation characteristics, as well as there being a variety of materials we can use, it can be seen that those materials are beneficial to produce fiber optic sensors. This paper aims to provide researchers with guidelines on the factors to consider when choosing a material for bent fiber optic sensors, depending on the application. Full article

Accurate pressure measurement is crucial for understanding ocean dynamics in marine research. However, pressure sensors based on strain measurement principles are significantly affected by temperature variations, impacting the accuracy of depth measurements. This study investigates the SBE37-SM sensor and presents an improved calibration method based on a constant-pressure, variable-temperature scheme that effectively addresses temperature-induced deviations in pressure measurement. Experiments were conducted across a pressure range of 2000 dbar to 6000 dbar and a temperature range of 2 °C to 35 °C, establishing a comprehensive pressure–temperature calibration grid. The results show that, at a pressure of 6000 dbar, temperature-induced variations in readings for brand new SBE37-SM sensors can reach up to 9 dbar, while, for used sensors, they exceed 12 dbar, following a U-shaped trend. After applying a polynomial regression model for calibration, these variations were reduced to within ±0.5 dbar, significantly reducing the measurement uncertainty of the sensors in complex marine environments. This method underscores the necessity of further optimizing the CTD system’s temperature compensation mechanism during calibration and highlights the importance of regular calibration to minimize measurement uncertainty. Full article

In order to solve the problem of a meteorology temperature sensor not being able to touch a liquid, an open gas cavity structure immersed in the liquid was designed. According to the characteristics that the temperature sensing position of the meteorological temperature sensor is in the bottom area of the gas cavity, a simulation and experimental study of the bottom temperature field of φ50 mm cylindrical and φ(50-35-25) mm stepped column gas cavities were carried out. The experimental results at (−30~30) °C show that the gas stability of the gas cavities was better than that of the liquid constant temperature bath, and the performance of the cylindrical gas cavity was the best. The gas temperature stability of the stepped column gas cavity and the liquid constant temperature bath follow a strong trend. The maximum stability of the cylindrical gas cavity is 0.0054 °C, and the maximum stability of the stepped column gas cavity is 0.0080 °C. The results also show that the maximum uniformity of the stepped gas cavity is 0.0077 °C, and the maximum uniformity of the cylindrical gas cavity is 0.0528 °C. The uncertainty introduced in the measurement process was evaluated to ensure the confidence of the experimental data. The maximum value of the extended uncertainty was U = 0.0027 °C (k = 2). Compared with the solid-state constant temperature bath calibration method, the temperature sensor of different shapes can be directly placed into the gas cavity without the need for the meteorological temperature sensor to be closely attached to the wall of the gas cavity, and a sealing plug is used to seal the cavity mouth. The operation is very convenient, rapid turnover of the calibration of the meteorological temperature sensor can be achieved, and the work efficiency can be improved. Superior stability and uniformity can be obtained compared to gas constant temperature cavities. This study provides a valuable reference for the structural design of large-volume gas cavities and provides support and guarantee for global climate change monitoring. Full article

The tunability of spectral lineshapes, ranging from Lorentzian to Fano profiles, is essential for advancing nanoscale photonic technologies. Conventional far-field techniques are insufficient for studying nanoscale phenomena, particularly within the terahertz (THz) range. In this work, we use a U-shaped resonant ring on a waveguide substrate to achieve precise modulation of Lorentzian, Fano, and antiresonance profiles. THz scattering scanning near-field optical microscopy (s-SNOM) reveals the underlying physical mechanism of these transitions, driven by time-domain phase shifts between the background excitation from the waveguide and the resonance of the U-shaped ring. Our approach reveals a pronounced asymmetry in the near-field response, which remains undetectable in far-field systems. The ability to control spectral lineshapes at the nanoscale presents promising applications in characterizing composite nanoresonators and developing nanoscale phase sensors. Full article

Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily on image-based approaches, which may not be able to fully characterize radar sensor data or efficiently further utilize them for perception tasks. This paper rethinks the approach to modeling radar signals and proposes a novel U-shaped multilayer perceptron network (U-MLPNet) that aims to enhance the learning of omni-dimensional spatio-temporal dependencies. Our method involves innovative signal processing techniques, including a 3D CNN for spatio-temporal feature extraction and an encoder–decoder framework with cross-shaped receptive fields specifically designed to capture the sparse and non-uniform characteristics of radar signals. We conducted extensive experiments using a diverse dataset of urban driving scenarios to characterize the sensor’s performance in multi-view semantic segmentation and object detection tasks. Experiments showed that U-MLPNet achieves competitive performance against state-of-the-art (SOTA) methods, improving the mAP by 3.0% and mDice by 2.7% in RD segmentation and AR and AP by 1.77% and 2.03%, respectively, in object detection. These improvements signify an advancement in radar-based perception for autonomous vehicles, potentially enhancing their reliability and safety across diverse driving conditions. Full article

In this paper, the impact of Lorentz forces and temperature on the natural frequencies of a piezoresistive sensor composed of two microcantilevers with integrated U-shaped thin-film aluminum heaters are investigated. Two types of experiments were performed. In the first, the sensor was placed in a magnetic field so that the current flowing in the heater, in addition to raising the temperature, produced Lorentz forces, inducing normal stresses in the plane of one of the microcantilevers. In the second, which were conducted without magnetic fields, only the temperature variation of the natural frequency was left. In processing of the results, the thermal variations were subtracted from the variations due to both Lorentz forces and temperature in the natural frequency, resulting in the influence of the Lorentz forces only. Theoretical relations for the Lorentz frequency offsets were derived. An indirect method of estimating the natural frequency of one of the cantilevers, through a particular cusp point in the amplitude–frequency response of the sensor, was used in the investigations. The findings show that for thin microcantilevers with silicon masses on the order of 4 × 10⁻⁷ g and currents of 25 µA, thermal eigenfrequency variations are dominant. The results may have applications in the design of similar microsensors with vibrational action. Full article