Search Results (487)

This article reports on the interrogation of fiber Bragg grating (FBG)-based sensors that are multiplexed in an asymmetric tree topology. At each stage in the topology, FBGs are connected at one output port of a 50:50 coupler with fibers of different lengths. This asymmetric structure allows the simultaneous interrogation of long-distance and parallel sensor networks to be realized. Time- and wavelength-division multiplexing techniques are used to multiplex the FBGs. Using the heterodyne detection technique, high-sensitivity detection of reflection signals that have been weakened by losses induced by a round-trip transmission through the couplers and long-distance propagation is performed. Quasi-distributed FBGs are interrogated simultaneously, over distances ranging from 15 m to 80 km. Full article

Light detection and ranging (LiDAR) sensors are essential for applications where high-resolution distance and velocity measurements are required. In particular, frequency-modulated continuous wave (FMCW) LiDAR, compared with other LiDAR implementations, provides superior receiver sensitivity, enhanced range resolution, and the capability to measure velocity. Integrating LiDARs into electronic and photonic semiconductor chips can lower their cost, size, and power consumption, making them affordable for cost-sensitive applications. Additionally, simple designs are required, such as FMCW signal generation by the direct modulation of the current of a semiconductor laser. However, semiconductor lasers are inherently nonlinear, and the driving waveform needs to be optimized to generate linear FMCW signals. In this paper, we employ pre-distortion techniques to compensate for chirp nonlinearity, achieving frequency nonlinearities of 0.0029% for the down-ramp and the up-ramp at 55 kHz. Experimental results demonstrate a highly accurate LiDAR system with a resolution of under 5 cm, operating over a 210-m range through single-mode fiber, which corresponds to approximately 308 m in free space, towards meeting the requirements for long-range autonomous driving. Full article

This review systematically examines recent advances in self-powered wireless sensing technologies based on triboelectric nanogenerators (TENGs), focusing on innovative methods that leverage breakdown discharge effects to achieve high-precision and long-distance signal transmission. These methods offer novel technical pathways and theoretical frameworks for next-generation wireless sensing systems. To address the core limitations of conventional wireless sensors, such as a restricted transmission range, high power consumption, and suboptimal integration, this analysis elucidates the mechanism of the generation of high-frequency electromagnetic waves through localized electric field ionization induced by breakdown discharge. Key research directions are synthesized to enhance TENG-based sensing capabilities, including novel device architectures, the optimization of RLC circuit models, the integration of machine learning algorithms, and power management strategies. While current breakdown discharge sensors face challenges such as energy dissipation, multimodal coupling complexity, and signal interpretation barriers, future breakthroughs in material engineering and structural design are anticipated to drive advancements in efficiency, miniaturization, and intelligent functionality in this field. Full article

In the field of intelligent decision-making, time-series data collected by sensors serves as the core carrier for interaction between the physical and digital worlds. Accurate analysis is the cornerstone of decision-making in critical scenarios, such as industrial monitoring and intelligent transportation. However, the inherent spatio-temporal coupling characteristics and cross-period long-range dependency of sensor data cause traditional time-series prediction methods to face performance bottlenecks in feature decoupling and multi-scale modeling. This study innovatively proposes a Spatio-Temporal Attention-Enhanced Network (TSEBG). Breaking through traditional structural designs, the model employs a Squeeze-and-Excitation Network (SENet) to reconstruct the convolutional layers of the Temporal Convolutional Network (TCN), strengthening the feature expression of key time steps through dynamic channel weight allocation to address the redundancy issue of traditional causal convolutions in local pattern capture. A Bidirectional Gated Recurrent Unit (BiGRU) variant based on a global attention mechanism is designed, leveraging the collaboration between gating units and attention weights to mine cross-period long-distance dependencies and effectively alleviate the gradient disappearance problem of Recurrent Neural Network (RNN-like) models in multi-scale time-series analysis. A hierarchical feature fusion architecture is constructed to achieve multi-dimensional alignment of local spatial and global temporal features. Through residual connections and the dynamic adjustment of attention weights, hierarchical semantic representations are output. Experiments show that TSEBG outperforms current dominant models in time-series single-step prediction tasks in terms of accuracy and performance, with a cross-dataset R² standard deviation of only 3.7%, demonstrating excellent generalization stability. It provides a novel theoretical framework for feature decoupling and multi-scale modeling of complex time-series data. Full article

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. 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

A coding ruler is a device that marks position information in the fordigital signals, and a code reader is a device that decodes the signals on the coding ruler and converts them into digital signals. The code reader and encoder ruler are key devices in ensuring the positioning accuracy of coke oven locomotives and the safety of coke production. They are common information transmission and positioning detection devices that can provide accurate monitoring and information feedback for the position and speed of coke oven locomotives. Four encoding methods were studied, namely, binary encoding, Gray code encoding, shift continuous encoding, and hybrid encoding. The application scenarios and encoding characteristics of each encoding method are summarized in this paper. Hybrid encoding combines the advantages of two different encoding methods, absolute and incremental encoding, to achieve higher accuracy and stability. Hybrid coding has high positioning accuracy in the long-range coke oven tampering tracks, ensuring the accuracy and high efficiency of the tampering operation. A certain number of opposing laser sensors are installed inside the code reader to obtain 0/1 encoding and read the movement displacement of the code reader on the ruler. In order to effectively detect the swing of the coding ruler, a certain number of distance sensors are installed on both sides and on the same side of the code reader. Ruler swing is accurately detected by the sensors, which output and process corresponding signals. Timely adjustment and correction measures are taken on the production line according to the test results, which not only improves detection accuracy but also enhances the stability and reliability of the system. Full article

The advent of digital mining has become a tangible reality in recent years. This digital evolution requires a predictive understanding of key elements, particularly considering the reliable communication infrastructures needed for autonomous machines. The LoRa technology and its underground propagation behavior can make an important contribution to this digitalization. Since LoRa operates with a high signal budget and long ranges in sub-GHz frequencies, its behavior is very promising for underground sensor networks. The aim of the development and series of measurements was to observe LoRa’s applicability and propagation behavior in active salt mines and to detect and identify effects arising from the special environment. The propagation of LoRa was measured via packet loss and signal strength in line-of-sight and non-line-of-sight configurations over entire mining sections. The aim was to analyze the performance of LoRa at the macroscopic level. LoRa operated at 868 MHz in the free band, and units were equipped with omni-directional antennas. The K+S Group’s active salt and potash mine Werra, Germany, was kindly opened as a distinctive experimental setting. The LoRa exhibited characteristics that were highly distinctive in this environment. The presence of the massive salt allowed the signal to bounce along drift edges with near-perfect reflection, which enabled travel over kilometers due to a waveguide-like effect. A packet loss of below $15\%$ showed that LoRa communication was possible over distances exceeding 1000 m with no line-of-sight in room-and-pillar structures. Measured differences of $\Delta 50\mathrm{dBm}$ values confirmed consistent path loss across different materials and tunnel geometries. This effect occurs due to the physical structure of the mining drifts, facilitating the containment and direction of signals, minimizing losses during propagation. Further modeling and measurements are of great interest, as they indicate that LoRa can achieve even better outcomes underground than in urban or indoor environments, as this waveguide effect has been consistently observed. Full article

System architecture was developed to solve the issues of short pupil distance and mismatch between the simulated wavelength range and the sensor in the simulator of small targets in space. The system consists of Liquid Crystal on Silicon (LCOS), a Polarizing Beam Splitter (PBS), a dual free-form surface-illumination system, and a long-exit-pupil-distance projection system. The innovatively designed long exit pupil distance projection system can achieve an exit pupil distance of 1250 mm, covering the visible and near-infrared bands from 400 to 950 nm. The dual free-form surface-illumination system reaches a divergence angle of ±4.3° and an illumination non-uniformity of 4.7%. Experimental validation shows that the system’s star position error is better than −3.94″, and the angular distance error between stars does not exceed −7.69″. The radiation simulation accuracy for stars ranging from magnitude 3 to 6 is between −0.049 and 0.085 magnitudes, demonstrating high-precision simulation capabilities for both geometric and radiation characteristics. The research results set a critical theoretical foundation for the development of high-fidelity space target simulators, and the proposed dual free-form surface-design method and wide-spectrum aberration compensation technology provide a new paradigm for precision optical system design. Full article

Resonators are passive components that respond to an excitation signal by oscillating at their natural frequency with an exponentially decreasing amplitude. When combined with antennas, resonators enable purely passive chipless sensors that can be read wirelessly. In this contribution, we investigate the properties of dielectric resonators, which combine the following functionalities: They store the readout signal for a sufficiently long time and couple to free space electromagnetic waves to act as antennas. Their mode spectrum, along with their resonant frequencies, quality factor, and coupling to electromagnetic waves, is investigated using a commercial finite element program. The fundamental mode exhibits a too-low overall Q factor. However, some higher modes feature overall Q factors of several thousand, which allows them to act as transponders operating without integrated circuits, batteries, or antennas. To experimentally verify the simulations, isolated dielectric resonators exhibiting modes with similarly high radiation-induced and dissipative quality factors were placed on a low-loss, low permittivity ceramic holder, allowing their far-field radiation properties to be measured. The radiation patterns investigated in the laboratory and outdoors agree well with the simulations. The resulting radiation patterns show a directivity of approximately 7.5 dBi at 2.5 GHz. The sensor was then heated in a ceramic furnace with the readout antenna located outside at room temperature. Wireless temperature measurements up to 700 °C with a resolution of 0.5 °C from a distance of 1 m demonstrated the performance of dielectric resonators for practical applications. Full article

Monitoring urban microenvironments using low-cost sensors effectively addresses the spatiotemporal limitations of conventional monitoring networks. However, their widespread adoption is hindered by concerns regarding data quality. Calibrating these sensors is crucial for enabling their large-scale deployment and increasing confidence among researchers and users. This study focuses on an internet of things (IoT) application in Wuhan, China, aiming to enhance the quality of long-term hourly air temperature data collected by low-cost sensors through on-site calibration. Multiple linear regression (MLR) and light gradient boosting machine (LightGBM) algorithms were employed for calibration, with leave-one-out cross-validation (LOOCV) being used for model evaluation. Factors, such as multiple scenarios, spatial distances, and seasonal variations, were also examined for their influence on long-term data calibration. The experimental findings revealed that the LightGBM method consistently outperformed MLR. Calibration using this approach markedly improved the sensor data quality, with the R-squared (R²) value of the sensor with the poorest raw data increasing from 0.416 to 0.957, its mean absolute error (MAE) decreasing from 6.255 to 1.680, and its root mean square error (RMSE) being reduced from 7.881 to 2.148. This study demonstrates the application potential of using LightGBM as an advanced machine learning (ML) method in innovative low-cost sensors, thereby providing a method of obtaining high-quality and real-time information for urban environmental and public health research. Full article

Unmanned aerial vehicles (UAVs) equipped with hyperspectral hardware systems are widely used in urban planning and land classification. However, hyperspectral sensors generate large volumes of data that are rich in both spatial and spectral information, making its efficient processing in resource-constrained devices challenging. While transformers have been widely adopted for hyperspectral image classification due to their global feature extraction capabilities, their quadratic computational complexity limits their applicability for resource-constrained devices. To address this limitation and enable the real-time processing of hyperspectral data on UAVs, we propose a lightweight multi-head MambaOut with a CosTaylorFormer (LMHMambaOut-CosTaylorFormer). First, 3D-2D CNN is used to extract both spatial and spectral shallow features from hyperspectral images. Following this, one branch employs a linear transformer, CosTaylorFormer, to extract global spectral information. More specifically, we propose CosTaylorFormer with a cosine function, adjusting the weights based on the spectral curve distribution, which is more conducive to establishing long-distance spectral dependencies. Meanwhile, compared with other linearized transformers, the CosTaylorFormer we propose better improves model performance. For the other branch, we propose multi-head MambaOut to extract global spatial features and enhance the network classification effect. Moreover, a dynamic information fusion strategy is proposed to adaptively fuse spatial and spectral information. The proposed network is validated on four datasets (IP, WHU-Longkou, SA, and PU) and compared with several models, demonstrating its superior classification accuracy; however, the number of model parameters is only 0.22 M, thus achieving better balance between model complexity and accuracy. Full article

LoRa, as a leading LPWAN technology, plays a pivotal role in enabling long-range, low-power wireless communication, especially in agricultural IoT applications. This study examines the propagation characteristics of 433 MHz LoRa signals in maize fields, focusing on signal attenuation, RSSI, SNR, and packet loss under dense crop conditions. Field experiments were conducted in Wuwei, Gansu Province, with validation tests in Tongliao, Inner Mongolia. The effects of transmitter and receiver antenna heights on signal quality and propagation distance were systematically analyzed. Results show a consistent improvement in signal quality and range with increased antenna height. Path loss models were developed using regression analysis, achieving high predictive accuracy (R² > 0.9). Validation confirmed the models’ reliability, offering valuable insights for deploying wireless sensor networks (WSNs) in agriculture. Future research will integrate machine learning for dynamic modeling and explore variations across crop growth stages. Full article

The Multi-Objective Optimization Problem (MOOP) in Wireless Sensor Networks (WSNs) is a challenging issue that requires balancing multiple conflicting objectives, such as maintaining coverage, connectivity, and network lifetime all together. These objectives are important for a functioning WSN safety-critical applications, whether in environmental monitoring, military surveillance, or smart cities. To address these challenges, we propose a novel bio-inspired Bird Flocking Node Scheduling algorithm, which takes inspiration from the natural flocking behavior of birds migrating over long distance to optimize sensor node activity in a distributed and energy-efficient manner. The proposed algorithm integrates the Lyapunov function to maintain connected coverage while optimizing energy efficiency, ensuring service availability and reliability. The effectiveness of the algorithm is evaluated through extensive simulations, namely MATLAB R2018b simulator coupled with a Pareto front, comparing its performance with our previously developed BAT node scheduling algorithm. The results demonstrate significant improvements across key performance metrics, specifically, enhancing network coverage by 8%, improving connectivity by 10%, and extending network lifetime by an impressive 80%. These findings highlight the potential of bio-inspired Bird Flocking optimization techniques in advancing WSN dependability, making them more sustainable and suitable for real-world WSN safety-critical systems. Full article

The development of underwater high-precision navigation technology is of great significance for the application of autonomous underwater vehicles (AUVs). Traditional long baseline (LBL) positioning systems require pre-deployment and the calibration of multiple beacons, which consumes valuable time and manpower. In contrast, the range-only single-beacon (ROSB) positioning technology can help autonomous underwater vehicles (AUVs) obtain accurate position information by deploying only one beacon. This method greatly reduces the time and workload of deploying beacons, showing high application potential and cost ratio. Given the operational constraints of AUV open-ocean navigation with single-beacon weak observations and absence of valid a priori positioning data in calibration zones, a multi-sensor underwater virtual beacon localization framework was established, proposing a differential Chan–Gauss–Newton (DCGN) methodology for submerged vehicles. Based on inertial navigation, the method uses the distance measurement information from a single beacon and observations from multiple sensors, such as the Doppler velocity log (DVL) and pressure sensor, to obtain accurate position estimates by discriminating the initial position of multiple hypotheses. A simulation experiment and lake test show that the proposed method not only significantly improves the positioning accuracy and convergence speed, but also shows high reliability. Full article