Sensors

26 pages, 3638 KiB

Open AccessArticle

Data Collection and Remote Control of an IoT Electronic Nose Using Web Services and the MQTT Protocol

by Juan J. Pérez-Solano and Antonio Ruiz-Canales

Sensors 2025, 25(14), 4356; https://doi.org/10.3390/s25144356 - 11 Jul 2025

An electronic nose is a device capable of characterizing samples of substances and products by their aroma. The development of such devices relies on a series of non-specific sensors that react to gases and generate different signals, which can be used for compound [...] Read more.

An electronic nose is a device capable of characterizing samples of substances and products by their aroma. The development of such devices relies on a series of non-specific sensors that react to gases and generate different signals, which can be used for compound identification and sample classification. The deployment of such devices often requires the possibility of having remote access over the Internet to manage their operation and to collect the sampled data. In this context, the application of web technologies to the monitoring and supervision of these systems connected to the Internet, which can be considered as an Internet of Things (IoT) device, offers the advantage of not requiring the development of client-side applications. Users can employ a browser to connect to the IoT device and monitor or control its operation. Moreover, web design enables the development of cross-platform web monitoring systems. In addition, the inclusion of the MQTT protocol and the utilization of a virtual private network (VPN) enable a secure transmission and collection of the sampled data. In this work, all these technologies have been applied in the development of a system to manage and collect data to monitor rot in lemons treated with sodium benzoate before harvest. Full article

(This article belongs to the Special Issue Electronic Nose and Artificial Olfaction)

22 pages, 942 KiB

Open AccessArticle

Iterative Learning Control Without Resetting Conditions of an Algorithm Based on a Finite-Time Zeroing Neural Network

by Yuanyuan Chai, Furong Zhang, Donglin Jiang, Liying Shao, Jing Wang and Jing Li

Sensors 2025, 25(14), 4355; https://doi.org/10.3390/s25144355 - 11 Jul 2025

Abstract

In this paper, an iterative learning control without resetting conditions based on a finite-time zeroing neural network (NRCILC-FTZNN) is designed for trajectory tracking of a robotic manipulator operating under external disturbances and executing repetitive tasks. A finite-time zeroing neural network (FTZNN) is developed [...] Read more.

In this paper, an iterative learning control without resetting conditions based on a finite-time zeroing neural network (NRCILC-FTZNN) is designed for trajectory tracking of a robotic manipulator operating under external disturbances and executing repetitive tasks. A finite-time zeroing neural network (FTZNN) is developed to eliminate external disturbances and enhance convergence. Furthermore, an iterative learning control without resetting conditions based on the FTZNN is proposed to automatically provide the initial state value in each iteration, thereby eliminating the need for reset conditions. The trajectory-tracking errors, measured by the mean absolute error (MAE), are reduced by 46.89% and 63.29% compared to other schemes. Furthermore, the tracking errors of the proposed NRCILC-FTZNN method converge to zero in fewer iterations than those of the other methods. Simulation results demonstrate the convergence of the robotic manipulator system under disturbances to confirm the effectiveness of NRCILC-FTZNN scheme. Full article

(This article belongs to the Section Sensors and Robotics)

20 pages, 108154 KiB

Open AccessArticle

Masks-to-Skeleton: Multi-View Mask-Based Tree Skeleton Extraction with 3D Gaussian Splatting

by Xinpeng Liu, Kanyu Xu, Risa Shinoda, Hiroaki Santo and Fumio Okura

Sensors 2025, 25(14), 4354; https://doi.org/10.3390/s25144354 - 11 Jul 2025

Abstract

Accurately reconstructing tree skeletons from multi-view images is challenging. While most existing works use skeletonization from 3D point clouds, thin branches with low-texture contrast often involve multi-view stereo (MVS) to produce noisy and fragmented point clouds, which break branch connectivity. Leveraging the recent [...] Read more.

Accurately reconstructing tree skeletons from multi-view images is challenging. While most existing works use skeletonization from 3D point clouds, thin branches with low-texture contrast often involve multi-view stereo (MVS) to produce noisy and fragmented point clouds, which break branch connectivity. Leveraging the recent development in accurate mask extraction from images, we introduce a mask-guided graph optimization framework that estimates a 3D skeleton directly from multi-view segmentation masks, bypassing the reliance on point cloud quality. In our method, a skeleton is modeled as a graph whose nodes store positions and radii while its adjacency matrix encodes branch connectivity. We use 3D Gaussian splatting (3DGS) to render silhouettes of the graph and directly optimize the nodes and the adjacency matrix to fit given multi-view silhouettes in a differentiable manner. Furthermore, we use a minimum spanning tree (MST) algorithm during the optimization loop to regularize the graph to a tree structure. Experiments on synthetic and real-world plants show consistent improvements in completeness and structural accuracy over existing point-cloud-based and heuristic baseline methods. Full article

(This article belongs to the Section Remote Sensors)

► Show Figures

Figure 1

17 pages, 2681 KiB

Open AccessArticle

Magnetically Retrievable Nanoparticles with Tailored Surface Ligands for Investigating the Interaction and Removal of Water-Soluble PFASs in Natural Water Matrices

by Yunfei Zhang, Jacqueline Ortiz, Shi He, Xianzhi Li, Bableen Kaur, Bing Cao, Zachariah Seiden, Shuo Wu and He Wei

Sensors 2025, 25(14), 4353; https://doi.org/10.3390/s25144353 - 11 Jul 2025

Abstract

Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals widely used in industrial applications and have become persistent environmental contaminants due to their chemical stability. Water-soluble PFASs with fewer than ten carbon atoms, such as perfluorooctanoic acid (PFOA), are particularly concerning because of their [...] Read more.

Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals widely used in industrial applications and have become persistent environmental contaminants due to their chemical stability. Water-soluble PFASs with fewer than ten carbon atoms, such as perfluorooctanoic acid (PFOA), are particularly concerning because of their high solubility in water, environmental mobility, and resistance to degradation. In this work, we present an eco-friendly Fe₃O₄ magnetic nanoparticle (MNP)-based platform for the detection and removal of PFOA from water. The synthesized iron oxide MNPs exhibit rapid and strong magnetic responsiveness, enabling efficient magnetic separation for both PFOA detection and removal. To optimize surface affinity for PFOA, we functionalized the MNPs with distinctive ligands, including polyethylene glycol (PEG), β-cyclodextrin (βCD), and dopamine (DA). Among these, PEG and DA showed notable binding affinity toward PFOA, as confirmed by infrared spectroscopy and colorimetric assays. After incubation with the functionalized MNPs followed by magnetic retrieval, we achieved over 90% PFOA removal efficiencies, demonstrating the potential for future research in PFAS remediation technologies. Importantly, the system was validated using deionized, tap, and lake water, all of which yielded comparable and promising results. This study provides a promising, eco-friendly, and recyclable nanomaterial platform for investigating the crucial role of surface chemistry in nanoparticle–PFAS interactions through ligand-mediated magnetic separation. Full article

(This article belongs to the Special Issue Chemical Sensors for Toxic Chemical Detection: 2nd Edition)

► Show Figures

Figure 1

11 pages, 1204 KiB

Open AccessArticle

High-Power Coupled Wideband Low-Frequency Antenna Design for Enhanced Long-Range Loran-C Timing Synchronization

by Jingqi Wu, Xueyun Wang, Juncheng Liu, Chenyang Fan, Chenxi Zhang, Zilun Zeng, Liwei Wang and Jianchun Xu

Sensors 2025, 25(14), 4352; https://doi.org/10.3390/s25144352 - 11 Jul 2025

Abstract

Precise timing synchronization remains a fundamental requirement for modern navigation and communication systems, where the miniaturization of Loran-C infrastructure presents both technical challenges and practical significance. Conventional miniaturized loop antennas cannot simultaneously meet the requirements of the Loran-C signal for both radiation intensity [...] Read more.

Precise timing synchronization remains a fundamental requirement for modern navigation and communication systems, where the miniaturization of Loran-C infrastructure presents both technical challenges and practical significance. Conventional miniaturized loop antennas cannot simultaneously meet the requirements of the Loran-C signal for both radiation intensity and bandwidth due to inherent quality factor (Q) limitations. A sub-cubic-meter impedance matching (IM) antenna is proposed, featuring a −20 dB bandwidth of 18 kHz and over 7-fold radiation enhancement. The proposed design leverages a planar-transformer-based impedance matching network to enable efficient 100 kHz operation in a compact form factor, while a resonant coil structure is adopted at the receiver side to enhance the system’s sensitivity. The miniaturized Loran-C timing system incorporating the IM antenna achieves an extended decoding range of >100 m with merely 100 W input power, exceeding conventional loop antennas limited to 30 m operation. This design successfully achieves overall miniaturization of the Loran-C timing system while breaking through the current transmission distance limitations of compact antennas, extending the effective transmission range to the hundred-meter scale. The design provides a case for developing compact yet high-performance Loran-C systems. Full article

(This article belongs to the Section Communications)

37 pages, 6556 KiB

Open AccessArticle

Research on the Bearing Remaining Useful Life Prediction Method Based on Optimized BiLSTM

by Yi Zou, Wenlei Sun, Tiantian Xu and Bingkai Wang

Sensors 2025, 25(14), 4351; https://doi.org/10.3390/s25144351 - 11 Jul 2025

Abstract

The predictive performance of the remaining useful life (RUL) estimation model for bearings is of utmost importance, and the setting method of the bearing degradation threshold is crucial for detecting its early degradation point, as it significantly affects the performance of the RUL [...] Read more.

The predictive performance of the remaining useful life (RUL) estimation model for bearings is of utmost importance, and the setting method of the bearing degradation threshold is crucial for detecting its early degradation point, as it significantly affects the performance of the RUL prediction model. To solve these problems, a bearing RUL prediction method based on early degradation detection and optimized BiLSTM is proposed: an optimized VMD combined with the Pearson correlation coefficient is used to denoise the bearing signal. Afterward, multi-domain features are extracted and evaluated using different metrics. The optimal degradation feature is then selected. Furthermore, KPCA is used to integrate the features and establish the health indicators (HIs) for early degradation detection of bearings using a sliding window method combined with the 3σ (3-sigma) criterion and the quartile method. The RUL prediction model is developed by combining the BiLSTM network with the attention mechanism and by employing the SSA to adaptively update the network parameters. The proposed RUL prediction model is tested on various datasets to evaluate its generalization ability and applicability. The obtained results demonstrate that the proposed denoising method has high performance. The dynamic 3σ-threshold setting method accurately detects the early degradation points of bearings. The proposed RUL prediction model has high performance and fitting capacity, as well as very high generalization ability and applicability, enabling the early prediction of bearing RUL. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

16 pages, 6605 KiB

Open AccessArticle

Analysis of Doline Microtopography in Karst Mountainous Terrain Using UAV LiDAR: A Case Study of ‘Gulneomjae’ in Mungyeong City, South Korea

by Juneseok Kim and Ilyoung Hong

Sensors 2025, 25(14), 4350; https://doi.org/10.3390/s25144350 - 11 Jul 2025

Abstract

This study utilizes UAV-based LiDAR to analyze doline microtopography within a karst mountainous terrain. The study area, ‘Gulneomjae’ in Mungyeong City, South Korea, features steep slopes, limited accessibility, and abundant vegetation—conditions that traditionally hinder accurate topographic surveying. UAV LiDAR data were acquired using [...] Read more.

This study utilizes UAV-based LiDAR to analyze doline microtopography within a karst mountainous terrain. The study area, ‘Gulneomjae’ in Mungyeong City, South Korea, features steep slopes, limited accessibility, and abundant vegetation—conditions that traditionally hinder accurate topographic surveying. UAV LiDAR data were acquired using the DJI Matrice 300 RTK equipped with a Zenmuse L2 sensor, enabling high-density point cloud generation (98 points/m²). The point clouds were processed to remove non-ground points and generate a 0.25 m resolution DEM using TIN interpolation. A total of seven dolines were detected and delineated, and their morphometric characteristics—including area, perimeter, major and minor axes, and elevation—were analyzed. These results were compared with a 1:5000-scale DEM derived from the 2013 National Basic Map. Visual and numerical comparisons highlighted significant improvements in spatial resolution and feature delineation using UAV LiDAR. Although the 1:5000-scale DEM enables general doline detection, UAV LiDAR facilitates more precise boundary extraction and morphometric analysis. The study demonstrates the effectiveness of UAV LiDAR for detailed topographic mapping in complex karst terrains and offers a foundation for future automated classification and temporal change analysis. Full article

(This article belongs to the Special Issue Advancing Land Monitoring through Synergistic Harmonization of Optical, Radar and Lidar Satellite Technologies)

21 pages, 3097 KiB

Open AccessArticle

Hydrodynamic Characterisation of the Inland Valley Soils of the Niger Delta Area for Sustainable Agricultural Water Management

by Peter Uloho Osame and Taimoor Asim

Sensors 2025, 25(14), 4349; https://doi.org/10.3390/s25144349 - 11 Jul 2025

Abstract

Since farmers in the inland valley region of the Niger Delta mostly rely on experience rather than empirical evidence when it comes to irrigation, flood irrigation being the most popular technique, the region’s agricultural sector needs more efficient water management. In order to [...] Read more.

Since farmers in the inland valley region of the Niger Delta mostly rely on experience rather than empirical evidence when it comes to irrigation, flood irrigation being the most popular technique, the region’s agricultural sector needs more efficient water management. In order to better understand the intricate hydrodynamics of water flow through the soil subsurface, this study aimed to develop a soil column laboratory experimental setup for soil water infiltration. The objective was to measure the soil water content and soil matric potential at 10 cm intervals to study the soil water characteristic curve as a relationship between the two hydraulic parameters, mimicking drip soil subsurface micro-irrigation. A specially designed cylindrical vertical soil column rig was built, and an EQ3 equitensiometer of Delta-T Devices was used in the laboratory as a precision sensor to measure the soil matric potential Ψ (kPa), and the volumetric soil water content θ (%) was measured using a WET150 sensor of Delta-T Devices. The relationship between the volumetric soil water content and the soil matric potential resulted in the generation of the soil water characteristic curve. Two separate monoliths of undisturbed soil samples from Ivrogbo and Oleh in the Nigerian inland valley of the Niger Delta, as well as a uniformly packed sample of soil from Aberdeen, UK, for comparison, were used in gravity-driven flow experiments. In each case, tests were performed once on the monoliths of undisturbed soil samples. In contrast, the packed sample was subjected to an experiment before being further agitated to simulate ploughing and then subjected to an infiltration experiment, resulting in a total of four samples. The Van Genuchten model of the soil water characteristic curve was used for the verification of the experimental results. Comparing the four samples’ volumetric soil water contents and soil matric potentials at various depths revealed a significant variation in their behaviour. However, compared to the predicted curve, the range of values was narrower. Compared to n = 2 in the Van Genuchten curve, the value of n at 200 mm depth was found to be 15, with θr of 0.046 and θs of 0.23 for the packed soil sample, resulting in a percentage difference of 86.7%. Additionally, n = 10 for the ploughed sample resulted in an 80% difference, yet θr = 0.03 and θs = 0.23. For the Ivrogbo sample and the Oleh sample, the range of the matric potential was relatively too small for the comparison. The pre-experiment moisture content of the soil samples was part of the cause of this, in addition to differences in the soil types. Furthermore, the data revealed a remarkable agreement between the measured behaviour and the projected technique of the soil water characteristic curve. Full article

(This article belongs to the Special Issue Smart Sensors for Sustainable Agriculture)

► Show Figures

Figure 1

19 pages, 2641 KiB

Open AccessArticle

MSFF-Net: Multi-Sensor Frequency-Domain Feature Fusion Network with Lightweight 1D CNN for Bearing Fault Diagnosis

by Miao Dai, Hangyeol Jo, Moonsuk Kim and Sang-Woo Ban

Sensors 2025, 25(14), 4348; https://doi.org/10.3390/s25144348 - 11 Jul 2025

Abstract

This study proposes MSFF-Net, a lightweight deep learning framework for bearing fault diagnosis based on frequency-domain multi-sensor fusion. The vibration and acoustic signals are initially converted into the frequency domain using the fast Fourier transform (FFT), enabling the extraction of temporally invariant spectral [...] Read more.

This study proposes MSFF-Net, a lightweight deep learning framework for bearing fault diagnosis based on frequency-domain multi-sensor fusion. The vibration and acoustic signals are initially converted into the frequency domain using the fast Fourier transform (FFT), enabling the extraction of temporally invariant spectral features. These features are processed by a compact one-dimensional convolutional neural network, where modality-specific representations are fused at the feature level to capture complementary fault-related information. The proposed method demonstrates robust and superior performance under both full and scarce data conditions, as verified through experiments on a publicly available dataset. Experimental results on a publicly available dataset indicate that the proposed model attains an average accuracy of 99.73%, outperforming state-of-the-art (SOTA) methods in both accuracy and stability. With only about 70.3% of the parameters of the SOTA model, it offers faster inference and reduced computational cost. Ablation studies confirm that multi-sensor fusion improves all classification metrics over single-sensor setups. Under few-shot conditions with 20 samples per class, the model retains 94.69% accuracy, highlighting its strong generalization in data-limited scenarios. The results validate the effectiveness, computational efficiency, and practical applicability of the model for deployment in data-constrained industrial environments. Full article

(This article belongs to the Special Issue Condition Monitoring in Manufacturing with Advanced Sensors)

► Show Figures

Figure 1

36 pages, 2413 KiB

Open AccessArticle

Rain-Induced Vibration Energy Harvesting Using Nonlinear Plates with Piezoelectric Integration and Power Management

by Yi-Ren Wang, Wei Ting Lin and Bo-Jang Huang

Sensors 2025, 25(14), 4347; https://doi.org/10.3390/s25144347 - 11 Jul 2025

Abstract

Vibration energy offers promising potential for renewable energy harvesting, especially in conditions where conventional sources such as solar power may be limited or intermittent. This study proposes a rain energy harvester (REH) that converts the kinetic energy of raindrops into electrical energy using [...] Read more.

Vibration energy offers promising potential for renewable energy harvesting, especially in conditions where conventional sources such as solar power may be limited or intermittent. This study proposes a rain energy harvester (REH) that converts the kinetic energy of raindrops into electrical energy using nonlinear thin plates, integrated with piezoelectric elements. Two plate configurations—fully hinged (H-H-H-H) and clamped–hinged–free–hinged (C-H-F-H)—are investigated. Theoretical modeling and simulation results are compared with experimental data, with special attention paid to the role of slapping forces in improving prediction accuracy. A power management system is also introduced to stabilize and regulate the harvested voltage. Results confirm the feasibility of rain-induced energy harvesting, showing potential for application in rain-prone areas and integration with existing infrastructure such as solar panels, tents, or canopies. Full article

(This article belongs to the Special Issue Advances in Energy Harvesting and Sensor Systems)

19 pages, 4857 KiB

Open AccessArticle

Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model

by Shuo Feng, James Wainwright, Chong Wang, Jun Wang, Goncalo Rodrigues Pardal, Jian Qin, Yi Yin, Shakirudeen Lasisi, Jialuo Ding and Stewart Williams

Sensors 2025, 25(14), 4346; https://doi.org/10.3390/s25144346 - 11 Jul 2025

Abstract

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based [...] Read more.

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

37 pages, 708 KiB

Open AccessSystematic Review

Quantifying the Multidimensional Impact of Cyber Attacks in Digital Financial Services: A Systematic Literature Review

by Olumayowa Adefowope Adekoya, Hany F. Atlam and Harjinder Singh Lallie

Sensors 2025, 25(14), 4345; https://doi.org/10.3390/s25144345 - 11 Jul 2025

Abstract

The increasing frequency and sophistication of cyber attacks have posed significant challenges for digital financial organisations, particularly in quantifying their multidimensional impacts. These challenges are largely attributed to the lack of a standardised cyber impact taxonomy, limited data availability, and the evolving nature [...] Read more.

The increasing frequency and sophistication of cyber attacks have posed significant challenges for digital financial organisations, particularly in quantifying their multidimensional impacts. These challenges are largely attributed to the lack of a standardised cyber impact taxonomy, limited data availability, and the evolving nature of technological threats. As a result, organisations often struggle with ineffective security investment prioritisation, reactive incident response planning, and the inability to implement robust, risk-based controls. Hence, an efficient and comprehensive approach is needed to quantify the diverse impacts of cyber attacks in digital financial services. This paper presents a systematic review and examination of the state of the art in cyber impact quantification, with a particular focus on digital financial organisations. Based on a structured search strategy, 44 articles (out of 637) were selected for in-depth analysis. The review investigates the terminologies used to describe cyber impacts, categorises current quantification techniques (pre-attack and post-attack), and identifies the most commonly utilised internal and external data sources. Furthermore, it explores the application of Machine Learning (ML) and Deep Learning (DL) techniques in cyber security risk quantification. Our findings reveal a significant lack of standardised taxonomy for describing and quantifying the multidimensional impact of cyberattacks across physical, digital, economic, psychological, reputational, and societal dimensions. Lastly, open issues and future research directions are discussed. This work provides insights for researchers and professionals by consolidating and identifying quantification technique gaps in cyber security risk quantification. Full article

(This article belongs to the Special Issue Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (2nd Edition))

28 pages, 1653 KiB

Open AccessArticle

Blockchain-Based Trusted Data Management with Privacy Preservation for Secure IoT Systems

by Haojie Zhou, Hongmin Gao, Zhaofeng Ma and Guanhui Lai

Sensors 2025, 25(14), 4344; https://doi.org/10.3390/s25144344 - 11 Jul 2025

Abstract

With the explosive growth of the Internet of Things (IoT), the traditional single data sharing scheme has difficulty satisfying the data sharing needs of both same-domain and cross-domain IoT devices. In order to realize efficient data sharing of IoT devices in the same [...] Read more.

With the explosive growth of the Internet of Things (IoT), the traditional single data sharing scheme has difficulty satisfying the data sharing needs of both same-domain and cross-domain IoT devices. In order to realize efficient data sharing of IoT devices in the same domain with data privacy protection and efficient collaboration between IoT devices in different domains, this paper proposes a trusted data sharing scheme in IoT systems based on multi-channel blockchain. The scheme adopts a multi-channel mechanism to isolate the ledger data between IoT devices of different domains; IoT devices of the same domain utilize hybrid encryption to achieve efficient data sharing through smart contracts, and IoT devices of different domains utilize the CKKS algorithm to achieve cross-domain data sharing with privacy protection through proxy nodes (PNs). In addition, this paper adopts decentralized identity (DID) to achieve autonomous identity management to avoid privacy leakage in IoT devices and adopts InterPlanetary File System (IPFS) to store data files to solve the blockchain storage capacity limitation problem. The security analysis proves that this scheme satisfies the IND-CPA security model, and the performance analysis proves that this scheme has good utility in trusted data sharing of IoT devices. Full article

(This article belongs to the Special Issue Blockchain-Based Solutions to Secure IoT)

25 pages, 2824 KiB

Open AccessArticle

Real-Time Wing Deformation Monitoring via Distributed Fiber Bragg Grating and Adaptive Federated Filtering

by Zhen Ma, Xiyuan Chen, Cundeng Wang and Bingbo Cui

Sensors 2025, 25(14), 4343; https://doi.org/10.3390/s25144343 - 11 Jul 2025

Abstract

To address the issues of decreased accuracy and poor stability in distributed transfer alignment caused by factors such as wing deflection and deformation in complex flight environments, this paper proposes a wing-distributed transfer alignment method based on Fiber Bragg Grating (FBG). This paper [...] Read more.

To address the issues of decreased accuracy and poor stability in distributed transfer alignment caused by factors such as wing deflection and deformation in complex flight environments, this paper proposes a wing-distributed transfer alignment method based on Fiber Bragg Grating (FBG). This paper establishes a flexural deformation model based on FBGs, establishes a coupling angle model and a dynamic lever arm model, derives the motion parameter relationship model between the main and the sub-nodes, establishes the corresponding transfer alignment filter, and proposes a federated adaptive filter based on allocation coefficients and an updated federated adaptive filter. The results show that the federated adaptive filtering algorithm based on allocation coefficients improved the pitch angle accuracy of the Inertial Measurement Unit (IMU) by 66.38% and the position estimation accuracy by 75.67%, compared to traditional algorithms. The arm estimation accuracy was also improved in the east and sky directions. Compared with traditional algorithms, the updated federated adaptive filtering algorithm improved the pitch angle accuracy of the sub IMU by 76.72%, the position estimation accuracy by 63.51%, and the lever arm estimation accuracy. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

14 pages, 897 KiB

Open AccessArticle

Multi-Robot Path Planning for High-Density Parking Environments Considering Efficiency and Fairness

by Jinhyuk Lee and Woojin Chung

Sensors 2025, 25(14), 4342; https://doi.org/10.3390/s25144342 - 11 Jul 2025

Abstract

As parking congestion at airport parking lots intensifies, high-density parking (HDP) systems with multiple parking robots are gaining attention for improving operational efficiency. However, conventional multi-agent pathfinding (MAPF) methods primarily focus on overall efficiency improvement, often neglecting the priority of individual parking tasks. [...] Read more.

As parking congestion at airport parking lots intensifies, high-density parking (HDP) systems with multiple parking robots are gaining attention for improving operational efficiency. However, conventional multi-agent pathfinding (MAPF) methods primarily focus on overall efficiency improvement, often neglecting the priority of individual parking tasks. Additionally, these methods assume robots are ideal agents, resulting in physically infeasible paths for parking robots. We propose a multi-robot path planning approach that balances efficiency and priority. The proposed method improves priority-based search (PBS) by dynamically adjusting priorities, thereby ensuring both operational efficiency and priority of individual vehicles. A simulator replicating a real airport parking environment with 100 parking slots and parking robots under development was implemented to validate the approach. Real-world parking data from an airport was used as input, demonstrating that the proposed autonomous parking system can effectively handle peak-season parking demand. The proposed method achieves a throughput exceeding 41 vehicles per hour with appropriate weight value, meeting the peak-season demand while maintaining acceptable fairness. Our approach provides a practical foundation for establishing time-based parking operation strategies and estimating the number of robots recommended for a given parking scenario. Full article

(This article belongs to the Special Issue AI and Smart Sensors for Intelligent Transportation Systems)

20 pages, 1592 KiB

Open AccessArticle

Influence of Flow Field on the Imaging Quality of Star Sensors for Hypersonic Vehicles in near Space

by Siyao Wu, Ting Sun, Fei Xing, Haonan Liu, Kang Yang, Jiahui Song, Shijie Yu and Lianqing Zhu

Sensors 2025, 25(14), 4341; https://doi.org/10.3390/s25144341 - 11 Jul 2025

Abstract

When hypersonic vehicles fly in near space, the flow field near the optical window leads to light displacement, jitter, blurring, and energy attenuation of the star sensor. This ultimately affects the imaging quality and navigation accuracy. In order to investigate the impact of [...] Read more.

When hypersonic vehicles fly in near space, the flow field near the optical window leads to light displacement, jitter, blurring, and energy attenuation of the star sensor. This ultimately affects the imaging quality and navigation accuracy. In order to investigate the impact of aerodynamic optical effects on imaging, the fourth-order Runge–Kutta and the fourth-order Adams–Bashforth–Moulton (ABM) predictor-corrector methods are used for ray tracing on the density data. A comparative analysis of the imaging quality results from the two methods reveals their respective strengths and limitations. The influence of the optical system is included in the image quality calculations to make the results more representative of real data. The effects of altitude, velocity, and angle of attack on the imaging quality are explored when the optical window is located at the tail of the vehicle. The results show that altitude significantly affects imaging results, and higher altitudes reduce the impact of the flow field on imaging quality. When the optical window is located at the tail of the vehicle, the relationship between velocity and offset is no longer simply linear. This research provides theoretical support for analyzing the imaging quality and navigation accuracy of a star sensor when a vehicle is flying at hypersonic speeds in near space. Full article

(This article belongs to the Special Issue Sensors and Sensing Technologies for Precision Optical Measurement and Metrology)

19 pages, 1383 KiB

Open AccessArticle

Research on Nonlinear Error Compensation and Intelligent Optimization Method for UAV Target Positioning

by Yinglei Li, Qingping Hu, Shiyan Sun, Wenjian Ying and Xiaojia Yan

Sensors 2025, 25(14), 4340; https://doi.org/10.3390/s25144340 - 11 Jul 2025

Abstract

The realization of high-precision target positioning requires the systematic suppression of nonlinear perturbations in the UAV optoelectronic system and the optimization of the cumulative deviation of coordinate transformations through error transfer modeling. This study proposes an error allocation method based on the improved [...] Read more.

The realization of high-precision target positioning requires the systematic suppression of nonlinear perturbations in the UAV optoelectronic system and the optimization of the cumulative deviation of coordinate transformations through error transfer modeling. This study proposes an error allocation method based on the improved raccoon optimization algorithm (KYCOA) to resolve the problem of degradation of positioning accuracy due to multi-source error coupling during UAV target positioning. Firstly, a multi-coordinate system transformation model is established to analyze the nonlinear transfer characteristics of the error, and the Taylor expansion is used to linearize the error transfer process and derive the synthetic error model under the geocentric coordinate system. Secondly, the KYCOA is proposed to optimize the error allocation by combining the good point set initialization strategy to enhance the population diversity, and the golden sine algorithm to improve the position updating mechanism in response to the defect of the traditional optimization algorithm, which easily falls into the local optimum. Simulation experiments show that the positioning error distance of the KYCOA is reduced by 66.75%, 41.89%, and 62.06% when compared with that of the original Coati Optimization Algorithm (COA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), respectively. In the real flight test, the target point localization error of the KYCOA is reduced by more than 40% on average when compared with that of other algorithms, which verifies the effectiveness of the proposed method in improving the target localization accuracy and robustness of UAVs. Full article

(This article belongs to the Section Navigation and Positioning)

23 pages, 592 KiB

Open AccessArticle

Binary Secretary Bird Optimization Clustering by Novel Fitness Function Based on Voronoi Diagram in Wireless Sensor Networks

by Mohammed Abdulkareem, Hadi S. Aghdasi, Pedram Salehpour and Mina Zolfy

Sensors 2025, 25(14), 4339; https://doi.org/10.3390/s25144339 - 11 Jul 2025

Abstract

Minimizing energy consumption remains a critical challenge in wireless sensor networks (WSNs) because of their reliance on nonrechargeable batteries. Clustering-based hierarchical communication has been widely adopted to address this issue by improving residual energy and balancing the network load. In this architecture, cluster [...] Read more.

Minimizing energy consumption remains a critical challenge in wireless sensor networks (WSNs) because of their reliance on nonrechargeable batteries. Clustering-based hierarchical communication has been widely adopted to address this issue by improving residual energy and balancing the network load. In this architecture, cluster heads (CHs) are responsible for data collection, aggregation, and forwarding, making their optimal selection essential for prolonging network lifetime. The effectiveness of CH selection is highly dependent on the choice of metaheuristic optimization method and the design of the fitness function. Although numerous studies have applied metaheuristic algorithms with suitably designed fitness functions to tackle the CH selection problem, many existing approaches fail to fully capture both the spatial distribution of nodes and dynamic energy conditions. To address these limitations, we propose the binary secretary bird optimization clustering (BSBOC) method. BSBOC introduces a binary variant of the secretary bird optimization algorithm (SBOA) to handle the discrete nature of CH selection. Additionally, it defines a novel multiobjective fitness function that, for the first time, considers the Voronoi diagram of CHs as an optimization objective, besides other well-known objectives. BSBOC was thoroughly assessed via comprehensive simulation experiments, benchmarked against two advanced methods (MOBGWO and WAOA), under both homogeneous and heterogeneous network models across two deployment scenarios. Findings from these simulations demonstrated that BSBOC notably decreased energy usage and prolonged network lifetime, highlighting its effectiveness as a reliable method for energy-aware clustering in WSNs. Full article

(This article belongs to the Section Sensor Networks)

17 pages, 5189 KiB

Open AccessArticle

YOLO-Extreme: Obstacle Detection for Visually Impaired Navigation Under Foggy Weather

by Wei Wang, Bin Jing, Xiaoru Yu, Wei Zhang, Shengyu Wang, Ziqi Tang and Liping Yang

Sensors 2025, 25(14), 4338; https://doi.org/10.3390/s25144338 - 11 Jul 2025

Abstract

Visually impaired individuals face significant challenges in navigating safely and independently, particularly under adverse weather conditions such as fog. To address this issue, we propose YOLO-Extreme, an enhanced object detection framework based on YOLOv12, specifically designed for robust navigation assistance in foggy environments. [...] Read more.

Visually impaired individuals face significant challenges in navigating safely and independently, particularly under adverse weather conditions such as fog. To address this issue, we propose YOLO-Extreme, an enhanced object detection framework based on YOLOv12, specifically designed for robust navigation assistance in foggy environments. The proposed architecture incorporates three novel modules: the Dual-Branch Bottleneck Block (DBB) for capturing both local spatial and global semantic features, the Multi-Dimensional Collaborative Attention Module (MCAM) for joint spatial-channel attention modeling to enhance salient obstacle features and reduce background interference in foggy conditions, and the Channel-Selective Fusion Block (CSFB) for robust multi-scale feature integration. Comprehensive experiments conducted on the Real-world Task-driven Traffic Scene (RTTS) foggy dataset demonstrate that YOLO-Extreme achieves state-of-the-art detection accuracy and maintains high inference speed, outperforming existing dehazing-and-detect and mainstream object detection methods. To further verify the generalization capability of the proposed framework, we also performed cross-dataset experiments on the Foggy Cityscapes dataset, where YOLO-Extreme consistently demonstrated superior detection performance across diverse foggy urban scenes. The proposed framework significantly improves the reliability and safety of assistive navigation for visually impaired individuals under challenging weather conditions, offering practical value for real-world deployment. Full article

(This article belongs to the Section Navigation and Positioning)

► Show Figures

Figure 1

Journal Description

Sensors

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI