Sensors

16 pages, 3500 KiB

Open AccessArticle

Non-Uniform Corrosion Monitoring of Steel Pipes Using Distributed Optical Fiber Sensors in the Fluctuation Zone of a Coastal Wharf

by Jiguo Chen, Ruiqi Zhang, Qianwu Li, Hongke Wang, Qiangqiang Ma, Qi Fan, Liang Fan and Zequan Lin

Sensors 2025, 25(10), 3194; https://doi.org/10.3390/s25103194 - 19 May 2025

Abstract

Steel pipes, while essential for modern infrastructure due to their high strength and load-bearing capacity, are prone to corrosion in the marine environment, leading to material degradation, compromised structural integrity, and elevated safety risks and economic losses. In this study, distributed fiber-optic sensors [...] Read more.

Steel pipes, while essential for modern infrastructure due to their high strength and load-bearing capacity, are prone to corrosion in the marine environment, leading to material degradation, compromised structural integrity, and elevated safety risks and economic losses. In this study, distributed fiber-optic sensors were deployed on steel pipe surfaces to monitor corrosion in the splash zone (a region particularly vulnerable to cyclic wet–dry conditions). The sensors were engineered to withstand aggressive marine exposure. Strain variations induced by expansive corrosion products were detected via the fiber-optic array and used to calculate localized mass loss. Color-coded corrosion severity maps were generated to visualize the non-uniform corrosion distribution. Experimental results demonstrate that sensor-derived mass loss values align with 3D laser scanning measurements, validating the operational efficacy of distributed fiber-optic sensing for marine corrosion monitoring. This approach provides quantitative insights into the field applicability of optical sensing in structural health monitoring. Full article

(This article belongs to the Special Issue Advances in Optical Fiber Sensors and Fiber Lasers)

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11 pages, 205 KiB

Open AccessArticle

In-Match Physical Requirements and Team Performance in Cerebral Palsy Football Across a Competitive Season

by Juan Francisco Maggiolo, Alejandro Javaloyes, Manuel Moya-Ramón and Iván Peña-González

Sensors 2025, 25(10), 3193; https://doi.org/10.3390/s25103193 - 19 May 2025

Abstract

This study aimed to analyze the in-match physical requirements of cerebral palsy football (CP football) players over an entire national league season (56 matches) and their relationship with team performance-related metrics. Key variables examined included total distance, distance at different intensities, acceleration/deceleration patterns, [...] Read more.

This study aimed to analyze the in-match physical requirements of cerebral palsy football (CP football) players over an entire national league season (56 matches) and their relationship with team performance-related metrics. Key variables examined included total distance, distance at different intensities, acceleration/deceleration patterns, and ball contacts at various intensities. Statistical analyses (one-way ANOVA, t-tests, Pearson’s correlations, and multiple linear regressions) were conducted to identify differences and predictive relationships between these physical requirements and team success indicators (ranking position, points, and goal-related outcomes). Higher-ranked teams covered significantly greater total and walking distances (t = 2.73 and 3.09, p < 0.01). Total distance had the strongest relationship with team performance (r = 0.91–0.99, R² = 0.82–0.99), followed by walking and low-intensity distances (r = 0.71–0.92, R² = 0.66–0.88) and certain acceleration/deceleration actions. In contrast, no significant differences were found in high-intensity actions or ball contact patterns between teams with different performance-related outcomes. These findings suggest that success in CP football is closely related to total distance, particularly at low intensities, indicating a strong connection between physical requirements and tactical strategies. These insights are valuable for coaches and sports professionals, helping to optimize match strategies and training approaches to enhance team performance. Full article

(This article belongs to the Special Issue Movement Biomechanics Applications of Wearable Inertial Sensors)

21 pages, 1262 KiB

Open AccessArticle

NeuroDetect: Deep Learning-Based Signal Detection in Phase-Modulated Systems with Low-Resolution Quantization

by Chanula Luckshan, Samiru Gayan, Hazer Inaltekin, Ruhui Zhang and David Akman

Sensors 2025, 25(10), 3192; https://doi.org/10.3390/s25103192 - 19 May 2025

Abstract

This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes [...] Read more.

This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes a neural network architecture to learn the nonlinear relationship between quantized received signals and transmitted symbols directly from data. It achieves near-optimum performance, within a worst-case

12 %

margin of the maximum likelihood detector that assumes perfect channel knowledge. We rigorously investigate the interplay between ADC resolution and detection accuracy, introducing novel penalty metrics that quantify the effects of both quantization and learning errors. Our results shed light on the design trade-offs between ADC resolution and detection accuracy, providing future directions for developing energy-efficient high-speed and wideband wireless systems. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks: 3rd Edition)

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23 pages, 718 KiB

Open AccessSystematic Review

Tiny Machine Learning and On-Device Inference: A Survey of Applications, Challenges, and Future Directions

by Soroush Heydari and Qusay H. Mahmoud

Sensors 2025, 25(10), 3191; https://doi.org/10.3390/s25103191 - 19 May 2025

Abstract

The growth in artificial intelligence and its applications has led to increased data processing and inference requirements. Traditional cloud-based inference solutions are often used but may prove inadequate for applications requiring near-instantaneous response times. This review examines Tiny Machine Learning, also known as [...] Read more.

The growth in artificial intelligence and its applications has led to increased data processing and inference requirements. Traditional cloud-based inference solutions are often used but may prove inadequate for applications requiring near-instantaneous response times. This review examines Tiny Machine Learning, also known as TinyML, as an alternative to cloud-based inference. The review focuses on applications where transmission delays make traditional Internet of Things (IoT) approaches impractical, thus necessitating a solution that uses TinyML and on-device inference. This study, which follows the PRISMA guidelines, covers TinyML’s use cases for real-world applications by analyzing experimental studies and synthesizing current research on the characteristics of TinyML experiments, such as machine learning techniques and the hardware used for experiments. This review identifies existing gaps in research as well as the means to address these gaps. The review findings suggest that TinyML has a strong record of real-world usability and offers advantages over cloud-based inference, particularly in environments with bandwidth constraints and use cases that require rapid response times. This review discusses the implications of TinyML’s experimental performance for future research on TinyML applications. Full article

(This article belongs to the Special Issue Edge AI for Wearables and IoT)

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18 pages, 3160 KiB

Open AccessArticle

Ultrasonic Beamforming-Based Visual Localisation of Minor and Multiple Gas Leaks Using a Microelectromechanical System (MEMS) Microphone Array

by Tao Wang, Jiawen Ji, Jianglong Lan and Bo Wang

Sensors 2025, 25(10), 3190; https://doi.org/10.3390/s25103190 - 19 May 2025

Abstract

The development of a universal method for real-time gas leak localisation imaging is crucial for preventing substantial financial losses and hazardous incidents. To achieve this objective, this study integrates array signal processing and electronic techniques to construct an ultrasonic sensor array for gas [...] Read more.

The development of a universal method for real-time gas leak localisation imaging is crucial for preventing substantial financial losses and hazardous incidents. To achieve this objective, this study integrates array signal processing and electronic techniques to construct an ultrasonic sensor array for gas leak detection and localisation. A digital microelectromechanical system microphone array is used to capture spatial ultrasonic information. By processing the array signals using beamforming algorithms, an acoustic spatial power spectrum is obtained, which facilitates the estimation of the locations of potential gas leak sources. In the pre-processing of beamforming, the Hilbert transform is employed instead of the fast Fourier transform to save computational resources. Subsequently, the spatial power spectrum is fused with visible-light images to generate acoustic localisation images, which enables the visualisation of gas leak sources. Experimental validation demonstrates that the system detects minor and multiple gas leaks in real time, meeting the sensitivity and accuracy requirements of embedded industrial applications. These findings contribute to the development of practical, cost-effective, and scalable gas leak detection systems for industrial and environmental safety applications. Full article

(This article belongs to the Section Physical Sensors)

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26 pages, 4212 KiB

Open AccessArticle

Autonomous Driving of Trackless Transport Vehicles: A Case Study in Underground Mines

by Yunjie Sun, Linxin Zhang, Junhong Liu, Yonghe Xu and Xiaoquan Li

Sensors 2025, 25(10), 3189; https://doi.org/10.3390/s25103189 - 19 May 2025

Abstract

The introduction of autonomous vehicles in underground mine trackless transportation systems can significantly reduce safety risks for personnel in production operations and improve transportation efficiency. Current autonomous mining vehicle technology is characterized by complex algorithms and high deployment costs, which limit its widespread [...] Read more.

The introduction of autonomous vehicles in underground mine trackless transportation systems can significantly reduce safety risks for personnel in production operations and improve transportation efficiency. Current autonomous mining vehicle technology is characterized by complex algorithms and high deployment costs, which limit its widespread application in underground mines. This paper proposes a light-band-guided autonomous driving method for trackless mining vehicles, where a continuous, digitally controllable light band is installed at the tunnel ceiling to provide uninterrupted vehicle guidance. The light band is controlled by an independent hardware system and uses different colors to indicate vehicle movement status, enabling vehicles to navigate simply by following the designated light trajectory. We designed the necessary hardware and software systems and built a physical model for validation. The system enabled multiple vehicles to be guided simultaneously within the same area to perform diverse transportation tasks according to operational requirements. The model vehicles maintained a safe distance from tunnel walls. In GPS-denied environments, positioning was achieved using dead reckoning and periodic location updates at designated points based on the known light-band trajectory. The proposed method demonstrates high potential for practical applications. Full article

(This article belongs to the Section Vehicular Sensing)

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23 pages, 4473 KiB

Open AccessArticle

Highly Sensitive Pressure Transducer for Measuring Arterial Pulse Wave Velocity Based on Giant Magneto-Impedance Sensors

by Lizeth Stefanía Benavides Cabrera, Eduardo Costa da Silva and Elisabeth Costa Monteiro

Sensors 2025, 25(10), 3188; https://doi.org/10.3390/s25103188 - 19 May 2025

Abstract

Pulse wave velocity (PWV) has been recognised as the gold standard for assessing arterial stiffness and a relevant indicator in diagnosing cardiovascular disease. Conventional approaches can be affected by factors such as the size of the probe, its positioning on the skin with [...] Read more.

Pulse wave velocity (PWV) has been recognised as the gold standard for assessing arterial stiffness and a relevant indicator in diagnosing cardiovascular disease. Conventional approaches can be affected by factors such as the size of the probe, its positioning on the skin with the appropriate angle and magnitude of the incident force, or influenced by optical properties. Aiming at improving the assessment of PWV parameter, an important cardiovascular risk marker, the present study introduces a new arterial pulse wave measurement technique based on measurements of the impedance phase characteristics of giant magneto-impedance (GMI) sensors submitted to slight magnetic field variations caused by the displacement of a small magnetic marker placed on the patient’s skin, whose movement is coordinated by the local pressure wave. The proposed method eliminates the necessity of using probes with mechanical amplification, enhancing spatial resolution and usability in hard-to-reach anatomical regions through a contactless device unaffected by optical parameters. The obtained experimental results indicate the potential of the developed measurement system in measuring arterial pulse waveform and PWV. Full article

(This article belongs to the Special Issue Sensing Signals for Biomedical Monitoring)

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15 pages, 1295 KiB

Open AccessArticle

Fabric Tactile Prediction Method Based on Spider Diagram

by Ruifeng Xie, Shuyang Ding, Zeyu Cheng, Luowei Ma and Yanzhu Yang

Sensors 2025, 25(10), 3187; https://doi.org/10.3390/s25103187 - 19 May 2025

Abstract

The detection and quantification of fabric tactile sensations are crucial in textile production and marketing as they are closely linked to textile comfort and serve as key criteria for consumers when selecting fabrics. Previous studies have predominantly focused on measuring the physical properties [...] Read more.

The detection and quantification of fabric tactile sensations are crucial in textile production and marketing as they are closely linked to textile comfort and serve as key criteria for consumers when selecting fabrics. Previous studies have predominantly focused on measuring the physical properties of fabrics, often neglecting correlations between these parameters and tactile sensations. This oversight complicates customers’ ability to assess the tactile experience of fabrics during online purchasing. This study first obtained subjective evaluations of three types of fabric tactile sensations through experiments involving volunteer participants. Subsequently, five objective physical properties that characterize fabric tactile properties were proposed and experimentally tested on 15 fabric samples categorized by yarn weight, weave pattern, and material. A fabric tactile spider diagram was created by normalizing the values of the five physical properties across the 15 fabric samples. The grading of the physical properties was then performed based on the proposed evaluation index. These spider diagrams were compared with the subjective evaluation results to analyze the physical properties that most significantly influenced subjective perception, ultimately leading to the development of a highly reliable fabric touch prediction model. Full article

(This article belongs to the Section Intelligent Sensors)

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14 pages, 7865 KiB

Open AccessArticle

Time-Interval-Guided Event Representation for Scene Understanding

by Boxuan Wang, Wenjun Yang, Kunqi Wu, Rui Yang, Jiayue Xie and Huixiang Liu

Sensors 2025, 25(10), 3186; https://doi.org/10.3390/s25103186 - 19 May 2025

Abstract

The recovery of scenes under extreme lighting conditions is pivotal for effective image analysis and feature detection. Traditional cameras face challenges with low dynamic range and limited spectral response in such scenarios. In this paper, we advocate for the adoption of event cameras [...] Read more.

The recovery of scenes under extreme lighting conditions is pivotal for effective image analysis and feature detection. Traditional cameras face challenges with low dynamic range and limited spectral response in such scenarios. In this paper, we advocate for the adoption of event cameras to reconstruct static scenes, particularly those in low illumination. We introduce a new method to elucidate the phenomenon where event cameras continue to generate events even in the absence of brightness changes, highlighting the crucial role played by noise in this process. Furthermore, we substantiate that events predominantly occur in pairs and establish a correlation between the time interval of event pairs and the relative light intensity of the scene. A key contribution of our work is the proposal of an innovative method to convert sparse event streams into dense intensity frames without dependence on any active light source or motion, achieving the static imaging of event cameras. This method expands the application of event cameras in static vision fields such as HDR imaging and leads to a practical application. The feasibility of our method was demonstrated through multiple experiments. Full article

(This article belongs to the Special Issue Computational Optical Sensing and Imaging)

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12 pages, 1606 KiB

Open AccessArticle

Measurement Grid Optimization for OTA Testing of 5G Smart Watches

by Xudong An, Fei Liu, Meijun Qu and Siyang Sun

Sensors 2025, 25(10), 3185; https://doi.org/10.3390/s25103185 - 19 May 2025

Abstract

Over-the-air (OTA) testing is crucial for optimizing wireless performance of 5G smart watches and improving their user experience. However, the current required test time is so long that it is almost impossible to complete the entire OTA testing without recharging and repositioning, which [...] Read more.

Over-the-air (OTA) testing is crucial for optimizing wireless performance of 5G smart watches and improving their user experience. However, the current required test time is so long that it is almost impossible to complete the entire OTA testing without recharging and repositioning, which is unacceptable for the industry. Therefore, test-time reduction is significant. The objective of this work is to optimize measurement grids for OTA testing of 5G smart watches, which balance accuracy with efficiency. In this research, passive patterns from a typical 5G commercial smart watch are measured at different bands as reference patterns, which represent general radiation properties of 5G commercial smart watches. The effect of various coarse grids on OTA testing precision is characterized quantitatively by analyzing their accuracy in reconstructing reference patterns. The related measurement uncertainty (MU) terms are then evaluated and determined quantitatively based on statistical analysis. According to the derived MU limits for grid configurations, reducing grid points from currently required 62 (30/30) to 26 (45/45), and from 266 (15/15) to 62 (30/30) could save roughly 60% and 75% of the test time, respectively, with an uncertainty increase of 0.1 dB for both Total Isotropic Sensitivity (TIS) and Total Radiated Power (TRP) testing, which is considered acceptable. Furthermore, the feasibility of the proposed MU analysis and recommended grids have been experimentally verified. Full article

(This article belongs to the Special Issue Challenges and Future Trends in Antenna Technology)

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12 pages, 2111 KiB

Open AccessArticle

Static Thermal Model of a Fibre-Optic Rotational Seismometer

by Piotr Zając, Marcin Janicki and Cezary Maj

Sensors 2025, 25(10), 3184; https://doi.org/10.3390/s25103184 - 19 May 2025

Abstract

Fibre-optic rotational seismometers are devices capable of high-accuracy measurements of rotation rates. Taking into account that temperature significantly influences their operation, particularly the optical signal path, accurate thermal models of these devices are required. This paper presents a static thermal model of such [...] Read more.

Fibre-optic rotational seismometers are devices capable of high-accuracy measurements of rotation rates. Taking into account that temperature significantly influences their operation, particularly the optical signal path, accurate thermal models of these devices are required. This paper presents a static thermal model of such a fibre-optic rotational seismometer. This compact thermal model is based on a thermal resistor network, where the temperature values are computed only in the most important locations of the device. The values of thermal model elements are estimated based on thermocouple temperature measurements. Owing to the accurate determination of temperature values, especially in fibre-optic loops, it was possible to achieve the desired device sensitivity. Full article

(This article belongs to the Section Electronic Sensors)

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21 pages, 7991 KiB

Open AccessArticle

Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors

by Mehmet Taştan

Sensors 2025, 25(10), 3183; https://doi.org/10.3390/s25103183 - 19 May 2025

Abstract

Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability. In this study, an Internet of [...] Read more.

Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability. In this study, an Internet of Things (IoT)-based air quality monitoring system was developed and tested using the most commonly preferred sensor types for air quality measurement: fine particulate matter (PM_2.5), carbon dioxide (CO₂), temperature, and humidity sensors. To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). Sensor performance was evaluated by comparing measurements with a reference device, and the best-performing ML model was determined for each sensor. The results indicate that GB and kNN achieved the highest accuracy. For CO₂ sensor calibration, GB achieved R² = 0.970, RMSE = 0.442, and MAE = 0.282, providing the lowest error rates. For the PM_2.5 sensor, kNN delivered the most successful results, with R² = 0.970, RMSE = 2.123, and MAE = 0.842. Additionally, for temperature and humidity sensors, GB demonstrated the highest accuracy with the lowest error values (R² = 0.976, RMSE = 2.284). These findings demonstrate that, by identifying suitable ML methods, ML-based calibration techniques can significantly enhance the accuracy of LCSs. Consequently, they offer a viable and cost-effective alternative to traditional high-cost air quality monitoring systems. Future studies should focus on long-term data collection, testing under diverse environmental conditions, and integrating additional sensor types to further advance this field. Full article

(This article belongs to the Special Issue Intelligent Sensor Calibration: Techniques, Devices and Methodologies)

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30 pages, 20203 KiB

Open AccessArticle

Multi-Feature Fusion Method Based on Adaptive Dilation Convolution for Small-Object Detection

by Lin Cao, Jin Wu, Zongmin Zhao, Chong Fu and Dongfeng Wang

Sensors 2025, 25(10), 3182; https://doi.org/10.3390/s25103182 - 18 May 2025

Abstract

This paper addresses the challenge of small-object detection in traffic surveillance by proposing a hybrid network architecture that combines attention mechanisms with convolutional layers. The network introduces an innovative attention mechanism into the YOLOv8 backbone, which effectively enhances the detection accuracy and robustness [...] Read more.

This paper addresses the challenge of small-object detection in traffic surveillance by proposing a hybrid network architecture that combines attention mechanisms with convolutional layers. The network introduces an innovative attention mechanism into the YOLOv8 backbone, which effectively enhances the detection accuracy and robustness of small objects through fine-grained and coarse-grained attention routing on feature maps. During the feature fusion stage, we employ adaptive dilated convolution, which dynamically adjusts the dilation rate spatially based on frequency components. This adaptive convolution kernel helps preserve the details of small objects while strengthening their feature representation. It also expands the receptive field, which is beneficial for capturing contextual information and the overall features of small objects. Our method demonstrates an improvement in Average Precision (AP) by 1% on the UA-DETRAC-test dataset and 3% on the VisDrone-test dataset when compared to state-of-the-art methods. The experiments indicate that the new architecture achieves significant performance improvements across various evaluation metrics. To fully leverage the potential of our approach, we conducted extended research on radar–camera systems. Full article

(This article belongs to the Section Sensing and Imaging)

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59 pages, 3221 KiB

Open AccessSystematic Review

A Systematic Review of AI-Based Techniques for Automated Waste Classification

by Farnaz Fotovvatikhah, Ismail Ahmedy, Rafidah Md Noor and Muhammad Umair Munir

Sensors 2025, 25(10), 3181; https://doi.org/10.3390/s25103181 - 18 May 2025

Abstract

Waste classification is a critical step in waste management that is time-consuming and necessitates automation to replace traditional approaches. Recently, machine learning (ML) and deep learning (DL) have gained attention from researchers seeking to automate waste classification by providing alternative computational techniques to [...] Read more.

Waste classification is a critical step in waste management that is time-consuming and necessitates automation to replace traditional approaches. Recently, machine learning (ML) and deep learning (DL) have gained attention from researchers seeking to automate waste classification by providing alternative computational techniques to address various waste-related challenges. Significant research on waste classification has emerged in recent years, reflecting the growing focus on this domain. This systematic literature review (SLR) explores the role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in automating waste classification. Using Kitchenham’s and PRISMA guidelines, we analyze over 97 studies, categorizing AI-based techniques into ML-based, DL-based, and hybrid models. We further present an in-depth review of over fifteen publicly available waste classification datasets, highlighting key limitations such as dataset imbalance, real-world variability, and standardization issues. Our analysis reveals that deep learning and hybrid approaches dominate the current research landscape, with CNN-based architecture and transfer learning techniques showing particularly promising results. To guide future advancements, this study also proposes a structured roadmap that organizes challenges and opportunities into short-, mid-, and long-term priorities. The roadmap integrates insights on model accuracy, system efficiency, and sustainability goals to support the practical deployment of AI-powered waste classification systems. This work provides researchers with a comprehensive understanding of the state-of-the-art in ML and DL for waste classification and offers insights into areas that remain unexplored. Full article

(This article belongs to the Section Cross Data)

22 pages, 126226 KiB

Open AccessTechnical Note

A Corner Detection Method for Noisy Checkerboard Images

by Hui Liu, Ligen Shan, Jiahao Feng and Shuanghao Wang

Sensors 2025, 25(10), 3180; https://doi.org/10.3390/s25103180 - 18 May 2025

Abstract

This article proposes a novel approach for corner detection in noisy checkerboard images, comprising several methodical steps: (1) an initial extraction of corners utilizing the cross features present in the edge image of the checkerboard; (2) the elimination of erroneous corners through an [...] Read more.

This article proposes a novel approach for corner detection in noisy checkerboard images, comprising several methodical steps: (1) an initial extraction of corners utilizing the cross features present in the edge image of the checkerboard; (2) the elimination of erroneous corners through an analysis of the periodic consistency among the detected corners; (3) the identification of the outermost corners and the subsequent generation of a rectangular bounding box based on the total number of input checkerboard corners; (4) the reconstruction of missing corners, which may have been obscured by noise, by leveraging the periodic characteristics of the corners. Experimental findings indicate that this methodology is capable of effectively detecting all corners of the checkerboard across varying levels of noise, thereby significantly enhancing the success rate of corner detection in noisy images. This makes the proposed method particularly advantageous for camera calibration in special scenarios where noise or contamination in checkerboard images is unavoidable. Full article

(This article belongs to the Section Sensing and Imaging)

21 pages, 499 KiB

Open AccessArticle

Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm

by Rui Liang, Dingzhao Li, Haixin Sun and Liangpo Hong

Sensors 2025, 25(10), 3179; https://doi.org/10.3390/s25103179 - 18 May 2025

Abstract

Efficient and adaptive formation planning is critical for unmanned surface vehicle (USV) swarms equipped with sensor networks and smart sensors to perform cooperative detection tasks in complex marine environments. Existing formation optimization methods often overlook the nonlinear coupling between sensor-based detection performance, communication [...] Read more.

Efficient and adaptive formation planning is critical for unmanned surface vehicle (USV) swarms equipped with sensor networks and smart sensors to perform cooperative detection tasks in complex marine environments. Existing formation optimization methods often overlook the nonlinear coupling between sensor-based detection performance, communication constraints, and obstacle avoidance. We propose a multi-objective formation optimization framework based on an improved genetic algorithm that simultaneously considers the detection coverage area, forward detection width, inter-agent communication, and static obstacle avoidance. We formulate a probabilistic cooperative detection model, introduce normalized detection efficiency indicators, and embed multiple geometric and environmental constraints into the optimization process. Simulation results show that the proposed method significantly improves the spatial efficiency of cooperative sensing, yielding a 32.76% increase in effective coverage area and 20.97% improvement in forward detection width compared to unoptimized formations. This strategy, supported by multi-sensor positioning and navigation, offers a robust and generalizable approach for intelligent maritime USV deployment in dynamic, multi-constraint scenarios. Full article

(This article belongs to the Section Intelligent Sensors)

55 pages, 913 KiB

Open AccessReview

Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods

by Zhishui You, Yuzhu Guo, Xiulei Zhang and Yifan Zhao

Sensors 2025, 25(10), 3178; https://doi.org/10.3390/s25103178 - 18 May 2025

Abstract

Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to [...] Read more.

Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

29 pages, 2476 KiB

Open AccessReview

Flexible Epidermal Sensor Power Systems: Innovations in Multidimensional Materials and Biomedical Applications

by Sheng Zhang, Shulan Zhou, Zhaotao He, Oresegun Olakunle Ibrahim, Chen Liu, Mengwei Wu, Chunge Wang and Qianqian Wang

Sensors 2025, 25(10), 3177; https://doi.org/10.3390/s25103177 - 18 May 2025

Abstract

Epidermal sensors are pivotal components of next-generation wearable technologies. They offer transformative potential in health monitoring, motion tracking, and biomedical applications. This potential stems from their ultra-thin design, skin compatibility, and ability to continuously detect physiological signals. The long-term functionality relies on advanced [...] Read more.

Epidermal sensors are pivotal components of next-generation wearable technologies. They offer transformative potential in health monitoring, motion tracking, and biomedical applications. This potential stems from their ultra-thin design, skin compatibility, and ability to continuously detect physiological signals. The long-term functionality relies on advanced power systems balancing flexibility, energy density, and environmental resilience. This review highlights four key power strategies: chemical batteries, biofuel cells, environmental energy harvesters, and wireless power transfer. Breakthroughs in multidimensional materials address challenges in ion transport, catalytic stability, and mechanical durability. Structural innovations mitigate issues like dendrite growth and enzyme degradation. These systems enable applications spanning biomarker analysis, motion sensing, and environmental monitoring. By integrating these advancements, this review concludes with a prospective outlook on future directions for epidermal sensor power systems. Full article

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

21 pages, 8626 KiB

Open AccessArticle

LSCD-Pose: A Feature Point Detection Model for Collaborative Perception in Airports

by Ruifeng Meng, Jinlei Wang, Yuanhao Huang, Zhaofeng Xue, Yihao Hu and Biao Li

Sensors 2025, 25(10), 3176; https://doi.org/10.3390/s25103176 - 18 May 2025

Abstract

Ensuring safety on busy airport aprons remains challenging, particularly in preventing aircraft wingtip collisions. In this study, first, a simplified coordinate mapping method converts pixel detections into accurate spatial coordinates, improving aircraft position and velocity estimates. Next, an innovative dynamic warning area with [...] Read more.

Ensuring safety on busy airport aprons remains challenging, particularly in preventing aircraft wingtip collisions. In this study, first, a simplified coordinate mapping method converts pixel detections into accurate spatial coordinates, improving aircraft position and velocity estimates. Next, an innovative dynamic warning area with a classification mechanism is introduced to enable faster responses from airport staff. Finally, this study proposes LSCD-Pose, a real-time detection network enhanced by lightweight shared modules, significantly reducing model size and computational load without sacrificing accuracy. Experiments on real airport datasets representing various apron scenarios demonstrate frame rates up to 461.7 FPS and a 90.5% reduction in model size compared with the baseline. Visualizations confirm the solution’s versatility and efficiency in effectively mitigating wingtip collisions and enhancing apron safety. Full article

(This article belongs to the Special Issue Applications of Advanced Sensors and Interoperability Technologies in Autonomous Transportation Systems)

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