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Sensors

Sensors is an international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB), International Society for the Measurement of Physical Behaviour (ISMPB) and Chinese Society of Micro-Nano Technology (CSMNT) and more are affiliated with Sensors and their members receive a discount on the article processing charges.

Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
Journal Rank: JCR - Q2 (Chemistry, Analytical) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.6 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about Sensors.
Companion journals for Sensors include: Chips, JCP and Targets.

Impact Factor: 3.4 (2023); 5-Year Impact Factor: 3.7 (2023)

Imprint Information Journal Flyer Open Access ISSN: 1424-8220

Latest Articles

14 pages, 962 KiB

Open AccessArticle

Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study

by Ka-Ming Lai and Kenneth N. K. Fong

Sensors 2025, 25(8), 2516; https://doi.org/10.3390/s25082516 - 16 Apr 2025

Falls pose a significant health risk for older people, necessitating accurate predictive tools for fall prevention. This study evaluated the sensitivity of a wearable waist-belt sensor, the Booguu Aspire system, in predicting prospective fall incidents among 37 community-dwelling older people in Hong Kong. [...] Read more.

Falls pose a significant health risk for older people, necessitating accurate predictive tools for fall prevention. This study evaluated the sensitivity of a wearable waist-belt sensor, the Booguu Aspire system, in predicting prospective fall incidents among 37 community-dwelling older people in Hong Kong. A prospective cohort design was employed, involving two analytical groups: the overall cohort and a subset with cognitive performance data available, measured using the Montreal Cognitive Assessment (MoCA). Participants were categorized into moderate- or high-risk groups for falls using the sensor and further assessed with physical function tests, including the Single Leg Stand Test (SLST), 6 Meter Walk Test (6MWT), and Five Times Sit to Stand Test (5STS). Fall incidents were monitored for 12 months through quarterly follow-up phone calls. Statistical analyses showed no significant differences in physical performance between high- and moderate-risk groups and no significant correlations between sensor-based fall risk ratings and physical function test outcomes. The SLST, 6MWT, 5STS, and MoCA tests classified sensor-determined fall risk ratings with accuracies of 51.4%, 64.9%, 59.5%, and 50%. The sensor showed low sensitivity, with 13.51% true positives for fallers and a 20% sensitivity for high-risk individuals. ROC analysis yielded an Area Under the Curve of 0.688. Our findings indicate that the wearable waist-belt Sensor System may not be a sensitive tool in predicting prospective fall incidents. The algorithm for fall risk classification in the wearable sensor merits further exploration. Full article

(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)

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20 pages, 4447 KiB

Open AccessArticle

Bottom Plate Damage Localization Method for Storage Tanks Based on Bottom Plate-Wall Plate Synergy

by Yunxiu Ma, Linzhi Hu, Yuxuan Dong, Lei Chen and Gang Liu

Sensors 2025, 25(8), 2515; https://doi.org/10.3390/s25082515 - 16 Apr 2025

Ultrasonic guided waves can be employed for in-service defect detection in storage tank bottom plates; however, conventional single-array approaches face challenges from boundary scattering noise at side connection welds. This study proposes a collaborative bottom plate-wall plate detection methodology to address these limitations. [...] Read more.

Ultrasonic guided waves can be employed for in-service defect detection in storage tank bottom plates; however, conventional single-array approaches face challenges from boundary scattering noise at side connection welds. This study proposes a collaborative bottom plate-wall plate detection methodology to address these limitations. Sensor arrays were strategically deployed on both the bottom plate and wall plate, achieving multidimensional signal acquisition through bottom plate array excitation and dual-array reception from both the bottom plate and tank wall. A correlation coefficient-based matching algorithm was developed to distinguish damage echoes from weld-induced scattering noise by exploiting path-dependent signal variations between the two arrays. The investigation revealed that guided wave signals processed through data matching effectively preserved damage echo signals while substantially attenuating boundary scattering signals. Building upon these findings, correlation matching was implemented on guided wave signals received by corresponding array elements from both the bottom plate and wall plate, followed by total focusing imaging (TFM) using the processed signals. Results demonstrate that the collaborative bottom plate-wall plate detection imaging cloud maps, after implementing signal correlation matching, effectively suppress artifacts compared with imaging results obtained solely from bottom plate arrays. The maximum relative localization error was measured as 5.4%, indicating superior detection accuracy. Full article

(This article belongs to the Special Issue Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing)

28 pages, 26593 KiB

Open AccessArticle

Geometry-Constrained Learning-Based Visual Servoing with Projective Homography-Derived Error Vector

by Yueyuan Zhang, Arpan Ghosh, Yechan An, Kyeongjin Joo, SangMin Kim and Taeyong Kuc

Sensors 2025, 25(8), 2514; https://doi.org/10.3390/s25082514 - 16 Apr 2025

We propose a novel geometry-constrained learning-based method for camera-in-hand visual servoing systems that eliminates the need for camera intrinsic parameters, depth information, and the robot’s kinematic model. Our method uses a cerebellar model articulation controller (CMAC) to execute online Jacobian estimation within the [...] Read more.

We propose a novel geometry-constrained learning-based method for camera-in-hand visual servoing systems that eliminates the need for camera intrinsic parameters, depth information, and the robot’s kinematic model. Our method uses a cerebellar model articulation controller (CMAC) to execute online Jacobian estimation within the control framework. Specifically, we introduce a fixed-dimension, uniform-magnitude error function based on the projective homography matrix. The fixed-dimension error function ensures a constant Jacobian size regardless of the number of feature points, thereby reducing computational complexity. By not relying on individual feature points, the approach maintains robustness even when some features are occluded. The uniform magnitude of the error vector elements simplifies neural network input normalization, thereby enhancing online training efficiency. Furthermore, we incorporate geometric constraints between feature points (such as collinearity preservation) into the network update process, ensuring that model predictions conform to the fundamental principles of projective geometry and eliminating physically impossible control outputs. Experimental and simulation results demonstrate that our approach achieves superior robustness and faster learning rates compared to other model-free image-based visual servoing methods. Full article

(This article belongs to the Section Intelligent Sensors)

19 pages, 1468 KiB

Open AccessArticle

Adaptive Kalman Filtering for Compensating External Effects in On-Line Spectroscopic Measurements

by Daniel Sbarbaro, Tor Arne Johansen and Jorge Yañez

Sensors 2025, 25(8), 2513; https://doi.org/10.3390/s25082513 - 16 Apr 2025

This study addresses the challenges of real-time spectroscopic sensing in industrial applications, where external factors such as temperature fluctuations, pressure variations, and particle size distribution significantly impact measurement accuracy. Conventional quantitative analytical methods often neglect these dynamic influences, leading to erroneous concentration estimates. [...] Read more.

This study addresses the challenges of real-time spectroscopic sensing in industrial applications, where external factors such as temperature fluctuations, pressure variations, and particle size distribution significantly impact measurement accuracy. Conventional quantitative analytical methods often neglect these dynamic influences, leading to erroneous concentration estimates. To overcome these limitations, we propose an integrated modeling framework that combines a discrete-time process model with a physics-based spectroscopic sensor model, explicitly accounting for the dynamic properties of the system. A key innovation of this work is the development and application of an Adaptive Kalman Filter (AKF) to systematically correct for measurement distortions caused by external disturbances. Unlike conventional filtering techniques, the AKF dynamically adjusts to changing process conditions by leveraging real-time observability analysis, ensuring robustness even in the presence of sensor noise and environmental variability. Furthermore, to address cases where full observability is not achievable, we introduce a reduced-order Adaptive Kalman Filter (rAKF), which optimally estimates concentrations while minimizing computational complexity. A comprehensive series of simulations was conducted to assess the sensitivity of the estimation to variations in external signal type, noise levels, and initial values for parameters and states. The findings of this study demonstrate the superior performance of both AKF and rAKF in comparison to conventional filtering techniques, including the Extended Kalman Filter. The proposed approaches have been shown to enhance the reliability of spectroscopic sensor measurements, enabling more precise real-time estimations that can be used for monitoring and advanced process control strategies in industrial settings. Full article

(This article belongs to the Special Issue Advanced Spectroscopy-Based Sensors and Spectral Analysis Technology)

24 pages, 9147 KiB

Open AccessArticle

Quantitative Retrieval of Soil Salinity in Arid Regions: A Radar Feature Space Approach with Fully Polarimetric SAR Data

by Ilyas Nurmemet, Aihepa Aihaiti, Yilizhati Aili, Xiaobo Lv, Shiqin Li and Yu Qin

Sensors 2025, 25(8), 2512; https://doi.org/10.3390/s25082512 - 16 Apr 2025

Soil salinization is a critical factor affecting land desertification and limiting agricultural development in arid regions, and the rapid acquisition of salinized soil information is crucial for prevention and mitigation efforts. In this study, we selected the Yutian Oasis in Xinjiang, China as [...] Read more.

Soil salinization is a critical factor affecting land desertification and limiting agricultural development in arid regions, and the rapid acquisition of salinized soil information is crucial for prevention and mitigation efforts. In this study, we selected the Yutian Oasis in Xinjiang, China as the study area and utilized Gaofen-3 synthetic aperture radar (SAR) remote sensing data and field measurements to analyze the correlations between the salinized soil properties and 36 polarimetric radar feature components. Based on the analysis results, two components with the highest correlation, namely, Yamaguchi4_vol (p < 0.01) and Freeman3_vol (p < 0.01), were selected to construct a two-dimensional feature space, named Yamaguchi4_vol-Freeman3_vol. Based on this feature space, a radar salinization monitoring index (RSMI) model was developed. The results indicate that the RSMI exhibited a strong correlation with the surface soil salinity, with a correlation coefficient of 0.85. The simulated values obtained using the RSMI model were well-fitted to the measured soil electrical conductivity (EC) values, achieving an R² value of 0.72 and a root mean square error (RMSE) of 7.28 dS/m. To assess the model’s generalizability, we applied the RSMI to RADARSAT-2 SAR data from the environmentally similar Weiku Oasis. The validation results showed comparable accuracy (R² = 0.70, RMSE = 9.29 dS/m), demonstrating the model’s robustness for soil salinity retrieval across different arid regions. This model offers a rapid and reliable approach for quantitative monitoring and assessment of soil salinization in arid regions using fully polarimetric radar remote sensing. Furthermore, it lays the groundwork for further exploring the application potential of Gaofen-3 satellite data and expanding its utility in soil salinization monitoring. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Remote Sensing: Challenges and Future Direction)

18 pages, 3974 KiB

Open AccessArticle

A LAMP Detection System Based on a Microfluidic Chip for Pyricularia grisea

by Chenda Wu, Jianing Cheng, Yinchao Zhang and Ping Yao

Sensors 2025, 25(8), 2511; https://doi.org/10.3390/s25082511 - 16 Apr 2025

As one of the major rice fungal diseases, blast poses a serious threat to the yield and quality of rice globally. It is caused by the pathogen Pyricularia grisea. Therefore, the development of rapid, accurate, and portable microfluidic detection system for Pyricularia [...] Read more.

As one of the major rice fungal diseases, blast poses a serious threat to the yield and quality of rice globally. It is caused by the pathogen Pyricularia grisea. Therefore, the development of rapid, accurate, and portable microfluidic detection system for Pyricularia grisea is important for the control of rice blast. This study presents an integrated microfluidic detection system for the rapid and sensitive detection of Pyricularia grisea using the LAMP detection method. The microfluidic detection system includes a microfluidic chip, a temperature control module, and an OpenMv camera. The micro-mixing channels with shear structures improve the mixing efficiency to about 98%. Flow-blocking valves are used to reduce reagent loss in the reaction chamber. The temperature control module is used to heat the reaction chamber, maintaining a stable temperature of 65 °C. The microfluidic chip detection chamber is used for image inspection using an OpenMv camera. The developed system can detect Pyricularia grisea in the range of 10 copies/μL–10⁵ copies/μL within 45 min. Specificity and interference experiments were performed on Pyricularia grisea, validating the method’s good reliability. This LAMP detection system based on a microfluidic chip has strong potential in the early and effective detection of rice blast. Full article

(This article belongs to the Section Smart Agriculture)

22 pages, 378 KiB

Open AccessArticle

Reevaluating the Potential of a Vanilla Transformer Encoder for Unsupervised Time-Series Anomaly Detection in Sensor Applications

by Chan Sik Han, HyungWon Kim and Keon Myung Lee

Sensors 2025, 25(8), 2510; https://doi.org/10.3390/s25082510 - 16 Apr 2025

Sensors generate extensive time-series data across various domains, and effective methods for detecting anomalies in such data are still in high demand. Unsupervised time-series anomaly detection provides practical approaches to addressing the challenges of collecting anomalous data. For effective anomaly detection, a range [...] Read more.

Sensors generate extensive time-series data across various domains, and effective methods for detecting anomalies in such data are still in high demand. Unsupervised time-series anomaly detection provides practical approaches to addressing the challenges of collecting anomalous data. For effective anomaly detection, a range of deep-learning-based models have been explored to handle temporal patterns inherent in time-series data. In particular, Transformer encoders have gained significant attention due to their ability to efficiently capture temporal dependencies. Various studies have attempted the architectural improvements of Transformer encoders to address the inherent complexity of time-series data analysis. Unlike the previous studies, this work demonstrates that a vanilla Transformer encoder-based framework remains yet a competitive model for time-series anomaly detection. Instead of architectural modification of the Transformer encoder, we identify key design choices and propose an asymmetric autoencoder-based framework incorporating those design choices with a vanilla Transformer encoder and a linear layer decoder. The proposed framework has been evaluated on a range of unsupervised time-series anomaly-detection benchmarks, and the experimental results show that it achieves performance that is either superior or competitive compared to state-of-the-art models. Full article

(This article belongs to the Section Electronic Sensors)

27 pages, 2836 KiB

Open AccessArticle

Efficient Internet of Things Communication System Based on Near-Field Communication and Long Range Radio

by Ting Chai, Dongryool Kim and Seungsoo Shin

Sensors 2025, 25(8), 2509; https://doi.org/10.3390/s25082509 - 16 Apr 2025

Efficient communication in the Internet of Things (IoT) is essential for enabling smart applications. While NFC excels in near-field device interaction, its limited communication range hinders LoRa’s long-range communication due to its low data throughput. Together with NFC and LoRa technologies, Raspberry Pi [...] Read more.

Efficient communication in the Internet of Things (IoT) is essential for enabling smart applications. While NFC excels in near-field device interaction, its limited communication range hinders LoRa’s long-range communication due to its low data throughput. Together with NFC and LoRa technologies, Raspberry Pi is used as a microcontroller (MCU) in this paper to look into how to make near-field and long-distance communication work better together and fix the issue of an imbalance between communication range and energy consumption in the IoT system. By optimizing the communication algorithm and parameter tuning, the power consumption of the system is significantly reduced, and the communication range and data throughput are improved. Our research gives you the technical information you need to make an IoT communication system that works well, uses little power, and has a wide coverage area. This kind of system is good for situations where you need to collect data from a close distance and keep an eye on things from afar. This makes the system more power-efficient and better at communicating, which also makes it easier for users to manage data. It is suitable for a wide range of application scenarios, such as warehousing, healthcare, agriculture, and smart cities. Full article

(This article belongs to the Section Internet of Things)

14 pages, 9159 KiB

Open AccessArticle

Copper Nanoclusters Anchored on Crumpled N-Doped MXene for Ultra-Sensitive Electrochemical Sensing

by Hanxue Yang, Chao Rong, Shundong Ge, Tao Wang, Bowei Zhang and Fu-Zhen Xuan

Sensors 2025, 25(8), 2508; https://doi.org/10.3390/s25082508 - 16 Apr 2025

Simultaneous detection of dopamine (DA) and uric acid (UA) is essential for diagnosing neurological and metabolic diseases but hindered by overlapping electrochemical signals. We present an ultrasensitive electrochemical sensor using copper nanoclusters anchored on nitrogen-doped crumpled Ti₃C₂T_x MXene [...] Read more.

Simultaneous detection of dopamine (DA) and uric acid (UA) is essential for diagnosing neurological and metabolic diseases but hindered by overlapping electrochemical signals. We present an ultrasensitive electrochemical sensor using copper nanoclusters anchored on nitrogen-doped crumpled Ti₃C₂T_x MXene (Cu-N/Ti₃C₂T_x). The engineered 3D crumpled architecture prevents MXene restacking, exposes active sites, and enhances ion transport, while Cu nanoclusters boost electrocatalytic activity via accelerated electron transfer. Structural analyses confirm uniform Cu dispersion (3.0 wt%), Ti-N bonding, and strain-induced wrinkles, synergistically improving conductivity. The sensor achieves exceptional sensitivity (1958.3 and 1152.7 μA·mM⁻¹·cm⁻² for DA/UA), ultralow detection limits (0.058 and 0.099 μM for DA/UA), rapid response (<1.5 s), and interference resistance (e.g., ascorbic acid). Differential pulse voltammetry enables independent linear detection ranges (DA: 2–60 μM; UA: 5–100 μM) in biofluids, with 94.4% stability retention over 7 days. The designed sensor exhibits excellent capabilities for DA and UA detection. This work provides a novel design strategy for developing high-performance electrochemical sensors. Full article

(This article belongs to the Special Issue Nanomaterial-Optimized Device Construction and AI-Enhanced Signal Analysis of Gas Sensors)

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19 pages, 2612 KiB

Open AccessArticle

Kalman Filter-Based Epidemiological Model for Post-COVID-19 Era Surveillance and Prediction

by Yuanyou Shi, Xinhang Zhu, Xinhe Zhu, Baiqi Cheng and Yongmin Zhong

Sensors 2025, 25(8), 2507; https://doi.org/10.3390/s25082507 - 16 Apr 2025

In the post-COVID-19 era, the dynamic spread of COVID-19 poses new challenges to epidemiological modelling, particularly due to the absence of large-scale screening and the growing complexity introduced by immune failure and reinfections. This paper proposes an AEIHD (antibody-acquired, exposed, infected, hospitalised, and [...] Read more.

In the post-COVID-19 era, the dynamic spread of COVID-19 poses new challenges to epidemiological modelling, particularly due to the absence of large-scale screening and the growing complexity introduced by immune failure and reinfections. This paper proposes an AEIHD (antibody-acquired, exposed, infected, hospitalised, and deceased) model to analyse and predict COVID-19 transmission dynamics in the post-COVID-19 era. This model removes the susceptible compartment and combines the recovered and vaccinated compartments into an “antibody-acquired” compartment. It also introduces a new hospitalised compartment to monitor severe cases. The model incorporates an antibody-acquired infection rate to account for immune failure. The Extended Kalman Filter based on the AEIHD model is proposed for real-time state and parameter estimation, overcoming the limitations of fixed-parameter approaches and enhancing adaptability to nonlinear dynamics. Simulation studies based on reported data from Australia validate the AEIHD model, demonstrating its capability to accurately capture COVID-19 transmission dynamics with limited statistical information. The proposed approach addresses the key limitations of traditional SIR and SEIR models by integrating hospitalisation data and time-varying parameters, offering a robust framework for monitoring and predicting epidemic behaviours in the post-COVID-19 era. It also provides a valuable tool for public health decision-making and resource allocation to handle rapidly evolving epidemiology. Full article

(This article belongs to the Special Issue Advances in Signal Processing for Biomedical Applications and Healthcare)

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14 pages, 16915 KiB

Open AccessArticle

Performance Assessment of Change Detection Based on Robust PCA for Wavelength Resolution SAR Images Using Nonidentical Flight Passes

by Lucas P. Ramos, Viet T. Vu, Mats I. Pettersson, Patrik Dammert, Leonardo T. Duarte and Renato Machado

Sensors 2025, 25(8), 2506; https://doi.org/10.3390/s25082506 - 16 Apr 2025

One of the main challenges in Synthetic Aperture Radar (SAR) change detection involves using SAR images from different flight passes. Depending on the flight pass, objects have different specular reflections since the radar cross-sections of these objects can be totally different between passes. [...] Read more.

One of the main challenges in Synthetic Aperture Radar (SAR) change detection involves using SAR images from different flight passes. Depending on the flight pass, objects have different specular reflections since the radar cross-sections of these objects can be totally different between passes. Then, it is common knowledge that the flight passes must be close to identical for conventional SAR change detection. Wavelength-resolution SAR refers to a SAR system with a spatial resolution approximately equal to the wavelength. This high relative resolution helps to stabilize the ground clutter in the SAR images. Consequently, the restricted requirement about identical flight passes for SAR change detection can be relaxed, and SAR change detection becomes possible with nonidentical passes. This paper shows that robust principal component analysis (RPCA) is efficient for change detection even using wavelength-resolution SAR images acquired with very different flight passes. It presents several SAR change detection experimental results using flight pass differences up to 95°. For slightly different passes, e.g., 5°, our method reached a false alarm rate (FAR) of approximately one false alarm per square kilometer for a probability of detection (PD) above 90%. In a particular setting, it achieves a PD of 97.5% for a FAR of 0.917 false alarms per square kilometer, even using SAR images acquired with nonidentical passes. Full article

(This article belongs to the Special Issue Application of SAR and Remote Sensing Technology in Earth Observation)

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30 pages, 1471 KiB

Open AccessArticle

Distributed Sensor Network Calibration Under Sensor Nonlinearities with Applications in Aerodynamic Pressure Sensing

by Srdjan S. Stanković, Miloš S. Stanković, Mladen Veinović, Ivana Jokić and Miloš Frantlović

Sensors 2025, 25(8), 2505; https://doi.org/10.3390/s25082505 - 16 Apr 2025

The theoretical part of this paper is devoted to a class of distributed blind calibration algorithms for large sensor networks based on consensus. The basic blind calibration method starts from affine sensor models and calibration functions, aiming to equalize corrected sensor offsets and [...] Read more.

The theoretical part of this paper is devoted to a class of distributed blind calibration algorithms for large sensor networks based on consensus. The basic blind calibration method starts from affine sensor models and calibration functions, aiming to equalize corrected sensor offsets and gains without requiring any a priori knowledge of the measured signal. The main focus is to systematically and rigorously analyze the behavior of the calibration algorithms of the stochastic approximation type under nonlinear sensor models and stochastic environments, and to provide recommendations that are relevant to practice. It is demonstrated that the calibration algorithm—based on consensus with respect to all the calibration parameters—is far less robust to unknown sensor nonlinearities than the modified algorithm, taking one micro-calibrated sensor as a reference. Stability proofs of the algorithms are given in the bounded input–bounded output sense. The influences of measurement and communication noises are also analyzed using the theory of stochastic approximation. Numerous simulation results provide a comprehensive picture of the algorithm properties that are relevant to practice. This is followed by an important verification of the theoretical results, obtained by applying the analyzed blind calibration algorithms to an originally designed multichannel instrument for aerodynamic pressure sensing. A description of the new instrument is given, together with essential aspects of the implementation of the blind calibration algorithm. It is shown that the selected algorithm can be seen as a simple and efficient practical tool for blind online real-time re-calibration of complex sensor networks during normal system operations. Full article

(This article belongs to the Section Sensor Networks)

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23 pages, 4132 KiB

Open AccessArticle

Mechanism–Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection

by Wenhao Chen, Yong Zou, Zhengzhou Li, Shengrong Zhong, Haolin Gan and Aoran Li

Sensors 2025, 25(8), 2504; https://doi.org/10.3390/s25082504 - 16 Apr 2025

Multi-feature-based maritime radar target detection algorithms often rely on statistical models to accurately characterize sea clutter variations. However, it is a big challenge for these models to accurately characterize sea clutter due to the complexity of the marine environment. Moreover, the distribution of [...] Read more.

Multi-feature-based maritime radar target detection algorithms often rely on statistical models to accurately characterize sea clutter variations. However, it is a big challenge for these models to accurately characterize sea clutter due to the complexity of the marine environment. Moreover, the distribution of training samples captured from dynamic observation conditions is imbalanced. These multi-features extracted from inaccurate models and imbalanced data lead to overfitting or underfitting and degrade detection performance. To tackle these challenges, this paper proposes a mechanism–data collaborative method using the scattering coefficient as a representative feature. By establishing a mapping relationship between measured data and empirical values, the classical model is piecewise fitted to the measured data. A fusion strategy is then used to compensate for interval discontinuities, enabling accurate characterization of clutter properties in the current maritime environment. Based on the characterized clutter properties, a hybrid feature selection strategy is further proposed to construct a diverse and compact training sample set by integrating global density distribution with local gradient variation. The experiments based on field data are included to evaluate the effectiveness of the proposed method including sea clutter characterization accuracy and training sample selection across various scenarios. Experimental results demonstrate that the proposed method provides a more accurate representation of sea clutter characteristics. Moreover, the detectors trained with the proposed training samples exhibit strong generalization capability across diverse maritime environments under the condition of identical features and classifiers. These achievements highlight the importance of accurate sea clutter modeling and optimal training sample selection in improving target detection performance and ensuring the reliability of radar-based maritime surveillance. Full article

(This article belongs to the Special Issue Maritime Information Sensing and Big Data)

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20 pages, 4940 KiB

Open AccessArticle

Estimation of Flood Inundation Area Using Soil Moisture Active Passive Fractional Water Data with an LSTM Model

by Rekzi D. Febrian, Wanyub Kim, Yangwon Lee, Jinsoo Kim and Minha Choi

Sensors 2025, 25(8), 2503; https://doi.org/10.3390/s25082503 - 16 Apr 2025

Accurate flood monitoring and forecasting techniques are important and continue to be developed for improved disaster preparedness and mitigation. Flood estimation using satellite observations with deep learning algorithms is effective in detecting flood patterns and environmental relationships that may be overlooked by conventional [...] Read more.

Accurate flood monitoring and forecasting techniques are important and continue to be developed for improved disaster preparedness and mitigation. Flood estimation using satellite observations with deep learning algorithms is effective in detecting flood patterns and environmental relationships that may be overlooked by conventional methods. Soil Moisture Active Passive (SMAP) fractional water (FW) was used as a reference to estimate flood areas in a long short-term memory (LSTM) model using a combination of soil moisture information, rainfall forecasts, and floodplain topography. To perform flood modeling in LSTM, datasets with different spatial resolutions were resampled to 30 m spatial resolution using bicubic interpolation. The model’s efficacy was quantified by validating the LSTM-based flood inundation area with a water mask from Senti-nel-1 SAR images for regions with different topographic characteristics. The average area under the curve (AUC) value of the LSTM model was 0.93, indicating a high accuracy estimation of FW. The confusion matrix-derived metrics were used to validate the flood inundation area and had a high-performance accuracy of ~0.9. SMAP FW showed optimal performance in low-covered vegetation, seasonal water variations and flat regions. The estimates of flood inundation areas show the methodological promise of the proposed framework for improved disaster preparedness and resilience. Full article

(This article belongs to the Special Issue Deep Learning-Based Image and Signal Sensing and Processing: 2nd Edition)

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38 pages, 20801 KiB

Open AccessArticle

A Hybrid Method to Solve the Multi-UAV Dynamic Task Assignment Problem

by Shahad Alqefari and Mohamed El Bachir Menai

Sensors 2025, 25(8), 2502; https://doi.org/10.3390/s25082502 - 16 Apr 2025

In the rapidly evolving field of aerial robotics, the coordinated management of multiple unmanned aerial vehicle (multi-UAV) systems to address complex and dynamic environments is increasingly critical. Multi-UAV systems promise enhanced efficiency and effectiveness in various applications, from disaster response to infrastructure inspection, [...] Read more.

In the rapidly evolving field of aerial robotics, the coordinated management of multiple unmanned aerial vehicle (multi-UAV) systems to address complex and dynamic environments is increasingly critical. Multi-UAV systems promise enhanced efficiency and effectiveness in various applications, from disaster response to infrastructure inspection, by leveraging the collective capabilities of UAV fleets. However, the dynamic nature of such environments presents significant challenges in task allocation and real-time adaptability. This paper introduces a novel hybrid algorithm designed to optimize multi-UAV task assignments in dynamic environments. State-of-the-art solutions in this domain have exhibited limitations, particularly in rapidly responding to dynamic changes and effectively scaling to large-scale environments. The proposed solution bridges these gaps by combining clustering to group and assign tasks in an initial offline phase with a dynamic partial reassignment process that locally updates assignments in response to real-time changes, all within a centralized–distributed communication topology. The simulation results validate the superiority of the proposed solution and demonstrate its improvements in efficiency and responsiveness over existing solutions. Additionally, the results highlight the scalability of the solution in handling large-scale problems and demonstrate its ability to efficiently manage a growing number of UAVs and tasks. It also demonstrated robust adaptability and enhanced mission effectiveness across a wide range of dynamic events and different scale scenarios. Full article

(This article belongs to the Section Sensors and Robotics)

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23 pages, 1848 KiB

Open AccessArticle

Calibration of Mobile Robots Using ATOM

by Bruno Silva, Diogo Vieira, Manuel Gomes, Miguel Riem Oliveira and Eurico Pedrosa

Sensors 2025, 25(8), 2501; https://doi.org/10.3390/s25082501 - 16 Apr 2025

The calibration of mobile manipulators requires accurate estimation of both the transformations provided by the localization system and the transformations between sensors and the motion coordinate system. Current works offer limited flexibility when dealing with mobile robotic systems with many different sensor modalities. [...] Read more.

The calibration of mobile manipulators requires accurate estimation of both the transformations provided by the localization system and the transformations between sensors and the motion coordinate system. Current works offer limited flexibility when dealing with mobile robotic systems with many different sensor modalities. In this work, we propose a calibration approach that simultaneously estimates these transformations, enabling precise calibration even when the localization system is imprecise. This approach is integrated into Atomic Transformations Optimization Method (ATOM), a versatile calibration framework designed for multi-sensor, multi-modal robotic systems. By formulating calibration as an extended optimization problem, ATOM estimates both sensor poses and calibration pattern positions. The proposed methodology is validated through simulations and real-world case studies, demonstrating its effectiveness in improving calibration accuracy for mobile manipulators equipped with diverse sensor modalities. Full article

(This article belongs to the Collection Sensors and Data Processing in Robotics)

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19 pages, 19461 KiB

Open AccessArticle

MEF-CAAN: Multi-Exposure Image Fusion Based on a Low-Resolution Context Aggregation Attention Network

by Wenxiang Zhang, Chunmeng Wang and Jun Zhu

Sensors 2025, 25(8), 2500; https://doi.org/10.3390/s25082500 - 16 Apr 2025

Recently, deep learning-based multi-exposure image fusion methods have been widely explored due to their high efficiency and adaptability. However, most existing multi-exposure image fusion methods have insufficient feature extraction ability for recovering information and details in extremely exposed areas. In order to solve [...] Read more.

Recently, deep learning-based multi-exposure image fusion methods have been widely explored due to their high efficiency and adaptability. However, most existing multi-exposure image fusion methods have insufficient feature extraction ability for recovering information and details in extremely exposed areas. In order to solve this problem, we propose a multi-exposure image fusion method based on a low-resolution context aggregation attention network (MEF-CAAN). First, we feed the low-resolution version of the input images to CAAN to predict their low-resolution weight maps. Then, the high-resolution weight maps are generated by guided filtering for upsampling (GFU). Finally, the high-resolution fused image is generated by a weighted summation operation. Our proposed network is unsupervised and adaptively adjusts the weights of channels to achieve better feature extraction. Experimental results show that our method outperforms existing state-of-the-art methods by both quantitative and qualitative evaluation. Full article

(This article belongs to the Special Issue Advances in Image and Signal Processing and Biomedical Engineering Informatics)

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12 pages, 2593 KiB

Open AccessArticle

Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations

by Francesco Di Nardo, Rocco De Marco, Daniel Li Veli, Laura Screpanti, Benedetta Castagna, Alessandro Lucchetti and David Scaradozzi

Sensors 2025, 25(8), 2499; https://doi.org/10.3390/s25082499 - 16 Apr 2025

Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for [...] Read more.

Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for classifying dolphin vocalizations from a PAM acoustic recording using a convolutional neural network (CNN). Four types of common bottlenose dolphin (Tursiops truncatus) vocalizations were identified from underwater recordings: whistles, echolocation clicks, burst pulse sounds, and feeding buzzes. To enhance classification performances, edge-detection filters were applied to spectrograms, with the aim of removing unwanted noise components. A dataset of nearly 10,000 spectrograms was used to train and test the CNN through a 10-fold cross-validation procedure. The results showed that the CNN achieved an average accuracy of 95.2% and an F1-score of 87.8%. The class-specific results showed a high accuracy for whistles (97.9%), followed by echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%). The highest F1-score was obtained for whistles, exceeding 95%, while the other three vocalization typologies maintained an F1-score above 80%. This method provides a promising step toward improving the passive acoustic monitoring of dolphins, contributing to both species conservation and the mitigation of conflicts with fisheries. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 8999 KiB

Open AccessArticle

Real-Time Monitoring-Based Stability Analysis of an Extra-Large LNG Tank Roof Under Construction

by Yong Yang, Tuanhai Chen, Kezheng Zhang and Yu Song

Sensors 2025, 25(8), 2498; https://doi.org/10.3390/s25082498 - 16 Apr 2025

The predominant failure mode of ultra-large LNG tanks is buckling. Current stability analysis methods for the roofs of these tanks face challenges, such as inaccurate buckling load simulations and on-site monitoring data scarcity. This article presents a novel method for analyzing buckling, systematically [...] Read more.

The predominant failure mode of ultra-large LNG tanks is buckling. Current stability analysis methods for the roofs of these tanks face challenges, such as inaccurate buckling load simulations and on-site monitoring data scarcity. This article presents a novel method for analyzing buckling, systematically investigating the key factors and mechanisms affecting roof stability based on real-time monitoring during construction. Firstly, a method including the “element birth and death” technique is proposed for roof stability calculation, and its accuracy is validated through real-time monitoring data. Secondly, the roof stability at each construction stage is analyzed. Finally, the impact of three key structural parameters is explored. The results indicate that the “element birth and death” technique can accurately simulate roof stability under construction conditions. The roof plate thickness, beam cross-sectional dimensions, and rise–span ratio all have a positive influence on stability. Among the factors considered, the section size of the longitudinal beam and the rise–span ratio have the most significant impact on roof stability, followed by the section size of the circular beam. In terms of material consumption, enhancing roof stability by increasing the rise–span ratio is an effective option. Full article

(This article belongs to the Special Issue Recent Development and Applications of Sensing Technology in Resilient and Sustainable Infrastructure)

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19 pages, 4412 KiB

Open AccessArticle

A Reconfigurable Digital Beamformer Implemented on a Field-Programmable Gate Array for Real-Time and Resource-Efficient Direction-of-Arrival Estimation

by Yuting Wang, Liyuan Zhu, Tianxiang Wu and Shunli Ma

Sensors 2025, 25(8), 2497; https://doi.org/10.3390/s25082497 - 16 Apr 2025

A medium- or large-scale receiving antenna array using digital beamforming can achieve high-resolution direction-of-arrival (DOA) estimation at the receiver. However, it typically suffers from high cost and complexity. This paper proposes an efficient reconfigurable digital beamformer that can achieve real-time angle estimation with [...] Read more.

A medium- or large-scale receiving antenna array using digital beamforming can achieve high-resolution direction-of-arrival (DOA) estimation at the receiver. However, it typically suffers from high cost and complexity. This paper proposes an efficient reconfigurable digital beamformer that can achieve real-time angle estimation with high accuracy while making effective use of hardware resources. The digital beamformer operates in two modes: beamforming mode and angle estimation mode. In the angle estimation mode, the phase shift steps required for beam scanning can be flexibly adjusted according to the desired angular resolution. By dynamically switching operational modes and fine-tuning the granularity of processing tasks, this architecture maximizes the efficient use of Field-Programmable Gate Array (FPGA) resources, ensuring optimal performance and flexibility in real-time signal processing applications. Simulation results show that with an input signal-to-noise ratio of 10 dB, the beamformer can complete DOA estimation with an error of less than 1° within microsecond-level delay. Full article

(This article belongs to the Section Electronic Sensors)

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