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A Sensor Employing an Array of Silicon Photomultipliers for Detection of keV Ions in Time-of-Flight Mass Spectrometry
On-Line Monitoring of Vitamin C in Fruit Juice in Processing Plants by PEDOT-Modified Electrochemical Sensor
Resonant Drive Techniques for Electrostatic Microelectromechanical Systems (MEMS): A Comparative Study
Acquisition and Modeling of Material Appearance Using a Portable, Low Cost, Device
Super Learner Calibration of Low-Cost IoT Air Quality Sensors for Accurate PM Monitoring

Journal Description

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 (Instruments and Instrumentation) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.7 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the first half of 2025).
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, Targets and AI Sensors.
Journal Cluster of Instruments and Instrumentation: Actuators, AI Sensors, Instruments, Micromachines and Sensors.

Impact Factor: 3.5 (2024); 5-Year Impact Factor: 3.7 (2024)

Imprint Information Journal Flyer Open Access ISSN: 1424-8220

Latest Articles

21 pages, 912 KB

Open AccessArticle

UAV-Enabled Maritime IoT D2D Task Offloading: A Potential Game-Accelerated Framework

by Baiyi Li, Jian Zhao and Tingting Yang

Sensors 2025, 25(18), 5820; https://doi.org/10.3390/s25185820 (registering DOI) - 18 Sep 2025

Maritime Internet of Things (IoT) with unmanned surface vessels (USVs) faces tight onboard computing and sparse wireless links. Compute-intensive vision and sensing workloads often exceed latency budgets, which undermines timely decisions. In this paper, we propose a novel distributed computation offloading framework for [...] Read more.

Maritime Internet of Things (IoT) with unmanned surface vessels (USVs) faces tight onboard computing and sparse wireless links. Compute-intensive vision and sensing workloads often exceed latency budgets, which undermines timely decisions. In this paper, we propose a novel distributed computation offloading framework for maritime IoT scenarios. By leveraging the limited computational resources of USVs within a device-to-device (D2D)-assisted edge network and the mobility advantages of UAV-assisted edge computing, we design a breadth-first search (BFS)-based distributed computation offloading game. Building upon this, we formulate a global latency minimization problem that jointly optimizes UAV hovering coordinates and arrival times. This problem is solved by decomposing it into subproblems addressed via a joint Alternating Direction Method of Multipliers (ADMM) and Successive Convex Approximation (SCA) approach, effectively reducing the time between UAV arrivals and hovering coordinates. Extensive simulations verify the effectiveness of our framework, demonstrating up to a 49.6% latency reduction compared with traditional offloading schemes. Full article

(This article belongs to the Special Issue Artificial Intelligence and Edge Computing in IoT-Based Applications)

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14 pages, 3180 KB

Open AccessArticle

Real-Time Structural Health Monitoring of Reinforced Concrete Under Seismic Loading Using Dynamic OFDR

by Jooyoung Lee, Hyoyoung Jung, Myoung Jin Kim and Young Ho Kim

Sensors 2025, 25(18), 5818; https://doi.org/10.3390/s25185818 (registering DOI) - 18 Sep 2025

This paper presents a compact dynamic optical frequency domain reflectometry (D-OFDR) platform enabling millimeter-scale, distributed strain sensing for real-time structural health monitoring (SHM) of reinforced concrete subjected to seismic loading. The proposed D-OFDR interrogator employs a dual-interferometer architecture: a main interferometer for strain [...] Read more.

This paper presents a compact dynamic optical frequency domain reflectometry (D-OFDR) platform enabling millimeter-scale, distributed strain sensing for real-time structural health monitoring (SHM) of reinforced concrete subjected to seismic loading. The proposed D-OFDR interrogator employs a dual-interferometer architecture: a main interferometer for strain sensing and an auxiliary interferometer for nonlinear frequency sweep compensation. Both signals are detected by photodetectors and digitized via a dual-channel FPGA-based DAQ board, enabling high-speed embedded signal processing. A dual-edge triggering scheme exploits both the up-chirp and down-chirp of a 50 Hz bidirectional sweep to achieve a 100 Hz interrogation rate without increasing the sweep speed. Laboratory validation tests on stainless steel cantilever beams showed sub-hertz frequency fidelity (an error of 0.09 Hz) relative to conventional strain gauges. Shake-table tests on a 2 m RC column under incremental seismic excitations (scaled 10–130%, peak acceleration 0.864 g) revealed distinct damage regimes. Distributed strain data and frequency-domain analysis revealed a clear frequency reduction from approximately 3.82 Hz to 1.48 Hz, signifying progressive stiffness degradation and structural yielding prior to visible cracking. These findings demonstrate that the bidirectional sweep-triggered D-OFDR method offers enhanced real-time monitoring capabilities, substantially outperforming traditional point sensors in the early and precise detection of seismic-induced structural damage. Full article

(This article belongs to the Special Issue Sensor-Based Structural Health Monitoring of Civil Infrastructure)

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17 pages, 1317 KB

Open AccessArticle

Long-Term Stability Improvements of the Miniature Atomic Clock Through Enhanced Thermal Environmental Control

by Emily Gokie, Jon Omaraie and Thejesh N. Bandi

Sensors 2025, 25(18), 5817; https://doi.org/10.3390/s25185817 (registering DOI) - 18 Sep 2025

Advancement of compact atomic clocks has centered on reducing footprint and power consumption. Such developments come at the cost of the clock’s stability performance. Various commercial and military applications demand reduced size, weight, and power (SWaP) requirements but desire an enhanced stability performance [...] Read more.

Advancement of compact atomic clocks has centered on reducing footprint and power consumption. Such developments come at the cost of the clock’s stability performance. Various commercial and military applications demand reduced size, weight, and power (SWaP) requirements but desire an enhanced stability performance beyond what is achieved with the lower-profile standards, such as Microchip’s chip-scale atomic clock (CSAC) or miniature atomic clock (MAC). Furthermore, a high-performing space-rated clock will enhance small satellite missions by providing capability for alternate PNT, one-way radiometric ranging, and eventual lunar PNT purposes. The MAC is a strong candidate as it has modest SWaP parameters. Enhanced performance improvement to the MAC, particularly in the medium to long-term stability over a day and beyond will strengthen its candidacy as an on-board clock in small satellite missions and other ground-based applications. In this work, using external thermal control methods, we demonstrate an improvement of the MAC performance by at least a factor of five, showing a superior stability of σ_y = 4.2 × 10⁻¹³ compared to the best-performing miniaturized standard on the market for averaging intervals of τ > 10⁴ s up to 4 days. Full article

(This article belongs to the Topic Navigation and Positioning System: Opportunities and Obstacles)

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14 pages, 1375 KB

Open AccessArticle

Development of 2D Microfluidics Surface with Low-Frequency Electric Fields for Cell Separation Applications

by Madushan Wickramasinghe and Dharmakeerthi Nawarathna

Sensors 2025, 25(18), 5816; https://doi.org/10.3390/s25185816 (registering DOI) - 18 Sep 2025

Cell separation techniques are widely used in many biomedical and clinical applications for the development of screening, diagnosis and therapeutic tests. Current 3D microfluidics-based cell separation methods have limited applications in part due to low throughput and technical complexity. To address these critical [...] Read more.

Cell separation techniques are widely used in many biomedical and clinical applications for the development of screening, diagnosis and therapeutic tests. Current 3D microfluidics-based cell separation methods have limited applications in part due to low throughput and technical complexity. To address these critical needs, we have developed a 2D microfluidics surface which is the miniaturized version of a 3D microfluids cell separation device. Using low-frequency electric fields (1–10 Vpp and 1 kHz–20 MHz), we have first studied dielectrophoresis, AC electro-osmosis and capillary flow within a sessile drop, and finally utilized the results to develop the 2D cell separation surface. Our study has demonstrated that frequency-dependent dielectrophoretic force and AC electro-osmotic flow can be integrated to minimize the capillary flow and subsequently produce clusters of target cells within the 2D microfluidics surface. To demonstrate the concept, we have isolated the blood cells from a red blood cell-lysed blood sample. Cell isolation results show that significant improvement in throughout up to about 120-fold over 3D microfluidics devices. Additionally, due to the technical simplicity, this device offers great potential for use in a wide range of biomedical and clinical applications. Full article

(This article belongs to the Section Biomedical Sensors)

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24 pages, 5065 KB

Open AccessArticle

Benchmark Dataset and Deep Model for Monocular Camera Calibration from Single Highway Images

by Wentao Zhang, Wei Jia and Wei Li

Sensors 2025, 25(18), 5815; https://doi.org/10.3390/s25185815 (registering DOI) - 18 Sep 2025

Single-image based camera auto-calibration holds significant value for improving perception efficiency in traffic surveillance systems. However, existing approaches face dual challenges: scarcity of real-world datasets and poor adaptability to multi-view scenarios. This paper presents a systematic solution framework. First, we constructed a large-scale [...] Read more.

Single-image based camera auto-calibration holds significant value for improving perception efficiency in traffic surveillance systems. However, existing approaches face dual challenges: scarcity of real-world datasets and poor adaptability to multi-view scenarios. This paper presents a systematic solution framework. First, we constructed a large-scale synthetic dataset containing 36 highway scenarios using the CARLA 0.9.15 simulation engine, generating approximately 336,000 virtual frames with precise calibration parameters. The dataset achieves statistical consistency with real-world scenes by incorporating diverse view distributions, complex weather conditions, and varied road geometries. Second, we developed DeepCalib, a deep calibration network that explicitly models perspective projection features through the triplet attention mechanism. This network simultaneously achieves road direction vanishing point localization and camera pose estimation using only a single image. Finally, we adopted a progressive learning paradigm: robust pre-training on synthetic data establishes universal feature representations in the first stage, followed by fine-tuning on real-world datasets in the second stage to enhance practical adaptability. Experimental results indicate that DeepCalib attains an average calibration precision of 89.6%. Compared to conventional multi-stage algorithms, our method achieves a single-frame processing speed of 10 frames per second, showing robust adaptability to dynamic calibration tasks across diverse surveillance views. Full article

(This article belongs to the Collection Applications of Convolutional Neural Networks in Imaging and Sensing)

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17 pages, 3797 KB

Open AccessArticle

Using the Injection System as a Sensor to Analyze the State of the Electronic Automotive System

by Matej Kucera, Miroslav Gutten, Daniel Korenciak, Michal Prazenica and Tomasz N. Koltunowicz

Sensors 2025, 25(18), 5814; https://doi.org/10.3390/s25185814 (registering DOI) - 18 Sep 2025

This paper presents a novel diagnostic method that employs the fuel injection system as a sensor for monitoring internal combustion engine pressure, analysing series resistance to detect connector degradation, and evaluating needle movement within the injector’s magnetic core. Experimental results showed that reducing [...] Read more.

This paper presents a novel diagnostic method that employs the fuel injection system as a sensor for monitoring internal combustion engine pressure, analysing series resistance to detect connector degradation, and evaluating needle movement within the injector’s magnetic core. Experimental results showed that reducing the injector needle opening from 100% to 20% of its maximum displacement caused up to a 35% reduction in peak current amplitude and a 0.2 ms delay in coil charging. Increasing fuel pressure from 0.3 bar to 2.5 bar resulted in a rise in peak current by approximately 35% and an extension of coil charging delay by 0.4 ms. Furthermore, increasing the series resistance from 0.2 Ω to 2.0 Ω reduced the current amplitude by nearly 50% and significantly distorted the waveform, simulating connector oxidation or wear. Comparative analysis of reference and fault current waveforms confirmed that variations in electrical parameters correlate with injector needle displacement, fuel pressure, and resistance changes. Finally, an automated diagnostic system was developed that achieved over 90% accuracy in independently detecting injector faults based on current waveform characteristics. Full article

(This article belongs to the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition)

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18 pages, 5667 KB

Open AccessArticle

Verification of Vision-Based Terrain-Referenced Navigation Using the Iterative Closest Point Algorithm Through Flight Testing

by Taeyun Kim, Seongho Nam, Hyungsub Lee and Juhyun Oh

Sensors 2025, 25(18), 5813; https://doi.org/10.3390/s25185813 (registering DOI) - 17 Sep 2025

Terrain-referenced navigation (TRN) provides an alternative navigation method for environments with limited GPS availability. This paper proposes a vision-based TRN framework that employs stereo imagery and a rotation-invariant iterative closest point (ICP) algorithm to align reconstructed elevation maps with a terrain elevation database. [...] Read more.

Terrain-referenced navigation (TRN) provides an alternative navigation method for environments with limited GPS availability. This paper proposes a vision-based TRN framework that employs stereo imagery and a rotation-invariant iterative closest point (ICP) algorithm to align reconstructed elevation maps with a terrain elevation database. In contrast to conventional ICP, which is sensitive to camera intrinsic errors, the proposed approach improves robustness at high altitudes. Its feasibility and effectiveness are demonstrated through full-scale flight tests using a Cessna aircraft equipped with an IMU, camera, and barometric altimeter. The results show that the proposed method consistently enhances positioning accuracy and robustness compared with a filter-based approach, particularly under challenging high-altitude conditions where image resolution is reduced. The algorithm proved capable of maintaining reliable performance across varying flight altitudes, demonstrating its robustness under high-altitude conditions. This study establishes the novelty of integrating rotation-invariant ICP with vision-based TRN and provides real-world validation through actual flight testing. The findings offer valuable implications for future research and potential applications in unmanned aerial vehicles and long-range guided systems, where passive and GPS-independent navigation is critical for mission success. Full article

(This article belongs to the Section Navigation and Positioning)

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23 pages, 9679 KB

Open AccessArticle

Control Gain Determination Method for Robust Time-Delay Control of Industrial Robot Manipulators Based on an Improved State Observer

by Yu Chen, Jianwan Ding, Tianchang Xu and Yanbing Liu

Sensors 2025, 25(18), 5812; https://doi.org/10.3390/s25185812 - 17 Sep 2025

High-precision control of robotic manipulators plays a vital role in improving the efficiency and quality of industrial manufacturing. However, the inherent nonlinear and time-varying characteristics of robotic systems make high-accuracy control a challenging task, and external noise interference further complicates reliable state estimation. [...] Read more.

High-precision control of robotic manipulators plays a vital role in improving the efficiency and quality of industrial manufacturing. However, the inherent nonlinear and time-varying characteristics of robotic systems make high-accuracy control a challenging task, and external noise interference further complicates reliable state estimation. Conventional time-delay control methods often involve computationally intensive procedures for gain determination and are limited in their ability to suppress noise effectively. To overcome these limitations, this paper proposes a robust time-delay control strategy based on an improved state observer. By deriving a linearized form of the dynamic model, an offline computation scheme for control gain determination is developed, which eliminates the need for additional tuning parameters and simplifies the design process. Furthermore, the proposed state observer integrates model reference estimation with noise suppression techniques, enabling accurate acquisition of joint states and improving system robustness under noisy conditions. Experimental results validate the effectiveness of the proposed method, showing that it can efficiently determine control gains and significantly outperform existing advanced approaches in terms of trajectory tracking accuracy and overall control performance. Full article

(This article belongs to the Special Issue Intelligent Robots: Control and Sensing)

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25 pages, 2051 KB

Open AccessArticle

Response of Nearby Sensors to Variable Doses of Nitrogen Fertilization in Winter Fodder Crops Under Mediterranean Climate

by Luís Silva, Caroline Brunelli, Raphael Moreira, Sofia Barbosa, Manuela Fernandes, Andreia Miguel, Benvindo Maçãs, Constantino Valero, Manuel Patanita, Fernando Cebola Lidon and Luís Alcino Conceição

Sensors 2025, 25(18), 5811; https://doi.org/10.3390/s25185811 - 17 Sep 2025

The sustainable intensification of forage production in Mediterranean climates requires technological solutions that optimize the use of agricultural inputs. This study aimed to evaluate the performance of proximal optical sensors in recommending and monitoring variable rate nitrogen fertilization in winter forage crops cultivated [...] Read more.

The sustainable intensification of forage production in Mediterranean climates requires technological solutions that optimize the use of agricultural inputs. This study aimed to evaluate the performance of proximal optical sensors in recommending and monitoring variable rate nitrogen fertilization in winter forage crops cultivated under Mediterranean conditions. A handheld multispectral active sensor (HMA), a multispectral camera on an unmanned aircraft vehicle (UAV), and one passive on-the-go sensor (OTG) were used to generate real-time nitrogen (N) application prescriptions. The sensors were assessed for their correlation with agronomic parameters such as plant fresh matter (PFM), plant dry matter (PDM), plant N content (PNC), crude protein (CP) in%, crude protein yield (CPyield) per unit of area, and N uptake (NUp). The real-time N fertilization stood out by promoting a 15.23% reduction in the total N fertilizer applied compared to a usual farmer-fixed dose of 150 kg ha⁻¹, saving 22.90 kg ha⁻¹ without compromising crop productivity. Additionally, NDVI_OTG showed moderate simple linear correlation with PFM (R² = 0.52), confirming its effectiveness in prescription based on vegetative vigor. UAV_II (NDVI after fertilization) showed even stronger correlations with CP (R² = 0.58), CPyield (R² = 0.53), and NUp (R² = 0.53), highlighting its sensitivity to physiological responses induced by N fertilization. Although the HMA sensor operates via point readings, it also proved effective, with significant correlations to NUp (R² = 0.55) and CPyield (R² = 0.53). It is concluded that integrating sensors enables both precise input prescription and efficient monitoring of plant physiological responses, fostering cost-effectiveness, sustainability, and improved agronomic efficiency. Full article

(This article belongs to the Section Smart Agriculture)

42 pages, 4295 KB

Open AccessArticle

Research on Shape–Performance Integrated Monitoring Technology for Planetary Gearboxes Based on the Integration of Artificial Intelligence, Finite Element Analysis, and Multibody Dynamics Simulation

by Yanping Cui, Boshuo An, Zhe Wu, Ziao Shang and Xuanrui Zhang

Sensors 2025, 25(18), 5810; https://doi.org/10.3390/s25185810 - 17 Sep 2025

To address gear tooth damage and the difficulty of acquiring performance data under high-speed and high-load operating conditions of planetary gearboxes, a digital twin-based system for operational state recognition and performance prediction is proposed, integrating morphological and functional characteristics. Driven by experimental data, [...] Read more.

To address gear tooth damage and the difficulty of acquiring performance data under high-speed and high-load operating conditions of planetary gearboxes, a digital twin-based system for operational state recognition and performance prediction is proposed, integrating morphological and functional characteristics. Driven by experimental data, the system incorporates finite element analysis, multibody dynamics simulation, artificial intelligence algorithms, and 3D visualization to achieve a virtual mapping of the gearbox’s geometric configuration, structural properties, and dynamic behavior. Structural performance is represented using finite element and dynamic simulation techniques combined with texture mapping, visualized through color gradients; dynamic performance is captured through multibody dynamics simulations and stored in a time-series database, presented as sequential images. The integrated system is constructed by combining a structural performance surrogate model, a system-driven model, and a dynamic performance database, enabling comprehensive functionality. Results demonstrate that the maximum error of the structural performance model is 3%, occurring only under specific working conditions, with negligible impact on the overall meshing performance evaluation of the sun gear. The maximum error in dynamic performance prediction is 1.68%, showing strong consistency with experimental data. Full article

(This article belongs to the Section Physical Sensors)

39 pages, 445 KB

Open AccessArticle

A Study on IoT Device Authentication Using Artificial Intelligence

by Shahram Miri Kelaniki and Nikos Komninos

Sensors 2025, 25(18), 5809; https://doi.org/10.3390/s25185809 - 17 Sep 2025

Designing reliable authentication mechanisms for IoT devices is increasingly necessary to protect citizens’ private information and data. One of the most significant issues in today’s digital age is authentication. As IoT device technology advances and data grow rapidly, machine learning techniques improve the [...] Read more.

Designing reliable authentication mechanisms for IoT devices is increasingly necessary to protect citizens’ private information and data. One of the most significant issues in today’s digital age is authentication. As IoT device technology advances and data grow rapidly, machine learning techniques improve the accuracy and efficiency of authentication and offer advantages over traditional methods, making them valuable in both academia and industry. Device authentication aims to verify legitimate computing devices and identify impostors based on their behavioral data. This paper explores research that applies artificial intelligence algorithms to enhance device authentication mechanisms. We discuss AI authentication models, including deep learning algorithms, convolutional neural networks, and reinforcement learning. We also highlight research challenges and provide recommendations for future studies to support innovation in this field. Full article

(This article belongs to the Section Intelligent Sensors)

21 pages, 2603 KB

Open AccessArticle

Sensing What You Do Not See: Alerting of Approaching Objects with a Haptic Vest

by Albina Rurenko, Devbrat Anuragi, Ahmed Farooq, Marja Salmimaa, Zoran Radivojevic, Sanna Kumpulainen and Roope Raisamo

Sensors 2025, 25(18), 5808; https://doi.org/10.3390/s25185808 - 17 Sep 2025

Workplace accidents in high-risk environments remain a major safety concern, particularly when workers’ visual and auditory channels are overloaded. Haptic feedback offers a promising alternative for alerting individuals to unseen dangers and enhancing situational awareness. Motivated by challenges commonly observed in construction, this [...] Read more.

Workplace accidents in high-risk environments remain a major safety concern, particularly when workers’ visual and auditory channels are overloaded. Haptic feedback offers a promising alternative for alerting individuals to unseen dangers and enhancing situational awareness. Motivated by challenges commonly observed in construction, this study investigates haptic alerting strategies applicable across dynamic, attentionally demanding contexts. We present two empirical experiments exploring how wearable vibration cues can inform users about approaching objects outside their field of view. The first experiment evaluated variations of pattern-based vibrations to simulate motion and examined the relationship between signal parameters and perceived urgency. A negative correlation between urgency and pulse duration emerged, identifying a key design factor. The second experiment conducted a novel comparison of pattern-based and location-based haptic alerts in a complex virtual environment, with tasks designed to simulate cognitive engagement with work processes. Results indicate that location-based alerts were more efficient for hazard detection. These findings offer insights into the design of effective user-centred haptic-based safety systems and provide a foundation for future development and deployment in real-world settings. This work contributes a generalisable step toward wearable alerting technologies for safety-critical occupations, including but not limited to construction. Full article

(This article belongs to the Section Wearables)

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17 pages, 2612 KB

Open AccessArticle

An Efficient and Robust Dual-Channel Signal Gluing Method for Atmospheric Lidar

by Tong Wu, Kai Zhong, Xianzhong Zhang, Fangjie Li, Xinqi Li, Guxi Chen, Degang Xu and Jianquan Yao

Sensors 2025, 25(18), 5807; https://doi.org/10.3390/s25185807 - 17 Sep 2025

Lidar serves as a vital active remote sensing instrument for exploring the atmosphere. However, the detection range of lidar is significantly constrained by the dynamic range of photo-detectors. To mitigate this limitation, atmospheric lidars are commonly equipped with multiple channels to capture signals [...] Read more.

Lidar serves as a vital active remote sensing instrument for exploring the atmosphere. However, the detection range of lidar is significantly constrained by the dynamic range of photo-detectors. To mitigate this limitation, atmospheric lidars are commonly equipped with multiple channels to capture signals from different altitude ranges, making the high-quality gluing of multi-channel echo signals crucial for accurate data retrieval. In this paper, an efficient dual-channel signal gluing method based on the improved whale optimization algorithm (IMWOA) and the entropy weight method (EWM), named IMWOA-EWM, was proposed. Here, the IMWOA method was used to optimize the fitness function, achieving higher computational efficiency. The weights of the correlation coefficient R, regression stability coefficient S and mean fit deviation D were determined using EWM, which together constitute the fitness function. Through signal gluing experiments conducted with ground-based aerosol lidar data, IMWOA-EWM can accurately identify the optimal gluing region, due to IMWOA’s excellent global search capability and the higher weight assigned to the objective function S by EWM. Meanwhile, regarding computational efficiency, its runtime is only half that of IGWO-RSD. Additionally, the applicable conditions of the weights in IMWOA-EWM were explored, which indicate that IMWOA-EWM has good robustness for atmospheric lidar signal gluing. Full article

(This article belongs to the Special Issue Remote Sensing in Atmospheric Measurements)

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29 pages, 4744 KB

Open AccessArticle

Intelligent Soft Sensor for Spindle Convective Heat Transfer Coefficient Under Varying Operating Conditions Using Improved Grey Wolf Optimization Algorithm

by Jinxiang Pian and Gen Li

Sensors 2025, 25(18), 5806; https://doi.org/10.3390/s25185806 - 17 Sep 2025

The thermal deformation of high-precision CNC machine tools has long been a significant barrier to improving machining accuracy. Accurately characterizing the thermal properties of the spindle, especially the convective heat transfer coefficients (CHTC), is essential for precise thermal analysis. However, due to the [...] Read more.

The thermal deformation of high-precision CNC machine tools has long been a significant barrier to improving machining accuracy. Accurately characterizing the thermal properties of the spindle, especially the convective heat transfer coefficients (CHTC), is essential for precise thermal analysis. However, due to the lack of dedicated instruments for directly measuring the CHTC, thermal analysis of the spindle faces substantial challenges. This study presents an innovative approach that combines multi-sensor data with intelligent optimization algorithms to address this issue. A distributed temperature monitoring network is constructed to capture real-time thermal field data across the spindle. At the same time, an improved Grey Wolf Optimization (IGWO) algorithm is employed to dynamically and accurately identify the CHTC. The proposed algorithm introduces an adaptive weight adjustment mechanism, which overcomes the limitations of traditional optimization methods in dynamic operating conditions. Experimental results show that the proposed method significantly outperforms conventional approaches in terms of temperature prediction accuracy across a broad operating range. This research provides a novel technical solution for machine tool thermal error compensation and establishes a scalable intelligent indirect measurement framework, even in the absence of specialized measurement instruments. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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29 pages, 7882 KB

Open AccessArticle

From Concept to Representation: Modeling Driving Capability and Task Demand with a Multimodal Large Language Model

by Haoran Zhou, Alexander Carballo, Keisuke Fujii and Kazuya Takeda

Sensors 2025, 25(18), 5805; https://doi.org/10.3390/s25185805 - 17 Sep 2025

Driving safety hinges on the dynamic interplay between task demand and driving capability, yet these concepts lack a unified, quantifiable formulation. In this work, we present a framework based on a multimodal large language model that transforms heterogeneous driving signals—scene images, maneuver descriptions, [...] Read more.

Driving safety hinges on the dynamic interplay between task demand and driving capability, yet these concepts lack a unified, quantifiable formulation. In this work, we present a framework based on a multimodal large language model that transforms heterogeneous driving signals—scene images, maneuver descriptions, control inputs, and surrounding traffic states—into low-dimensional embeddings of task demand and driving capability. By projecting both embeddings into a shared latent space, the framework yields an interpretable measurement of task difficulty that alerts to capability shortfalls before unsafe behavior arises. Built upon a customized BLIP 2 backbone and fine-tuned on diverse simulated driving scenarios, the model respects consistency within tasks, captures impairment-related capability degradation, and can transfer to real-world motorway data without additional training. These findings endorse the framework as a concise yet effective step toward proactive, explainable risk assessment in intelligent vehicles. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems Based on Sensor Fusion: 2nd Edition)

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13 pages, 1716 KB

Open AccessArticle

Hybrid Approach to Enabling Cross-Domain Service Orchestration over Heterogeneous Infrastructures

by Jane Frances Pajo, Geir Egeland, Sarang Kahvazadeh, Hamzeh Khalili, Martin Tolan, Ryan McCloskey, Min Xie and Olai Bendik Erdal

Sensors 2025, 25(18), 5804; https://doi.org/10.3390/s25185804 (registering DOI) - 17 Sep 2025

Efforts to lower the barriers for 5G uptake have brought forth accelerated innovation rates in a wide range of verticals. Consequently, the demands for 5G experimentation facilities have recently emerged from small- and medium-sized enterprises (SMEs) and 3rd party developers, requiring the abstraction [...] Read more.

Efforts to lower the barriers for 5G uptake have brought forth accelerated innovation rates in a wide range of verticals. Consequently, the demands for 5G experimentation facilities have recently emerged from small- and medium-sized enterprises (SMEs) and 3rd party developers, requiring the abstraction of the complexities of the underlying infrastructure, platform, and related management and orchestration (MANO) systems, especially in multi-domain scenarios. This paper proposes a novel approach towards cross-domain service orchestration, which combines the flexibility of supporting different 5G Service Orchestrators (SOs) in various domains, while preserving compatibility through NetApps. The Cross-domain Service Orchestrator (CDSO) is based on ETSI’s Open Source MANO (OSM), with a Requests Handler module on top, which acts as the main integration point to the different domains’ 5G SOs and other custom systems that are northbound. This would facilitate the interworking among independently orchestrated domains in supporting multi-domain services. The EU Horizon 2020 project, 5GMediaHUB, is presented as a use case, together with a first implementation of the Requests Handler and corresponding integrations. Nonetheless, the proposed approach is vertical-agnostic and is foreseen to accelerate service innovation and digitalization in any industry by laying the foundations in terms of service management and interconnectivity. Full article

(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2025)

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11 pages, 493 KB

Open AccessArticle

Effects of Measurement Distance on Linear Taylor Patterns with Reduced Inner Sidelobes

by Aldara Seoane-Campos, María Elena López-Martín, Juan Antonio Rodriguez-Gonzalez and Francisco Jose Ares-Pena

Sensors 2025, 25(18), 5803; https://doi.org/10.3390/s25185803 - 17 Sep 2025

The influence of distance on Taylor diagrams with one, two, and three depressed inner lobes was analyzed in the context of linear distributions. High sidelobes are tolerated in these patterns, except in the case of the three inner lobes, which are positioned at [...] Read more.

The influence of distance on Taylor diagrams with one, two, and three depressed inner lobes was analyzed in the context of linear distributions. High sidelobes are tolerated in these patterns, except in the case of the three inner lobes, which are positioned at a significantly lower level to minimize interference and optimize efficiency. The classical method described by Elliott was used to compute the necessary roots in the Taylor distribution. The study was conducted considering

\bar{n}

equal to 6 and a sidelobe level (SLL) of −20 dB for all lobes except the first inner positioned at −40 dB. Full article

(This article belongs to the Special Issue Antenna Technology for Advanced Communication and Sensing Systems)

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21 pages, 5337 KB

Open AccessArticle

SC-NBTI: A Smart Contract-Based Incentive Mechanism for Federated Knowledge Sharing

by Yuanyuan Zhang, Jingwen Liu, Jingpeng Li, Yuchen Huang, Wang Zhong, Yanru Chen and Liangyin Chen

Sensors 2025, 25(18), 5802; https://doi.org/10.3390/s25185802 - 17 Sep 2025

With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered [...] Read more.

With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered by privacy risks, high communication overhead, and fragmented ownership of data. Federated learning promises to overcome these barriers by enabling collaborative model training without exchanging raw knowledge artifacts, but its success depends on motivating data holders to undertake the additional computational and communication costs. Most existing incentive schemes, which are based on non-cooperative game formulations, neglect unstructured interactions and communication efficiency, thereby limiting their applicability in knowledge-driven scenarios. To address these challenges, we introduce SC-NBTI, a smart contract and Nash bargaining-based incentive framework for federated learning in knowledge collaboration environments. We cast the reward allocation problem as a cooperative game, devise a heuristic algorithm to approximate the NP-hard Nash bargaining solution, and integrate a probabilistic gradient sparsification method to trim communication costs while safeguarding privacy. Experiments on the FMNIST image classification task show that SC-NBTI requires fewer training rounds while achieving 5.89% higher accuracy than the DRL-Incentive baseline. Full article

(This article belongs to the Section Internet of Things)

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16 pages, 7132 KB

Open AccessArticle

A Radar Waveform Design Method Based on Multicarrier Phase Coding for Suppressing Autocorrelation Sidelobes

by Ji Li, Liu Ye and Wei Wang

Sensors 2025, 25(18), 5801; https://doi.org/10.3390/s25185801 - 17 Sep 2025

Multicarrier phase-coded radar waveforms show significant potential in broadband radar applications by integrating phase coding with orthogonal frequency division multiplexing (OFDM) technology. However, their inherent high autocorrelation sidelobe levels limit system performance. To address this challenge, this paper proposes a two-stage joint optimization [...] Read more.

Multicarrier phase-coded radar waveforms show significant potential in broadband radar applications by integrating phase coding with orthogonal frequency division multiplexing (OFDM) technology. However, their inherent high autocorrelation sidelobe levels limit system performance. To address this challenge, this paper proposes a two-stage joint optimization waveform design method. In the first stage, we construct an AC-MCPC signal by introducing chaotic coding in the time domain and applying a hamming window in the frequency domain, achieving effective sidelobe suppression. In the second stage, to achieve even lower sidelobe levels, we further propose the AC-MCPC-g signal. While retaining chaotic coding in the time domain, we employ a genetic algorithm in the frequency domain to optimize the window function parameters, thereby further reducing the sidelobe levels of the AC-MCPC signal. The results indicate that the AC-MCPC signal has significantly reduced sidelobes compared to the MCPC signal, while the AC-MCPC-g signal has achieved further suppression based on the AC-MCPC. Full article

(This article belongs to the Section Radar Sensors)

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32 pages, 3467 KB

Open AccessArticle

Multi-Epoch Differential Pseudorange Joint Positioning Using GNSS Signals and Terrestrial Cellular Signals-of-Opportunity

by Pei Zhang, Tian Jin, James Chakwizira and Yuchen Wang

Sensors 2025, 25(18), 5800; https://doi.org/10.3390/s25185800 - 17 Sep 2025

When the global navigation satellite systems (GNSSs) are unavailable, cellular signals of opportunity (SOPs) can be used to achieve positioning. However, in low observability environments where both GNSS signals and cellular SOPs are less than 2, the current research on cellular SOPs–GNSS signals [...] Read more.

When the global navigation satellite systems (GNSSs) are unavailable, cellular signals of opportunity (SOPs) can be used to achieve positioning. However, in low observability environments where both GNSS signals and cellular SOPs are less than 2, the current research on cellular SOPs–GNSS signals fusion positioning faces challenges regarding the difficulty in precise position initialization and spatiotemporal uncertainty. To address these issues, a cellular SOPs and GNSS signals fusion positioning model by the pseudorange single difference at multi-epoch (PSDM) is proposed. The spatiotemporal uncertainty of fusion positioning is solved by differential pseudorange. Then, to solve the problem of difficult precise location initialization during the differential pseudorange positioning process, a pseudo-linearization closed-form method was derived, and its limitations were analyzed. Moreover, the pseudo-linearization equation was reconstructed. Based on this, a constrained multi-step weighted least squares (CMWLS) method is proposed that reduces the impact of noise on the PSDM positioning models and improves global convergence. According to the simulation and field test results, the proposed fusion positioning method shows good positioning performance in low-observability environments. For urban positioning in such environments, this study provides a new solution strategy and avoids the requirement of the prior information of the receiver’s initial position for positioning. Full article

(This article belongs to the Section Navigation and Positioning)

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