Sensors

18 pages, 1372 KiB

Open AccessArticle

Enhanced Calibration Method for Robotic Flexible 3D Scanning System

by Zhilong Zhou, Jinyong Shangguan, Xuemei Sun, Yunlong Liu, Xu Zhang, Dengbo Zhang and Haoran Liu

Sensors 2025, 25(15), 4661; https://doi.org/10.3390/s25154661 (registering DOI) - 27 Jul 2025

Abstract

Large-sized components with numerous small key local features are essential in advanced manufacturing. Achieving high-precision quality control necessitates accurate and highly efficient three-dimensional (3D) measurement techniques. A flexible measurement system integrating a fringe-projection-based 3D scanner with an industrial robot is developed to enable [...] Read more.

Large-sized components with numerous small key local features are essential in advanced manufacturing. Achieving high-precision quality control necessitates accurate and highly efficient three-dimensional (3D) measurement techniques. A flexible measurement system integrating a fringe-projection-based 3D scanner with an industrial robot is developed to enable the rapid measurement of large object surfaces. To enhance overall measurement accuracy, we propose an enhanced calibration method utilizing a multidimensional ball-based calibrator to simultaneously calibrate for hand-eye transformation and robot kinematic parameters. Firstly, a preliminary hand-eye calibration method is introduced to compensate for measurement errors at observation points, leveraging geometric-constraint-based optimization and a virtual single point derived via the barycentric calculation method. Subsequently, a distance-constrained calibration method is proposed to jointly estimate the hand-eye transformation and robot kinematic parameters, wherein a distance error model is constructed to link parameter errors with the measured deviations of a virtual single point. Finally, calibration and validation experiments were carried out, and the results indicate that the maximum and average measurement errors were reduced from 1.053 mm and 0.814 mm to 0.421 mm and 0.373 mm, respectively, thereby confirming the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors: 2nd Edition)

24 pages, 3480 KiB

Open AccessArticle

MFPI-Net: A Multi-Scale Feature Perception and Interaction Network for Semantic Segmentation of Urban Remote Sensing Images

by Xiaofei Song, Mingju Chen, Jie Rao, Yangming Luo, Zhihao Lin, Xingyue Zhang, Senyuan Li and Xiao Hu

Sensors 2025, 25(15), 4660; https://doi.org/10.3390/s25154660 (registering DOI) - 27 Jul 2025

Abstract

To improve semantic segmentation performance for complex urban remote sensing images with multi-scale object distribution, class similarity, and small object omission, this paper proposes MFPI-Net, an encoder–decoder-based semantic segmentation network. It includes four core modules: a Swin Transformer backbone encoder, a diverse dilation [...] Read more.

To improve semantic segmentation performance for complex urban remote sensing images with multi-scale object distribution, class similarity, and small object omission, this paper proposes MFPI-Net, an encoder–decoder-based semantic segmentation network. It includes four core modules: a Swin Transformer backbone encoder, a diverse dilation rates attention shuffle decoder (DDRASD), a multi-scale convolutional feature enhancement module (MCFEM), and a cross-path residual fusion module (CPRFM). The Swin Transformer efficiently extracts multi-level global semantic features through its hierarchical structure and window attention mechanism. The DDRASD’s diverse dilation rates attention (DDRA) block combines convolutions with diverse dilation rates and channel-coordinate attention to enhance multi-scale contextual awareness, while Shuffle Block improves resolution via pixel rearrangement and avoids checkerboard artifacts. The MCFEM enhances local feature modeling through parallel multi-kernel convolutions, forming a complementary relationship with the Swin Transformer’s global perception capability. The CPRFM employs multi-branch convolutions and a residual multiplication–addition fusion mechanism to enhance interactions among multi-source features, thereby improving the recognition of small objects and similar categories. Experiments on the ISPRS Vaihingen and Potsdam datasets show that MFPI-Net outperforms mainstream methods, achieving 82.57% and 88.49% mIoU, validating its superior segmentation performance in urban remote sensing. Full article

(This article belongs to the Section Sensing and Imaging)

► Show Figures

Figure 1

19 pages, 3060 KiB

Open AccessArticle

Research on Damage Identification in Transmission Tower Structures Based on Cross-Correlation Function Amplitude Vector

by Qing Zhang, Xing Fu, Wenqiang Jiang and Hengdong Jin

Sensors 2025, 25(15), 4659; https://doi.org/10.3390/s25154659 (registering DOI) - 27 Jul 2025

Abstract

Transmission towers constitute critical power infrastructure, yet structural damage may accumulate over their long-term service, underscoring the paramount importance of research on damage identification. This paper presents a cross-correlation function amplitude vector (CorV) method for damage localization based on time-domain response analysis. The [...] Read more.

Transmission towers constitute critical power infrastructure, yet structural damage may accumulate over their long-term service, underscoring the paramount importance of research on damage identification. This paper presents a cross-correlation function amplitude vector (CorV) method for damage localization based on time-domain response analysis. The approach involves calculating the CorV of structural members before and after damage using dynamic response data, employing the CorV assurance criterion (CVAC) to quantify changes in CorV, and introducing first-order differencing for damage localization. Taking an actual transmission tower in Jiangmen as the engineering backdrop, a finite element model is established. Damage conditions are simulated by reducing the stiffness of specific members, and parameter analyses are conducted to validate the proposed method. Furthermore, experimental validation in a lab is performed to provide additional confirmation. The results indicate that the CVAC value of the damaged structure is significantly lower than that in the healthy state. By analyzing the relative changes in the components of CorV, the damage location can be accurately determined. Notably, this method only requires acquiring the time-domain response signals of the transmission tower under random excitation to detect both the existence and location of damage. Consequently, it is well suited for structural health monitoring of transmission towers under environmental excitation. Full article

(This article belongs to the Special Issue Sensors for Non-Destructive Testing and Structural Health Monitoring)

► Show Figures

Figure 1

19 pages, 2106 KiB

Open AccessArticle

Rethinking Infrared and Visible Image Fusion from a Heterogeneous Content Synergistic Perception Perspective

by Minxian Shen, Gongrui Huang, Mingye Ju and Kaikuang Ma

Sensors 2025, 25(15), 4658; https://doi.org/10.3390/s25154658 (registering DOI) - 27 Jul 2025

Abstract

Infrared and visible image fusion (IVIF) endeavors to amalgamate the thermal radiation characteristics from infrared images with the fine-grained texture details from visible images, aiming to produce fused outputs that are more robust and information-rich. Among the existing methodologies, those based on generative [...] Read more.

Infrared and visible image fusion (IVIF) endeavors to amalgamate the thermal radiation characteristics from infrared images with the fine-grained texture details from visible images, aiming to produce fused outputs that are more robust and information-rich. Among the existing methodologies, those based on generative adversarial networks (GANs) have demonstrated considerable promise. However, such approaches are frequently constrained by their reliance on homogeneous discriminators possessing identical architectures, a limitation that can precipitate the emergence of undesirable artifacts in the resultant fused images. To surmount this challenge, this paper introduces HCSPNet, a novel GAN-based framework. HCSPNet distinctively incorporates heterogeneous dual discriminators, meticulously engineered for the fusion of disparate source images inherent in the IVIF task. This architectural design ensures the steadfast preservation of critical information from the source inputs, even when faced with scenarios of image degradation. Specifically, the two structurally distinct discriminators within HCSPNet are augmented with adaptive salient information distillation (ASID) modules, each uniquely structured to align with the intrinsic properties of infrared and visible images. This mechanism impels the discriminators to concentrate on pivotal components during their assessment of whether the fused image has proficiently inherited significant information from the source modalities—namely, the salient thermal signatures from infrared imagery and the detailed textural content from visible imagery—thereby markedly diminishing the occurrence of unwanted artifacts. Comprehensive experimentation conducted across multiple publicly available datasets substantiates the preeminence and generalization capabilities of HCSPNet, underscoring its significant potential for practical deployment. Additionally, we also prove that our proposed heterogeneous dual discriminators can serve as a plug-and-play structure to improve the performance of existing GAN-based methods. Full article

(This article belongs to the Section Sensing and Imaging)

29 pages, 2830 KiB

Open AccessArticle

BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding

by Muhammad Zulkifal Aziz, Xiaojun Yu, Xinran Guo, Xinming He, Binwen Huang and Zeming Fan

Sensors 2025, 25(15), 4657; https://doi.org/10.3390/s25154657 (registering DOI) - 27 Jul 2025

Abstract

Motor imagery (MI) electroencephalograms (EEGs) are pivotal cortical potentials reflecting cortical activity during imagined motor actions, widely leveraged for brain-computer interface (BCI) system development. However, effectively decoding these MI EEG signals is often overshadowed by flawed methods in signal processing, deep learning methods [...] Read more.

Motor imagery (MI) electroencephalograms (EEGs) are pivotal cortical potentials reflecting cortical activity during imagined motor actions, widely leveraged for brain-computer interface (BCI) system development. However, effectively decoding these MI EEG signals is often overshadowed by flawed methods in signal processing, deep learning methods that are clinically unexplained, and highly inconsistent performance across different datasets. We propose BCINetV1, a new framework for MI EEG decoding to address the aforementioned challenges. The BCINetV1 utilizes three innovative components: a temporal convolution-based attention block (T-CAB) and a spectral convolution-based attention block (S-CAB), both driven by a new convolutional self-attention (ConvSAT) mechanism to identify key non-stationary temporal and spectral patterns in the EEG signals. Lastly, a squeeze-and-excitation block (SEB) intelligently combines those identified tempo-spectral features for accurate, stable, and contextually aware MI EEG classification. Evaluated upon four diverse datasets containing 69 participants, BCINetV1 consistently achieved the highest average accuracies of 98.6% (Dataset 1), 96.6% (Dataset 2), 96.9% (Dataset 3), and 98.4% (Dataset 4). This research demonstrates that BCINetV1 is computationally efficient, extracts clinically vital markers, effectively handles the non-stationarity of EEG data, and shows a clear advantage over existing methods, marking a significant step forward for practical BCI applications. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

► Show Figures

Figure 1

21 pages, 3448 KiB

Open AccessArticle

A Welding Defect Detection Model Based on Hybrid-Enhanced Multi-Granularity Spatiotemporal Representation Learning

by Chenbo Shi, Shaojia Yan, Lei Wang, Changsheng Zhu, Yue Yu, Xiangteng Zang, Aiping Liu, Chun Zhang and Xiaobing Feng

Sensors 2025, 25(15), 4656; https://doi.org/10.3390/s25154656 (registering DOI) - 27 Jul 2025

Abstract

Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability [...] Read more.

Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability of deep learning models, this paper proposes a multi-granularity spatiotemporal representation learning algorithm based on the hybrid enhancement of handcrafted and deep learning features. A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. A multi-granularity attention-based feature aggregation module is developed to select key interference-free frames using cross-frame attention, generate multi-granularity features via grouped pooling, and apply the Convolutional Block Attention Module (CBAM) at each granularity level. Finally, these multi-granularity spatiotemporal features are adaptively fused. Meanwhile, an independent branch utilizes the Histogram of Oriented Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) features to extract long-term spatial structural information from historical edge images, enhancing the model’s interpretability. The proposed method achieves an accuracy of 99.187% on a self-constructed dataset. Additionally, it attains a real-time inference speed of 20.983 ms per sample on a hardware platform equipped with an Intel i9-12900H CPU and an RTX 3060 GPU, thus effectively balancing accuracy, speed, and interpretability. Full article

(This article belongs to the Topic Applied Computing and Machine Intelligence (ACMI))

► Show Figures

Figure 1

22 pages, 6689 KiB

Open AccessArticle

Design and Implementation of a Sun Outage Simulation System with High Uniformity and Stray Light Suppression Capability

by Zhen Mao, Zhaohui Li, Yong Liu, Limin Gao and Jianke Zhao

Sensors 2025, 25(15), 4655; https://doi.org/10.3390/s25154655 (registering DOI) - 27 Jul 2025

Abstract

To enable accurate evaluation of satellite laser communication terminals under solar outage interference, this paper presents the design and implementation of a solar radiation simulation system targeting the 1540–1560 nm communication band. The system reconstructs co-propagating interference conditions through standardized and continuously tunable [...] Read more.

To enable accurate evaluation of satellite laser communication terminals under solar outage interference, this paper presents the design and implementation of a solar radiation simulation system targeting the 1540–1560 nm communication band. The system reconstructs co-propagating interference conditions through standardized and continuously tunable output, based on high irradiance and spectral uniformity. A compound beam homogenization structure—combining a multimode fiber and an apodizator—achieves 85.8% far-field uniformity over a 200 mm aperture. A power–spectrum co-optimization strategy is introduced for filter design, achieving a spectral matching degree of 78%. The system supports a tunable output from 2.5 to 130 mW with a 50× dynamic range and maintains power control accuracy within ±0.9%. To suppress internal background interference, a BRDF-based optical scattering model is established to trace primary and secondary stray light paths. Simulation results show that by maintaining the surface roughness of key mirrors below 2 nm and incorporating a U-shaped reflective light trap, stray light levels can be reduced to 5.13 × 10⁻¹² W, ensuring stable detection of a 10⁻¹⁰ W signal at a 10:1 signal-to-background ratio. Experimental validation confirms that the system can faithfully reproduce solar outage conditions within a ±3° field of view, achieving consistent performance in spectrum shaping, irradiance uniformity, and background suppression. The proposed platform provides a standardized and practical testbed for ground-based anti-interference assessment of optical communication terminals. Full article

(This article belongs to the Section Communications)

► Show Figures

Figure 1

23 pages, 5485 KiB

Open AccessArticle

Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning

by Dominic Benintendi, Kevin M. Tennant, Edward M. Sabolsky and Jay Wilhelm

Sensors 2025, 25(15), 4654; https://doi.org/10.3390/s25154654 (registering DOI) - 27 Jul 2025

Abstract

Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the [...] Read more.

Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the sensor was performed using a Vector Network Analyzer (VNA) and a pair of interrogation antennas to capture resonance behavior under varying thermal and spatial conditions with sensitivities ranging from 0.052 to 0.20

\frac{M H z}{° C}

. Sensor calibration was conducted using a Long Short-Term Memory (LSTM) model, which leveraged temporal patterns to account for hysteresis effects. The calibration method demonstrated improved performance when combined with an LSTM model, achieving up to a 76% improvement in temperature estimation error when compared with Linear Regression (LR). The experiments highlighted an innovative solution for patch antenna-based non-contact temperature measurement, which addresses limitations with conventional methods such as RFID-based systems, infrared, and thermocouples. Full article

(This article belongs to the Special Issue Advanced Sensing Techniques for Environmental and Energy Systems)

► Show Figures

Figure 1

14 pages, 16349 KiB

Open AccessCommunication

Fault Detection in Real-Time Kinematic Positioning Using Multiple Reference Stations

by Euiho Kim and Soomin Lee

Sensors 2025, 25(15), 4653; https://doi.org/10.3390/s25154653 (registering DOI) - 27 Jul 2025

Abstract

Multiple-reference-station-based real-time kinematics (MR-RTK) is an advanced RTK technique that leverages global navigation satellite system (GNSS) measurements from multiple reference stations and their known baselines. This study investigates the fault detection capabilities of MR-RTK by employing additional measurements from continuously operating reference stations [...] Read more.

Multiple-reference-station-based real-time kinematics (MR-RTK) is an advanced RTK technique that leverages global navigation satellite system (GNSS) measurements from multiple reference stations and their known baselines. This study investigates the fault detection capabilities of MR-RTK by employing additional measurements from continuously operating reference stations (CORSs) to evaluate the probability of missed detection. The proposed method was validated using test data from a ground rover and a few CORSs within a 10 km radius. The test results show that the missed detection probability decreased by up to 55.0% as the number of reference stations increased up to four. Full article

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

21 pages, 5205 KiB

Open AccessArticle

SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation

by Haijiao Yun, Qingyu Du, Ziqing Han, Mingjing Li, Le Yang, Xinyang Liu, Chao Wang and Weitian Ma

Sensors 2025, 25(15), 4652; https://doi.org/10.3390/s25154652 (registering DOI) - 27 Jul 2025

Abstract

Segmentation of skin lesions in dermoscopic images is critical for the accurate diagnosis of skin cancers, particularly malignant melanoma, yet it is hindered by irregular lesion shapes, blurred boundaries, low contrast, and artifacts, such as hair interference. Conventional deep learning methods, typically based [...] Read more.

Segmentation of skin lesions in dermoscopic images is critical for the accurate diagnosis of skin cancers, particularly malignant melanoma, yet it is hindered by irregular lesion shapes, blurred boundaries, low contrast, and artifacts, such as hair interference. Conventional deep learning methods, typically based on UNet or Transformer architectures, often face limitations in regard to fully exploiting lesion features and incur high computational costs, compromising precise lesion delineation. To overcome these challenges, we propose SGNet, a structure-guided network, integrating a hybrid CNN–Mamba framework for robust skin lesion segmentation. The SGNet employs the Visual Mamba (VMamba) encoder to efficiently extract multi-scale features, followed by the Dual-Domain Boundary Enhancer (DDBE), which refines boundary representations and suppresses noise through spatial and frequency-domain processing. The Semantic-Texture Fusion Unit (STFU) adaptively integrates low-level texture with high-level semantic features, while the Structure-Aware Guidance Module (SAGM) generates coarse segmentation maps to provide global structural guidance. The Guided Multi-Scale Refiner (GMSR) further optimizes boundary details through a multi-scale semantic attention mechanism. Comprehensive experiments based on the ISIC2017, ISIC2018, and PH2 datasets demonstrate SGNet’s superior performance, with average improvements of 3.30% in terms of the mean Intersection over Union (mIoU) value and 1.77% in regard to the Dice Similarity Coefficient (DSC) compared to state-of-the-art methods. Ablation studies confirm the effectiveness of each component, highlighting SGNet’s exceptional accuracy and robust generalization for computer-aided dermatological diagnosis. Full article

(This article belongs to the Section Biomedical Sensors)

► Show Figures

Figure 1

29 pages, 17807 KiB

Open AccessArticle

Low-Cost Microalgae Cell Concentration Estimation in Hydrochemistry Applications Using Computer Vision

by Julia Borisova, Ivan V. Morshchinin, Veronika I. Nazarova, Nelli Molodkina and Nikolay O. Nikitin

Sensors 2025, 25(15), 4651; https://doi.org/10.3390/s25154651 (registering DOI) - 27 Jul 2025

Abstract

Accurate and efficient estimation of microalgae cell concentration is critical for applications in hydrochemical monitoring, biofuel production, pharmaceuticals, and ecological studies. Traditional methods, such as manual counting with a hemocytometer, are time-consuming and prone to human error, while automated systems are often costly [...] Read more.

Accurate and efficient estimation of microalgae cell concentration is critical for applications in hydrochemical monitoring, biofuel production, pharmaceuticals, and ecological studies. Traditional methods, such as manual counting with a hemocytometer, are time-consuming and prone to human error, while automated systems are often costly and require extensive training data. This paper presents a low-cost, automated approach for estimating cell concentration in Chlorella vulgaris suspensions using classical computer vision techniques. The proposed method eliminates the need for deep learning by leveraging the Hough circle transform to detect and count cells in microscope images, combined with a conversion factor to translate pixel measurements into metric units for direct concentration calculation (cells/mL). Validation against manual hemocytometer counts demonstrated strong agreement, with a Pearson correlation coefficient of 0.96 and a mean percentage difference of 17.96%. The system achieves rapid processing (under 30 s per image) and offers interpretability, allowing specialists to verify results visually. Key advantages include affordability, minimal hardware requirements, and adaptability to other microbiological applications. Limitations, such as sensitivity to cell clumping and impurities, are discussed. This work provides a practical, accessible solution for laboratories lacking expensive automated equipment, bridging the gap between manual methods and high-end technologies. Full article

(This article belongs to the Section Environmental Sensing)

► Show Figures

Figure 1

25 pages, 1170 KiB

Open AccessArticle

A Kinodynamic Model for Dubins-Based Trajectory Planning in Precision Oyster Harvesting

by Weiyu Chen, Chiao-Yi Wang, Kaustubh Joshi, Alan Williams, Anjana Hevaganinge, Xiaomin Lin, Sandip Sharan Senthil Kumar, Allen Pattillo, Miao Yu, Nikhil Chopra, Matthew Gray and Yang Tao

Sensors 2025, 25(15), 4650; https://doi.org/10.3390/s25154650 (registering DOI) - 27 Jul 2025

Abstract

Oyster aquaculture in the U.S. faces severe inefficiencies due to the absence of precise path planning tools, resulting in environmental degradation and resource waste. Current dredging techniques lack trajectory planning, often leading to redundant seabed disturbance and suboptimal shell distribution. Existing vessel models—such [...] Read more.

Oyster aquaculture in the U.S. faces severe inefficiencies due to the absence of precise path planning tools, resulting in environmental degradation and resource waste. Current dredging techniques lack trajectory planning, often leading to redundant seabed disturbance and suboptimal shell distribution. Existing vessel models—such as the Nomoto or Dubins models—are not designed to map steering inputs directly to spatial coordinates, presenting a research gap in maneuver planning for underactuated boats. This research fills that gap by introducing a novel hybrid vessel kinetics model that integrates the Nomoto model with Dubins motion primitives. Our approach links steering inputs directly to the vessel motion, enabling Cartesian coordinate path generation without relying on intermediate variables like yaw velocity. Field trials in the Chesapeake Bay demonstrate consistent trajectory following performance across varied path complexities, with average offsets of 0.01 m, 1.35 m, and 0.42 m. This work represents a scalable, efficient step toward real-time, constraint-aware automation in oyster harvesting, with broader implications for sustainable aquaculture operations. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

► Show Figures

Figure 1

26 pages, 34763 KiB

Open AccessArticle

A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions

by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba and Ying Tian

Sensors 2025, 25(15), 4649; https://doi.org/10.3390/s25154649 (registering DOI) - 27 Jul 2025

Abstract

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and [...] Read more.

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. The suggested approach begins with the implementation of Variational Mode Decomposition (VMD) on the initial vibration signal. This is succeeded by a denoising process that utilizes the goodness-of-fit test based on the Anderson–Darling (AD) distance for enhanced accuracy. This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions. Full article

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

► Show Figures

Figure 1

13 pages, 1220 KiB

Open AccessArticle

Uncertainty Evaluation of Two-Dimensional Horizontal Distributed Photometric Sensor Based on MCM for Illuminance Measurement Task

by Jianguo Sun, Yueyao Wang, Yinbao Cheng, Guanghu Zhu, Jianwen Shao and Yuebing Sha

Sensors 2025, 25(15), 4648; https://doi.org/10.3390/s25154648 (registering DOI) - 27 Jul 2025

Abstract

In response to the demand for precise measurement of illuminance distribution in the quality control of LED monitoring fill light products and the iterative direction of secondary optical design, distributed photometric sensors have shown advantages, but their measurement uncertainty assessment faces challenges. This [...] Read more.

In response to the demand for precise measurement of illuminance distribution in the quality control of LED monitoring fill light products and the iterative direction of secondary optical design, distributed photometric sensors have shown advantages, but their measurement uncertainty assessment faces challenges. This paper addresses the problem of uncertainty evaluation in photometric parameter measurement with a two-dimensional horizontal distributed photometric sensor and proposes an uncertainty evaluation framework for this task. We have established an uncertainty analysis model for the measurement system and provided two uncertainty synthesis methods, The Guide to the Expression of Uncertainty in Measurement and the Monte Carlo method. This study designed illuminance measurement experiments to validate the feasibility of the proposed uncertainty evaluation method. The results demonstrate that the actual probability distribution of the measurement data follows a trapezoidal distribution. Furthermore, the expanded uncertainty calculated using the GUM method was 21.1% higher than that obtained by the MCM. This work effectively addresses the uncertainty evaluation challenge for illuminance measurement tasks using a two-dimensional horizontal distributed photometric sensor. The findings offer valuable reference for the uncertainty assessment of other high-precision optical instruments and possess significant engineering value in enhancing the reliability of optical metrology systems. Full article

(This article belongs to the Special Issue Optical Sensors for Industrial Applications)

► Show Figures

Figure 1

27 pages, 1587 KiB

Open AccessArticle

Incorporating Uncertainty Estimation and Interpretability in Personalized Glucose Prediction Using the Temporal Fusion Transformer

by Antonio J. Rodriguez-Almeida, Carmelo Betancort, Ana M. Wägner, Gustavo M. Callico, Himar Fabelo and on behalf of the WARIFA Consortium

Sensors 2025, 25(15), 4647; https://doi.org/10.3390/s25154647 (registering DOI) - 26 Jul 2025

Abstract

More than 14% of the world’s population suffered from diabetes mellitus in 2022. This metabolic condition is defined by increased blood glucose concentrations. Among the different types of diabetes, type 1 diabetes, caused by a lack of insulin secretion, is particularly challenging to [...] Read more.

More than 14% of the world’s population suffered from diabetes mellitus in 2022. This metabolic condition is defined by increased blood glucose concentrations. Among the different types of diabetes, type 1 diabetes, caused by a lack of insulin secretion, is particularly challenging to treat. In this regard, automatic glucose level estimation implements Continuous Glucose Monitoring (CGM) devices, showing positive therapeutic outcomes. AI-based glucose prediction has commonly followed a deterministic approach, usually with a lack of interpretability. Therefore, these AI-based methods do not provide enough information in critical decision-making scenarios, like in the medical field. This work intends to provide accurate, interpretable, and personalized glucose prediction using the Temporal Fusion Transformer (TFT), and also includes an uncertainty estimation. The TFT was trained using two databases, an in-house-collected dataset and the OhioT1DM dataset, commonly used for glucose forecasting benchmarking. For both datasets, the set of input features to train the model was varied to assess their impact on model interpretability and prediction performance. Models were evaluated using common prediction metrics, diabetes-specific metrics, uncertainty estimation, and interpretability of the model, including feature importance and attention. The obtained results showed that TFT outperforms existing methods in terms of RMSE by at least 13% for both datasets. Full article

(This article belongs to the Collection Deep Learning in Biomedical Informatics and Healthcare)

► Show Figures

Figure 1

20 pages, 3386 KiB

Open AccessArticle

Design of Realistic and Artistically Expressive 3D Facial Models for Film AIGC: A Cross-Modal Framework Integrating Audience Perception Evaluation

by Yihuan Tian, Xinyang Li, Zuling Cheng, Yang Huang and Tao Yu

Sensors 2025, 25(15), 4646; https://doi.org/10.3390/s25154646 (registering DOI) - 26 Jul 2025

Abstract

The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this [...] Read more.

The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. Full article

(This article belongs to the Special Issue Convolutional Neural Network Technology for 3D Imaging and Sensing)

► Show Figures

Figure 1

20 pages, 5340 KiB

Open AccessArticle

System-Level Assessment of Ka-Band Digital Beamforming Receivers and Transmitters Implementing Large Thinned Antenna Array for Low Earth Orbit Satellite Communications

by Giovanni Lasagni, Alessandro Calcaterra, Monica Righini, Giovanni Gasparro, Stefano Maddio, Vincenzo Pascale, Alessandro Cidronali and Stefano Selleri

Sensors 2025, 25(15), 4645; https://doi.org/10.3390/s25154645 (registering DOI) - 26 Jul 2025

Abstract

In this paper, we present a system-level model of a digital multibeam antenna designed for Low Earth Orbit satellite communications operating in the Ka-band. We initially develop a suitable array topology, which is based on a thinned lattice, then adopt it as the [...] Read more.

In this paper, we present a system-level model of a digital multibeam antenna designed for Low Earth Orbit satellite communications operating in the Ka-band. We initially develop a suitable array topology, which is based on a thinned lattice, then adopt it as the foundation for evaluating its performance within a digital beamforming architecture. This architecture is implemented in a system-level simulator to evaluate the performance of the transmitter and receiver chains. This study advances the analysis of the digital antennas by incorporating both the RF front-end and digital sections non-idealities into a digital-twin framework. This approach enhances the designer’s ability to optimize the system with a holistic approach and provides insights into how various impairments affect the transmitter and receiver performance, identifying the subsystems’ parameter limits. To achieve this, we analyze several subsystems’ parameters and impairments, assessing their effects on both the antenna radiation and quality of the transmitted and received signals in a real applicative context. The results of this study reveal the sensitivity of the system to the impairments and suggest strategies to trade them off, emphasizing the importance of selecting appropriate subsystem features to optimize overall system performance. Full article

(This article belongs to the Special Issue Advanced Subsystems and Technologies for Space and Airborne Communication Networks)

24 pages, 10103 KiB

Open AccessArticle

Design Technique and Efficient Polyphase Implementation for 2D Elliptically Shaped FIR Filters

by Doru Florin Chiper and Radu Matei

Sensors 2025, 25(15), 4644; https://doi.org/10.3390/s25154644 (registering DOI) - 26 Jul 2025

Abstract

This paper presents a novel analytical approach for the efficient design of a particular class of 2D FIR filters, having a frequency response with an elliptically shaped support in the frequency plane. The filter design is based on a Gaussian shaped prototype filter, [...] Read more.

This paper presents a novel analytical approach for the efficient design of a particular class of 2D FIR filters, having a frequency response with an elliptically shaped support in the frequency plane. The filter design is based on a Gaussian shaped prototype filter, which is frequently used in signal and image processing. In order to express the Gaussian prototype frequency response as a trigonometric polynomial, we developed it into a Fourier series up to a specified order, given by the imposed approximation precision. We determined analytically a 1D to 2D frequency transformation, which was applied to the factored frequency response of the prototype, yielding directly the factored frequency response of a directional, elliptically shaped 2D filter, with specified selectivity and an orientation angle. The designed filters have accurate shapes and negligible distortions. We also designed a 2D uniform filter bank of elliptical filters, which was then applied in decomposing a test image into sub-band images, thus proving its usefulness as an analysis filter bank. Then, the original image was accurately reconstructed from its sub-band images. Very selective directional elliptical filters can be used in efficiently extracting straight lines with specified orientations from images, as shown in simulation examples. A computationally efficient implementation at the system level was also discussed, based on a polyphase and block filtering approach. The proposed implementation is illustrated for a smaller size of the filter kernel and input image and is shown to have reduced computational complexity due to its parallel structure, being much more arithmetically efficient compared not only to the direct filtering approach but also with the most recent similar implementations. Full article

(This article belongs to the Section Sensing and Imaging)

► Show Figures

Figure 1

14 pages, 7807 KiB

Open AccessArticle

Flexible FLIG-Based Temperature Sensor Enabled by Femtosecond Laser Direct Writing for Thermal Monitoring in Health Systems

by Huansheng Wu, Cong Wang, Linpeng Liu and Ji’an Duan

Sensors 2025, 25(15), 4643; https://doi.org/10.3390/s25154643 (registering DOI) - 26 Jul 2025

Abstract

In this study, a facile and mask-free femtosecond laser direct writing (FLDW) approach is proposed to fabricate porous graphene (FLIG) patterns directly on polyimide (PI) substrates. By systematically adjusting the laser scanning spacing (10–25 μm), denser and more continuous microstructures are obtained, resulting [...] Read more.

In this study, a facile and mask-free femtosecond laser direct writing (FLDW) approach is proposed to fabricate porous graphene (FLIG) patterns directly on polyimide (PI) substrates. By systematically adjusting the laser scanning spacing (10–25 μm), denser and more continuous microstructures are obtained, resulting in significantly enhanced thermal sensitivity. The optimized sensor demonstrated a temperature coefficient of 0.698% °C⁻¹ within the range of 40–120 °C, with response and recovery times of 10.3 s and 20.9 s, respectively. Furthermore, it exhibits remarkable signal stability across multiple thermal cycles, a testament to its reliability in extreme conditions. Moreover, the sensor was successfully integrated into a 3D-printed robotic platform, achieving both contact and non-contact temperature detection. These results underscore the sensor’s practical adaptability for real-time thermal sensing. This work presents a viable and scalable methodology for fabricating high-performance FLIG-based flexible temperature sensors, with extensive application prospects in wearable electronics, electronic skin, and intelligent human–machine interfaces. Full article

(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

Journal Description

Sensors

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI