Sensors

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)

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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, 9225 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)

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)

14 pages, 1682 KiB

Open AccessArticle

Recording of Cardiac Excitation Using a Novel Magnetocardiography System with Magnetoresistive Sensors Outside a Magnetic Shielded Room

by Leo Yaga, Miki Amemiya, Yu Natsume, Tomohiko Shibuya and Tetsuo Sasano

Sensors 2025, 25(15), 4642; https://doi.org/10.3390/s25154642 (registering DOI) - 26 Jul 2025

Abstract

Magnetocardiography (MCG) provides a non-invasive, contactless technique for evaluating the magnetic fields generated by cardiac electrical activity, offering unique spatial insights into cardiac electrophysiology. However, conventional MCG systems depend on superconducting quantum interference devices that require cryogenic cooling and magnetic shielded environments, posing [...] Read more.

Magnetocardiography (MCG) provides a non-invasive, contactless technique for evaluating the magnetic fields generated by cardiac electrical activity, offering unique spatial insights into cardiac electrophysiology. However, conventional MCG systems depend on superconducting quantum interference devices that require cryogenic cooling and magnetic shielded environments, posing considerable impediments to widespread clinical adoption. In this study, we present a novel MCG system utilizing a high-sensitivity, wide-dynamic-range magnetoresistive sensor array operating at room temperature. To mitigate environmental interference, identical sensors were deployed as reference channels, enabling adaptive noise cancellation (ANC) without the need for traditional magnetic shielding. MCG recordings were obtained from 40 healthy participants, with signals processed using ANC, R-peak-synchronized averaging, and Bayesian spatial signal separation. This approach enabled the reliable detection of key cardiac components, including P, QRS, and T waves, from the unshielded MCG recordings. Our findings underscore the feasibility of a cost-effective, portable MCG system suitable for clinical settings, presenting new opportunities for noninvasive cardiac diagnostics and monitoring. Full article

(This article belongs to the Special Issue Novel Optical Sensors for Biomedical Applications—2nd Edition)

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19 pages, 3392 KiB

Open AccessArticle

Denoising Algorithm for High-Resolution and Large-Range Phase-Sensitive SPR Imaging Based on PFA

by Zihang Pu, Xuelin Wang, Wanwan Chen, Zhexian Liu and Peng Wang

Sensors 2025, 25(15), 4641; https://doi.org/10.3390/s25154641 (registering DOI) - 26 Jul 2025

Abstract

Phase-sensitive surface plasmon resonance (SPR) detection is widely employed in molecular dynamics studies and SPR imaging owing to its real-time capability, high sensitivity, and compatibility with imaging systems. A key research objective is to achieve higher measurement resolution of refractive index under optimal [...] Read more.

Phase-sensitive surface plasmon resonance (SPR) detection is widely employed in molecular dynamics studies and SPR imaging owing to its real-time capability, high sensitivity, and compatibility with imaging systems. A key research objective is to achieve higher measurement resolution of refractive index under optimal dynamic range conditions. We present an enhanced SPR phase imaging system combining a quad-polarization filter array for phase differential detection with a novel polarization pair, block matching, and 4D filtering (PPBM4D) algorithm to extend the dynamic range and enhance resolution. By extending the BM3D framework, PPBM4D leverages inter-polarization correlations to generate virtual measurements for each channel in the quad-polarization filter, enabling more effective noise suppression through collaborative filtering. The algorithm demonstrates 57% instrumental noise reduction and achieves 1.51 × 10⁻⁶ RIU resolution (1.333–1.393 RIU range). The system’s algorithm performance is validated through stepwise NaCl solution switching experiments (0.0025–0.08%) and protein interaction assays (0.15625–20 μg/mL). This advancement establishes a robust framework for high-resolution SPR applications across a broad dynamic range, particularly benefiting live-cell imaging and high-throughput screening. Full article

(This article belongs to the Section Biosensors)

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19 pages, 7699 KiB

Open AccessArticle

Development of Low-Cost Single-Chip Automotive 4D Millimeter-Wave Radar

by Yongjun Cai, Jie Bai, Hui-Liang Shen, Libo Huang, Bing Rao and Haiyang Wang

Sensors 2025, 25(15), 4640; https://doi.org/10.3390/s25154640 (registering DOI) - 26 Jul 2025

Abstract

Traditional 3D millimeter-wave radars lack target height information, leading to identification failures in complex scenarios. Upgrading to 4D millimeter-wave radars enables four-dimensional information perception, enhancing obstacle detection and improving the safety of autonomous driving. Given the high cost-sensitivity of in-vehicle radar systems, single-chip [...] Read more.

Traditional 3D millimeter-wave radars lack target height information, leading to identification failures in complex scenarios. Upgrading to 4D millimeter-wave radars enables four-dimensional information perception, enhancing obstacle detection and improving the safety of autonomous driving. Given the high cost-sensitivity of in-vehicle radar systems, single-chip 4D millimeter-wave radar solutions, despite technical challenges in imaging, are of great research value. This study focuses on developing single-chip 4D automotive millimeter-wave radar, covering system architecture design, antenna optimization, signal processing algorithm creation, and performance validation. The maximum measurement error is approximately ±0.2° for azimuth angles within the range of ±30° and around ±0.4° for elevation angles within the range of ±13°. Extensive road testing has demonstrated that the designed radar is capable of reliably measuring dynamic targets such as vehicles, pedestrians, and bicycles, while also accurately detecting static infrastructure like overpasses and traffic signs. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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21 pages, 1622 KiB

Open AccessArticle

Enhancing Wearable Fall Detection System via Synthetic Data

by Minakshi Debnath, Sana Alamgeer, Md Shahriar Kabir and Anne H. Ngu

Sensors 2025, 25(15), 4639; https://doi.org/10.3390/s25154639 (registering DOI) - 26 Jul 2025

Abstract

Deep learning models rely heavily on extensive training data, but obtaining sufficient real-world data remains a major challenge in clinical fields. To address this, we explore methods for generating realistic synthetic multivariate fall data to supplement limited real-world samples collected from three fall-related [...] Read more.

Deep learning models rely heavily on extensive training data, but obtaining sufficient real-world data remains a major challenge in clinical fields. To address this, we explore methods for generating realistic synthetic multivariate fall data to supplement limited real-world samples collected from three fall-related datasets: SmartFallMM, UniMib, and K-Fall. We apply three conventional time-series augmentation techniques, a Diffusion-based generative AI method, and a novel approach that extracts fall segments from public video footage of older adults. A key innovation of our work is the exploration of two distinct approaches: video-based pose estimation to extract fall segments from public footage, and Diffusion models to generate synthetic fall signals. Both methods independently enable the creation of highly realistic and diverse synthetic data tailored to specific sensor placements. To our knowledge, these approaches and especially their application in fall detection represent rarely explored directions in this research area. To assess the quality of the synthetic data, we use quantitative metrics, including the Fréchet Inception Distance (FID), Discriminative Score, Predictive Score, Jensen–Shannon Divergence (JSD), and Kolmogorov–Smirnov (KS) test, and visually inspect temporal patterns for structural realism. We observe that Diffusion-based synthesis produces the most realistic and distributionally aligned fall data. To further evaluate the impact of synthetic data, we train a long short-term memory (LSTM) model offline and test it in real time using the SmartFall App. Incorporating Diffusion-based synthetic data improves the offline F1-score by 7–10% and boosts real-time fall detection performance by 24%, confirming its value in enhancing model robustness and applicability in real-world settings. Full article

(This article belongs to the Special Issue Sensors Network and Wearables for People Activities and Wellbeing Monitoring)

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16 pages, 1863 KiB

Open AccessArticle

Improving Data Communication of Enhanced Loran Systems Using 128-ary Polar Codes

by Ruochen Jia, Yunxiao Li and Daiming Qu

Sensors 2025, 25(15), 4638; https://doi.org/10.3390/s25154638 (registering DOI) - 26 Jul 2025

Abstract

The enhanced Loran (eLoran) system, a critical terrestrial backup for the Global Satellite Navigation System (GNSS), traditionally utilizes a Reed-Solomon (RS) code for its data communication, which presents limitations in error performance, particularly due to its decoding method. This paper introduces a significant [...] Read more.

The enhanced Loran (eLoran) system, a critical terrestrial backup for the Global Satellite Navigation System (GNSS), traditionally utilizes a Reed-Solomon (RS) code for its data communication, which presents limitations in error performance, particularly due to its decoding method. This paper introduces a significant advancement by proposing the replacement of the conventional RS code with a 128-ary polar code, which is designed to maintain compatibility with the established 128-ary Pulse Position Modulation (PPM) scheme integral to eLoran’s positioning function. A Soft–Soft (SS) demodulation method, based on a correlation receiver, is developed to provide the requisite soft information for the effective Successive Cancellation List (SCL) decoding of the 128-ary polar code. Comprehensive simulations demonstrate that the proposed 128-ary polar code with SS demodulation achieves a substantial error performance improvement, yielding an approximate 9.3 dB gain at the 0.01 FER level over the RS code in eLoran data communication with EPD-MD demodulation. Additionally, the proposed scheme improves data transmission efficiency—either reducing transmission duration by 2/3 or increasing message bit number by 250% for comparable error performance—without impacting the system’s primary positioning capabilities. Full article

(This article belongs to the Section Communications)

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20 pages, 5843 KiB

Open AccessArticle

Accurate and Robust Train Localization: Fusing Degeneracy-Aware LiDAR-Inertial Odometry and Visual Landmark Correction

by Lin Yue, Peng Wang, Jinchao Mu, Chen Cai, Dingyi Wang and Hao Ren

Sensors 2025, 25(15), 4637; https://doi.org/10.3390/s25154637 (registering DOI) - 26 Jul 2025

Abstract

To overcome the limitations of current train positioning systems, including low positioning accuracy and heavy reliance on track transponders or GNSS signals, this paper proposes a novel LiDAR-inertial and visual landmark fusion framework. Firstly, an IMU preintegration factor considering the Earth’s rotation and [...] Read more.

To overcome the limitations of current train positioning systems, including low positioning accuracy and heavy reliance on track transponders or GNSS signals, this paper proposes a novel LiDAR-inertial and visual landmark fusion framework. Firstly, an IMU preintegration factor considering the Earth’s rotation and a LiDAR-inertial odometry factor accounting for degenerate states are constructed to adapt to railway train operating environments. Subsequently, a lightweight network based on YOLO improvement is used for recognizing reflective kilometer posts, while PaddleOCR extracts numerical codes. High-precision vertex coordinates of kilometer posts are obtained by jointly using LiDAR point cloud and an image detection box. Next, a kilometer post factor is constructed, and multi-source information is optimized within a factor graph framework. Finally, onboard experiments conducted on real railway vehicles demonstrate high-precision landmark detection at 35 FPS with 94.8% average precision. The proposed method delivers robust positioning within 5 m RMSE accuracy for high-speed, long-distance train travel, establishing a novel framework for intelligent railway development. Full article

(This article belongs to the Section Navigation and Positioning)

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17 pages, 2373 KiB

Open AccessArticle

Discrete Unilateral Constrained Extended Kalman Filter in an Embedded System

by Leonardo Herrera and Rodrigo Méndez-Ramírez

Sensors 2025, 25(15), 4636; https://doi.org/10.3390/s25154636 (registering DOI) - 26 Jul 2025

Abstract

Since its publication in the 1960s, the Kalman Filter (KF) has been a powerful tool in optimal state estimation. However, the KF and most of its variants have mainly focused on the state estimation of smooth systems. In this work, we propose a [...] Read more.

Since its publication in the 1960s, the Kalman Filter (KF) has been a powerful tool in optimal state estimation. However, the KF and most of its variants have mainly focused on the state estimation of smooth systems. In this work, we propose a new algorithm called the Discrete Unilateral Constrained Extended Kalman Filter (DUCEKF) that expands the capabilities of the Extended Kalman Filter (EKF) to a class of hybrid mechanical systems known as systems with unilateral constraints. Such systems are non-smooth in position and discontinuous in velocity. Lyapunov stability theory is invoked to establish sufficient conditions for the estimation error stability of the proposed algorithm. A comparison of the proposed algorithm with the EKF is conducted in simulation through a case study to demonstrate the superiority of the DUCEKF for the state estimation tasks in this class of systems. Simulations and an experiment were developed in this case study to validate the performance of the proposed algorithm. The experiment was conducted using electronic hardware that consists of an Embedded System (ES) called “Mikromedia for dsPIC33EP” and an external DAC-12 Click board, which includes a Digital-to-Analog Converter (DAC) from Texas Instruments. Full article

(This article belongs to the Section Electronic Sensors)

24 pages, 74760 KiB

Open AccessArticle

The Application of Mobile Devices for Measuring Accelerations in Rail Vehicles: Methodology and Field Research Outcomes in Tramway Transport

by Michał Urbaniak, Jakub Myrcik, Martyna Juda and Jan Mandrysz

Sensors 2025, 25(15), 4635; https://doi.org/10.3390/s25154635 (registering DOI) - 26 Jul 2025

Abstract

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems [...] Read more.

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

(This article belongs to the Collection Sensors and Actuators for Intelligent Vehicles)

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17 pages, 1915 KiB

Open AccessArticle

Thermocouple Sensor Response in Hot Airstream

by Jacek Pieniazek

Sensors 2025, 25(15), 4634; https://doi.org/10.3390/s25154634 (registering DOI) - 26 Jul 2025

Abstract

The response of a temperature sensor in a gas stream depends on several heat transfer phenomena. The temperature of the thermocouple’s hot junction in the hot stream is lower than the measured temperature, which causes a measurement error. Compensation for this error and [...] Read more.

The response of a temperature sensor in a gas stream depends on several heat transfer phenomena. The temperature of the thermocouple’s hot junction in the hot stream is lower than the measured temperature, which causes a measurement error. Compensation for this error and interpretation of the values indicated by the temperature sensor are possible by using a sensor dynamics model. Changes over time of the hot junction temperature as well as the entire thermocouple temperature in a stream are solved using the finite element method. Fluid flow and heat transfer equations are solved for a particular sensor geometry. This article presents a method for identifying a temperature sensor model using the results of numerical modeling of the response to temperature changes of the fluid stream, in which the input and output signal waveforms are recorded and then used by the estimator of a model coefficient. It is demonstrated that the dynamics of a bare-bead thermocouple sensor are well-described by a first-order transfer function. The proposed method was used to study the influence of stream velocity on the reaction of two sensors differing in the diameter of the wires, and the effect of radiative heat transfer on the model coefficients was examined by enabling and disabling selected models. The results obtained at several calculation points show the influence of the stream outflow velocity and selected geometric parameters on the value of the transfer function coefficients, i.e., transfer function gain and time constant. This study provides quantitative models of changes in sensor dynamics as functions of the coefficients. Full article

(This article belongs to the Section Industrial Sensors)

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13 pages, 5974 KiB

Open AccessArticle

Proof of Concept and Validation of Single-Camera AI-Assisted Live Thumb Motion Capture

by Huy G. Dinh, Joanne Y. Zhou, Adam Benmira, Deborah E. Kenney and Amy L. Ladd

Sensors 2025, 25(15), 4633; https://doi.org/10.3390/s25154633 (registering DOI) - 26 Jul 2025

Abstract

Motion analysis can be useful for multiplanar analysis of hand kinematics. The carpometacarpal (CMC) joint has been traditionally difficult to capture with surface-based motion analysis but is the most commonly arthritic joint of the hand and is of particular clinical interest. Traditional 3D [...] Read more.

Motion analysis can be useful for multiplanar analysis of hand kinematics. The carpometacarpal (CMC) joint has been traditionally difficult to capture with surface-based motion analysis but is the most commonly arthritic joint of the hand and is of particular clinical interest. Traditional 3D motion capture of the CMC joint using multiple cameras and reflective markers and manual goniometer measurement has been challenging to integrate into clinical workflow. We therefore propose a markerless single-camera artificial intelligence (AI)-assisted motion capture method to provide real-time estimation of clinically relevant parameters. Our study enrolled five healthy subjects, two male and three female. Fourteen clinical parameters were extracted from thumb interphalangeal (IP), metacarpal phalangeal (MP), and CMC joint motions using manual goniometry and live motion capture with the Google AI MediaPipe Hands landmarker model. Motion capture measurements were assessed for accuracy, precision, and correlation with manual goniometry. Motion capture demonstrated sufficient accuracy in 11 and precision in all 14 parameters, with mean error of −2.13 ± 2.81° (95% confidence interval [CI]: −5.31, 1.05). Strong agreement was observed between both modalities across all subjects, with a combined Pearson correlation coefficient of 0.97 (p < 0.001) and an intraclass correlation coefficient of 0.97 (p < 0.001). The results suggest AI-assisted live motion capture can be an accurate and practical thumb assessment tool, particularly in virtual patient encounters, for enhanced range of motion (ROM) analysis. Full article

(This article belongs to the Special Issue Smart Sensor Technologies for Accurate Movement Monitoring and Connectivity)

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43 pages, 10460 KiB

Open AccessArticle

State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms

by Romel Carrera, Leonidas Quiroz, Cesar Guevara and Patricia Acosta-Vargas

Sensors 2025, 25(15), 4632; https://doi.org/10.3390/s25154632 (registering DOI) - 26 Jul 2025

Abstract

This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under [...] Read more.

This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter

α

. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. Full article

(This article belongs to the Section Electronic Sensors)

26 pages, 2457 KiB

Open AccessReview

Crack Detection in Civil Infrastructure Using Autonomous Robotic Systems: A Synergistic Review of Platforms, Cognition, and Autonomous Action

by Rong Dai, Rui Wang, Chang Shu, Jianming Li and Zhe Wei

Sensors 2025, 25(15), 4631; https://doi.org/10.3390/s25154631 (registering DOI) - 26 Jul 2025

Abstract

Traditional manual crack inspection methods often face limitations in terms of efficiency, safety, and consistency. To overcome these issues, a new approach based on autonomous robotic systems has gained attention, combining robotics, artificial intelligence, and advanced sensing technologies. However, most existing reviews focus [...] Read more.

Traditional manual crack inspection methods often face limitations in terms of efficiency, safety, and consistency. To overcome these issues, a new approach based on autonomous robotic systems has gained attention, combining robotics, artificial intelligence, and advanced sensing technologies. However, most existing reviews focus on individual components in isolation and fail to present a complete picture of how these systems work together. This study focuses on robotic crack detection and proposes a structured framework that connects three core modules: the physical platform (robots and sensors), the cognitive core (crack detection algorithms), and autonomous action (navigation and planning). We analyze key technologies, their interactions, and the challenges involved in real-world implementation. The aim is to provide a clear roadmap of current progress and future directions, helping researchers and engineers better understand the field and develop smart, deployable systems for infrastructure crack inspection. Full article

(This article belongs to the Special Issue Innovative Synergies: Robotics, AI, and Sensor Technologies in Field Autonomous Systems)

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20 pages, 3528 KiB

Open AccessArticle

High-Precision Optimization of BIM-3D GIS Models for Digital Twins: A Case Study of Santun River Basin

by Zhengbing Yang, Mahemujiang Aihemaiti, Beilikezi Abudureheman and Hongfei Tao

Sensors 2025, 25(15), 4630; https://doi.org/10.3390/s25154630 (registering DOI) - 26 Jul 2025

Abstract

The integration of Building Information Modeling (BIM) and 3D Geographic Information System (3D GIS) models provides high-precision spatial data for digital twin watersheds. To tackle the challenges of large data volumes and rendering latency in integrated models, this study proposes a three-step framework [...] Read more.

The integration of Building Information Modeling (BIM) and 3D Geographic Information System (3D GIS) models provides high-precision spatial data for digital twin watersheds. To tackle the challenges of large data volumes and rendering latency in integrated models, this study proposes a three-step framework that uses Industry Foundation Classes (IFCs) as the base model and Open Scene Graph Binary (OSGB) as the target model: (1) geometric optimization through an angular weighting (AW)-controlled Quadric Error Metrics (QEM) algorithm; (2) Level of Detail (LOD) hierarchical mapping to establish associations between the IFC and OSGB models, and redesign scene paging logic; (3) coordinate registration by converting the IFC model’s local coordinate system to the global coordinate system and achieving spatial alignment via the seven-parameter method. Applied to the Santun River Basin digital twin project, experiments with 10 water gate models show that the AW-QEM algorithm reduces average loading time by 15% compared to traditional QEM, while maintaining 97% geometric accuracy, demonstrating the method’s efficiency in balancing precision and rendering performance. Full article

(This article belongs to the Section Intelligent Sensors)

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12 pages, 1888 KiB

Open AccessArticle

Surface-Enhanced Raman Spectroscopy for Adenine Detection in Five Selected Bacterial Strains Under Stress Conditions

by Mona Ghazalová, Pavlína Modlitbová, Ota Samek, Katarína Rebrošová, Martin Šiler, Jan Ježek and Zdeněk Pilát

Sensors 2025, 25(15), 4629; https://doi.org/10.3390/s25154629 (registering DOI) - 26 Jul 2025

Abstract

This pilot study investigated the metabolic responses of five selected bacteria to physiological stress. Surface-enhanced Raman spectroscopy was used to analyze spectral changes associated with the release of adenine, a key metabolite indicative of stress conditions. Laboratory-synthesized spherical silver and gold nanoparticles, which [...] Read more.

This pilot study investigated the metabolic responses of five selected bacteria to physiological stress. Surface-enhanced Raman spectroscopy was used to analyze spectral changes associated with the release of adenine, a key metabolite indicative of stress conditions. Laboratory-synthesized spherical silver and gold nanoparticles, which remained stable over an extended period, were employed as enhanced surfaces. Bacterial cultures were analyzed under standard conditions and in the presence of a selected stressor—demineralized water—inducing osmotic stress. The results showed that the adenine signal originated from metabolites released into the surrounding environment rather than directly from the bacterial cell wall. The study confirms the suitability of these cost-effective and easily synthesized stable nanoparticles for the qualitative detection of bacterial metabolites using a commercially available Raman instrument. Full article

(This article belongs to the Section Sensors Development)

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20 pages, 77932 KiB

Open AccessArticle

Image Alignment Based on Deep Learning to Extract Deep Feature Information from Images

by Lin Zhu, Yuxing Mao and Jianyu Pan

Sensors 2025, 25(15), 4628; https://doi.org/10.3390/s25154628 (registering DOI) - 26 Jul 2025

Abstract

To overcome the limitations of traditional image alignment methods in capturing deep semantic features, a deep feature information image alignment network (DFA-Net) is proposed. This network aims to enhance image alignment performance through multi-level feature learning. DFA-Net is based on the deep residual [...] Read more.

To overcome the limitations of traditional image alignment methods in capturing deep semantic features, a deep feature information image alignment network (DFA-Net) is proposed. This network aims to enhance image alignment performance through multi-level feature learning. DFA-Net is based on the deep residual architecture and introduces spatial pyramid pooling to achieve cross-scalar feature fusion, effectively enhancing the feature’s adaptability to scale. A feature enhancement module based on the self-attention mechanism is designed, with key features that exhibit geometric invariance and high discriminative power, achieved through a dynamic weight allocation strategy. This improves the network’s robustness to multimodal image deformation. Experiments on two public datasets, MSRS and RoadScene, show that the method performs well in terms of alignment accuracy, with the RMSE metrics being reduced by 0.661 and 0.473, and the SSIM, MI, and NCC improved by 0.155, 0.163, and 0.211; and 0.108, 0.226, and 0.114, respectively, compared with the benchmark model. The visualization results validate the significant improvement in the features’ visual quality and confirm the method’s advantages in terms of stability and discriminative properties of deep feature extraction. Full article

(This article belongs to the Section Sensing and Imaging)

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