Sensors

16 pages, 4100 KiB

Open AccessArticle

Analysis and Experiments of Resonant Coupling Wireless Power Transfer System for Nonuniform Powering of Multiple Sensors

by Thuc Phi Duong, Ngoc Hung Phi, Bilal Ahmad, Sasani Jayasekara and Jong-Wook Lee

Sensors 2025, 25(7), 2342; https://doi.org/10.3390/s25072342 - 7 Apr 2025

Viewed by 227

Abstract

With a quickly increasing number of Internet of Things (IoT) involving different power levels, wireless power transfer (WPT) systems need the capability to deliver energy to multiple receivers simultaneously. The nonuniform powering of multiple receivers is also necessary, considering the different power levels [...] Read more.

With a quickly increasing number of Internet of Things (IoT) involving different power levels, wireless power transfer (WPT) systems need the capability to deliver energy to multiple receivers simultaneously. The nonuniform powering of multiple receivers is also necessary, considering the different power levels that IoT sensors demand. This paper investigates asymmetric resonant coupling WPT systems for powering multiple receivers. We propose a simple method for achieving the specified power ratio of the multiple receivers using the equivalent circuit model and reflected impedance technique. The results are generalized for a system with an N number of multiple receivers. Experiments are performed for powering two receivers with power ratios of 1.5 and 2.5, which achieve a power transfer efficiency of 91.7% and 88.6%, respectively. Another experiment performed for powering four receivers, which have power ratios of 1.0, 1.5, 2.0, and 0.75, shows an efficiency of up to 89.9%, which agrees well with the simulation result. Our result shows that the distance between the source loop and the transmitting resonator can be varied to maximize efficiency without altering the power division. Full article

(This article belongs to the Special Issue Advanced Sensing Technology and Data Analytics for Power Equipment Security and Energy System)

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17 pages, 4785 KiB

Open AccessArticle

Fabrication and Characterization of a Flexible Non-Enzymatic Electrochemical Glucose Sensor Using a Cu Nanoparticle/Laser-Induced Graphene Fiber/Porous Laser-Induced Graphene Network Electrode

by Taeheon Kim and James Jungho Pak

Sensors 2025, 25(7), 2341; https://doi.org/10.3390/s25072341 - 7 Apr 2025

Viewed by 210

Abstract

We demonstrate a flexible electrochemical biosensor for non-enzymatic glucose detection under different bending conditions. The novel flexible glucose sensor consists of a Cu nanoparticle (NP)/laser-induced graphene fiber (LIGF)/porous laser-induced graphene (LIG) network structure on a polyimide film. The bare LIGF/LIG electrode fabricated using [...] Read more.

We demonstrate a flexible electrochemical biosensor for non-enzymatic glucose detection under different bending conditions. The novel flexible glucose sensor consists of a Cu nanoparticle (NP)/laser-induced graphene fiber (LIGF)/porous laser-induced graphene (LIG) network structure on a polyimide film. The bare LIGF/LIG electrode fabricated using an 8.9 W laser power shows a measured sheet resistance and thickness of 6.8 Ω/□ and ~420 μm, respectively. In addition, a conventional Cu NP electroplating method is used to fabricate a Cu/LIGF/LIG electrode-based glucose sensor that shows excellent glucose detection characteristics, including a sensitivity of 1438.8 µA/mM∙cm², a limit of detection (LOD) of 124 nM, and a broad linear range at an applied potential of +600 mV. Significantly, the Cu/LIGF/LIG electrode-based glucose sensor exhibits a relatively high sensitivity, low LOD, good linear detection range, and long-term stability at bending angles of 0°, 45°, 90°, 135°, and 180°. Full article

(This article belongs to the Section Chemical Sensors)

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25 pages, 4669 KiB

Open AccessArticle

Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework

by Arnd Pettirsch and Alvaro Garcia-Hernandez

Sensors 2025, 25(7), 2340; https://doi.org/10.3390/s25072340 - 7 Apr 2025

Viewed by 168

Abstract

Roadside camera systems are commonly used for traffic data collection, yet conventional optical systems are limited by poor performance in varying weather and light conditions and are often restricted by data privacy regulations. Thermal imaging overcomes these issues, enabling reliable detection across all [...] Read more.

Roadside camera systems are commonly used for traffic data collection, yet conventional optical systems are limited by poor performance in varying weather and light conditions and are often restricted by data privacy regulations. Thermal imaging overcomes these issues, enabling reliable detection across all conditions without collecting personal data. However, its widespread use is hindered by the scarcity of diverse, annotated thermal training data, especially since fixed cameras installed at the side of the road produce very similar images with the same backgrounds. This paper presents two key innovations to address these challenges: a novel dataset of 11,400 annotated images and 142 unannotated video clips, the largest and most diverse available for thermal roadside imaging to date, and a weakly supervised incremental learning framework tailored for thermal roadside imagery. The dataset supports the development of self-supervised algorithms, and the learning framework allows efficient adaptation to new camera viewpoints and diverse environmental conditions without additional labelling. Together, these contributions enable cost-effective and reliable thermal-based traffic monitoring across varied locations, achieving an 8.9-point increase in mean average precision for previously unseen viewpoints. Full article

(This article belongs to the Section Sensing and Imaging)

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26 pages, 1941 KiB

Open AccessReview

Boron-Doped Diamond Electrodes for Toxins Sensing in Environmental Samples—A Review

by Aleksandar Mijajlović, Vesna Stanković, Tijana Mutić, Sladjana Djurdjić, Filip Vlahović and Dalibor Stanković

Sensors 2025, 25(7), 2339; https://doi.org/10.3390/s25072339 - 7 Apr 2025

Viewed by 230

Abstract

Boron-doped diamond electrodes have found applications in the detection, monitoring, and mitigation of toxic chemicals resulting from various industries and human activities. The boron-doped diamond electrode is a widely applicable technology in this field, primarily due to its excellent surface characteristics: minimal to [...] Read more.

Boron-doped diamond electrodes have found applications in the detection, monitoring, and mitigation of toxic chemicals resulting from various industries and human activities. The boron-doped diamond electrode is a widely applicable technology in this field, primarily due to its excellent surface characteristics: minimal to no adsorption, a wide operating potential range, robustness, and high selectivity. These extraordinary properties can be further enhanced through surface termination, which can additionally improve the analytical performance of boron-doped diamond (BDD) electrodes. The high accuracy and precision of the developed methods indicate the broad practical applicability of these electrodes across various sample matrices. Some studies have shown that different strategies can lead to enhanced sensitivity and selectivity, such as modifying the electrode surface (nanostructuring), forming different composite materials based on BDD, or implementing miniaturization techniques. Thus, this review summarizes the recent literature on the electroanalytical applications of BDDE surfaces, with a particular focus on environmental applications. Full article

(This article belongs to the Special Issue Chemical Sensors for Toxic Chemical Detection: 2nd Edition)

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20 pages, 5129 KiB

Open AccessArticle

Multi-Band Analog Radio-over-Fiber Mobile Fronthaul System for Indoor Positioning, Beamforming, and Wireless Access

by Hang Yang, Wei Tian, Jianhua Li and Yang Chen

Sensors 2025, 25(7), 2338; https://doi.org/10.3390/s25072338 (registering DOI) - 7 Apr 2025

Viewed by 245

Abstract

In response to the urgent demands of the Internet of Things for precise indoor target positioning and information interaction, this paper proposes a multi-band analog radio-over-fiber mobile fronthaul system. The objective is to obtain the target’s location in indoor environments while integrating remote [...] Read more.

In response to the urgent demands of the Internet of Things for precise indoor target positioning and information interaction, this paper proposes a multi-band analog radio-over-fiber mobile fronthaul system. The objective is to obtain the target’s location in indoor environments while integrating remote beamforming capabilities to achieve wireless access to the targets. Vector signals centered at 3, 4, 5, and 6 GHz for indoor positioning and centered at 30 GHz for wireless access are generated centrally in the distributed unit (DU) and fiber-distributed to the active antenna unit (AAU) in the multi-band analog radio-over-fiber mobile fronthaul system. Target positioning is achieved by radiating electromagnetic waves indoors through four omnidirectional antennas in conjunction with a pre-trained neural network, while high-speed wireless communication is realized through a phased array antenna (PAA) comprising four antenna elements. Remote beamforming for the PAA is implemented through the integration of an optical true time delay pool in the multi-band analog radio-over-fiber mobile fronthaul system. This integration decouples the weight control of beamforming from the AAU, enabling centralized control of beam direction at the DU and thereby reducing the complexity and cost of the AAU. Simulation results show that the average accuracy of localization classification can reach 86.92%, and six discrete beam directions are achieved via the optical true time delay pool. In the optical transmission layer, when the received optical power is 10 dBm, the error vector magnitudes (EVMs) of vector signals in all frequency bands remain below 3%. In the wireless transmission layer, two beam directions were selected for verification. Once the beam is aligned with the target device at maximum gain and the received signal is properly processed, the EVM of millimeter-wave vector signals remains below 11%. Full article

(This article belongs to the Section Communications)

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22 pages, 2355 KiB

Open AccessArticle

DBO-AWOA: An Adaptive Whale Optimization Algorithm for Global Optimization and UAV 3D Path Planning

by Tao Xu and Chaoyue Chen

Sensors 2025, 25(7), 2336; https://doi.org/10.3390/s25072336 - 7 Apr 2025

Viewed by 227

Abstract

The rapid expansion of unmanned aerial vehicle (UAV) applications in complex environments presents significant challenges in 3D path planning, particularly in overcoming the limitations of traditional methods for dynamic obstacle avoidance and computational efficiency. To address these challenges, this study introduces an adaptive [...] Read more.

The rapid expansion of unmanned aerial vehicle (UAV) applications in complex environments presents significant challenges in 3D path planning, particularly in overcoming the limitations of traditional methods for dynamic obstacle avoidance and computational efficiency. To address these challenges, this study introduces an adaptive whale optimization algorithm (DBO-AWOA), which incorporates chaotic mapping, nonlinear convergence factors, adaptive inertia mechanisms, and dung beetle optimizer-inspired reproductive behaviors. Specifically, the algorithm utilizes ICMIC chaotic mapping to enhance initial population diversity, a cosine-based nonlinear convergence factor to balance exploration and exploitation, and a hybrid strategy inspired by the dung beetle optimizer to mitigate stagnation in local optima. When evaluated on the CEC2017 benchmark suite, DBO-AWOA demonstrates superior convergence precision and robustness, achieving the lowest minimum and average values across 72% of test functions. In 3D path-planning simulations within mountainous environments, DBO-AWOA generates smoother, shorter, and safer trajectories compared to existing variants, with fitness values reduced by 5–25%. Although the algorithm demonstrates slight instability in highly dynamic hybrid functions, its overall performance marks an improvement in global optimization and UAV path planning. Full article

(This article belongs to the Section Vehicular Sensing)

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23 pages, 9656 KiB

Open AccessArticle

Full Cross-Sectional Profile Measurement of a High-Aspect-Ratio Micro-Groove Using a Deflection Probe Measuring System

by Zhong-Hao Cao, Jinyan Tang, Zhongwei Li and Yuan-Liu Chen

Sensors 2025, 25(7), 2335; https://doi.org/10.3390/s25072335 (registering DOI) - 7 Apr 2025

Viewed by 210

Abstract

For the full cross-sectional profile measurement of high-aspect-ratio micro-grooves, traditional measurement methods have blind measurement areas in the vertical sidewall and its intersection area with the bottom. This paper proposes a deflection-based scanning method that utilizes a large length-to-diameter ratio probe to achieve [...] Read more.

For the full cross-sectional profile measurement of high-aspect-ratio micro-grooves, traditional measurement methods have blind measurement areas in the vertical sidewall and its intersection area with the bottom. This paper proposes a deflection-based scanning method that utilizes a large length-to-diameter ratio probe to achieve a full cross-sectional profile measurement of micro-grooves. Blind measurement areas were eliminated by a deflection-based scanning method. The complete groove profile was obtained by stitching the positive and reversal deflection-based measurement results. The optimal deflection angle of the probe was calculated by considering the profile-stitching setting and the principle of minimizing the probe deformation during the measurement process. A four-axis measurement system was established to measure high-aspect-ratio micro-grooves, which incorporated a force feedback mechanism to maintain a constant contact force during the measurement and an integrated error separation module to modify the measurement results. The measurement method and system were experimentally validated to achieve a full cross-sectional profile measurement of micro-grooves with a width of 50 μm and an aspect ratio of no less than 3. The standard deviation of the measurement results was 82 nm, and the expanded uncertainty was 108 nm. Full article

(This article belongs to the Section Physical Sensors)

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16 pages, 2942 KiB

Open AccessArticle

Electrochemical Sensor Based on DNA Aptamers Immobilized on V₂O₅/rGO Nanocomposite for the Sensitive Detection of Hg(II)

by Mahesh A. Takte, Shubham S. Patil, Akash V. Fulari, Tibor Hianik and Mahendra D. Shirsat

Sensors 2025, 25(7), 2334; https://doi.org/10.3390/s25072334 - 7 Apr 2025

Viewed by 323

Abstract

We developed a sensor consisting of V₂O₅ nanorods and a reduced graphene oxide (rGO) nanocomposite (V₂O₅/rGO) with immobilized DNA aptamers (Apt-NH@V₂O₅/rGO) for the sensitive electrochemical detection of Hg (II). The V₂ [...] Read more.

We developed a sensor consisting of V₂O₅ nanorods and a reduced graphene oxide (rGO) nanocomposite (V₂O₅/rGO) with immobilized DNA aptamers (Apt-NH@V₂O₅/rGO) for the sensitive electrochemical detection of Hg (II). The V₂O₅ nanorods anchored on rGO nanosheets were synthesized using a hydrothermal method. The nanocomposite was analyzed by various powerful physical methods that include X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), field emission scanning electron microscopy (FE-SEM), Raman spectroscopy, the Brunauer–Emmett–Teller (BET) method, and Fourier transform infrared spectroscopy (FTIR). The FE-SEM of V₂O₅ disclosed the nanorod-like structure and uniform anchoring of V₂O₅ on the rGO nanosheet. Moreover, the BET results showed that the V₂O₅/rGO nanocomposite possesses excellent porosity. Furthermore, a glassy carbon electrode (GCE) was modified with Apt-NH@V₂O₅/rGO and used for the electrochemical detection of Hg(II) by differential pulse voltammetry (DPV). The aptasensor exhibited excellent sensitivity and selectivity toward Hg(II) detection, with a limit of detection (LOD) of 5.57 nM, which is below the maximum permissible limit established by WHO for rivers (30 nM). The sensor also exhibited significant stability and good repeatability. Full article

(This article belongs to the Special Issue Electrochemical DNA- and Aptasensors for the Detection of Low-Molecular Compounds)

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24 pages, 8194 KiB

Open AccessArticle

A New Pallet-Positioning Method Based on a Lightweight Component Segmentation Network for AGV Toward Intelligent Warehousing

by Bin Wu, Shijie Wang, Yi Lu, Yang Yi, Di Jiang and Mengmeng Qiao

Sensors 2025, 25(7), 2333; https://doi.org/10.3390/s25072333 (registering DOI) - 7 Apr 2025

Viewed by 245

Abstract

In human–robot hybrid intelligent warehouses, pallets often come in various shapes and sizes, posing challenges for AGVs to automate pallet picking. This, in turn, reduces the overall operational efficiency of the warehouse. To address this issue, this paper proposes a lightweight component segmentation [...] Read more.

In human–robot hybrid intelligent warehouses, pallets often come in various shapes and sizes, posing challenges for AGVs to automate pallet picking. This, in turn, reduces the overall operational efficiency of the warehouse. To address this issue, this paper proposes a lightweight component segmentation network using a dual-attention mechanism to achieve precise segmentation of the pallet’s stringer board and accurate localization of the pallet slots. To overcome the challenge of redundant computations in existing semantic segmentation models, which are unable to balance spatial details and high-level semantic information, this network utilizes a dual-branch attention mechanism within an encoder–decoder architecture to effectively capture spatial details. On this basis, a residual structure is introduced to reduce redundant network parameters, addressing issues like vanishing and exploding gradients during training. Due to the lack of a public pallet image segmentation dataset, the network was tested using a custom-made dataset. The results show that by extracting intermediate-, low-, and high-level features from dual-branch input images and integrating them to construct multi-scale images, precise segmentation of various types of pallets can be achieved with limited annotated images. Furthermore, to comprehensively evaluate the model’s robustness, additional pallet localization experiments were conducted under varying illumination conditions and background noise levels. The results demonstrate that the proposed method can effectively identify and locate multi-category pallet targets while maintaining high segmentation accuracy under different lighting conditions and background interferences, verifying the model’s robustness in complex warehousing environments. Compared to the traditional model, the proposed model in this paper achieves a 10.41% improvement in accuracy and a 32.8% increase in image processing speed. The segmentation network we proposed is used for pallet-positioning experiments and has achieved good positioning results in pallet images taken from different distances and angles. Full article

(This article belongs to the Section Sensing and Imaging)

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21 pages, 7669 KiB

Open AccessArticle

Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest

by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu and Guangyu Bin

Sensors 2025, 25(7), 2332; https://doi.org/10.3390/s25072332 (registering DOI) - 7 Apr 2025

Viewed by 309

Abstract

Objective: Clinically, patients in a coma after cardiac arrest are given the prognosis of “neurological recovery” to minimize discrepancies in opinions and reduce judgment errors. This study aimed to analyze the background patterns of electroencephalogram (EEG) signals from such patients to identify the [...] Read more.

Objective: Clinically, patients in a coma after cardiac arrest are given the prognosis of “neurological recovery” to minimize discrepancies in opinions and reduce judgment errors. This study aimed to analyze the background patterns of electroencephalogram (EEG) signals from such patients to identify the key indicators for assessing the prognosis after coma. Approach: Standard machine learning models were applied sequentially as feature selectors and filters. CatBoost demonstrated superior performance as a classification method compared to other approaches. In addition, Shapley additive explanation (SHAP) values were utilized to rank and analyze the importance of the features. Results: Our results indicated that the three different EEG features helped achieve a fivefold cross-validation receiver-operating characteristic (ROC) of 0.87. Our evaluation revealed that functional connectivity features contribute the most to classification at 70%. Among these, low-frequency long-distance functional connectivity (45%) was associated with a poor prognosis, whereas high-frequency short-distance functional connectivity (25%) was linked with a good prognosis. Burst suppression ratio is 20%, concentrated in the left frontal–temporal and right occipital–temporal regions at high thresholds (10/15 mV), demonstrating its strong discriminative power. Significance: Our research identifies key electroencephalographic (EEG) biomarkers, including low-frequency connectivity and burst suppression thresholds, to improve early and objective prognosis assessments. By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. These findings provide a clinically actionable framework for advancing neurological prognosis and optimizing patient care. Full article

(This article belongs to the Special Issue Brain Activity Monitoring and Measurement (2nd Edition))

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16 pages, 5303 KiB

Open AccessArticle

Electroacoustic Analysis and Optimization of Needle-Rod Electrodes for Low-Power Impulse Sound Source

by Xiao Du, Jing Zhou and Xu Gao

Sensors 2025, 25(7), 2331; https://doi.org/10.3390/s25072331 - 7 Apr 2025

Viewed by 256

Abstract

In acoustic deep detection technology, conventional monopole, dipole, and phased-array sound sources are far inferior to impulsive sound sources in frequency and amplitude. But impulse sound sources mostly work under high-power, high-voltage, and high-current conditions, which are difficult to be applied downhole. The [...] Read more.

In acoustic deep detection technology, conventional monopole, dipole, and phased-array sound sources are far inferior to impulsive sound sources in frequency and amplitude. But impulse sound sources mostly work under high-power, high-voltage, and high-current conditions, which are difficult to be applied downhole. The purpose of this paper is to reduce the power of the impulse sound source system and at the same time to stimulate excellent impulse wave characteristics. Firstly, an experimental impulse sound source system using needle-rod electrodes was constructed, and the discharge experimental results were analysed. Secondly, a finite element model of the needle-rod electrodes of the impulse sound source was established based on the experimental conditions, and the effects of the charging voltage, electrode gap, and liquid conductivity on the power and electroacoustic parameters of the needle-rod electrodes system were investigated separately. Finally, the optimised electroacoustic parameters and curves of the needle-rod electrodes of the low-power impulse sound source were obtained. The results show that the charging voltage is the most significant parameter affecting the power of the needle-rod electrode system; a larger liquid conductivity and a suitable electrode gap are required for the optimal impulse wave parameters. The optimised low-power impulse sound source system with needle-bar electrodes with a power of 20.95 kW achieves an impulse wave intensity of 4.78 MPa, with a sound pressure level above 295 dB up to 1 kHz and above 225 dB from 1 kHz to 300 kHz. Optimised needle-rod electrodes for low-power impulse sound sources have the advantages of a wide bandwidth and high energy. This makes the downhole application of low-power impulse sound sources possible, which will play an important role in oil exploration and other drilling exploration fields. Full article

(This article belongs to the Section Electronic Sensors)

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25 pages, 1017 KiB

Open AccessArticle

A Testing and Evaluation Framework for Indoor Navigation and Positioning Systems

by Zhang Zhang, Qu Wang, Wenfeng Wang, Meijuan Feng and Liangliang Guo

Sensors 2025, 25(7), 2330; https://doi.org/10.3390/s25072330 - 6 Apr 2025

Viewed by 334

Abstract

The lack of a testing framework for various indoor positioning technologies brings huge challenges to the systematic and fair evaluation of positioning systems, which greatly hinders the development and industrialization of indoor positioning technology. In order to solve this problem, this article refers [...] Read more.

The lack of a testing framework for various indoor positioning technologies brings huge challenges to the systematic and fair evaluation of positioning systems, which greatly hinders the development and industrialization of indoor positioning technology. In order to solve this problem, this article refers to international standards, such as ISO/IEC 18305, and uses the China Electronics Standardization Institute’s rich experience in indoor positioning technology research and testing to build a universal positioning performance testing and evaluation framework. First, this paper introduces the experimental environment in detail from the aspects of the coordinate system definition, test point selection, building type definition, motion mode definition, and motion trajectory setting. Then, this paper comprehensively measures performance evaluation indicators from dimensions such as the accuracy index, relative accuracy, startup time, fault tolerance, power consumption, size, and cost. Finally, this paper elaborates on the testing methods and processes of positioning precision, accuracy, relative accuracy, floor identification, indoor–outdoor distinction, latency, relative accuracy, success rate, and movement speed tests. Full article

(This article belongs to the Section Navigation and Positioning)

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23 pages, 7658 KiB

Open AccessArticle

An Accurate Altimetry Method for High-Altitude Airburst Fuze Based on Two-Dimensional Joint Extension Characteristics

by Liwen Pan, Yao Zhang, Qianyu Wang, Shuhuan He and Xi Pan

Sensors 2025, 25(7), 2329; https://doi.org/10.3390/s25072329 - 6 Apr 2025

Viewed by 244

Abstract

Considering the challenge of precise altimetry for high-altitude airburst fuzes, this paper proposes a two-dimensional joint extension characteristic altimetry method based on an improved constant false alarm rate (CFAR) detection and an accurate feature region extraction approach. First, an improved CFAR detection method [...] Read more.

Considering the challenge of precise altimetry for high-altitude airburst fuzes, this paper proposes a two-dimensional joint extension characteristic altimetry method based on an improved constant false alarm rate (CFAR) detection and an accurate feature region extraction approach. First, an improved CFAR detection method with secondary protection windows is introduced to effectively mitigate the masking effect caused by conventional CFAR algorithms. The fuze-to-ground distance-based height measurement is achieved by leveraging the geometric relationship between the maximum and minimum slant distances and the impact angle. Then, to enhance altimetry accuracy under low signal-to-noise ratio (SNR) conditions, a 2D joint accurate altimetry approach is implemented by integrating Doppler-dimension extension characteristics with the conventional range-based method. The estimated impact angle is further refined using the proposed feature region extraction method. The final results demonstrate that for high-altitude airburst fuzes operating at burst altitudes between 70 m and 100 m, the proposed 2D joint altimetry algorithm provides more accurate and robust distance measurements. Under an SNR of −10 dB, the root mean square error (RMSE) is less than 2.38 m, with an error rate of approximately 3%. Notably, even at an SNR of −15 dB, the RMSE remains below 4.76 m, with an error rate not exceeding 5%, highlighting the robustness of the proposed method under low-SNR conditions. Full article

(This article belongs to the Section Communications)

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31 pages, 17450 KiB

Open AccessArticle

Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning

by Huachao Jiao, Wenlei Sun, Hongwei Wang and Xiaojing Wan

Sensors 2025, 25(7), 2328; https://doi.org/10.3390/s25072328 - 6 Apr 2025

Viewed by 205

Abstract

The vibration signals of faulty bearings contain rich feature information in both the time and frequency domains. Effectively leveraging this information is crucial, especially when addressing imbalanced bearing fault datasets, as it can significantly enhance the performance of fault diagnosis models. However, existing [...] Read more.

The vibration signals of faulty bearings contain rich feature information in both the time and frequency domains. Effectively leveraging this information is crucial, especially when addressing imbalanced bearing fault datasets, as it can significantly enhance the performance of fault diagnosis models. However, existing GAN models and diagnostic methods do not fully exploit these domain-specific features. To overcome this limitation, a novel fault diagnosis method is proposed, based on the Adaptive Wavelet-Like Transform Generative Adversarial Network (AWLT-GAN) and ensemble learning. In the first stage, AWLT-GAN is used to balance the bearing fault dataset by integrating time- and frequency-domain feature information. AWLT-GAN embeds an adaptive wavelet-like transform neural network into the generator as an adaptive layer and employs a dual-discriminator architecture. This design allows the network to simultaneously learn fault characteristics from both domains within a single training session, enhancing the quality of the synthetic fault data. Next, an ensemble learning approach is applied, combining time- and frequency-domain models, with the final classification determined through a soft voting mechanism. Experimental results demonstrate that the vibration signals generated by AWLT-GAN effectively replicate the feature distribution of real data, confirming its high performance. The fault diagnosis model, developed using these high-quality synthetic samples, accurately captures fault characteristics embedded in both the time and frequency domains, resulting in enhanced diagnostic performance. The proposed approach not only addresses the imbalance in bearing fault datasets but also significantly improves diagnostic accuracy. Full article

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

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12 pages, 1885 KiB

Open AccessArticle

Relationship Between Lower-Extremity Co-Contraction and Jerk During Gait

by Toshinori Miyashita, Kengo Kawanishi and Shintarou Kudo

Sensors 2025, 25(7), 2327; https://doi.org/10.3390/s25072327 - 6 Apr 2025

Viewed by 241

Abstract

The elderly exhibit increased co-contraction (CC) during gait, reducing movement smoothness. The jerk has been used to quantitatively smoothness. This study aimed to investigate the relationship between lower-leg jerk and lower-extremity CC during gait. Participants were 30 healthy middle-aged and elderly people. Surface [...] Read more.

The elderly exhibit increased co-contraction (CC) during gait, reducing movement smoothness. The jerk has been used to quantitatively smoothness. This study aimed to investigate the relationship between lower-leg jerk and lower-extremity CC during gait. Participants were 30 healthy middle-aged and elderly people. Surface electromyography (EMG) was measured from the tibialis anterior (TA), gastrocnemius lateralis (GL), vastus lateralis (VL), and biceps femoris (BF). An inertial measurement unit was attached to the lower-leg. Jerk was calculated from inertial measurement unit (IMU) acceleration data, and CC was quantified as the percent co-contraction index (CCI) for TAGL, VLBF, and VLGL. To examine the correlation between CCI and jerk, the part with the highest correlation between jerk and CC during gait was used as the dependent variable, and a multiple regression analysis was performed to obtain the estimated CC values (p < 0.05). VLGL CCI increased with higher jerk during the second half of the stance phase and also increased as gait speed declined. The CCI of the VLGL in-creased with age. The multiple regression analysis adjusted for age and gait speed revealed a relationship between jerks and CCI. The CCI of the VLGL is most closely related to lower-leg jerks, which affect the gait of the elderly. Full article

(This article belongs to the Section Wearables)

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10 pages, 1219 KiB

Open AccessArticle

Using an Electronic Goniometer to Assess the Influence of Single-Application Kinesiology Taping on Unstable Shoulder Proprioception and Function

by Ewa Bręborowicz, Izabela Olczak, Przemysław Lubiatowski, Piotr Ogrodowicz, Marta Ślęzak, Maciej Bręborowicz and Leszek Romanowski

Sensors 2025, 25(7), 2326; https://doi.org/10.3390/s25072326 - 6 Apr 2025

Viewed by 322

Abstract

Background: Glenohumeral joint instability is associated with a proprioception deficit. Joint position sense can be improved through targeted exercises and kinesiology taping (KT). While previous studies have examined the effects of KT on proprioception, most have focused on the knee joint, with limited [...] Read more.

Background: Glenohumeral joint instability is associated with a proprioception deficit. Joint position sense can be improved through targeted exercises and kinesiology taping (KT). While previous studies have examined the effects of KT on proprioception, most have focused on the knee joint, with limited research on unstable shoulder joints. Most studies have used commonly available equipment (e.g., the Biodex system). An electronic goniometer, the “Propriometer”, is a useful tool for assessing proprioception in shoulder joint instability; however, its application in evaluating the effects of KT on shoulder proprioception remains unexplored. This study aimed to (1) assess the usability of the Propriometer for evaluating the effects of KT on unstable shoulders and (2) determine the impact of a single KT application on joint position sense and limb function in individuals with anterior, post-traumatic shoulder joint instability. Methods and Materials: The study included 30 individuals with anterior, unilateral, post-traumatic shoulder joint instability (8 women, 22 men, mean age 26 years). A control group consisted of 35 healthy volunteers (9 women, 26 men, mean age 24 years). Proprioception assessment (active joint position reproduction evaluation) was performed in both groups using the Propriometer, which measures joint position in real time with an accuracy of 0.1° across all axes. The study methodology was validated and used to examine shoulder proprioception. The current study focused on assessing the effects of KT, which had not been previously tested with this device Assessments were conducted before KT application and three days’ post-application. Additionally, patients completed the Western Ontario Shoulder Instability Index (WOSI) self-assessment questionnaire before and three days after the therapy. Results: Results of the mean joint position reproduction error indicate a proprioceptive deficit in patients with shoulder joint instability. However, the analyzed KT application did not show a significant change in the magnitude of the active joint position reproduction error. Conversely, KT therapy significantly improved patients’ subjective assessment of shoulder function and stability as measured by the WOSI. Conclusions: The Propriometer goniometer and testing methodology are effective tools for assessing the impact of KT on proprioception in shoulder instability. While KT application did not significantly influence shoulder proprioception, it did improve patients’ perceived joint stability and function. Full article

(This article belongs to the Special Issue Human Movement Monitoring Using Wearable Sensor Technology—2nd Edition)

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19 pages, 10041 KiB

Open AccessArticle

Intelligent Detection and Recognition of Marine Plankton by Digital Holography and Deep Learning

by Xianfeng Xu, Weilong Luo, Zhanhong Ren and Xinjiu Song

Sensors 2025, 25(7), 2325; https://doi.org/10.3390/s25072325 - 6 Apr 2025

Viewed by 305

Abstract

The detection, observation, recognition, and statistics of marine plankton are the basis of marine ecological research. In recent years, digital holography has been widely applied to plankton detection and recognition. However, the recording and reconstruction of digital holography require a strictly controlled laboratory [...] Read more.

The detection, observation, recognition, and statistics of marine plankton are the basis of marine ecological research. In recent years, digital holography has been widely applied to plankton detection and recognition. However, the recording and reconstruction of digital holography require a strictly controlled laboratory environment and time-consuming iterative computation, respectively, which impede its application in marine plankton imaging. In this paper, an intelligent method designed with digital holography and deep learning algorithms is proposed to detect and recognize marine plankton (IDRMP). An accurate integrated A-Unet network is established under the principle of deep learning and trained by digital holograms recorded with publicly available plankton datasets. This method can complete the work of reconstructing and recognizing a variety of plankton organisms stably and efficiently by a single hologram, and a system interface of YOLOv5 that can realize the task of the end-to-end detection of plankton by a single frame is provided. The structural similarities of the images reconstructed by IDRMP are all higher than 0.97, and the average accuracy of the detection of four plankton species, namely, Appendicularian, Chaetognath, Echinoderm and Hydromedusae,, reaches 91.0% after using YOLOv5. In optical experiments, typical marine plankton collected from Weifang, China, are employed as samples. For randomly selected samples of Copepods, Tunicates and Polychaetes, the results are ideal and acceptable, and a batch detection function is developed for the learning of the system. Our test and experiment results demonstrate that this method is efficient and accurate for the detection and recognition of numerous plankton within a certain volume of space after they are recorded by digital holography. Full article

(This article belongs to the Special Issue Digital Holography in Optics: Techniques and Applications)

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15 pages, 4195 KiB

Open AccessArticle

Comparative Analysis of Spectral Broadening Techniques for Optical Temperature Sensing in Yttrium Fluoride (YF₃) Doped with Neodymium

by Ruan P. R. Moura, Bárbara M. Cruz, Tatiane S. Lilge, Adriano B. Andrade, Mario E. G. Valerio, Zélia S. Macedo, José J. Rodrigues, Jr. and Márcio A. R. C. Alencar

Sensors 2025, 25(7), 2324; https://doi.org/10.3390/s25072324 - 6 Apr 2025

Viewed by 258

Abstract

In this work, YF₃:Nd³⁺ powder was synthesized using the microwave-assisted hydrothermal method at a low temperature (140 °C) and short synthesis time (1 h). The photoluminescence and optical temperature sensing properties of YF₃:Nd³⁺ were examined using 800 [...] Read more.

In this work, YF₃:Nd³⁺ powder was synthesized using the microwave-assisted hydrothermal method at a low temperature (140 °C) and short synthesis time (1 h). The photoluminescence and optical temperature sensing properties of YF₃:Nd³⁺ were examined using 800 nm laser excitation, focusing on the emission corresponding to the ⁴F_3/2 → ⁴I_9/2 transition of Nd³⁺. The performance of YF₃:Nd³⁺ as an optical temperature sensor was evaluated using the full width at half maximum (FWHM), band broadening at 30% of maximum intensity (Δλ_30%), and valley-to-peak intensity ratio (VPR) techniques. All techniques demonstrated good repeatability and reproducibility. The best results were obtained using the VPR (V1/P1) method, which exhibited the highest relative sensitivity and the lowest temperature uncertainty, with values of 0.69 ± 0.02% K⁻¹ and 0.46 ± 0.09 K at 303 K, respectively. YF₃:Nd³⁺ shows promise as an optical temperature sensor operating entirely within the first biological window. Full article

(This article belongs to the Special Issue Recent Advances and Tendencies in Highly Sensitive Fiber Optic Sensors)

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19 pages, 8003 KiB

Open AccessArticle

Dynamic Coherent Diffractive Imaging with Modulus Enforced Probe and Low Spatial Frequency Constraints

by Yingling Zhang, Zijian Xu, Bo Zhao, Xiangzhi Zhang, Ruoru Li, Sheng Chen and Shuhan Wu

Sensors 2025, 25(7), 2323; https://doi.org/10.3390/s25072323 - 6 Apr 2025

Viewed by 320

Abstract

Dynamic behavior is prevalent in biological and condensed matter systems at the nano- and mesoscopic scales. Typically, we capture images as “snapshots” to demonstrate the evolution of a system, and coherent X-ray diffraction imaging (CDI), as a lensless imaging technique, provides a nanoscale [...] Read more.

Dynamic behavior is prevalent in biological and condensed matter systems at the nano- and mesoscopic scales. Typically, we capture images as “snapshots” to demonstrate the evolution of a system, and coherent X-ray diffraction imaging (CDI), as a lensless imaging technique, provides a nanoscale resolution, allowing us to clearly observe these microscopic phenomena. This paper presents a new dynamic CDI method based on zone-plate optics aiming to overcome the limitations of existing techniques in imaging fast dynamic processes by integrating the spatio-temporal dual constraint with a probe constraint. In this method, the modulus-enforced probe constraint and the temporal correlation of the dynamic sample low-frequency information are exploited and combined with an empty static region constraint in the dynamic sample. Using this method, we achieved a temporal resolution of 20 Hz and a spatial resolution of 13.2 nm, which were verified by visualized experimental results. Further comparisons showed that the reconstructed images were consistent with the ptychography reconstruction results, confirming the accuracy and feasibility of the method. This work is expected to provide a new tool for materials science and mesoscopic life sciences, promoting a deeper understanding of complex dynamic processes. Full article

(This article belongs to the Special Issue Advanced Sensing and Imaging Techniques for Structural Dynamics, Health Monitoring, and Integrity Assessment)

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24 pages, 4478 KiB

Open AccessArticle

DNA-Inspired Lightweight Cryptographic Algorithm for Secure and Efficient Image Encryption

by Mahmoud A. Abdelaal, Abdellatif I. Moustafa, H. Kasban, H. Saleh, Hanaa A. Abdallah and Mohamed Yasin I. Afifi

Sensors 2025, 25(7), 2322; https://doi.org/10.3390/s25072322 - 6 Apr 2025

Viewed by 404

Abstract

As IoT devices proliferate in critical areas like healthcare or nuclear safety, it necessitates the provision of cryptographic solutions with security and computational efficiency. Very well-established encryption mechanisms such as AES, RC4, and XOR cannot strike a balance between speed, energy consumption, and [...] Read more.

As IoT devices proliferate in critical areas like healthcare or nuclear safety, it necessitates the provision of cryptographic solutions with security and computational efficiency. Very well-established encryption mechanisms such as AES, RC4, and XOR cannot strike a balance between speed, energy consumption, and robustness. Moreover, most DNA-based solutions are not cognizant of the hardware limitations of IoT platforms such as Arduino R3. This paper proposes an improved encryption technique incorporating stochastic DNA-inspired processing with optical computing in a resource-constrained environment. The proposed algorithm employs stochastic pixel selection with DNA-encoded key generation and is further enhanced by parallel optical processing to overcome the trade-offs of conventional techniques during implementation. Experimental trials performed on Arduino R3 established superior performance in terms of an encryption time of 3956 μs and memory usage of 773 bytes, placing it ahead of AES and XOR-based approaches. Apart from the tests performed, security analyses have revealed a strong resistant position upon differential cryptanalysis (DP = 0.051) and linear cryptanalysis (LP = 0.045), with an almost-ideal key entropy (7.99 bits/key) and minimal autocorrelation (0.018). This research offers practical applications in real-time medical monitoring and nuclear radiation detection systems by closing the existing gap in hardware-aware DNA cryptography. Full article

(This article belongs to the Section Sensing and Imaging)

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28 pages, 7048 KiB

Open AccessArticle

ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking

by Chih-Yu Hsu, Chih-Yin Chang, Yin-Chi Chen, Jasper Wu and Shuo-Tsung Chen

Sensors 2025, 25(7), 2321; https://doi.org/10.3390/s25072321 - 5 Apr 2025

Viewed by 267

Abstract

Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by the heart. To evaluate sensor performance under realistic conditions, diverse ECG signals with embedded watermarks are generated, enabling an assessment of how [...] Read more.

Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by the heart. To evaluate sensor performance under realistic conditions, diverse ECG signals with embedded watermarks are generated, enabling an assessment of how effectively the sensor and its signal-conditioning circuitry handle these modified signals. A Variational Autoencoder (VAE) framework is employed to generate the watermarked ECG signals, addressing critical concerns in the digital era, such as data security, authenticity, and copyright protection. Three watermarking strategies are examined in this study: embedding watermarks in the mean (μ) of the VAE’s latent space, embedding them through the latent variable (z), and using post-reconstruction watermarking in the frequency domain. Experimental results demonstrate that watermarking applied through the mean (μ) and in the frequency domain achieves a low Mean Squared Error (MSE) while maintaining stable signal fidelity across varying watermark strengths (α), latent space dimensions, and noise levels. These findings indicate that the mean (μ) and frequency domain methods offer robust performance and are minimally affected by changes in these parameters, making them particularly suitable for preserving ECG signal quality. By contrasting these methods, this study provides insights into selecting the most appropriate watermarking technique for ECG sensor applications. Incorporating watermarking into sensor design not only strengthens data security and authenticity but also supports reliable signal acquisition in modern healthcare environments. Overall, the results underscore the effectiveness of combining VAEs with watermarking strategies to produce high-fidelity, resilient ECG signals for both sensor performance evaluation and the protection of digital content. Full article

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

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20 pages, 8423 KiB

Open AccessArticle

Design and Implementation of a Low-Power Biopotential Amplifier in 28 nm CMOS Technology with a Compact Die-Area of 2500 μm² and an Ultra-High Input Impedance

by Esmaeil Ranjbar Koleibi, William Lemaire, Konin Koua, Maher Benhouria, Reza Bostani, Mahziar Serri Mazandarani, Luis-Philip Gauthier, Marwan Besrour, Jérémy Ménard, Mahdi Majdoub, Benoit Gosselin, Sébastien Roy and Réjean Fontaine

Sensors 2025, 25(7), 2320; https://doi.org/10.3390/s25072320 (registering DOI) - 5 Apr 2025

Viewed by 274

Abstract

Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors [...] Read more.

Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a −3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μ

V_{rms}

, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μ

m^{2}

, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency. Full article

(This article belongs to the Special Issue (Bio)sensors for Physiological Monitoring)

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22 pages, 9081 KiB

Open AccessArticle

Microstrip Patch Sensor for Characterizing Saline Solution Based on Complimentary Split-Ring Resonators (SC-SRRs)

by Hussein Jasim, Sadiq Ahmed, Iulia Andreea Mocanu and Amer Abbood Al-Behadili

Sensors 2025, 25(7), 2319; https://doi.org/10.3390/s25072319 - 5 Apr 2025

Viewed by 296

Abstract

This article presents a novel microstrip patch sensor featuring four rectangular rings represented by single complementary split-ring resonance (SC-SRR) to calculate the complex permittivity of saline solutions within the range of 0 ppt to 100 ppt. This sensor operates via the turbulence technique, [...] Read more.

This article presents a novel microstrip patch sensor featuring four rectangular rings represented by single complementary split-ring resonance (SC-SRR) to calculate the complex permittivity of saline solutions within the range of 0 ppt to 100 ppt. This sensor operates via the turbulence technique, utilizing its resonant properties as indicators to find the parameters of the liquid under test (LUT), which arise due to the variations in the salt concentration altering the complex permittivity. This alteration influences the resonant frequency (f_r), reflection coefficient (S₁₁), and quality factor (Q). The sensor was designed by using a high-frequency structure simulator (HFSS) and by using an FR-4 substrate and a Teflon box with a height of 1.4 mm and 13.7 mm, respectively. The values of S₁₁ at resonance frequency were −34.48 dB, and 2.1328 GHz, respectively. A computer numerical control (CNC) machine was used to fabricate the sensor and Teflon box, and the Teflon box was situated above the four rings to create a strong interaction between the induced electric field and the LUT, thereby achieving high sensitivity in a non-contacting and non-destructive manner. The measurement and simulation results were consistent and aligned with those of Klien and Meissner (in comparison to the theoretical values derived from the single and double Debye models). We derived numerical equations for the conductivity (S/m), dielectric constant permittivity, and concentrations (ppt) using curve fitting origin software, and the results are in good agreement. Due to its performance, we expect that the proposed sensor could be used in agricultural applications to identify freshwater and in medical applications to detect the concentration of salt in saliva or blood and to identify diseases, in addition to many other applications involving mixed liquids. Full article

(This article belongs to the Section Physical Sensors)

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36 pages, 18532 KiB

Open AccessArticle

A Heavy Metal Ion Water Quality Detection Model Based on Spectral Analysis: New Methods for Enhancing Detection Speed and Visible Spectral Denoising

by Bingyang Sun, Shunsheng Yang and Xu Cheng

Sensors 2025, 25(7), 2318; https://doi.org/10.3390/s25072318 - 5 Apr 2025

Viewed by 218

Abstract

This paper analyzes the current state of water quality detection equipment and, based on the demand for portable water quality detection systems that are on-site, rapid, accurate, cost-effective, and capable of multi-parameter measurements using spectral analysis, represents the future development direction of water [...] Read more.

This paper analyzes the current state of water quality detection equipment and, based on the demand for portable water quality detection systems that are on-site, rapid, accurate, cost-effective, and capable of multi-parameter measurements using spectral analysis, represents the future development direction of water quality detection. By focusing on indicators of heavy metal ion water pollution, this study aims to achieve the “rapid and accurate detection of water quality using spectral analysis” and emphasizes key technologies such as “visible absorption spectroscopy in photoelectric detection technology and spectral analysis”, “spectral denoising methods”, and “Convolutional Neural Network (CNN) modeling and deployment”. A novel combined denoising method integrating Ensemble Empirical Mode Decomposition (EEMD) and Singular Value Decomposition (SVD) is developed and applied for the first time in spectral water quality detection to improve accuracy. The system uses a ZYNQ-based spectral analysis platform to detect heavy metal ion concentrations, enhancing detection speed. Comparative tests with copper ion standard solutions against Chinese national standards show good accuracy and reproducibility. The developed EEMD-SVD method demonstrates superior denoising effectiveness in processing actual spectral data within the water quality detection system. Full article

(This article belongs to the Section Environmental Sensing)

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18 pages, 5582 KiB

Open AccessArticle

Extending Sensing Range by Physics Constraints in Multiband-Multiline Absorption Spectroscopy for Flame Measurement

by Tengfei Jiao, Sheng Kou, Liuhao Ma, Kin-Pang Cheong and Wei Ren

Sensors 2025, 25(7), 2317; https://doi.org/10.3390/s25072317 - 5 Apr 2025

Viewed by 197

Abstract

The present numerical study proposes a technique to extend the sensing range of tunable diode laser absorption spectroscopy (TDLAS) for flame measurement by involving physics constraints on both gas condition and spectroscopic parameters in the interpretation of spectra from multiple bands. A total [...] Read more.

The present numerical study proposes a technique to extend the sensing range of tunable diode laser absorption spectroscopy (TDLAS) for flame measurement by involving physics constraints on both gas condition and spectroscopic parameters in the interpretation of spectra from multiple bands. A total of 24 major spectral lines for 2 spectral segments 4029–4031 cm⁻¹ and 7185–7186 cm⁻¹ are determined by specially designed detection function and contribution filtering. Numerical tests on uniform and complicated combustion fields prove the high accuracy, strong robustness to noise, wide sensing range, and good compatibility with tomography. The present study provides a strong technique for future complex combustion detection with advanced laser sources of broad spectrum. Full article

(This article belongs to the Special Issue Advances in Optical Sensing, Instrumentation and Systems: 2nd Edition)

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19 pages, 5758 KiB

Open AccessArticle

Detection of Pesticide Residues Using Three-Dimensional SERS Substrate Based on CNTs/Ag/AgNWs/SiO₂

by Jianjun Ding, Niansong Liu, Ganglin Wang, Naiyu Guo and Chao Sun

Sensors 2025, 25(7), 2316; https://doi.org/10.3390/s25072316 - 5 Apr 2025

Viewed by 241

Abstract

In response to the shortcomings of traditional surface-enhanced Raman spectroscopy (SERS) substrates, such as short shelf life, poor uniformity, and low selectivity, this study innovatively proposed a three-dimensional composite substrate of CNTs/Ag/AgNWs/SiO₂. This substrate demonstrates excellent SERS enhancement effects, with a [...] Read more.

In response to the shortcomings of traditional surface-enhanced Raman spectroscopy (SERS) substrates, such as short shelf life, poor uniformity, and low selectivity, this study innovatively proposed a three-dimensional composite substrate of CNTs/Ag/AgNWs/SiO₂. This substrate demonstrates excellent SERS enhancement effects, with a detection limit of 10⁻¹² mol/L for the probe molecule Rhodamine 6G (R6G) and an enhancement factor (EF) of 8.947 × 10⁸. Further experiments confirmed the substrate’s superior uniformity and stability. The enhancement mechanism was investigated using both experimental methods and the Finite Difference Time Domain (FDTD) approach. When commonly used pesticide thiram was used as the target analyte, the detection limit of the substrate reached 0.1 mg/L, which is significantly lower than the pesticide residue standards of China and the European Union. Additionally, the genetic algorithm (GA)-optimized Back Propagation (BP) neural network was introduced for the quantitative analysis of thiram concentrations. The experimental results indicated that the GA-BP algorithm achieved the training prediction accuracy of 92.5% for thiram, demonstrating good network performance. This method shows good selectivity and has broad application prospects in the detection of toxic chemicals, environmental pollutants, and food additives. Full article

(This article belongs to the Special Issue Advances in Nanomaterial-Based Electrochemical and Optical Biosensors)

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38 pages, 18311 KiB

Open AccessArticle

Design of an Interactive Exercise and Leisure System for the Elderly Integrating Artificial Intelligence and Motion-Sensing Technology

by Chao-Ming Wang, Cheng-Hao Shao and Yu-Ching Lin

Sensors 2025, 25(7), 2315; https://doi.org/10.3390/s25072315 (registering DOI) - 5 Apr 2025

Viewed by 225

Abstract

In response to the global trend of population aging, the issue of providing elderly individuals suitable leisure and entertainment has become increasingly important. In this study, it aims to utilize artificial intelligence (AI) technology to offer the elderly with a healthy and enjoyable [...] Read more.

In response to the global trend of population aging, the issue of providing elderly individuals suitable leisure and entertainment has become increasingly important. In this study, it aims to utilize artificial intelligence (AI) technology to offer the elderly with a healthy and enjoyable exercise and leisure experience. A human–machine interactive system is designed using computer vision, a subfield of AI, to promote positive physical adaptation for the elderly. The relevant literature on the needs of the elderly, technology, exercise, leisure, and AI techniques is reviewed. Case studies of interactive devices for exercise and leisure for the elderly, both domestically and internationally, are summarized to establish the prototype concept for system design. The proposed interactive exercise and leisure system is developed by integrating motion-sensing interfaces and real-time object detection using the YOLO algorithm. The system’s effectiveness is evaluated through questionnaire surveys and participant interviews, with the collected survey data analyzed statistically using IBM SPSS 26 and AMOS 23. Findings indicate that (1) AI technology provides new and enjoyable interactive experiences for the elderly’s exercise and leisure; (2) positive impacts are made on the elderly’s health and well-being; and (3) the system’s acceptance and attractiveness increase when elements related to personal experiences are incorporated into the system. Full article

(This article belongs to the Special Issue AI-Based Computer Vision Sensors & Systems)

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23 pages, 1713 KiB

Open AccessArticle

Sensing the Inside Out: An Embodied Perspective on Digital Animation Through Motion Capture and Wearables

by Katerina El-Raheb, Lori Kougioumtzian, Vilelmini Kalampratsidou, Anastasios Theodoropoulos, Panagiotis Kyriakoulakos and Spyros Vosinakis

Sensors 2025, 25(7), 2314; https://doi.org/10.3390/s25072314 (registering DOI) - 5 Apr 2025

Viewed by 318

Abstract

Over the last few decades, digital technology has played an important role in innovating the pipeline, techniques, and approaches for creating animation. Sensors for motion capture not only enabled the incorporation of physical human movement in all its precision and expressivity but also [...] Read more.

Over the last few decades, digital technology has played an important role in innovating the pipeline, techniques, and approaches for creating animation. Sensors for motion capture not only enabled the incorporation of physical human movement in all its precision and expressivity but also created a field of collaboration between the digital and performing arts. Moreover, it has challenged the boundaries of cinematography, animation, and live action. In addition, wearable technology can capture biosignals such as heart rate and galvanic skin response that act as indicators of the emotional state of the performer. Such metrics can be used as metaphors to visualise (or sonify) the internal reactions and bodily sensations of the designed animated character. In this work, we propose a framework for incorporating the role of the performer in digital character animation as a real-time designer of the character’s affect, expression, and personality. Within this embodied perspective, sensors that capture the performer’s movement and biosignals are viewed as the means to build the nonverbal personality traits, cues, and signals of the animated character and their narrative. To do so, following a review of the state of the art and relevant literature, we provide a detailed description of what constitute nonverbal personality traits and expression in animation, social psychology, and the performing arts, and we propose a workflow of methodological and technological toolstowardsan embodied perspective for digital animation. Full article

(This article belongs to the Special Issue Sensing Technology and Wearables for Physical Activity)

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11 pages, 1267 KiB

Open AccessArticle

A Practical Cardiovascular Health Assessment for Manual Wheelchair Users During the 6-Minute Push Test

by Maja Goršič, Madisyn R. Adelman, Grace McClatchey and Jacob R. Rammer

Sensors 2025, 25(7), 2313; https://doi.org/10.3390/s25072313 - 5 Apr 2025

Viewed by 244

Abstract

Traditional VO_2max testing methods are often impractical for manual wheelchair users, as they rely on lower-body exercise protocols, require specialized equipment, and trained personnel. The 6-Minute Push Test (6MPT) is a widely used cardiovascular assessment that may provide a feasible alternative for [...] Read more.

Traditional VO_2max testing methods are often impractical for manual wheelchair users, as they rely on lower-body exercise protocols, require specialized equipment, and trained personnel. The 6-Minute Push Test (6MPT) is a widely used cardiovascular assessment that may provide a feasible alternative for estimating aerobic capacity in this population. This study aimed to develop a predictive model for VO_2max using physiological variables recorded during the 6MPT. Twenty-eight participants (14 novice and 14 expert manual wheelchair users) completed the test while wearing a VO₂ mask and heart rate monitor. Spearman correlation analysis showed that distance covered during the 6MPT significantly correlated with VO_2max (r = 0.685, p < 0.001). A stepwise linear regression identified two predictive models: one using distance alone (R² = 0.416, p < 0.001) and another incorporating both distance and maximum heart rate (R² = 0.561, p < 0.001). These models offer practical estimations of VO_2max, eliminating separate protocols. Our findings suggest that the 6MPT can serve as a simple, cost-effective alternative to laboratory-based VO₂ testing, facilitating routine cardiovascular fitness assessments for manual wheelchair users in clinical and community settings. Future research should focus on validating these models in a larger, more diverse cohort to enhance their generalizability. Full article

(This article belongs to the Special Issue Wearable Sensors for Rehabilitation and Remote Health Monitoring)

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15 pages, 3240 KiB

Open AccessArticle

Optimized Magnetization Distribution in Body-Centered Cubic Lattice-Structured Magnetoelastomer for High-Performance 3D Force–Tactile Sensors

by Hongfei Hou, Ziyin Xiang, Chaonan Zhi, Haodong Hu, Xingyu Zhu, Baoru Bian, Yuanzhao Wu, Yiwei Liu, Xiaohui Yi, Jie Shang and Run-Wei Li

Sensors 2025, 25(7), 2312; https://doi.org/10.3390/s25072312 - 5 Apr 2025

Viewed by 241

Abstract

Flexible magnetic tactile sensors hold transformative potential in robotics and human–computer interactions by enabling precise force detection. However, existing sensors face challenges in balancing sensitivity, detection range, and structural adaptability for sensing force. This study proposed a pre-compressed magnetization method to address these [...] Read more.

Flexible magnetic tactile sensors hold transformative potential in robotics and human–computer interactions by enabling precise force detection. However, existing sensors face challenges in balancing sensitivity, detection range, and structural adaptability for sensing force. This study proposed a pre-compressed magnetization method to address these limitations by amplifying the magnetoelastic effect through optimized magnetization direction distribution of the elastomer. A body-centered cubic lattice-structured magnetoelastomer featuring regular deformation under compression was fabricated via digital light processing (DLP) to validate this method. Finite element simulations and experimental analyses revealed that magnetizing the material under 60% compression strain optimized magnetization direction distribution, enhancing force–magnetic coupling. Integrating the magnetic elastomer with a hall sensor, the prepared tactile sensor demonstrated a low detection limit (1 mN), wide detection range (0.001–10 N), rapid response/recovery times (40 ms/50 ms), and durability (>1500 cycles). By using machine learning, the sensor enabled accurate 3D force prediction. Full article

(This article belongs to the Special Issue Flexible Pressure/Force Sensors and Their Applications)

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