Sensors最新文献

{"title":"Hardware Acceleration-Based Privacy-Aware Authentication Scheme for Internet of Vehicles Using Physical Unclonable Function.","authors":"Ujunwa Madububa Mbachu, Rabeea Fatima, Ahmed Sherif, Elbert Dockery, Mohamed Mahmoud, Maazen Alsabaan, Kasem Khalil","doi":"10.3390/s25051629","DOIUrl":"10.3390/s25051629","url":null,"abstract":"<p><p>Due to technological advancement, the advent of smart cities has facilitated the deployment of advanced urban management systems. This integration has been made possible through the Internet of Vehicles (IoV), a foundational technology. By connecting smart cities with vehicles, the IoV enhances the safety and efficiency of transportation. This interconnected system facilitates wireless communication among vehicles, enabling the exchange of crucial traffic information. However, this significant technological advancement also raises concerns regarding privacy for individual users. This paper presents an innovative privacy-preserving authentication scheme focusing on IoV using physical unclonable functions (PUFs). This scheme employs the k-nearest neighbor (KNN) encryption technique, which possesses a multi-multi searching property. The main objective of this scheme is to authenticate autonomous vehicles (AVs) within the IoV framework. An innovative PUF design is applied to generate random keys for our authentication scheme to enhance security. This two-layer security approach protects against various cyber-attacks, including fraudulent identities, man-in-the-middle attacks, and unauthorized access to individual user information. Due to the substantial amount of information that needs to be processed for authentication purposes, our scheme is implemented using hardware acceleration on an Nexys A7-100T FPGA board. Our analysis of privacy and security illustrates the effective accomplishment of specified design goals. Furthermore, the performance analysis reveals that our approach imposes a minimal communication and computational burden and optimally utilizes hardware resources to accomplish design objectives. The results show that the proposed authentication scheme exhibits a non-linear increase in encryption time with a growing AV ID size, starting at 5μs for 100 bits and rising to 39 μs for 800 bits. Also, the result demonstrates a more gradual, linear increase in the search time with a growing AV ID size, starting at less than 1 μs for 100 bits and rising to less than 8 μs for 800 bits. Additionally, for hardware utilization, our scheme uses only 25% from DSP slides and IO pins, 22.2% from BRAM, 5.6% from flip-flops, and 24.3% from LUTs.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Assessment of Upper Extremity Function with the Modified Mallet Score Using Single-Plane Smartphone Videos.","authors":"Cancan Su, Lianne Brandt, Guangwen Sun, Kaitlynn Sampel, Edward D Lemaire, Kevin Cheung, Albert Tu, Natalie Baddour","doi":"10.3390/s25051619","DOIUrl":"10.3390/s25051619","url":null,"abstract":"<p><p>The Modified Mallet Score (MMS) is widely used to assess upper limb function but requires evaluation by experienced clinicians. This study automated MMS assessments using smartphone videos, artificial intelligence (AI), and new algorithms. A total of 125 videos covering all MMS grades were recorded from four neurotypical participants. For all recordings, an expert physician provided manual scores as the ground truth. The OpenPose BODY25 model extracted body keypoint data, which were used to calculate joint angles for an automated scoring algorithm. The algorithm's scores were compared to the ground truth and expert manual scoring. High accuracy was achieved for the global abduction, hand-to-neck, hand-on-spine, and hand-to-mouth movements, with Pearson correlation coefficients (PCCs) > 0.9 and a low root mean square error (RMSE). Although slightly less accurate for global external rotation, the algorithm still showed strong agreement. This study demonstrates the potential of using AI and smartphone videos for reliable, remote upper limb assessments.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Guide to Expression of Uncertainty in Measurement and Monte Carlo Method for Evaluating Gauge Factor Calibration Test Uncertainty of High-Temperature Wire Strain Gauge.","authors":"Yazhi Zhao, Fengling Zhang, Yanting Ai, Jing Tian, Zhi Wang","doi":"10.3390/s25051633","DOIUrl":"10.3390/s25051633","url":null,"abstract":"<p><p>High-temperature strain gauges are widely used in the strain monitoring of the hot-end components of aero-engines. In the application of strain gauges, the calibration of the gauge factor (GF) is the most critical link. Evaluating the uncertainty of GF is of great significance to the accuracy analysis of measurement results. Firstly, the calibration test of the GF of the Pt-W high-temperature strain gauge was carried out in the range of 25 °C to 900 °C. The real test data required for the uncertainty evaluation were obtained. Secondly, the guide to the expression of uncertainty in measurement (GUM) and the Monte Carlo method (MCM) were used to evaluate the uncertainty of GF calibration test. The evaluation results of GUM and MCM were compared. Finally, the concept of the weight coefficient W was proposed to quantitatively analyze the influence of each input on the uncertainty of the output GF. The main uncertainty source was found, which had important engineering practical significance. The results show that the mean value of GF decreases with the increase in temperature nonlinearly. At 25 °C, GF is 3.29, and at 900 °C, GF decreases to 1.6. Through comparison and verification, the uncertainty interval given by MCM is closer to the real situation. MCM is superior to GUM, which only uses prior information for uncertainty assessment. MCM is more suitable for evaluating GF uncertainty. Among multiple uncertain sources, the weight coefficient W can effectively analyze Δε as the main uncertain source.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landsat Time Series Reconstruction Using a Closed-Form Continuous Neural Network in the Canadian Prairies Region.","authors":"Masoud Babadi Ataabadi, Darren Pouliot, Dongmei Chen, Temitope Seun Oluwadare","doi":"10.3390/s25051622","DOIUrl":"10.3390/s25051622","url":null,"abstract":"<p><p>The Landsat archive stands as one of the most critical datasets for studying landscape change, offering over 50 years of imagery. This invaluable historical record facilitates the monitoring of land cover and land use changes, helping to detect trends in and the dynamics of the Earth's system. However, the relatively low temporal frequency and irregular clear-sky observations of Landsat data pose significant challenges for multi-temporal analysis. To address these challenges, this research explores the application of a closed-form continuous-depth neural network (CFC) integrated within a recurrent neural network (RNN) called CFC-mmRNN for reconstructing historical Landsat time series in the Canadian Prairies region from 1985 to present. The CFC method was evaluated against the continuous change detection (CCD) method, widely used for Landsat time series reconstruction and change detection. The findings indicate that the CFC method significantly outperforms CCD across all spectral bands, achieving higher accuracy with improvements ranging from 33% to 42% and providing more accurate dense time series reconstructions. The CFC approach excels in handling the irregular and sparse time series characteristic of Landsat data, offering improvements in capturing complex temporal patterns. This study underscores the potential of leveraging advanced deep learning techniques like CFC to enhance the quality of reconstructed satellite imagery, thus supporting a wide range of remote sensing (RS) applications. Furthermore, this work opens up avenues for further optimization and application of CFC in higher-density time series datasets such as MODIS and Sentinel-2, paving the way for improved environmental monitoring and forecasting.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Angle of Arrival for the Beam Detection Method of Spatially Distributed Sensor Array.","authors":"Shan Zhao, Lei Zhu, Shiyang Shen, Heng Du, Xiangyu Wang, Lei Chen, Xiaodong Wang","doi":"10.3390/s25051625","DOIUrl":"10.3390/s25051625","url":null,"abstract":"<p><p>Laser space networks are an important development direction for inter-satellite communication. Detecting the angle of arrival (AOA) of multiple satellites in a wide field of view (FOV) is the key to realize inter-satellite laser communication networking. The traditional AOA detection method based on the lens system has a limited FOV. In this paper, we demonstrate a system that uses a spatially distributed sensor array to detect the AOA in a wide FOV. The basic concept is to detect AOA using the signal strength of each sensor at different spatial angles. An AOA detection model was developed, and the relationship of key structural parameters of the spatially distributed sensor array on the FOV and angular resolution was analyzed. Furthermore, a spatially distributed sensor array prototype consisting of 5 InGaAs PIN photodiodes distributed on a 3D-printed structure with an inclination angle of 30° was developed. In order to improve the angle calculation accuracy, a multi-sensor data fusion algorithm is proposed. The experimental results show that the prototype's maximum FOV is 110°. The root mean square error (RMSE) for azimuth is 0.6° within a 60° FOV, whereas the RMSE for elevation is 0.67°. The RMSE increases to 1.1° for azimuth and 1.7° for elevation when the FOV expands to 110°. The designed spatially distributed sensor array has the advantages of a wide FOV and low size, weight, and power (SWaP), presenting great potential for multi-satellite laser communication applications.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic Enhancement of Chemiresistive NO₂ Gas Sensors Using Nitrogen-Doped Reduced Graphene Oxide (N-rGO) Decorated with Nickel Oxide (NiO) Nanoparticles: Achieving sub-ppb Detection Limit.","authors":"Chiheb Walleni, Mounir Ben Ali, Mohamed Faouzi Ncib, Eduard Llobet","doi":"10.3390/s25051631","DOIUrl":"10.3390/s25051631","url":null,"abstract":"<p><p>Detecting low nitrogen dioxide concentrations (NO₂) is crucial for environmental monitoring. In this paper, we report the synergistic effect of decorating nitrogen-doped reduced graphene oxide (N-rGO) with nickel oxide (NiO) nanoparticles for developing highly selective and sensitive chemiresistive NO₂ gas sensors. The N-rGO/NiO sensor was synthesized straightforwardly, ensuring uniform decoration of NiO nanoparticles on the N-rGO surface. Comprehensive characterization using SEM, TEM, XRD, and Raman spectroscopy confirmed the successful integration of NiO nanoparticles with N-rGO and revealed key structural and morphological features contributing to its enhanced sensing performance. As a result, the NiO/N-rGO nanohybrids demonstrate a significantly enhanced response five orders of magnitude higher than that of N-rGO toward low NO₂ concentrations (<1 ppm) at 100 °C. Moreover, the present device has an outstanding performance, high sensitivity, and low limit of detection (<1 ppb). The findings pave the way for integrating these sensors into advanced applications, including environmental monitoring and IoT-enabled air quality management systems.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dielectric Properties of Transformer Resin Under Varying Conditions: Impact on Instrument Transformer Stability and Accuracy.","authors":"Simone Vincenzo Suraci, Jizhu Jin, Roberto Tinarelli, Lorenzo Peretto, Davide Fabiani, Alessandro Mingotti","doi":"10.3390/s25051626","DOIUrl":"10.3390/s25051626","url":null,"abstract":"<p><p>The accuracy of instrument transformers (ITs) is vital for the accurate measurement of electrical quantities. However, their performance is influenced by various factors during operation, including environmental conditions such as temperature, pressure, and humidity, as well as other factors like positioning, electromagnetic fields, and geometry. Given that IT accuracy is challenging to verify once installed in the field, it is essential to thoroughly understand its performance beforehand. This paper investigates how variations in resin properties affect IT accuracy. Samples prepared with different curing temperatures were subjected to aging tests, which included exposure to temperature and combined temperature-humidity conditions. Throughout the aging process, the dielectric properties of the samples were measured, and their impact on IT accuracy was evaluated. The results clearly demonstrate that the choice of resin properties is critical to ensure reliable IT performance, as improper selection can lead to significant accuracy deviations.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical Structure Design and Motion Simulation Analysis of a Lower Limb Exoskeleton Rehabilitation Robot Based on Human-Machine Integration.","authors":"Chenglong Zhao, Zhen Liu, Yuefa Ou, Liucun Zhu","doi":"10.3390/s25051611","DOIUrl":"10.3390/s25051611","url":null,"abstract":"<p><p>Population aging is an inevitable trend in contemporary society, and the application of technologies such as human-machine interaction, assistive healthcare, and robotics in daily service sectors continues to increase. The lower limb exoskeleton rehabilitation robot has great potential in areas such as enhancing human physical functions, rehabilitation training, and assisting the elderly and disabled. This paper integrates the structural characteristics of the human lower limb, motion mechanics, and gait features to design a biomimetic exoskeleton structure and proposes a human-machine integrated lower limb exoskeleton rehabilitation robot. Human gait data are collected using the Optitrack optical 3D motion capture system. SolidWorks 3D modeling software Version 2021 is used to create a virtual prototype of the exoskeleton, and kinematic analysis is performed using the standard Denavit-Hartenberg (D-H) parameter method. Kinematic simulations are carried out using the Matlab Robotic Toolbox Version R2018a with the derived D-H parameters. A physical prototype was fabricated and tested to verify the validity of the structural design and gait parameters. A controller based on BP fuzzy neural network PID control is designed to ensure the stability of human walking. By comparing two sets of simulation results, it is shown that the BP fuzzy neural network PID control outperforms the other two control methods in terms of overshoot and settling time. The specific conclusions are as follows: after multiple walking gait tests, the robot's walking process proved to be relatively safe and stable; when using BP fuzzy neural network PID control, there is no significant oscillation, with an overshoot of 5.5% and a settling time of 0.49 s, but the speed was slow, with a walking speed of approximately 0.18 m/s, a stride length of about 32 cm, and a gait cycle duration of approximately 1.8 s. The model proposed in this paper can effectively assist patients in recovering their ability to walk. However, the lower limb exoskeleton rehabilitation robot still faces challenges, such as a slow speed, large size, and heavy weight, which need to be optimized and improved in future research.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Industrial Potential of Formaldehyde Gas Sensor Based on PdPt Bimetallic Loaded SnO₂ Nanoparticles.","authors":"Bing Shen, Tongwei Yuan, Wenshuang Zhang, Xian Tan, Yang Chen, Jiaqiang Xu","doi":"10.3390/s25051627","DOIUrl":"10.3390/s25051627","url":null,"abstract":"<p><p>SnO₂-based semiconductor gas-sensing materials are regarded as some of the most crucial sensing materials, owing to their extremely high electron mobility, high sensitivity, and excellent stability. To bridge the gap between laboratory-scale SnO₂ and its industrial applications, low-cost and high-efficiency requirements must be met. This implies the need for simple synthesis techniques, reduced energy consumption, and satisfactory gas-sensing performances. In this study, we utilized a surfactant-free simple method to modify SnO₂ nanoparticles with PdPt noble metals, ensuring the stable state of the material. Under the synergistic catalytic effect of Pd and Pt, the composite material (1.0 wt%-PdPt-SnO₂) significantly enhanced its response to HCHO. This modification decreased the optimal working temperature to as low as 180 °C to achieve a response value (<i>R</i>_a/<i>R</i>_g = 8.2) and showcased lower operating temperatures, higher sensitivity, and better selectivity to detect 10 ppm of HCHO when compared with pristine SnO₂ or single noble metal-decorated SnO₂ sensors. Stability tests verified that the gas sensor signals based on PdPt-SnO₂ nanoparticles exhibit good reliability. Furthermore, a portable HCHO detector was designed for practical applications, such as in newly purchased cushions, indicating its potential for industrialization beyond the laboratory.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection.","authors":"Qi Zhou, Huicheng Li, Zhiling Cai, Yiwen Zhong, Fenglin Zhong, Xiaoyu Lin, Lijin Wang","doi":"10.3390/s25051635","DOIUrl":"10.3390/s25051635","url":null,"abstract":"<p><p>Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores-95.3% and 89.5%, respectively-surpassing previous benchmarks. Additionally, we tested the model's transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 5","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}