Sensors最新文献

{"title":"Compound Jamming Recognition Under Low JNR Setting Based on a Dual-Branch Residual Fusion Network.","authors":"Wen Lu, Junbao Li, Feng Xie, Huanyu Liu","doi":"10.3390/s25185881","DOIUrl":"10.3390/s25185881","url":null,"abstract":"<p><p>In complex electromagnetic environments, radar systems face increasing challenges from advanced jamming techniques. These challenges mainly stem from the diversity of jamming patterns, the complexity of compound jamming signals, and the difficulty of recognition under low jamming-to-noise ratio conditions. Accurate recognition of such signals is critical for enhancing radar anti-jamming capabilities. However, traditional methods often struggle with diverse and evolving jamming patterns. To address this issue, we propose a novel deep learning-based approach for accurate and robust recognition of complex radar jamming signals. Specifically, the proposed network adopts a dual-branch architecture that concurrently processes time-domain and time-frequency-domain features of jamming signals. It further incorporates a multi-branch convolutional structure to strengthen feature extraction and applies an effective feature fusion strategy to capture subtle patterns. Simulation results demonstrate that the proposed method outperforms six representative baseline approaches in recognition accuracy and noise robustness, particularly under low jamming-to-noise ratio conditions.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177925","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":"Demonstration of the Use of NSGA-II for Optimization of Sparse Acoustic Arrays.","authors":"Christopher E Petrin, Trevor C Wilson, Aaron S Alexander, Brian R Elbing","doi":"10.3390/s25185882","DOIUrl":"10.3390/s25185882","url":null,"abstract":"<p><p>Passive acoustic sensing with arrays has applications in many fields, including atmospheric monitoring of low frequency sounds (i.e., infrasound). Beamforming of array signals to gain spatial information about the signal is common, but the performance is often degraded due to limited resources (e.g., number of sensors, array size). Such sparse arrays create ambiguities due to reduced resolution and spatial aliasing. While previous work has focused on either maximizing array resolution or minimizing spatial aliasing, the current study demonstrates how evolutionary algorithms can be utilized to identify array configurations that optimize for both properties. The non-dominated sorting genetic algorithm II (NSGA-II) was used with the beamwidth and maximum sidelobe level as the fitness functions to iteratively identify a group of optimized synthesized array configurations. This group is termed a Pareto-front and is optimized such that one fitness function cannot be improved without a decrease in the other. These optimized solutions were studied for a single frequency (8 Hz) and a multi-frequency (3 to 20 Hz) signal using either a 36-element or 9-element array with a 60 m aperture. The performance of the synthesized arrays was compared against established array configurations (baseline) with most of the Pareto-front solutions outperforming these baseline configurations. The largest improvements to array performance using the synthesized configurations were with fewer array elements and the multi-frequency signal.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178146","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":"Audio Interference Suppressor in Analog Audio Interface.","authors":"Vladimir Olujić, Siniša Fajt, Vlado Sruk, Miljenko Krhen","doi":"10.3390/s25185868","DOIUrl":"10.3390/s25185868","url":null,"abstract":"<p><p>Audio systems with unbalanced connections are susceptible to interference from ground loops, which manifests as hum and noise. This paper introduces and evaluates a novel passive Audio Interference Suppressor in Analog Audio Interface (AISAAI) designed to mitigate this problem. The AISAAI circuit is inserted between an audio device's rectifier ground and its protective earth terminal, creating an optimized impedance path that reduces interference while ensuring safety. This approach is analyzed within a proposed Analog Audio Interconnection System (AAIS) framework. Experimental results show that common-mode voltages from protective earth potential differences are the primary source of interference. The optimized AISAAI suppressor achieves a consistent 15-30 dB reduction in measured audio interference across the audio band, regardless of the interconnect cable characteristics. This study confirms AISAAI as an effective solution for reducing ground loop noise in unbalanced audio systems and underlines the usefulness of the AAIS model for systemic analysis.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178106","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":"Bilateral Teleoperation of Aerial Manipulator with Hybrid Mapping Framework for Physical Interaction.","authors":"Lingda Meng, Yongfeng Rong, Wusheng Chou","doi":"10.3390/s25185844","DOIUrl":"10.3390/s25185844","url":null,"abstract":"<p><p>Bilateral teleoperation combines the agility of robotic manipulators with the ability to perform complex contact tasks guided by human expertise, thereby fulfilling a pivotal function in environments beyond human access. However, due to the limited workspace of existing master robots necessitating frequent mapping mode switches, coupled with the pronounced heterogeneity and asymmetry between the workspaces of the master and slave systems, achieving teleoperation of the mobile manipulator remains challenging. In this study, we innovatively introduced a 7 DOFs upper limb exoskeleton as the master control device, rigorously designed to align with the motion coordination of the human arm. Regarding teleoperation mapping, a hybrid heterogeneous teleoperation control framework with a variable mapping scheme, designed for an aerial manipulator performing physical operations, is proposed. The system incorporates mode switching driven by the operator's hand gestures, seamlessly and intuitively integrating the advantages of position control and rate control modalities to enable adaptive transitions adaptable to diverse task requirements. Comparative teleoperation experiments were conducted using a fully actuated aerial equipped with a compliant 3D end-effector performing physical aerial writing tasks. The mode-switching algorithm was effectively validated in experiments, demonstrating no instability during transitions and achieving a position tracking RMSE of 7.7% and 5.2% in the <i>X</i>,<i>Y</i>-axis, respectively. This approach holds significant potential for future applications in UAM inspection and physical operational scenarios.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178099","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":"Center of Mass (CoM) Motions and Foot Placement During Treadmill Walking Using One Time-of-Flight Camera.","authors":"Joshua T Chang, Alisha Ragatz, Anjana Ganesh, Ana P Quiros Padilla, Mikayla R Devins, Christina V Mihova, John G Milton","doi":"10.3390/s25185850","DOIUrl":"10.3390/s25185850","url":null,"abstract":"<p><p>Assessing the fall risk of a patient in a busy clinical setting is challenging. Tests such as the timed-up-and-go test and narrow beam walking are difficult to perform due to space restrictions. Moreover, it is not easy to directly connect the results of these tests to fundamental biomechanical principles of gait stability, which emphasize the interplay between the movements of the body's center of mass (CoM) and its base of support (BoS). Herein, we show how a 1.2 m-long treadmill and a single \"time-of-flight\" Azure Kinect camera can capture the CoM-BoS interplay within 5 min. The CoM was calculated by dividing the body into 14 segments determined from 20 joint positions measured by the Kinect camera's body tracking SDK. By tracking the CoM and joint positions from stride to stride, we can evaluate different gait stability metrics using a markerless, contactless, space-efficient approach. A large digital database of CoM movements relative to foot placement will be useful for the future development of statistical and machine learning techniques for identifying subjects at higher risk of falling.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178117","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":"Sequential Injection Analysis of Cholesterol Using an Oxidation-Reduction Electrode Detector.","authors":"Takato Imanaka, Takashi Masadome","doi":"10.3390/s25185863","DOIUrl":"10.3390/s25185863","url":null,"abstract":"<p><p>A new automated method for the determination of cholesterol in serum was developed by combining sequential injection analysis (SIA) with potentiometric detection using a gold oxidation-reduction potential (ORP) electrode because serum cholesterol is an important indicator of abnormal lipid metabolism, arteriosclerosis, and hypertension in clinical diagnosis. The method is based on enzymatic hydrolysis of cholesterol esters by cholesterol esterase (CE) to yield free cholesterol, followed by oxidation with cholesterol oxidase (COD) to produce hydrogen peroxide. In the presence of horseradish peroxidase (HRP) and potassium ferrocyanide (K₄[Fe(CN)₆]), hydrogen peroxide oxidizes ferrocyanide to ferricyanide (K₃[Fe(CN)₆]), and the concentration ratio of ferri-/ferrocyanide is determined potentiometrically. Experimental conditions were optimized as follows: 5.0 mM K₄[Fe(CN)₆], 2 min reaction time, 0.5 units/mL HRP, 0.75 units/mL COD for free cholesterol, 1.5 units/mL COD and 10.0 units/mL CE for total cholesterol, and 5.0% (<i>w</i>/<i>v</i>) Triton X-100 with 5.0% (<i>v</i>/<i>v</i>) isopropanol as solubilizing agents. Under these conditions, the calibration curve for total cholesterol exhibited a Nernstian slope of 47.6 mV/decade over the range of 1.0 × 10^-5-1.0 × 10^-3 M, with no significant interference from common serum constituents. This method offers low reagent consumption, high automation, and simple operation, making it promising for clinical cholesterol analysis.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473388/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178321","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":"Accuracy of a Novel Smartphone-Based Log Measurement App in the Prototyping Phase.","authors":"Mirella Elias, Gabriel Osei Forkuo, Gianni Picchi, Carla Nati, Stelian Alexandru Borz","doi":"10.3390/s25185847","DOIUrl":"10.3390/s25185847","url":null,"abstract":"<p><p>Recently, the development of smartphone apps has resulted in a wide range of services being offered related to wood supply chain management, supporting decision-making and narrowing the digital divide in this business. This study examined the performance of Tree Scanner (TS)-a LiDAR-based smartphone app prototype integrating advanced algorithms-in estimating and providing instant data on log volume through direct digital measurement. Digital log measurements were conducted by two researchers, who each performed two repetitions; in addition to accuracy, measurement-time efficiency was also considered in this study. The results indicate strong agreement between the standard (manual) and digital measurement estimates, with an R² > 0.98 and a low RMSE (0.0668 m³), as well as intra- and inter-user consistency. Moreover, the app showed significant potential for productivity improvement (38%), with digital measurements taking a median time of 21 s per log compared to 29 s per log with manual measurements. Its ease of use and integration of several key functionalities-such as Bluetooth transfer, remote server services, automatic species identification, the provision of instant volume estimates, compatibility with RFID tags and wood anatomy checking devices, and the ability to document the geographic location of measurements-make the Tree Scanner app a useful tool for integration into wood traceability systems.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178028","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":"Enhanced Path Travel Time Prediction via Guided Fusion of Heterogeneous Sensors Using Continuous-Time Dynamics.","authors":"Ang Li, Hanqiang Qian, Yanyan Chen","doi":"10.3390/s25185873","DOIUrl":"10.3390/s25185873","url":null,"abstract":"<p><p>Accurate path travel time prediction is often hindered by sparse and heterogeneous traffic data. This paper proposes FusionODE-TT, a novel model designed to address these challenges by modeling traffic as a continuous-time process. The model features a Recurrent Neural Network encoder that processes multi-source time-series data to initialize a latent state vector, which then evolves over the prediction horizon using a Neural Ordinary Differential Equation (NODE). The core innovation is a guided fusion mechanism that leverages sparse but high-fidelity Automatic Vehicle Identification (AVI) data to apply strong, event-based corrections to the model's continuous latent state, mitigating error accumulation in the prediction process. Experiments were conducted on a real-world dataset comprising AVI, GPS, and point sensor data from a major urban expressway. The experimental results demonstrate that the proposed model achieves superior accuracy, outperforming a suite of baseline models in terms of prediction accuracy and robustness. Furthermore, a comprehensive ablation study was performed to validate the efficacy of our design. The study quantitatively confirms that both the continuous-time dynamics modeled by the NODE and the guided fusion mechanism are essential components, each providing a significant and independent contribution to the model's overall performance.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178128","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":"Partitioned RIS-Assisted Vehicular Secure Communication Based on Meta-Learning and Reinforcement Learning.","authors":"Hui Li, Fengshuan Wang, Jin Qian, Pengcheng Zhu, Aiping Zhou","doi":"10.3390/s25185874","DOIUrl":"10.3390/s25185874","url":null,"abstract":"<p><p>This study tackles the issue of ensuring secure communications in vehicular ad hoc networks (VANETs) under dynamic eavesdropping threats, where eavesdroppers adaptively reposition to intercept transmissions. We introduce a scheme utilizing a partitioned reconfigurable intelligent surface (RIS) to assist in the joint transmission of confidential signals and artificial noise (AN) from a source station. The RIS is divided into segments: one enhances legitimate signal reflection toward the intended vehicular receiver, while the other directs AN toward eavesdroppers to degrade their reception. To maximize secrecy performance in rapidly changing environments, we introduce a joint optimization framework integrating meta-learning for RIS partitioning and reinforcement learning (RL) for reflection matrix optimization. The meta-learning component rapidly determines the optimal RIS partitioning ratio when encountering new eavesdropping scenarios, leveraging prior experience to adapt with minimal data. Subsequently, RL is employed to dynamically optimize both beamforming vectors as well as RIS reflection coefficients, thereby further improving the security performance. Extensive simulations demonstrate that the suggested approach attain a 28% higher secrecy rate relative to conventional RIS-assisted techniques, along with more rapid convergence compared to traditional deep learning approaches. This framework successfully balances signal enhancement with jamming interference, guaranteeing robust and energy-efficient security in highly dynamic vehicular settings.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178298","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":"Development and Evaluation of a Novel IoT Testbed for Enhancing Security with Machine Learning-Based Threat Detection.","authors":"Waleed Farag, Xin-Wen Wu, Soundararajan Ezekiel, Drew Rado, Jaylee Lassinger","doi":"10.3390/s25185870","DOIUrl":"10.3390/s25185870","url":null,"abstract":"<p><p>The Internet of Things (IoT) has revolutionized industries by enabling seamless data exchange between billions of connected devices. However, the rapid proliferation of IoT devices has introduced significant security challenges, as many of these devices lack robust protection against cyber threats such as data breaches and denial-of-service attacks. Addressing these vulnerabilities is critical to maintaining the integrity and trust of IoT ecosystems. Traditional cybersecurity solutions often fail in dynamic, heterogeneous IoT environments due to device diversity, limited computational resources, and inconsistent communication protocols, which hinder the deployment of uniform and scalable security mechanisms. Moreover, there is a notable lack of realistic, high-quality datasets for training and evaluating machine learning (ML) models for IoT security, limiting their effectiveness in detecting complex and evolving threats. This paper presents the development and implementation of a novel physical smart office/home testbed designed to evaluate ML algorithms for detecting and mitigating IoT security vulnerabilities. The testbed replicates a real-world office environment, integrating a variety of IoT devices, such as different types of sensors, cameras, smart plugs, and workstations, within a network generating authentic traffic patterns. By simulating diverse attack scenarios including unauthorized access and network intrusions, the testbed provides a controlled platform to train, test, and validate ML-based anomaly detection systems. Experimental results show that the XGBoost model achieved a balanced accuracy of up to 99.977% on testbed-generated data, comparable to 99.985% on the benchmark IoT-23 dataset. Notably, the SVM model achieved up to 96.71% accuracy using our testbed data, outperforming its results on IoT-23, which peaked at 94.572%. The findings demonstrate the testbed's effectiveness in enabling realistic security evaluations and ability to generate real-world datasets, highlighting its potential as a valuable tool for advancing IoT security research. This work contributes to the development of more resilient and adaptive security frameworks, offering valuable insights for safeguarding critical IoT infrastructures against evolving threats.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178073","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}