Sensors最新文献

{"title":"Effects of Sampling Frequency on Human Activity Recognition with Machine Learning Aiming at Clinical Applications.","authors":"Takahiro Yamane, Moeka Kimura, Mizuki Morita","doi":"10.3390/s25123780","DOIUrl":"https://doi.org/10.3390/s25123780","url":null,"abstract":"<p><p>Human activity recognition using wearable accelerometer data can be a useful digital biomarker for severity assessment and the diagnosis of diseases, where the relationship between onset and patient activity is crucial. For long-term monitoring in clinical settings, the volume of data collected over time should be minimized to reduce power consumption, computational load, and communication volume. This study aimed to determine the lowest sampling frequency that maintains recognition accuracy for each activity. Thirty healthy participants wore nine-axis accelerometer sensors at five body locations and performed nine activities. Machine-learning-based activity recognition was conducted using data sampled at 100, 50, 25, 20, 10, and 1 Hz. Data from the non-dominant wrist and chest, which have previously shown high recognition accuracy, were used. Reducing the sampling frequency to 10 Hz did not significantly affect the recognition accuracy for either location. However, lowering the frequency to 1 Hz decreases the accuracy of many activities, particularly brushing teeth. Using data with a 10 Hz sampling frequency can maintain recognition accuracy while decreasing data volume, enabling long-term patient monitoring and device miniaturization for clinical applications.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HETMCL: High-Frequency Enhancement Transformer and Multi-Layer Context Learning Network for Remote Sensing Scene Classification.","authors":"Haiyan Xu, Yanni Song, Gang Xu, Ke Wu, Jianguang Wen","doi":"10.3390/s25123769","DOIUrl":"https://doi.org/10.3390/s25123769","url":null,"abstract":"<p><p>Remote Sensing Scene Classification (RSSC) is an important and challenging research topic. Transformer-based methods have shown encouraging performance in capturing global dependencies. However, recent studies have revealed that Transformers perform poorly in capturing high frequencies that mainly convey local information. To solve this problem, we propose a novel method based on High-Frequency Enhanced Vision Transformer and Multi-Layer Context Learning (HETMCL), which can effectively learn the comprehensive features of high-frequency and low-frequency information in visual data. First, Convolutional Neural Networks (CNNs) extract low-level spatial structures, and the Adjacent Layer Feature Fusion Module (AFFM) reduces semantic gaps between layers to enhance spatial context. Second, the High-Frequency Information Enhancement Vision Transformer (HFIE) includes a High-to-Low-Frequency Token Mixer (HLFTM), which captures high-frequency details. Finally, the Multi-Layer Context Alignment Attention (MCAA) integrates multi-layer features and contextual relationships. On UCM, AID, and NWPU datasets, HETMCL achieves state-of-the-art OA of 99.76%, 97.32%, and 95.02%, respectively, outperforming existing methods by up to 0.38%.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness.","authors":"Yalin Gao, Dongwei Xu, Huiyan Zhu, Qi Xuan","doi":"10.3390/s25123779","DOIUrl":"https://doi.org/10.3390/s25123779","url":null,"abstract":"<p><p>In OFDM wireless communication systems, low-resolution channel characteristics and noise interference pose significant challenges to accurate channel estimation. To solve these problems, we propose a super-resolution denoising residual network (SDRNet), which combines the advantages of the super-resolution convolutional neural network (SRCNN) and the denoising convolutional neural network (DnCNN) to construct a pilot-based OFDM signal model, train SDRNet using OFDM pilot data containing Gaussian noise, and optimize its feature enhancement ability in frequency-selective fading channels. To further explore the role of channel estimation in communication security, we propose a frequency-domain adversarial attack method based on SDRNet output. This method first converts the time-domain signal to the frequency domain by using the Fourier transform and then applies Gaussian noise and selective masking. By integrating the channel gradient information, the adversarial perturbation we generated significantly improves the attack success rate compared with the non-channel awareness method. The experimental results show that SDRNet is superior to traditional algorithms (such as the least square method, minimum mean square error estimation, etc.) in both mean square error and bit error rate. Furthermore, the adversarial samples optimized through channel awareness frequency-domain masking exhibit stronger attack performance, confirming that accurate channel estimation can not only enhance communication reliability but also provide key guidance for adversarial perturbation. The experimental results show that under the same noise conditions, the MSE of SDRNet is significantly lower than that of LS and MMSE. The bit error rate is lower than 0.01 when the signal-to-noise ratio is 10 dB, which is significantly better than the traditional algorithm. The attack success rate of the proposed adversarial attack method reached 79.9%, which was 16.3% higher than that of the non-channel aware method, verifying the key role of accurate channel estimation in enhancing the effectiveness of the attack.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Enhanced Cascaded Deep Learning Framework for Multi-Cell Voltage Forecasting and State of Charge Estimation in Electric Vehicle Batteries Using LSTM Networks.","authors":"Supavee Pourbunthidkul, Narawit Pahaisuk, Popphon Laon, Nongluck Houngkamhang, Pattarapong Phasukkit","doi":"10.3390/s25123788","DOIUrl":"https://doi.org/10.3390/s25123788","url":null,"abstract":"<p><p>Enhanced Battery Management Systems (BMS) are essential for improving operational efficacy and safety within Electric Vehicles (EVs), especially in tropical climates where traditional systems encounter considerable performance constraints. This research introduces a novel two-tiered deep learning framework that utilizes a two-stage Long Short-Term Memory (LSTM) framework for precise prediction of battery voltage and SoC. The first tier employs LSTM-1 forecasts individual cell voltages across a full-scale 120-cell Lithium Iron Phosphate (LFP) battery pack using multivariate time-series data, including voltage history, vehicle speed, current, temperature, and load metrics, derived from dynamometer testing. Experiments simulate real-world urban driving, with speeds from 6 km/h to 40 km/h and load variations of 0, 10, and 20%. The second tier uses LSTM-2 for SoC estimation, designed to handle temperature-dependent voltage fluctuations in high-temperature environments. This cascade design allows the system to capture complex temporal and inter-cell dependencies, making it especially effective under high-temperature and variable-load environments. Empirical validation demonstrates a 15% improvement in SoC estimation accuracy over traditional methods under real-world driving conditions. This study marks the first deep learning-based BMS optimization validated in tropical climates, setting a new benchmark for EV battery management in similar regions. The framework's performance enhances EV reliability, supporting the growing electric mobility sector.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the Elastic Loss Characteristics of Acoustic Echoes from Underwater Corner Reflector.","authors":"Yi Luo, Dawei Xiao, Jingzhuo Zhang, Zuqiu Li","doi":"10.3390/s25123776","DOIUrl":"https://doi.org/10.3390/s25123776","url":null,"abstract":"<p><p>The underwater corner reflector is a \"concave\" elastic structure, and its acoustic echo exhibits large elastic loss, which affects its practical use. To study the acoustic echo elastic loss characteristics of underwater corner reflectors, based on the characteristics of small concave elastic structures of underwater corner reflectors, theoretical calculations were performed using the method of a combination of finite element and boundary element. Taking the underwater rigid corner reflector as the benchmark, the acoustic echo differences between similar types of underwater elastic corner reflectors were compared. The regular acoustic echo elastic loss of underwater corner reflectors was analyzed, and verified through pool experiments. The results show that, whether single-grid or multi-grid corner reflector, the actual acoustic echoes of underwater corner reflectors conform to the characteristics of elastic bodies, which differ significantly from rigid bodies and exhibit obvious elastic loss. The elastic loss mainly manifests as reduced target strength (TS), narrower directional pattern width, and poorer frequency stability of target strength, which is detrimental to practical use. This study provides assistance in proposing targeted methods to suppress elastic loss.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transverse Electric Inverse Scattering of Conductors Using Artificial Intelligence.","authors":"Chien-Ching Chiu, Po-Hsiang Chen, Yen-Chen Chang, Hao Jiang","doi":"10.3390/s25123774","DOIUrl":"https://doi.org/10.3390/s25123774","url":null,"abstract":"<p><p>Sensors are devices that can detect changes in the external environment and convert them into signals. They are widely used in fields like industrial automation, smart homes, medical devices, automotive electronics, and the Internet of Things (IoT), enabling real-time data collection to enhance system intelligence and efficiency. With advancements in technology, sensors are evolving toward miniaturization, high sensitivity, and multifunctional integration. This paper employs the Direct Sampling Method (DSM) and neural networks to reconstruct the shape of perfect electric conductors from the sensed electromagnetic field. Transverse electric (TE) electromagnetic waves are transmitted to illuminate the conductor. The scattered fields in the x- and y-directions are measured by sensors and used in the method of moments for forward scattering calculations, followed by the DSM for initial shape reconstruction. The preliminary shape data obtained from the DSM are then fed into a U-net for further training. Since the training parameters of deep learning significantly affect the reconstruction results, extensive tests are conducted to determine optimal parameters. Finally, the trained neural network model is used to reconstruct TE images based on the scattered fields in the x- and y-directions. Owing to the intrinsic strong nonlinearity in TE waves, different regularization factors are applied to improve imaging quality and reduce reconstruction errors after integrating the neural network. Numerical results show that compared to using the DSM alone, combining the DSM with a neural network enables the generation of high-resolution images with enhanced efficiency and superior generalization capability. In addition, the error rate has decreased to below 15%.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Depth from 2D Images: Development and Metrological Evaluation of System Uncertainty Applied to Agricultural Scenarios.","authors":"Bernardo Lanza, Cristina Nuzzi, Simone Pasinetti","doi":"10.3390/s25123790","DOIUrl":"https://doi.org/10.3390/s25123790","url":null,"abstract":"<p><p>This article describes the development, experimental validation, and uncertainty analysis of a simple-to-use model for monocular depth estimation based on optical flow. The idea is deeply rooted in the agricultural scenario, for which vehicles that move around the field are equipped with low-cost cameras. In the experiment, the camera was mounted on a robot moving linearly at five different constant speeds looking at the target measurands (ArUco markers) positioned at different depths. The acquired data was processed and filtered with a moving average window-based filter to reduce noise in the estimated apparent depths of the ArUco markers and in the estimated optical flow image speeds. Two methods are proposed for model validation: a generalized approach and a complete approach that separates the input data according to their image speed to account for the exponential nature of the proposed model. The practical result obtained by the two analyses is that, to reduce the impact of uncertainty on depth estimates, it is best to have image speeds higher than 500-800 px/s. This is obtained by either moving the camera faster or by increasing the camera's frame rate. The best-case scenario is achieved when the camera moves at 0.50-0.75 m/s and the frame rate is set to 60 fps (effectively reduced to 20 fps after filtering). As a further contribution, two practical examples are provided to offer guidance for untrained personnel in selecting the camera's speed and camera characteristics. The developed code is made publicly available on GitHub.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"All-Dielectric Metalenses for Long-Wavelength Infrared Imaging Applications: A Review.","authors":"Shinpei Ogawa, Misaki Hanaoka, Manabu Iwakawa, Shoichiro Fukushima, Masaaki Shimatani","doi":"10.3390/s25123781","DOIUrl":"https://doi.org/10.3390/s25123781","url":null,"abstract":"<p><p>Infrared imaging has gained considerable attention across diverse fields, including security, surveillance, and environmental monitoring. The need to minimize size, weight, power, and cost (SWaP-C) poses challenges for conventional optical systems like refractive lenses. Metalenses with subwavelength surface patterns have emerged as promising solutions to address these limitations. This review provides a comprehensive analysis of all-dielectric metalenses for long-wavelength infrared (LWIR) imaging applications, a critical spectral region for human detection and analytical applications (such as gas analysis). We examine the limitations of conventional infrared (IR) lens materials and highlight the performance advantages of LWIR metalenses. Key design principles, including chromatic and achromatic lens configurations, are discussed alongside their imaging performance. Additionally, we review advanced functionalities such as polarization control, multifocal capabilities, zoom, and reconfigurability. Theoretical performance limits and trade-offs are analyzed to provide insights into design optimization. We identify future challenges related to advanced design methods and fabrication techniques. LWIR metalenses can be expected to overcome the shortcomings of conventional LWIR lenses owing to meta-optics technologies, to achieve SWaP-C and advanced functionalities that cannot be achieved by conventional LWIR lenses. This review will guide researchers in academia and industry to develop LWIR metalenses to advance IR imaging technologies.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autogenetic Gravity Center Placement.","authors":"Timothy Sands","doi":"10.3390/s25123786","DOIUrl":"https://doi.org/10.3390/s25123786","url":null,"abstract":"<p><p>Operations by space drones mandate significant autonomy. This study experimentally evaluates key proposed applications of autonomy. Center of gravity auto-location is proposed using autonomous identification of mass properties, necessitating nonlinear state estimation. Nonlinear, coupled governing kinetics are strictly adopted as the control, and inversion provides closed-form estimates of mass properties. Seminally neglecting the diagonal inertia moments, the inertia cross-products are utilized to exactly find the mass center coordinates using the parallel axis theorem to parameterize the location coordinates. In December 2024, experiments were performed in space for hours, validating the approaches proposed. The findings indicate the longitudinal distribution was quite symmetric. Meanwhile, the lateral distribution was quite off-balance. Estimation convergence of the mass center coordinates was improved compared to the state-of-the-art comparative benchmark. In hundreds of days, the latter achieved millimeter convergence, while in minutes, the former achieved hundreds of millimeters convergence.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement of Low-Concentration Hydrogen in Inert Gas Within a Small Closed Volume.","authors":"Georgiy A Ivanov, Dmitry P Shornikov, Nikolay N Samotaev, Konstantin Y Oblov, Maya O Etrekova, Artur V Litvinov","doi":"10.3390/s25123771","DOIUrl":"https://doi.org/10.3390/s25123771","url":null,"abstract":"<p><p>A technique has been proposed and experimentally tested for measuring the hydrogen concentration in an inert atmosphere within a closed system. This method utilizes a metal-oxide-semiconductor field-effect capacity-type (MOSFEC) sensor under harsh conditions such as exposure to inert gases, pressure fluctuations, and varying temperatures. The measurement is performed during the thermal decomposition of metal hydrides in a liquid sodium environment. The developed measurement technique for determining hydrogen concentration released from metal hydride samples in a system with a closed gas path is cost-effective compared to standardized, resource-intensive open-volume flow measurement methods. The use of the developed MOSFEC sensor technique allows for rapid and efficient investigation of the in situ real-time dynamics of gas release from various metal hydride materials differing in their hydrogen content within a small closed volume. Additionally, this approach enables precise determination of the specific gas release temperatures.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 12","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}