IEEE Journal of Selected Areas in Sensors最新文献_第2页

Room-Temperature Low-Bias LPG Sensing via Barrier Modulation in WO3/Polyaniline Heterojunction Diodes 基于WO3/聚苯胺异质结二极管势垒调制的室温低偏置LPG传感

IEEE Journal of Selected Areas in Sensors Pub Date : 2026-01-01 Epub Date: 2026-04-13 DOI: 10.1109/JSAS.2026.3681240

Seungmin Oh;Chihoon Kim

{"title":"Room-Temperature Low-Bias LPG Sensing via Barrier Modulation in WO3/Polyaniline Heterojunction Diodes","authors":"Seungmin Oh;Chihoon Kim","doi":"10.1109/JSAS.2026.3681240","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3681240","url":null,"abstract":"We demonstrate a room-temperature liquefied petroleum gas (LPG) sensor based on a tungsten oxide /polyaniline heterojunction diode fabricated on a tungsten substrate. Tungsten oxide nanostructures were grown by hydrothermal synthesis, and polyaniline was deposited by cell-voltage-controlled electrodeposition, where the deposition time (10–25 s) tuned junction coverage. The optimized device (20 s) exhibited pronounced suppression of forward current upon LPG exposure at 300 K. To ensure interpretable quantification under variable ambient backgrounds, an air–N2–LPG sequence was employed and a purge-referenced normalized response, <italic>S(%), was defined using the N2-purged baseline at a fixed operating voltage (<italic>V* = 0.60 V). A baseline shift of 27.87% during air-to-N2 switching confirms strong sensitivity to background constituents, motivating the purge-referenced protocol to decouple LPG-induced modulation from ambient-dependent baseline variations. At <italic>V* = 0.60 V, the response increased monotonically over 100–900 ppm LPG and reached a representative value of 71.61% at 500 ppm. Within 300–700 ppm, a quasi-linear operating window was identified with a segment sensitivity of 0.0481%·ppm−1 (<inline-formula><tex-math>${bm{R}}_{mathrm{adj}}^2$</tex-math></inline-formula> = 0.966). The measured response/recovery times (<italic>t90 ≈ 20 s; Tr90 ≈ 7 s) represent system-level upper bounds under the present gas delivery configuration. Repeatability and device-to-device reproducibility of <italic>S(%) were 5.33% (<italic>n = 5) and 9.70% (<italic>n = 3) relative standard deviation (RSD), respectively. These results support low-bias diode readout as a practical route to room-temperature LPG sensing and provide a purge-referenced framework for interpreting diode-type gas signals under variable backgrounds.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"199-206"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11480967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital Twin-Assisted Large AI Task-Aware Edge Offloading and Resource Allocation for Low-Altitude Wireless Sensor Networks 面向低空无线传感器网络的数字双辅助大型AI任务感知边缘卸载与资源分配

IEEE Journal of Selected Areas in Sensors Pub Date : 2026-01-01 Epub Date: 2026-04-01 DOI: 10.1109/JSAS.2026.3679846

Bintao Hu;Hengyan Liu;Haotong Cao;Shijing Yuan;Xiaoli Chu;Shugong Xu

{"title":"Digital Twin-Assisted Large AI Task-Aware Edge Offloading and Resource Allocation for Low-Altitude Wireless Sensor Networks","authors":"Bintao Hu;Hengyan Liu;Haotong Cao;Shijing Yuan;Xiaoli Chu;Shugong Xu","doi":"10.1109/JSAS.2026.3679846","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3679846","url":null,"abstract":"Emerging vehicle-to-everything (V2X) applications, especially for smart transportation, require high-accuracy sensing and low-latency communications and computation; however, existing V2X architectures that deal with sensing, communication, and computation separately are ill-equipped to meet these coupled requirements. In this article, we propose a framework that combines integrated sensing and communications and digital twins with low-altitude edge intelligence for V2X, and formulate an optimization problem to minimize the total service delay (i.e., the transmission delay plus the task computation delay) of all vehicular users by jointly optimizing task offloading decisions, communication and computation resource allocation, and the association between unmanned aerial vehicles (UAVs) and vehicular users. To solve this problem under fast varying mobility and high-dimensional coupled constraints, we propose a task-aware multiagent resource allocation optimization algorithm, which enables scalable cooperative decision-making, including UAV-vehicular user association, and communication and computation resource allocation. Simulation results show substantial reductions in total service delay over the traditional deep reinforcement learning benchmark, especially in dense, dynamic low-altitude edge intelligent V2X scenarios.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"159-170"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11466417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast and Robust Stationary Crowd Counting With Commodity WiFi 快速和稳健的固定人群计数与商品WiFi

IEEE Journal of Selected Areas in Sensors Pub Date : 2025-12-26 DOI: 10.1109/JSAS.2025.3648956

Mert Torun;Alireza Parsay;Yasamin Mostofi

{"title":"Fast and Robust Stationary Crowd Counting With Commodity WiFi","authors":"Mert Torun;Alireza Parsay;Yasamin Mostofi","doi":"10.1109/JSAS.2025.3648956","DOIUrl":"https://doi.org/10.1109/JSAS.2025.3648956","url":null,"abstract":"This article introduces a novel method for estimating the size of seated crowds with commodity WiFi signals, by leveraging natural body fidgeting behaviors as a passive sensing cue. Departing from prior binary fidget representations, our approach leverages the bandwidth of the received signal as a finer-grained and robust indicator of crowd counts. More specifically, we propose a mathematical model that relates the probability distribution function (PDF) of the signal bandwidth to the crowd size, using a principled derivation based on the PDF of an individual's fidget-induced bandwidth. To characterize the individual fidgeting PDF, we use publicly available online videos, each of a seated individual, from which we extract body motion profiles using vision techniques, followed by a speed-to-bandwidth conversion inspired by Carson's Rule from analog FM radio design. Finally, to enhance robustness in real-world deployments where unrelated motions may occur nearby, we further introduce an anomaly detection module that filters out nonfidget movements. We validate our system through 42 experiments across two indoor environments with crowd sizes up to and including 13 people, achieving a mean absolute error of 1.04 and a normalized mean square error of 0.15, with an average convergence time of 51 s, significantly reducing the convergence time as compared to the state of the art. Additional simulation results demonstrate scalability to larger crowd sizes. Overall, our results showcase that the proposed pipeline enables fast, robust, and highly accurate counting of seated crowds.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"57-68"},"PeriodicalIF":0.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11316410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MedLiteNet: Lightweight Hybrid Medical Image Segmentation Model for the Medical Internet of Things (MIoT) MedLiteNet：用于医疗物联网（MIoT）的轻量级混合医学图像分割模型

IEEE Journal of Selected Areas in Sensors Pub Date : 2025-12-25 DOI: 10.1109/JSAS.2025.3648685

Pengyang Yu;Haoquan Wang;Gerard Marks;Tahar Kechadi;Laurence T. Yang;Sahraoui Dhelim;Nyothiri Aung

{"title":"MedLiteNet: Lightweight Hybrid Medical Image Segmentation Model for the Medical Internet of Things (MIoT)","authors":"Pengyang Yu;Haoquan Wang;Gerard Marks;Tahar Kechadi;Laurence T. Yang;Sahraoui Dhelim;Nyothiri Aung","doi":"10.1109/JSAS.2025.3648685","DOIUrl":"https://doi.org/10.1109/JSAS.2025.3648685","url":null,"abstract":"Accurate skin-lesion segmentation remains a key technical challenge for computer-aided diagnosis of skin cancer. Convolutional neural networks, while effective, are constrained by limited receptive fields and thus struggle to model long-range dependencies. Vision Transformers capture global context, yet their quadratic complexity and large parameter budgets hinder use on the small-sample medical datasets common in dermatology. We introduce the MedLiteNet, a lightweight CNN–Transformer hybrid tailored for dermoscopic segmentation that achieves high precision through hierarchical feature extraction and multiscale context aggregation. The encoder stacks depthwise mobile inverted bottleneck blocks to curb computation, inserts a bottleneck-level cross-scale token-mixing unit to exchange information between resolutions, and embeds a boundary-aware self-attention module to sharpen lesion contours. On the ISIC 2018 benchmark, a single MedLiteNet model attains a Dice score of <inline-formula><tex-math>$0.897 pm 0.010$</tex-math></inline-formula> and an IoU of <inline-formula><tex-math>$0.821 pm 0.015$</tex-math></inline-formula> with fewer than <inline-formula><tex-math>$3.3,mathrm{M}$</tex-math></inline-formula> parameters. A performance-weighted ensemble of three complementary variants raises accuracy to <inline-formula><tex-math>$0.904 pm 0.012$</tex-math></inline-formula> Dice and <inline-formula><tex-math>$0.830 pm 0.018$</tex-math></inline-formula> IoU while keeping the total parameter count below <inline-formula><tex-math>$10,mathrm{M}$</tex-math></inline-formula>—over 90% smaller than Vision-Transformer backbones. Qualitative results confirm superiority on irregular borders, low-contrast regions and multiscale lesions, indicating MedLiteNet’s suitability for real-time, resource-aware computer-aided dermatology.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"78-89"},"PeriodicalIF":0.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11316249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stress Classification From ECG Signals Using Vision Transformer 利用视觉变压器对心电信号进行应力分类

IEEE Journal of Selected Areas in Sensors Pub Date : 2025-12-25 DOI: 10.1109/JSAS.2025.3648684

Zeeshan Ahmad;Naimul Khan

{"title":"Stress Classification From ECG Signals Using Vision Transformer","authors":"Zeeshan Ahmad;Naimul Khan","doi":"10.1109/JSAS.2025.3648684","DOIUrl":"https://doi.org/10.1109/JSAS.2025.3648684","url":null,"abstract":"Vision transformers have shown tremendous success in numerous computer vision applications; however, they have not been exploited for stress assessment using physiological signals such as electrocardiogram (ECG). In order to get the maximum benefit from the vision transformer for multilevel stress assessment, in this article, we transform the raw ECG data into 2-D spectrograms using short-time Fourier transform. These spectrograms are divided into patches for feeding to the transformer encoder. We also perform experiments with 1-D convolutional neural network (CNN) and ResNet-18 (CNN model). We perform leave-one-subject-out cross validation (LOSOCV) experiments on wearable stress and affect detection (WESAD) and Ryerson Multimedia Lab (RML) dataset. One of the biggest challenges of LOSOCV-based experiments is to tackle the problem of intersubject variability. In this research, we address the issue of intersubject variability and show our success using 2-D spectrograms and the attention mechanism of transformer. Experiments show that vision transformer handles the effect of intersubject variability much better than CNN-based models and beats all previous state-of-the-art methods by a considerable margin. Moreover, our method is end-to-end, does not require handcrafted features, and can learn robust representations. The proposed method achieved 71.01% and 76.7% accuracies with RML dataset and WESAD dataset, respectively, for three class classification and 88.3% for binary classification on WESAD.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"47-56"},"PeriodicalIF":0.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11316267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Acoustic-Based Fault Detection for Robotic Arms 基于声学的机械臂故障检测

IEEE Journal of Selected Areas in Sensors Pub Date : 2025-12-01 DOI: 10.1109/JSAS.2025.3638975

Cihun-Siyong Gong;Chih-Hui Simon Su;Kuo-Wei Chao;Cheng-Yen Lin;Yu-Hua Chen

{"title":"Acoustic-Based Fault Detection for Robotic Arms","authors":"Cihun-Siyong Gong;Chih-Hui Simon Su;Kuo-Wei Chao;Cheng-Yen Lin;Yu-Hua Chen","doi":"10.1109/JSAS.2025.3638975","DOIUrl":"https://doi.org/10.1109/JSAS.2025.3638975","url":null,"abstract":"With the rise of Industry 4.0 and smart manufacturing, the integration of the Industrial Internet of Things and autonomous systems has become crucial for improving productivity and operational efficiency. For robotic arms, the core of automation, fault diagnosis is essential for ensuring production line reliability and sustainable operations. Traditional diagnostic methods, particularly those based on visual sensing, face challenges such as high deployment costs, insufficient contactlessness, and enormous data volumes. Acoustic-based fault detection has become a promising technique for monitoring the condition and ensuring the reliability of robotic arms. It is especially important in industrial automation, where noninvasive and real-time diagnostics are essential. This article systematically reviews recent state-of-the-art studies on acoustic signal acquisition, preprocessing, feature extraction, and classification for robotic arm fault diagnosis. Combining advanced signal processing techniques, such as time–frequency analysis, wavelet transforms, and empirical mode decomposition, with machine learning and deep learning models, has significantly improved fault detection in terms of accuracy and robustness. This review provides a detailed discussion of current research achievements and future directions, highlighting hybrid models. By integrating acoustic-sensing-based fault diagnosis with large-scale artificial intelligence models deployed in edge computing environments, users or companies can not only leverage neural network architectures to identify abnormal sound patterns amid complex background noise accurately but also directly address key challenges such as resource constraints, real-time performance, and energy efficiency at the edge.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flexible Near-Field Communication in Wearable Electronics: Antenna Design, Measurements, and System Demonstration 可穿戴电子产品中的灵活近场通信：天线设计、测量和系统演示

IEEE Journal of Selected Areas in Sensors Pub Date : 2025-11-21 DOI: 10.1109/JSAS.2025.3635540

Yichao Hu;Furong Yang;Linlin Xu;Yuchao Wang;Cheng Zhang;Chaoyun Song

{"title":"Flexible Near-Field Communication in Wearable Electronics: Antenna Design, Measurements, and System Demonstration","authors":"Yichao Hu;Furong Yang;Linlin Xu;Yuchao Wang;Cheng Zhang;Chaoyun Song","doi":"10.1109/JSAS.2025.3635540","DOIUrl":"https://doi.org/10.1109/JSAS.2025.3635540","url":null,"abstract":"This article presents the design, fabrication, and experimental evaluation of a flexible antenna for high-frequency near-field communication, specifically developed for wearable electronics and deployment on curved or moving surfaces. The antenna is fabricated on a polyimide substrate with electrodeposited copper and operates at 13.56 MHz, exhibiting a low reflection coefficient and stable bandwidth performance, even under bending conditions with a radius as small as 80 mm. Extensive measurements have confirmed that the device maintains reliable signal strength and stable communication performance under various mechanical deformations and dynamic conditions. The proposed reader system provides compatibility with various operating protocols, such as ISO/IEC 14443 and ISO/IEC 15693. Results highlight the antenna's capacity to maintain strong and stable wireless connectivity in realistic wearable scenarios, highlighting its suitability for integration into complex and changing scenarios. This work provides a cost-effective and adaptable solution for enabling seamless data exchange in next-generation wearable systems and lays the groundwork for future enhancements, such as multitag communication and expanded operating ranges.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"23-32"},"PeriodicalIF":0.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11264303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cybersecurity of Sensor Systems for State Sequence Estimation: A Machine Learning Approach 状态序列估计传感器系统的网络安全：一种机器学习方法

IEEE Journal of Selected Areas in Sensors Pub Date : 2025-11-17 DOI: 10.1109/JSAS.2025.3633237

Xubin Fang;Rick S. Blum;Ramesh Bharadwaj;Brian M. Sadler

{"title":"Cybersecurity of Sensor Systems for State Sequence Estimation: A Machine Learning Approach","authors":"Xubin Fang;Rick S. Blum;Ramesh Bharadwaj;Brian M. Sadler","doi":"10.1109/JSAS.2025.3633237","DOIUrl":"https://doi.org/10.1109/JSAS.2025.3633237","url":null,"abstract":"Due to possible devastating consequences, counteracting sensor data attacks is an extremely important topic, which has not seen sufficient study. To the best of our knowledge, this article develops the first methods that accurately identify/eliminate only the problematic attacked sensor data presented to a sequence estimation/regression algorithm under any attack from our attack model. The approach does not assume a known form for the statistical model of the sensor data, allowing data-driven and machine learning sequence estimation/regression algorithms to be protected. A simple protection approach for attackers not endowed with knowledge of the details of our protection approach is first developed, followed by additional processing for attacks based on protection system knowledge. Experimental results show that the simple approach achieves performance indistinguishable from that for an approach which knows which sensors are attacked. For cases where the attacker has knowledge of the protection approach, experimental results indicate the additional processing can be configured so that the worst-case degradation under the additional processing and a large number of sensors attacked can be made significantly smaller than the worst-case degradation of the simple approach, and close to an approach which knows which sensors are attacked, with just a slight degradation under no attacks. Mathematical descriptions of the worst-case attacks are used to demonstrate the additional processing will provide similar advantages for cases for which we do not have numerical results. All the data-driven/machine learning processing used in our approaches employ only unattacked training data.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"33-46"},"PeriodicalIF":0.0,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11250589","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Sensitivity Microwave Detection of Subwavelength Objects Using a Dielectric-Loaded Waveguide With Electromagnetic Jet Lens 电磁射流透镜介质负载波导对亚波长物体的高灵敏度微波探测

IEEE Journal of Selected Areas in Sensors Pub Date : 2025-09-30 DOI: 10.1109/JSAS.2025.3614641

Antoine Deubaibe;Ali Ghaddar;Baraka Mahamout Mahamat;Bernard Bayard;Bruno Sauviac

{"title":"High-Sensitivity Microwave Detection of Subwavelength Objects Using a Dielectric-Loaded Waveguide With Electromagnetic Jet Lens","authors":"Antoine Deubaibe;Ali Ghaddar;Baraka Mahamout Mahamat;Bernard Bayard;Bruno Sauviac","doi":"10.1109/JSAS.2025.3614641","DOIUrl":"https://doi.org/10.1109/JSAS.2025.3614641","url":null,"abstract":"This letter presents a high-sensitivity, noncontact sensing approach based on a 15 GHz electromagnetic jet for the detection of subwavelength-scale objects. The jet is generated by a rectangular waveguide partially filled with polytetrafluoroethylene (PTFE) and terminated by a rectangular dielectric lens extending into free space. An impedance-matching transition between the empty and PTFE-filled sections of the waveguide is optimized to minimize insertion loss resulting in a single, easily insertable dielectric component that ensures seamless integration into the waveguide. The lens geometry is designed through full-wave simulations to achieve subwavelength focusing. The proposed structure produces a focal spot with full width at half maximum of <inline-formula><tex-math>$0.41lambda$</tex-math></inline-formula> and <inline-formula><tex-math>$0.59lambda$</tex-math></inline-formula> in the <inline-formula><tex-math>$x$</tex-math></inline-formula>- and <inline-formula><tex-math>$y$</tex-math></inline-formula>-directions, respectively. Experimental results confirm the enhanced near-field confinement and demonstrate the detection of objects as small as <inline-formula><tex-math>$lambda /400$</tex-math></inline-formula>, highlighting advantages over conventional open-ended waveguide configurations. Owing to its fully guided architecture, compactness, and ease of use, the proposed device is particularly well suited for high-resolution, noninvasive sensing and offers strong potential for transfer to industrial applications.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"303-309"},"PeriodicalIF":0.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Interference Mitigation for Automotive Radar Sensor 汽车雷达传感器的实时干扰抑制

IEEE Journal of Selected Areas in Sensors Pub Date : 2025-09-11 DOI: 10.1109/JSAS.2025.3609274

Yubo Wu;Alexander Li;Wenjing Lou;Y. Thomas Hou

{"title":"Real-Time Interference Mitigation for Automotive Radar Sensor","authors":"Yubo Wu;Alexander Li;Wenjing Lou;Y. Thomas Hou","doi":"10.1109/JSAS.2025.3609274","DOIUrl":"https://doi.org/10.1109/JSAS.2025.3609274","url":null,"abstract":"Automotive radar sensor plays a crucial role in advanced driver assistance systems. As radar technology becomes increasingly common in vehicles, radar-to-radar interference poses a significant challenge, leading to a reduction in target detection performance. It is essential for an interference mitigation algorithm to effectively reduce this interference under dynamic driving conditions while adhering to strict processing time requirements. In this article, we present Soteria—a real-time interference mitigation algorithm for frequency modulated continuous wave radar systems, leveraging compressed sensing techniques. Soteria identifies interference by exploiting the sparsity of signals in the frequency-time domain, then separates the desired signal from interference using the orthogonal matching pursuit (OMP) algorithm. Additionally, Soteria utilizes the inherent correlation between input data from neighboring time frames to reduce the search space for the OMP algorithm. To further enhance processing speed, Soteria is implemented using a GPU-based parallel computing approach. Simulation results indicate that Soteria can achieve <inline-formula><tex-math>$sim$</tex-math></inline-formula>1 ms processing time, outperforming state-of-the-art methods in target detection accuracy.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"2 ","pages":"290-302"},"PeriodicalIF":0.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11159154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0