IEEE Sensors Journal最新文献_第5页

Analysis and Simulation Verification of the Strain Transfer Model for the FBG Sensor With Surface-Bonded in the Nongrating Region 非光栅区表面键合FBG传感器应变传递模型的分析与仿真验证

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-15 DOI: 10.1109/JSEN.2025.3597422

Xianhuan Luo;Baowu Zhang;Jianjun Cui;Kai Chen;Yihao Zhang;Lu Peng;Liang Pang;Bo Tang;Pinhong Yang;Depei Zeng

引用次数: 0

Colorimetric Sensor for Kanamycin Based on Peroxidase-Like Activity of Cu@Sch-HNT 基于Cu@Sch-HNT过氧化物酶样活性的卡那霉素比色传感器

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-14 DOI: 10.1109/JSEN.2025.3596715

Peng Song;Yuening Wang;Yan Gao;Bo Gong;Xin Ni;Zhaoying Zuo;Tao Wu;Xixi Zhu;Qingyun Liu

{"title":"Colorimetric Sensor for Kanamycin Based on Peroxidase-Like Activity of Cu@Sch-HNT","authors":"Peng Song;Yuening Wang;Yan Gao;Bo Gong;Xin Ni;Zhaoying Zuo;Tao Wu;Xixi Zhu;Qingyun Liu","doi":"10.1109/JSEN.2025.3596715","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596715","url":null,"abstract":"This study demonstrates the synthesis of a Cu@Sch-HNT nanocomposite via an oil-bath-assisted approach, exhibiting enhanced peroxidase-mimetic activity. Comprehensive characterization employing electron paramagnetic resonance (EPR) spectroscopy and radical scavenging assays established <inline-formula> <tex-math>${}^{bullet }$ </tex-math></inline-formula><inline-formula> <tex-math>${mathrm {O}}_{{2}}^{-}$ </tex-math></inline-formula> radicals as the predominant reactive species governing the catalytic mechanism. Optimal enzymatic activity was observed at physiological temperature, indicative of favorable biocompatibility. Capitalizing on these catalytic properties, a rapid colorimetric sensing platform was engineered for kanamycin detection. Quantitative analysis revealed a significant linear correlation between kanamycin concentration and absorbance at 652 nm, with detection limit determination conducted according to standard signal-to-noise ratio criteria. This methodology affords three principal advantages as follows: 1) visual analyte recognition through distinct chromogenic transitions; 2) high sensitivity confirmed by systematic detection limit assessment; and 3) practical utility validated through recovery analyses in complex matrices. The platform demonstrates significant potential for environmental surveillance and biosensing applications, particularly in resource-constrained environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34363-34369"},"PeriodicalIF":4.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GPS/UWB Tightly Coupled Vehicle Cooperative Positioning Based on AOO-CNN- BiGRU-Attention Model 基于AOO-CNN- BiGRU-Attention模型的GPS/UWB紧密耦合车辆协同定位

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-14 DOI: 10.1109/JSEN.2025.3596781

Wei Sun;Xinyu Qin;Wei Ding;Jingang Zhao;Chen Liang

{"title":"GPS/UWB Tightly Coupled Vehicle Cooperative Positioning Based on AOO-CNN- BiGRU-Attention Model","authors":"Wei Sun;Xinyu Qin;Wei Ding;Jingang Zhao;Chen Liang","doi":"10.1109/JSEN.2025.3596781","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596781","url":null,"abstract":"Accurate relative positioning is essential for the deployment of an intelligent transportation system. However, in complex environments such as urban canyons and tunnels, the global positioning system (GPS) signals are often blocked or interrupted, resulting in decreased or invalid positioning accuracy. To meet the demand for accurate vehicle positioning in complex environments of urban roads, this article proposes a deep learning model for GPS pseudo-range and Doppler shift prediction based on the fusion of the animated oat optimization (AOO), a convolutional neural network (CNN), a bidirectional gated recurrent unit (BiGRU), and an attention mechanism. CNN is applied to capture spatiotemporal features from the input sequence, while BiGRU explores the long-term dependencies in the data. The attention assigns varying weights according to the importance of input data, enabling the model to focus more effectively on critical parts. To improve predictive accuracy, the AOO algorithm is employed for hyperparameter optimization. Then, the predicted GPS pseudo-range and Doppler shift are used for GPS/ultrawide band (UWB) tightly coupled cooperative positioning by utilizing the characteristics of UWB technology that can provide high-precision ranging information. The results of the experiment show that the proposed fusion model improves the relative positioning accuracy by 13%, 29%, 33%, and 50% over CNN-BiGRU-Attention, CNN-BiGRU, BiGRU, and GRU models, respectively, during a GPS signal loss-of-lock environment, which significantly enhances the stability of vehicle positioning in complex environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35312-35322"},"PeriodicalIF":4.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Target Detection for Low Signal-to-Noise Ratio Scalar Magnetic Unexploded Ordnance Surveys: A Multilevel Orthogonal Basis Function Approach 低信噪比标量磁未爆弹药测量目标检测：一种多水平正交基函数方法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-14 DOI: 10.1109/JSEN.2025.3596895

Jianwei Zhao;Zhaofa Zeng;Shuai Zhou

{"title":"Target Detection for Low Signal-to-Noise Ratio Scalar Magnetic Unexploded Ordnance Surveys: A Multilevel Orthogonal Basis Function Approach","authors":"Jianwei Zhao;Zhaofa Zeng;Shuai Zhou","doi":"10.1109/JSEN.2025.3596895","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596895","url":null,"abstract":"With the increasing speed of magnetic data acquisition by uncrewed platforms, unexploded ordnance (UXO) surveys now face challenges such as susceptibility to environmental noise interference and low data acquisition. This study proposes a multilevel orthogonal basis function (MOBF) detection method to address the challenges of weak magnetic anomaly detection (MAD) in complex noise environments, particularly for UXO surveys. The MOBF method integrates discrete stationary wavelet transform (DSWT) and 2-D orthogonal basis function (2D-OBF) processing through a cascaded decomposition-fusion architecture. By leveraging DSWT’s shift-invariant multiscale decomposition, the method effectively separates colored noise (with a power spectral density (PSD) of 1/<inline-formula> <tex-math>${f}^{,alpha }$ </tex-math></inline-formula>) from target signals, while OBF enhances localized spatial correlations of anomalies. A variance-weighted energy fusion strategy is introduced to aggregate multiresolution features, significantly improving signal-to-noise ratio (SNR). Numerical simulations demonstrate MOBF’s robustness across diverse noise scenarios: at −20 dB SNR under Gaussian noise, the MOBF method has a higher detection probability and lower false alarm rate than traditional methods. In colored noise environments, MOBF maintains reliable detection at −15 dB SNR, whereas 2D-OBF fails. Field tests conducted in coastal areas with uncrewed aerial vehicle (UAV)-borne magnetic surveys validate MOBF’s practicality, successfully identifying ferromagnetic targets (anchors, iron tools) under challenging conditions (strip noise). Despite limitations in distinguishing UXOs from nonhazardous ferromagnetic objects, MOBF exhibits superior noise immunity and spatial resolution compared to existing methods. The proposed method provides a viable solution for real-time UXO detection on mobile platforms, particularly in low SNR scenarios with colored noise interference.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35157-35169"},"PeriodicalIF":4.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Multiphase Flow Water Cut Modeling Framework Based on Multilevel Multiscale Convolutional Neural Network 基于多层次多尺度卷积神经网络的多相流含水率建模框架

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-14 DOI: 10.1109/JSEN.2025.3597099

Weidong Dang;Xiaoyang Li;Ruiqi Wang;Haoyu Li;Zhongke Gao

引用次数: 0

On the Effectiveness of Sparse Linear Polarization Pixels for Face Anti-Spoofing 稀疏线性偏振像素在人脸抗欺骗中的有效性研究

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-14 DOI: 10.1109/JSEN.2025.3597155

JaeSeong Kim;Abraham Pelz;Michael Scherer;David Mendlovic

{"title":"On the Effectiveness of Sparse Linear Polarization Pixels for Face Anti-Spoofing","authors":"JaeSeong Kim;Abraham Pelz;Michael Scherer;David Mendlovic","doi":"10.1109/JSEN.2025.3597155","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597155","url":null,"abstract":"Robust face anti-spoofing (FAS) is essential for secure facial recognition systems. This article presents a novel hybrid sensor approach using sparsely linear polarization pixels integrated into an RGB pixel matrix to leverage both angle of linear polarization (AoLP) and degree of linear polarization (DoLP). The sparse pixel integration overcomes the vulnerabilities of conventional RGB-based methods and the complexity of multisensor solutions. By integrating polarization features into a lightweight convolutional neural network (CNN), our solution offers a cost-effective and reliable FAS under all light conditions. Experimental results show that combining AoLP and DoLP significantly boosts accuracy compared to methods relying solely on RGB or DoLP, achieving an average classification error rate (ACER) as low as 0.4%, even with an extremely sparse deployment of polarization pixels (1 per 400 RGB pixels). An ablation study quantifies the individual contributions of AoLP and DoLP. Moreover, the system sustains its efficacy in challenging low-light scenarios and delivers a 10-fold reduction in errors compared to RGB-based methods. These findings underscore the potential of this single-sensor, low-compute solution for secure, affordable deployments in mobile devices and embedded systems.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35178-35190"},"PeriodicalIF":4.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CNN-Based Data Processing for Enhanced Detection of Small Targets in Sea Clutter 基于cnn的海杂波小目标增强检测数据处理

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-13 DOI: 10.1109/JSEN.2025.3596263

Shuangyu Xu;Zhihang Wang;Zishu He

{"title":"CNN-Based Data Processing for Enhanced Detection of Small Targets in Sea Clutter","authors":"Shuangyu Xu;Zhihang Wang;Zishu He","doi":"10.1109/JSEN.2025.3596263","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596263","url":null,"abstract":"Detecting small targets within intricate sea clutter presents a formidable challenge. In previous methods, convolutional neural network (CNN)-based detectors have relied on handcrafted features extracted through the manual data processing, which may not fully capture the discriminative features necessary to distinguish targets from sea clutter. This article introduces a novel method of target detection that utilizes CNN-based data processing to directly handle raw data. The proposed CNN-based data processing can automatically extract higher level features from signals, which are often more discriminative and valuable for subsequent detection. The two-stage design of our method allows for the easy replacement of more advanced CNN-based detectors in future applications, providing flexibility for future improvements. Experimental results demonstrate that our method achieves probabilities of detection (PDs) of 0.9008 and 0.8433 on the IPIX and SDRDSP datasets, respectively, with a probability of false alarm (PFA) of 0.001, substantially surpassing other methods. The total FLOPs of our method are 206.42M, making it suitable for real-time applications. Further experiments confirm that our proposed CNN-based data processing can enhance various CNN-based detectors across different datasets, showcasing robustness and effectiveness. Moreover, our method maintains high detection performance even with a limited number of pulses.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35585-35596"},"PeriodicalIF":4.3,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regional Identification of Breast Tumors Using Multichannel Bioimpedance Spectroscopy 利用多通道生物阻抗谱技术进行乳腺肿瘤的区域识别

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-13 DOI: 10.1109/JSEN.2025.3596237

Yuanjing Zhang;Shuai Li;Jie He;Yang Wu;Hao Wang;Kai Liu;Jiafeng Yao

{"title":"Regional Identification of Breast Tumors Using Multichannel Bioimpedance Spectroscopy","authors":"Yuanjing Zhang;Shuai Li;Jie He;Yang Wu;Hao Wang;Kai Liu;Jiafeng Yao","doi":"10.1109/JSEN.2025.3596237","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596237","url":null,"abstract":"A multichannel bioimpedance spectroscopy (MC-BIS) method is proposed for regional identification of breast tumors. First, the sensor is partitioned to scan the breast across nine subregions, and an empty-field calibration algorithm is applied to normalize the impedance spectra. Next, numerical simulations are conducted to investigate the relationship between the electrical characteristics of unifocal tumors and their regional distribution. Subsequently, the performance of three classifiers—support vector machine (SVM), random forest (RF), and feedforward neural network (FNN)—is evaluated for bifocal tumor localization. The simulation results indicate significant differences in the impedance characteristics between tumor regions and normal tissue regions (<inline-formula> <tex-math>${P} lt 0.001$ </tex-math></inline-formula>). When a tumor is present in a subregion, the corresponding imaginary part relaxation impedance <inline-formula> <tex-math>${Z} _{text {imag-relax}}$ </tex-math></inline-formula> exceeds <inline-formula> <tex-math>$2.004~Omega $ </tex-math></inline-formula>. For bifocal breast tumor localization, the FNN classifier achieved the best performance, with a classification accuracy of 95.46% through fivefold cross-validation. To validate the simulation results, biological tissues with distinct electrical properties were selected to simulate tumor and normal tissue. The experimental accuracy reached 86.94%. The MC-BIS method enables rapid and accurate localization of tumor regions, providing a new technological approach for early screening and diagnosis of breast cancer.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35438-35446"},"PeriodicalIF":4.3,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neuromorphic Circuit for Temporal Odor Encoding in Turbulent Environments 湍流环境下时间气味编码的神经形态回路

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-13 DOI: 10.1109/JSEN.2025.3596564

Shavika Rastogi;Nik Dennler;Michael Schmuker;André van Schaik

{"title":"Neuromorphic Circuit for Temporal Odor Encoding in Turbulent Environments","authors":"Shavika Rastogi;Nik Dennler;Michael Schmuker;André van Schaik","doi":"10.1109/JSEN.2025.3596564","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596564","url":null,"abstract":"Natural odor environments present turbulent and dynamic conditions, causing chemical signals to fluctuate in space, time, and intensity. While many species have evolved highly adaptive behavioral responses to such variability, the emerging field of neuromorphic olfaction continues to grapple with the challenge of efficiently sampling and identifying odors in real-time. In this work, we investigate metal-oxide (MOx) gas sensor recordings of constant airflow-embedded artificial odor plumes. We discover a data feature that is representative of the presented odor stimulus at a certain concentration, irrespective of temporal variations caused by the plume dynamics. Furthermore, we design a neuromorphic electronic nose front-end circuit for extracting and encoding this feature into analog spikes for gas detection and concentration estimation. The design is loosely inspired by the spiking output of parallel neural pathways in the mammalian olfactory bulb (OB). We test the circuit for gas recognition and concentration estimation in artificial environments, where either single gas pulses or prerecorded odor plumes were deployed in a constant flow of air. For both environments, our results indicate that the gas concentration is encoded in—and inversely proportional to—the time difference of analog spikes emerging out of two parallel pathways. The resulting neuromorphic nose could enable data-efficient, real-time robotic plume navigation systems, advancing the capabilities of odor source localization in applications such as environmental monitoring and search-and-rescue.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35622-35630"},"PeriodicalIF":4.3,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight Gesture Recognition Model Based on CWT and Enhanced CBAM 基于CWT和增强CBAM的轻量级手势识别模型

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-08-13 DOI: 10.1109/JSEN.2025.3596600

Zhaoxia Zhang;Zhibin Liang;Xiaoyu Wang;Xuchao Feng

{"title":"Lightweight Gesture Recognition Model Based on CWT and Enhanced CBAM","authors":"Zhaoxia Zhang;Zhibin Liang;Xiaoyu Wang;Xuchao Feng","doi":"10.1109/JSEN.2025.3596600","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596600","url":null,"abstract":"As an interaction method, gesture is widely used in various fields because of its simplicity and intuition. At present, most radar-based gesture recognition methods use short-time Fourier transform (STFT) to process radar echo information, but STFT cannot improve time resolution and frequency resolution simultaneously. To fully utilize effective information, the continuous wavelet transform (CWT) is used to process the radar echo signals. In view of the complexity of gesture recognition networks, a novel network incorporating CWT and an enhanced convolutional block attention module (CBAM) mechanism is proposed. First, features are pre-extracted using a feature extraction network. Then, the CBAM module is improved and integrated. Finally, the classification result is formed. To verify the model’s effectiveness, experiments collected data for nine distinct gestures. The results demonstrate a recognition accuracy of 96.3% via participant-stratified cross validation. Moreover, the model parameters are optimized, facilitating relatively simple implementation. It also exhibits strong performance on unknown datasets, proving its excellent generalization capability.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35631-35641"},"PeriodicalIF":4.3,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0