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Impact Force Reconstruction in Composite Structures Using Wavelet Transform and Low-Frequency Response Components

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2025.3543362

Minghua Wang;Bowen Zhao;Yi Zhang;Di Wu;Yue Wang;Xinlin Qing;Yishou Wang

引用次数: 0

Infected and Noninfected Diseases Detection for Human Health Using Surface Plasmon Resonance Biosensors: A Review

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2025.3539783

Rajeev Kumar;Lokendra Singh;S. Malathi

{"title":"Infected and Noninfected Diseases Detection for Human Health Using Surface Plasmon Resonance Biosensors: A Review","authors":"Rajeev Kumar;Lokendra Singh;S. Malathi","doi":"10.1109/JSEN.2025.3539783","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3539783","url":null,"abstract":"Human health is affected by a vast spectrum of ailments, collectively called diseases, which obstruct the proper functioning of the body and may stem from diverse origins, including genetic alterations, environmental factors, or pathogenic infections. These diseases are often categorized into groups, such as infectious diseases, triggered by bacteria, viruses, fungi, or parasites, and noninfectious diseases, encompassing genetic disorders, autoimmune conditions, and persistent illnesses like cardiovascular disease and diabetes. Infectious diseases may disseminate through direct or indirect contact, air, food, water, or vectors, whereas noninfectious diseases are frequently associated with lifestyle practices, genetic predisposition, or environmental influences. Progress in modern medicine, including vaccines, antibiotics, and diagnostic technologies, has markedly reduced the impact of numerous diseases; however, challenges continue due to the emergence of new infections, antibiotic resistance, and the escalating prevalence of noncommunicable diseases. Confronting human diseases necessitates a holistic strategy that integrates public health measures, healthcare interventions, and biomedical research to enhance prevention, diagnosis, and treatment methods, consequently improving global health outcomes. Infectious and noninfectious diseases (autoimmune disorders, cancer, and cardiovascular diseases) caused by bacteria, viruses, and fungi can be identified quickly and accurately with surface plasmon resonance (SPR) sensors. The ease and capacity of SPR technology to track biomolecular interactions enhances its adaptability, which allows early diagnosis and individualized treatment plans for a variety of medical disorders.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10556-10565"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748957","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

The Effect of Length and Curvature on Quantum-Enhanced Temperature Sensing in a Fiber-Based Sagnac Loop

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2025.3542813

Hailong Wang;Zehua Chen;Tenghui Mao;Dongxu Wang;Cheng Peng;Yajuan Zhang;Chunliu Zhao

{"title":"The Effect of Length and Curvature on Quantum-Enhanced Temperature Sensing in a Fiber-Based Sagnac Loop","authors":"Hailong Wang;Zehua Chen;Tenghui Mao;Dongxu Wang;Cheng Peng;Yajuan Zhang;Chunliu Zhao","doi":"10.1109/JSEN.2025.3542813","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542813","url":null,"abstract":"In this work, we theoretically proposed and experimentally demonstrated the enhanced effect of prior curvature precondition on temperature sensing in a quantum-enhanced sensing system, which is constructed by combining an all-fiber two-mode squeezed light source and a Sagnac loop. By choosing the prior optimum length and curvature of polarization-maintaining fiber inside the Sagnac loop, both signal-to-noise ratio (SNR) and sensitivity can be effectively enhanced. The experimental results are summarized as follows. SNR is enhanced by the factor of 0.38 dB under balanced measurement condition. Under unbalanced measurement condition, the enhanced factors with regard to sensing depth are 0.22 and 2.34 dB for the classical double-channel light source and two-mode squeezed light source, respectively. With the support of curvature precondition, the sensing depth of the former one 15.62 dB is enhanced to 31.49 dB of the latter one by an enhanced factor of 15.87 dB, which is larger than 13.75 dB in the absence of curvature precondition, this enhanced effect still exists in the temperature sensitivity (0.982 dB/°C <1.134 dB/°C) and SNR (0.54 dB <1.7 dB). These results confirm the potential prospects of using the cross effects from other physical quantities to enhance temperature sensing in a quantum-enhanced sensing system.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11102-11110"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748973","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 Multimodal Hand Movement Recognition Framework Based on S-Transform and ISDNet

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2025.3542902

Lei Shi;Ranran Gui;Qunfeng Niu;Peng Li

{"title":"A Multimodal Hand Movement Recognition Framework Based on S-Transform and ISDNet","authors":"Lei Shi;Ranran Gui;Qunfeng Niu;Peng Li","doi":"10.1109/JSEN.2025.3542902","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542902","url":null,"abstract":"In the rehabilitation of hand movement disorders, multimodal signal-based hand movement recognition (HMR) plays a crucial role in enhancing therapeutic interventions and improving patient outcomes. However, existing methods face challenges such as suboptimal feature fusion and limited recognition performance. To address these issues, this article proposes a novel multimodal HMR framework. First, a signal fusion algorithm based on Spearman’s rank correlation coefficient (SRCC) is utilized to effectively integrate features from surface electromyography (sEMG) and triaxial acceleration signals (TASs), laying a solid foundation for subsequent feature fusion. Next, a feature fusion algorithm based on S-transform (S-T) and RGB image technology is developed, transforming signals into 3-D time-frequency fusion feature maps (3D-TFTTMs) to more comprehensively capture the time-frequency characteristics of the signals. Subsequently, a deep learning model, inception-SENet-DenseNet (ISDNet), is designed, incorporating both inception and squeeze-and-excitation network (SENet) modules. The inception module extracts fused features, while SENet dynamically adjusts channel weights, significantly enhancing recognition performance. Evaluation on the Ninapro DB2&3, DB5, and DB7 databases demonstrates that ISDNet achieves HMR accuracies of 97.02%, 93.78%, and 95.37%, respectively, significantly outperforming existing multimodal HMR methods. The results validate the effectiveness of the proposed framework in multimodal fusion and highlight its potential for advancing HMR technology, with broad application prospects in areas such as prosthetics, rehabilitation, and robotics.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11672-11682"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761366","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

Detection of Silver(I) Ions by Ion-Imprinted SPR Sensors

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2025.3543283

Duygu Çimen;Mitra Jalilzadeh;Adil Denizli

引用次数: 0

Multisensor Plug-and-Play Navigation Based on Resilient Information Filter

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2025.3540790

Qian Meng;Chang Su;Yingying Jiang;Weisong Wen;Xiaolin Meng

{"title":"Multisensor Plug-and-Play Navigation Based on Resilient Information Filter","authors":"Qian Meng;Chang Su;Yingying Jiang;Weisong Wen;Xiaolin Meng","doi":"10.1109/JSEN.2025.3540790","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3540790","url":null,"abstract":"To improve the positioning accuracy and resilience of the multisensor integration, a resilient plug-and-play navigation method based on information filter (IF) is proposed in this article. As the dual form of Kalman filter (KF), IF turns the likelihood product into a sum, which can fully utilize asynchronous sensor measurements for fusion and realize plug-and-play navigation flexibly. Furthermore, a resilient factor based on the principle of chi-square test is implemented to adjust the sensor information, aiming to reduce the impact of faulty measurements under challenging scenarios. By conducting and analyzing the vehicle experiments in the urban environment, the proposed method shows better performance over traditional KF, with the root mean square (rms) error reduced from 9.13 to 3.28 m. Plug-and-play navigation achieves a 51.37% improvement in positioning accuracy by utilizing more suitable sensor measurements and propagation intervals, which can decrease the sensitivity to measurement noise and faults. The resilient factor directly addresses the faults themselves and improves the positioning performance by 26.13%, further enhancing the system’s resilience and robustness to faulty information. This resilient IF method is fully validated as effective for multisensor plug-and-play navigation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11563-11573"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761556","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

AIS Data-Driven Maritime Traffic Flow Prediction and Density Visualization Using Multitime Scale Temporal Feature Fusion Network

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2024.3525094

Yukuan Wang;Ryan Wen Liu;Jingxian Liu;Lichao Yang;Yang Liu

{"title":"AIS Data-Driven Maritime Traffic Flow Prediction and Density Visualization Using Multitime Scale Temporal Feature Fusion Network","authors":"Yukuan Wang;Ryan Wen Liu;Jingxian Liu;Lichao Yang;Yang Liu","doi":"10.1109/JSEN.2024.3525094","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3525094","url":null,"abstract":"Maritime traffic flow prediction is essential for the development of intelligent transportation systems in the maritime domain. The widespread deployment of automatic identification system (AIS) sensors generates vast amounts of real-time trajectory data, which are crucial for traffic state perception and vessel position tracking. This study aims to improve maritime traffic state prediction by leveraging AIS data. The existing methods often face two key challenges: insufficient consideration of traffic flow characteristics across different time scales, which limits the comprehensive capture of temporal features, and difficulties in estimating the uncertainty in ship position distributions. To address these challenges, we propose a multitime scale temporal feature fusion network (MSTFFN) model. This model enhances the transformer architecture for maritime traffic flow prediction by extracting multiscale temporal features that encapsulate the dynamic nature of traffic patterns. Additionally, a Gaussian distribution process is employed to effectively visualize traffic density. Experiments on real-world datasets demonstrate the superior performance of the MSTFFN model in traffic flow prediction tasks. Compared to baseline models, such as transformer, GRU, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and CovLSTM—each improved through multitime scale feature fusion—the proposed MSTFFN achieves superior predictive accuracy. Moreover, the advanced visualization of traffic density facilitates more intuitive and efficient maritime traffic management, thereby enhancing the application of AIS data across ship-to-shore and management operations in the maritime industry.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11357-11365"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748793","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

Design, Preparation, and Performance Study of a Highly Reliable Temperature Measurement Array Based on NTC Thin Films

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2025.3542835

Tao Huang;Bo Yang;Yuxian Song;Yingying Dou;Wenwen Kong;Aimin Chang

{"title":"Design, Preparation, and Performance Study of a Highly Reliable Temperature Measurement Array Based on NTC Thin Films","authors":"Tao Huang;Bo Yang;Yuxian Song;Yingying Dou;Wenwen Kong;Aimin Chang","doi":"10.1109/JSEN.2025.3542835","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542835","url":null,"abstract":"This article presents the development of a highly reliable temperature measurement system based on negative temperature coefficient (NTC) thermistor thin films integrated into micro-electromechanical system (MEMS) sensor arrays. Leveraging COMSOL Multiphysics simulations, the system optimizes sensor array configuration to accurately predict and manage thermal gradients on wafer surfaces. It features several technical advancements previously uncombined in temperature sensing applications. First, a novel fabrication process involving magnetron sputtering significantly enhances the uniformity and sensitivity of the NTC thin films. Second, high-resolution photolithography ensures precise patterning of sensor elements, achieving the system’s ultrafast thermal response time of 0.34 s and exceptional measurement accuracy of <inline-formula> <tex-math>$0.5~^{circ }$ </tex-math></inline-formula>C across a broad operational range of <inline-formula> <tex-math>$0~^{circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$70~^{circ }$ </tex-math></inline-formula>C. The integrated sensors exhibit less than 3% resistance drift in high-stress testing environments, underscoring their stability and robustness. By accurately correlating temperature variations with process defects, and with a coefficient of determination (<inline-formula> <tex-math>${R}^{{2}}$ </tex-math></inline-formula>) calculated at 0.995, our system effectively enhances process control and contributes to yield improvement and device reliability in semiconductor manufacturing, thereby advancing temperature sensing technology in high-precision industrial applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10619-10627"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748963","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

AD2T: Multivariate Time-Series Anomaly Detection With Association Discrepancy Dual-Decoder Transformer

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2025.3543835

Zezhong Li;Wei Guo;Jianpeng An;Qi Wang;Yingchun Mei;Rongshun Juan;Tianshu Wang;Yang Li;Zhongke Gao

{"title":"AD2T: Multivariate Time-Series Anomaly Detection With Association Discrepancy Dual-Decoder Transformer","authors":"Zezhong Li;Wei Guo;Jianpeng An;Qi Wang;Yingchun Mei;Rongshun Juan;Tianshu Wang;Yang Li;Zhongke Gao","doi":"10.1109/JSEN.2025.3543835","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543835","url":null,"abstract":"Multivariate time-series (MTS) anomaly detection is of great importance in both condition monitoring and malfunction identification in multisensor systems. Current MTS anomaly detection approaches are typically based on reconstruction, prediction, or association discrepancy learning algorithms. These methods detect anomalies by learning hidden representations of entire sequences, modeling dependencies at a single time-step level, or calculating an association-based metric inherently distinguishable between regular and deviant points. However, most existing methods typically fail to leverage all three types of models simultaneously to enhance overall performance as well as often disregard the correlations between different sensors. To address the issues above, this article proposes a novel deep learning-based unsupervised MTS anomaly detection algorithm called association discrepancy dual-decoder transformer (AD2T). AD2T employs a dual-decoder architecture to accommodate reconstruction, prediction, and association discrepancy learning tasks, thereby effectively utilizing information across these tasks to better characterize MTS data. We further develop a min-max training strategy to jointly optimize all the aforementioned tasks. Additionally, we propose a compound embedding module based on dilated causal convolution to simultaneously capture correlations in both temporal and sensor dimensions. Extensive empirical studies on five multisensor system datasets from the aerospace, server, and water treatment domains have demonstrated the superiority of our method, achieving an average improvement of 1.96% in the <inline-formula> <tex-math>$F1$ </tex-math></inline-formula>-score compared to state-of-the-art (SOTA) methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11710-11721"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761492","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

ARAIM Protection Level Optimization Based on Feedback-Structure Subset Grouping

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-02-26 DOI: 10.1109/JSEN.2025.3532772

Jiashuang Yan;Zhibo Fang;Rui Sun;Ming Gao;Yi Mao;Cheng Jiang;Ying Xu

{"title":"ARAIM Protection Level Optimization Based on Feedback-Structure Subset Grouping","authors":"Jiashuang Yan;Zhibo Fang;Rui Sun;Ming Gao;Yi Mao;Cheng Jiang;Ying Xu","doi":"10.1109/JSEN.2025.3532772","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3532772","url":null,"abstract":"As an advanced algorithm for receiver autonomous integrity monitoring (RAIM), advanced RAIM (ARAIM) has gained considerable attention in the civil aviation sector and is gradually finding applications in other fields. However, with the increasing number of visible satellites, the number of fault subsets processed by the multiple hypothesis solution separation (MHSS) method grows exponentially, imposing a substantial computational burden on the receiver. Furthermore, ARAIM’s uniform distribution of integrity and continuity risks among fault subsets results in overly conservative protection levels (PLs). These challenges are often addressed as separate issues. However, this study proposes a novel PL optimization algorithm that incorporates a subset grouping method with a feedback structure to reduce the number of fault subsets, thereby decreasing detection time. In addition, an improved cuckoo search algorithm (ICSA) is developed to allocate integrity and continuity risks more effectively, optimizing the PLs. Experimental results demonstrate the effectiveness of the proposed method. Compared to ARAIM, without IMU, the protection level optimization of proposed algorithm improves by 34.38% and 35.06% in the vertical and horizontal directions, respectively; with IMU, the protection level optimization of proposed algorithm improves by 74.21% and 74.49% in the vertical and horizontal directions, respectively. In addition, due to the fault subsets reduction, the fault detection time is reduced by 54%, 47%, and 26% compared with ARAIM, FSPA, and Feedback ARAIM, respectively.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11823-11838"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761406","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