Pub Date : 2026-01-19DOI: 10.1109/TDMR.2026.3653227
{"title":"2025 Index IEEE Transactions on Device and Materials Reliability Vol. 25","authors":"","doi":"10.1109/TDMR.2026.3653227","DOIUrl":"https://doi.org/10.1109/TDMR.2026.3653227","url":null,"abstract":"","PeriodicalId":448,"journal":{"name":"IEEE Transactions on Device and Materials Reliability","volume":"25 4","pages":"1-33"},"PeriodicalIF":2.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1109/JSEN.2026.3653187
Seba Nur Alhasan;Burcu Arman Kuzubasoglu;Saygun Guler;Murat Kaya Yapici
Wearable electronic textiles (e-textiles) have gained significant attention in recent years, particularly for long-term biopotential signal monitoring. In this study, we present two wearable electrode designs—a headband and a neckband—tailored specifically for electrocardiogram (ECG) signal acquisition from behind the ear and around the neck regions. These designs were benchmarked against standard Ag/AgCl electrodes over 30 voluntary participants and demonstrated maximum correlation values as high as 98% and above for both designs, with an average correlation of 90.1% and 91.9% for the headband and neckband, respectively. A detailed investigation of six different electrode placements on the neck was also conducted to determine the optimal positions for recording ECG signals. The robustness of the designs was evaluated through 40-min ECG recordings and under various intense movement conditions. Furthermore, to advance the development of sustainable and reliable wearable e-textile systems, we evaluated the real-life performance of textile electrodes reduced using two different agents: L-ascorbic acid, an eco-friendly, bio-based compound, and sodium borohydride, a commonly used but toxic chemical. While both agents are already known to effectively reduce graphene oxide (GO), the primary objective was to comparatively assess the functional performance of the resulting electrodes under real-world conditions, specifically in scenarios relevant to wearable ECG monitoring applications. The reported results enhance the understanding of the efficiency and performance of the developed wearable e-textile designs for biopotential signal monitoring.
{"title":"Eco-Friendly Reduced Graphene e-Textile-Based Ergonomic Wearables Around-the-Neck and Behind-the-Ear for Vital Signs Monitoring","authors":"Seba Nur Alhasan;Burcu Arman Kuzubasoglu;Saygun Guler;Murat Kaya Yapici","doi":"10.1109/JSEN.2026.3653187","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3653187","url":null,"abstract":"Wearable electronic textiles (e-textiles) have gained significant attention in recent years, particularly for long-term biopotential signal monitoring. In this study, we present two wearable electrode designs—a headband and a neckband—tailored specifically for electrocardiogram (ECG) signal acquisition from behind the ear and around the neck regions. These designs were benchmarked against standard Ag/AgCl electrodes over 30 voluntary participants and demonstrated maximum correlation values as high as 98% and above for both designs, with an average correlation of 90.1% and 91.9% for the headband and neckband, respectively. A detailed investigation of six different electrode placements on the neck was also conducted to determine the optimal positions for recording ECG signals. The robustness of the designs was evaluated through 40-min ECG recordings and under various intense movement conditions. Furthermore, to advance the development of sustainable and reliable wearable e-textile systems, we evaluated the real-life performance of textile electrodes reduced using two different agents: L-ascorbic acid, an eco-friendly, bio-based compound, and sodium borohydride, a commonly used but toxic chemical. While both agents are already known to effectively reduce graphene oxide (GO), the primary objective was to comparatively assess the functional performance of the resulting electrodes under real-world conditions, specifically in scenarios relevant to wearable ECG monitoring applications. The reported results enhance the understanding of the efficiency and performance of the developed wearable e-textile designs for biopotential signal monitoring.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 5","pages":"7720-7729"},"PeriodicalIF":4.3,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299550","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}
This work presents the design and evaluation of a low-power data logger for multisensor data acquisition, intended for deployment in urban sewage infrastructure. The system integrates Internet of Things (IoT) communication capabilities to interface with a smart city’s centralized data platform. Two low-power wide area (LPWA) communication technologies—narrowband IoT (NB-IoT) and Sigfox/long range (LoRa)—are analyzed and compared in terms of power consumption, hardware cost, and data transmission performance. The results offer practical guidance for selecting the most suitable communication protocol based on application-specific constraints, such as message frequency, energy availability, and bidirectional communication requirements. Sigfox is selected for occasional alert messages, while NB-IoT is used for the daily bulk transmission of measurements.
这项工作介绍了用于多传感器数据采集的低功耗数据记录仪的设计和评估，旨在部署在城市污水基础设施中。该系统集成了物联网（IoT）通信功能，可与智慧城市的集中数据平台进行交互。对窄带物联网（NB-IoT）和Sigfox/long range （LoRa）两种低功耗广域通信技术进行了功耗、硬件成本和数据传输性能方面的分析和比较。研究结果为基于特定于应用程序的约束（如消息频率、能量可用性和双向通信需求）选择最合适的通信协议提供了实用指导。Sigfox被选择用于偶尔的警报消息，而NB-IoT用于日常批量传输测量值。
{"title":"Evaluating NB-IoT and Sigfox for Energy-Efficient Data Loggers in Sewer Infrastructure","authors":"Marcos Martínez-Peiró;Ruben Torres-Curado;Julio Gomis-Tena","doi":"10.1109/JSEN.2026.3653068","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3653068","url":null,"abstract":"This work presents the design and evaluation of a low-power data logger for multisensor data acquisition, intended for deployment in urban sewage infrastructure. The system integrates Internet of Things (IoT) communication capabilities to interface with a smart city’s centralized data platform. Two low-power wide area (LPWA) communication technologies—narrowband IoT (NB-IoT) and Sigfox/long range (LoRa)—are analyzed and compared in terms of power consumption, hardware cost, and data transmission performance. The results offer practical guidance for selecting the most suitable communication protocol based on application-specific constraints, such as message frequency, energy availability, and bidirectional communication requirements. Sigfox is selected for occasional alert messages, while NB-IoT is used for the daily bulk transmission of measurements.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 5","pages":"7706-7719"},"PeriodicalIF":4.3,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357469","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Distinguishing between indoor and outdoor environments is crucial for various location-based applications, including navigation tools and context-aware services. We present an innovative and highly efficient approach that combines air quality sensors with machine learning techniques to achieve this distinction. Extensive experiments across diverse settings—buildings and vehicles—and data from two cities, Helsinki, Finland, and Milan, Italy, with distinct environmental characteristics, demonstrate significant performance improvements. Specifically, our method achieves over 90% accuracy, a 30% increase compared to approaches relying solely on location information. This work highlights a novel application of air quality data and provides an enhanced methodology for accurately distinguishing indoor and outdoor spaces.
{"title":"Indoor–Outdoor Detection Using Low-Cost Air Quality Sensors","authors":"Qianqian Xia;Alberto Defendi;Samu Varjonen;Petteri Nurmi;Sasu Tarkoma;Martha Arbayani Zaidan;Naser Hossein Motlagh","doi":"10.1109/JSEN.2026.3652526","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3652526","url":null,"abstract":"Distinguishing between indoor and outdoor environments is crucial for various location-based applications, including navigation tools and context-aware services. We present an innovative and highly efficient approach that combines air quality sensors with machine learning techniques to achieve this distinction. Extensive experiments across diverse settings—buildings and vehicles—and data from two cities, Helsinki, Finland, and Milan, Italy, with distinct environmental characteristics, demonstrate significant performance improvements. Specifically, our method achieves over 90% accuracy, a 30% increase compared to approaches relying solely on location information. This work highlights a novel application of air quality data and provides an enhanced methodology for accurately distinguishing indoor and outdoor spaces.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 5","pages":"7692-7705"},"PeriodicalIF":4.3,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11353373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1109/JSEN.2025.3648925
Yi Zhang;Yuchen Zhang;Wenjun Yan;Qing Ling;Limin Zhang;Kangsheng Liu
Multiplatform specific emitter identification (SEI) shows performance degradation due to the significant distributional discrepancies in heterogeneous features. To address this challenge, we propose a graph convolutional deep residual network (ResNet) for classification. First, a platform–target association matrix is constructed based on the statistical features of radar data. Tag features are then implicitly modeled via tag embedding, and deep signal features are extracted using a deep residual convolutional module. Subsequently, the data and tag features are fused through attention-weighted feature splicing. To fully exploit tag dependencies, the graph convolutional network (GCN) is enhanced to accept the spliced features as input, enabling the generation of feature maps that yield learnable classifiers. The extracted features and generated classifiers are integrated to produce the final classification results. Experiments on real-world datasets reveal that the proposed method achieves the dynamic fusion and efficient classification of cross-platform multitag features. Compared to traditional independent multiclassification approaches, it improves accuracy and computational efficiency by 17% and 9.3%, respectively. Moreover, the integration of implicit tag modeling and feature splicing contributes an additional 8% gain in accuracy, fully meeting the practical application requirements of real-world scenarios.
{"title":"Cross-Platform Multitag Specific Emitter Identification Method","authors":"Yi Zhang;Yuchen Zhang;Wenjun Yan;Qing Ling;Limin Zhang;Kangsheng Liu","doi":"10.1109/JSEN.2025.3648925","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3648925","url":null,"abstract":"Multiplatform specific emitter identification (SEI) shows performance degradation due to the significant distributional discrepancies in heterogeneous features. To address this challenge, we propose a graph convolutional deep residual network (ResNet) for classification. First, a platform–target association matrix is constructed based on the statistical features of radar data. Tag features are then implicitly modeled via tag embedding, and deep signal features are extracted using a deep residual convolutional module. Subsequently, the data and tag features are fused through attention-weighted feature splicing. To fully exploit tag dependencies, the graph convolutional network (GCN) is enhanced to accept the spliced features as input, enabling the generation of feature maps that yield learnable classifiers. The extracted features and generated classifiers are integrated to produce the final classification results. Experiments on real-world datasets reveal that the proposed method achieves the dynamic fusion and efficient classification of cross-platform multitag features. Compared to traditional independent multiclassification approaches, it improves accuracy and computational efficiency by 17% and 9.3%, respectively. Moreover, the integration of implicit tag modeling and feature splicing contributes an additional 8% gain in accuracy, fully meeting the practical application requirements of real-world scenarios.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 6","pages":"9317-9329"},"PeriodicalIF":4.3,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440578","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}
Pub Date : 2026-01-14DOI: 10.1109/JSEN.2025.3650450
Hyun Bin Kwon;Ki Hun Jun;Heenam Yoon;Eun Yeon Joo;Sang Ho Choi
Sleep apnea is a common sleep-related condition defined by repeated pauses in respiration during sleep, which increases the risk of severe health complications. Polysomnography (PSG) is the diagnostic gold standard; however, its limited accessibility and poor suitability for long-term monitoring necessitate alternative home-based techniques. This study introduces an unconstrained sleep apnea detection system based on a convolutional vision transformer (Conv-ViT) with personalization via low-rank adaptation (LoRA). Data were collected from 121 participants who underwent PSG using a polyvinylidene fluoride (PVDF) sensor placed underneath a mattress topper to capture unconstrained signals. The Conv-ViT model combines a convolutional neural network (CNN) for localized features extraction with ViT for global representation learning and was fine-tuned on PVDF signals via transfer learning from PSG-derived airflow data. For personalization, LoRA tuning was applied to reflect the individual physiological variability. The evaluation results showed that Conv-ViT outperformed standalone CNN and ViT models, achieving Cohen’s kappa (KAPPA) of 0.736 and accuracy of 0.903 on a test dataset of 61 participants. Personalization with LoRA improved the performance, increasing the mean participant-level KAPPA from 0.674 to 0.728 and the event-level KAPPA from 0.736 to 0.763. Furthermore, the model effectively classified apnea severity, achieving a KAPPA of 0.73 and an average accuracy of 0.93. These findings demonstrate that an end-to-end Conv-ViT model using unconstrained PVDF signals can reliably detect sleep apnea and assess its severity, offering scalable, long-term, and individualized home-based sleep apnea monitoring.
{"title":"Unconstrained Sleep Apnea Detection With Conv-ViT Network: LoRA Tuning for Personalized Monitoring","authors":"Hyun Bin Kwon;Ki Hun Jun;Heenam Yoon;Eun Yeon Joo;Sang Ho Choi","doi":"10.1109/JSEN.2025.3650450","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3650450","url":null,"abstract":"Sleep apnea is a common sleep-related condition defined by repeated pauses in respiration during sleep, which increases the risk of severe health complications. Polysomnography (PSG) is the diagnostic gold standard; however, its limited accessibility and poor suitability for long-term monitoring necessitate alternative home-based techniques. This study introduces an unconstrained sleep apnea detection system based on a convolutional vision transformer (Conv-ViT) with personalization via low-rank adaptation (LoRA). Data were collected from 121 participants who underwent PSG using a polyvinylidene fluoride (PVDF) sensor placed underneath a mattress topper to capture unconstrained signals. The Conv-ViT model combines a convolutional neural network (CNN) for localized features extraction with ViT for global representation learning and was fine-tuned on PVDF signals via transfer learning from PSG-derived airflow data. For personalization, LoRA tuning was applied to reflect the individual physiological variability. The evaluation results showed that Conv-ViT outperformed standalone CNN and ViT models, achieving Cohen’s kappa (KAPPA) of 0.736 and accuracy of 0.903 on a test dataset of 61 participants. Personalization with LoRA improved the performance, increasing the mean participant-level KAPPA from 0.674 to 0.728 and the event-level KAPPA from 0.736 to 0.763. Furthermore, the model effectively classified apnea severity, achieving a KAPPA of 0.73 and an average accuracy of 0.93. These findings demonstrate that an end-to-end Conv-ViT model using unconstrained PVDF signals can reliably detect sleep apnea and assess its severity, offering scalable, long-term, and individualized home-based sleep apnea monitoring.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6331-6343"},"PeriodicalIF":4.3,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162188","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}
Pub Date : 2026-01-13DOI: 10.1109/JSEN.2026.3651726
Shanrong Ren;Jianliang Mao;Xixi He;Hanqing Yuan;Jun Li
This article proposes a physical-neural adaptive super-twisting momentum observer (ASTMO) to estimate interaction forces between a robot manipulator and uncertain environments. The proposed method reduces reliance on force/torque sensors while maintaining high estimation accuracy. Concretely, traditional dynamic modeling and identification methods often encounter challenges like unmodeled dynamics and parametric uncertainties, which may lead to significant modeling errors and adversely impact the accuracy of force estimation. To overcome these limitations, a hybrid model integrating physical dynamics with neural network (NN) corrections is proposed, introducing a unified framework that concurrently leverages model-based and data-driven strategies. The dynamic model is initially developed using the Newton–Euler method, followed by parameter identification to establish the physical model (PM). A backpropagation (BP) NN is then utilized to capture and correct residual model errors. Utilizing such a hybrid model, an ASTMO is constructed for external torque estimation. This effectively attenuates the chattering effect commonly associated with sliding mode control and improves estimation precision under uncertain operating conditions. Experimental validation on a six-degree-of-freedom (6-DoF) manipulator using a Beckhoff controller demonstrates the effectiveness in external torque estimation without a force/torque sensor.
{"title":"Sensorless Interaction Force Estimation via a Physical-Neural Adaptive Super-Twisting Momentum Observer","authors":"Shanrong Ren;Jianliang Mao;Xixi He;Hanqing Yuan;Jun Li","doi":"10.1109/JSEN.2026.3651726","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3651726","url":null,"abstract":"This article proposes a physical-neural adaptive super-twisting momentum observer (ASTMO) to estimate interaction forces between a robot manipulator and uncertain environments. The proposed method reduces reliance on force/torque sensors while maintaining high estimation accuracy. Concretely, traditional dynamic modeling and identification methods often encounter challenges like unmodeled dynamics and parametric uncertainties, which may lead to significant modeling errors and adversely impact the accuracy of force estimation. To overcome these limitations, a hybrid model integrating physical dynamics with neural network (NN) corrections is proposed, introducing a unified framework that concurrently leverages model-based and data-driven strategies. The dynamic model is initially developed using the Newton–Euler method, followed by parameter identification to establish the physical model (PM). A backpropagation (BP) NN is then utilized to capture and correct residual model errors. Utilizing such a hybrid model, an ASTMO is constructed for external torque estimation. This effectively attenuates the chattering effect commonly associated with sliding mode control and improves estimation precision under uncertain operating conditions. Experimental validation on a six-degree-of-freedom (6-DoF) manipulator using a Beckhoff controller demonstrates the effectiveness in external torque estimation without a force/torque sensor.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 5","pages":"7680-7691"},"PeriodicalIF":4.3,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299602","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}
Pub Date : 2026-01-13DOI: 10.1109/JSEN.2026.3651952
Nagina Ishaq;Ata Ullah;Mehreen Mushtaq;Osama A. Khashan;Matloub Hussain;Anwar Ghani
Continuous object tracking in the Internet of Things (IoT)-enabled wireless sensor networks (WSNs) requires fast, energy-efficient boundary detection, especially for dynamic phenomena such as toxic gas leakage or wildfire spread. However, existing approaches often rely on cloudcentric processing, resulting in high transmission delays and excessive energy consumption due to large-scale node activation. This article proposes boundary detection of continuous objects (BDCO), a fog-assisted scheme that reduces communication overhead and improves boundary accuracy. BDCO organizes the network into grid-based clusters where the cluster head (CH) filters anomalous data using a selective aggregation mechanism and forwards only relevant boundary-related information to the fog node (FN). The FN then applies a convex hull-based boundary estimation model, enabling precise boundary formulation while minimizing node activation. The proposed scheme is implemented in NS-2.35 and demonstrates substantial improvements in energy consumption (3.00E+06), service delay (22 ms), end-to-end delay (33 ms), packet loss ratio (3.0), and boundary accuracy (0.85) compared to existing approaches. Overall, the BDCO scheme provides a more energy-efficient and delay-aware solution for real-time BDCO in an IoT-enabled WSN environment.
{"title":"Efficient Continuous Object Tracking With Fog-Assisted Boundary Detection in IoT-Enabled WSN","authors":"Nagina Ishaq;Ata Ullah;Mehreen Mushtaq;Osama A. Khashan;Matloub Hussain;Anwar Ghani","doi":"10.1109/JSEN.2026.3651952","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3651952","url":null,"abstract":"Continuous object tracking in the Internet of Things (IoT)-enabled wireless sensor networks (WSNs) requires fast, energy-efficient boundary detection, especially for dynamic phenomena such as toxic gas leakage or wildfire spread. However, existing approaches often rely on cloudcentric processing, resulting in high transmission delays and excessive energy consumption due to large-scale node activation. This article proposes boundary detection of continuous objects (BDCO), a fog-assisted scheme that reduces communication overhead and improves boundary accuracy. BDCO organizes the network into grid-based clusters where the cluster head (CH) filters anomalous data using a selective aggregation mechanism and forwards only relevant boundary-related information to the fog node (FN). The FN then applies a convex hull-based boundary estimation model, enabling precise boundary formulation while minimizing node activation. The proposed scheme is implemented in NS-2.35 and demonstrates substantial improvements in energy consumption (3.00E+06), service delay (22 ms), end-to-end delay (33 ms), packet loss ratio (3.0), and boundary accuracy (0.85) compared to existing approaches. Overall, the BDCO scheme provides a more energy-efficient and delay-aware solution for real-time BDCO in an IoT-enabled WSN environment.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 5","pages":"7780-7792"},"PeriodicalIF":4.3,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299568","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}
Pub Date : 2026-01-13DOI: 10.1109/TPS.2026.3651943
{"title":"Special Issue on Selected Papers from APSPT-14 May 2027","authors":"","doi":"10.1109/TPS.2026.3651943","DOIUrl":"https://doi.org/10.1109/TPS.2026.3651943","url":null,"abstract":"","PeriodicalId":450,"journal":{"name":"IEEE Transactions on Plasma Science","volume":"54 1","pages":"352-352"},"PeriodicalIF":1.5,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11349680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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