Pub Date : 2025-01-20DOI: 10.1109/JSEN.2025.3525712
Yun Xiang;Kaihua Zhang;Tony Zhang;Zuohui Chen;Qi Xuan;Robert P. Dick
Camera-based inference techniques can be used to estimate $text {PM}_{{2.5}}$ concentrations in air based on the aggregate effects of particles on light scattering and absorption. These techniques can be spatially fine-grained, operate in real time, and substantially improve accuracy compared with particle counting sensors. However, existing camera-based techniques fail at night, when pollution exposure and production remain important. We describe the first vision-based technique for nighttime PM2.5 concentration estimation. The design approach differs substantially from that of daytime systems because the primary source of daytime information, the progression of color toward “airlight” color with increasing depth, is much less useful at night and the primary source of nighttime information, the glowing halation regions around artificial light sources, is insignificant during the day. We describe a nighttime pollution estimation technique that builds upon novel “illumination map (IM)” feature. We describe an IM-based dual-channel squeeze-and-excitation convolutional neural network (DSECNet) is to estimate PM2.5 concentrations. This method is evaluated on real-world data and images and outperforms the most advanced related existing (daytime) haze estimation methods, achieving a mean absolute error (MAE) of $8.65~mu text { g/m}^{{3}}$ , which is 16.99% lower than the state-of-the-art baseline method. To the best of the authors’ knowledge, this is the first vision-based nighttime nighttime PM2.5 estimation method.
{"title":"Halation-Based Nighttime PM2.5 Estimation","authors":"Yun Xiang;Kaihua Zhang;Tony Zhang;Zuohui Chen;Qi Xuan;Robert P. Dick","doi":"10.1109/JSEN.2025.3525712","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3525712","url":null,"abstract":"Camera-based inference techniques can be used to estimate <inline-formula> <tex-math>$text {PM}_{{2.5}}$ </tex-math></inline-formula> concentrations in air based on the aggregate effects of particles on light scattering and absorption. These techniques can be spatially fine-grained, operate in real time, and substantially improve accuracy compared with particle counting sensors. However, existing camera-based techniques fail at night, when pollution exposure and production remain important. We describe the first vision-based technique for nighttime PM2.5 concentration estimation. The design approach differs substantially from that of daytime systems because the primary source of daytime information, the progression of color toward “airlight” color with increasing depth, is much less useful at night and the primary source of nighttime information, the glowing halation regions around artificial light sources, is insignificant during the day. We describe a nighttime pollution estimation technique that builds upon novel “illumination map (IM)” feature. We describe an IM-based dual-channel squeeze-and-excitation convolutional neural network (DSECNet) is to estimate PM2.5 concentrations. This method is evaluated on real-world data and images and outperforms the most advanced related existing (daytime) haze estimation methods, achieving a mean absolute error (MAE) of <inline-formula> <tex-math>$8.65~mu text { g/m}^{{3}}$ </tex-math></inline-formula>, which is 16.99% lower than the state-of-the-art baseline method. To the best of the authors’ knowledge, this is the first vision-based nighttime nighttime PM2.5 estimation method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7015-7027"},"PeriodicalIF":4.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430469","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 : 2025-01-15DOI: 10.1109/JSEN.2025.3527471
Chaojian Xing;Shuxin Liu;Yankai Li;Jing Xu;Jing Li
The stage division and state recognition during ac contactor degradation process is an important prerequisite for realizing its self-perception. In the recognition of its degradation state, the traditional method cannot effectively identify similar and overlapping degradation states, which makes it impossible to make accurate judgments when evaluating the health state of ac contactor. To solve the above problems, a method of ac contactor’s contact degradation stage division and state recognition based on boundary detection and temporal convolutional network-transformer–bidirectional gated recurrent unit (TCN-Transformer–BiGRU) was proposed in this article. First, the characteristic parameters related to the degradation of the ac contactor were obtained through the full life test, and the kernel principal component analysis (KPCA) was introduced to fuse the characteristic parameters. Then, the degradation trend of the contact system was characterized, and the boundary detection method was used to divide the ac contactor degradation stage. Finally, the TCN-Transformer–BiGRU classification prediction model was used to accurately identify the degradation state of the ac contactor. Taking other samples of the same type of ac contactor as examples, it is verified that the method has good universality and high accuracy.
{"title":"Degradation Stage Division and Identification of AC Contactor’s Contact System","authors":"Chaojian Xing;Shuxin Liu;Yankai Li;Jing Xu;Jing Li","doi":"10.1109/JSEN.2025.3527471","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3527471","url":null,"abstract":"The stage division and state recognition during ac contactor degradation process is an important prerequisite for realizing its self-perception. In the recognition of its degradation state, the traditional method cannot effectively identify similar and overlapping degradation states, which makes it impossible to make accurate judgments when evaluating the health state of ac contactor. To solve the above problems, a method of ac contactor’s contact degradation stage division and state recognition based on boundary detection and temporal convolutional network-transformer–bidirectional gated recurrent unit (TCN-Transformer–BiGRU) was proposed in this article. First, the characteristic parameters related to the degradation of the ac contactor were obtained through the full life test, and the kernel principal component analysis (KPCA) was introduced to fuse the characteristic parameters. Then, the degradation trend of the contact system was characterized, and the boundary detection method was used to divide the ac contactor degradation stage. Finally, the TCN-Transformer–BiGRU classification prediction model was used to accurately identify the degradation state of the ac contactor. Taking other samples of the same type of ac contactor as examples, it is verified that the method has good universality and high accuracy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7068-7078"},"PeriodicalIF":4.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430502","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 : 2025-01-15DOI: 10.1109/JSEN.2025.3525662
Hui Xu;Chaorong Zhang;Qingying Wu;Benjamin K. Ng;Chan-Tong Lam;Halim Yanikomeroglu
In the next-generation wireless Internet-of-Things (IoT) networks empowered by modern communication technology, nonorthogonal multiple access (NOMA) and faster-than-Nyquist (FTN) signaling are purportedly two enabling technologies that enhance spectral efficiency (SE) without requiring additional spectrum resources. In addition, simultaneous wireless information and power transfer (SWIPT) technology enables IoT sensors and devices to harvest energy from radio frequency (RF) signals, effectively mitigating power supply limitations. This article proposes and investigates a novel SWIPT-NOMA system based on FTN technology, referred to as FTN-assisted SWIPT-NOMA, for IoT relay networks over Rayleigh fading channels. We provide a comprehensive analysis of the ergodic capacity and achievable rate of the FTN-assisted SWIPT-NOMA system applied in IoT relay networks. Specifically, we explore two distinct relaying architectures geared toward augmenting SE and energy utilization, i.e., power-splitting (PS) and time-switching (TS). We derive approximated expressions for the ergodic capacity and analyze high- signal-to-noise radio (SNR) slopes for sensor users in both architectures. Simulation results show that the ergodic capacity of the proposed system surpasses that of the conventional Nyquist SWIPT-NOMA system, with greater capacity improvements as the FTN acceleration factor $tau $ decreases. This highlights the substantial potential of FTN-assisted SWIPT-NOMA systems in enhancing the performance of IoT relay networks, particularly with respect to SE.
{"title":"FTN-Assisted SWIPT-NOMA Design for IoT Wireless Networks: A Paradigm in Wireless Efficiency and Energy Utilization","authors":"Hui Xu;Chaorong Zhang;Qingying Wu;Benjamin K. Ng;Chan-Tong Lam;Halim Yanikomeroglu","doi":"10.1109/JSEN.2025.3525662","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3525662","url":null,"abstract":"In the next-generation wireless Internet-of-Things (IoT) networks empowered by modern communication technology, nonorthogonal multiple access (NOMA) and faster-than-Nyquist (FTN) signaling are purportedly two enabling technologies that enhance spectral efficiency (SE) without requiring additional spectrum resources. In addition, simultaneous wireless information and power transfer (SWIPT) technology enables IoT sensors and devices to harvest energy from radio frequency (RF) signals, effectively mitigating power supply limitations. This article proposes and investigates a novel SWIPT-NOMA system based on FTN technology, referred to as FTN-assisted SWIPT-NOMA, for IoT relay networks over Rayleigh fading channels. We provide a comprehensive analysis of the ergodic capacity and achievable rate of the FTN-assisted SWIPT-NOMA system applied in IoT relay networks. Specifically, we explore two distinct relaying architectures geared toward augmenting SE and energy utilization, i.e., power-splitting (PS) and time-switching (TS). We derive approximated expressions for the ergodic capacity and analyze high- signal-to-noise radio (SNR) slopes for sensor users in both architectures. Simulation results show that the ergodic capacity of the proposed system surpasses that of the conventional Nyquist SWIPT-NOMA system, with greater capacity improvements as the FTN acceleration factor <inline-formula> <tex-math>$tau $ </tex-math></inline-formula> decreases. This highlights the substantial potential of FTN-assisted SWIPT-NOMA systems in enhancing the performance of IoT relay networks, particularly with respect to SE.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7431-7444"},"PeriodicalIF":4.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446285","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 : 2025-01-14DOI: 10.1109/JSEN.2025.3526951
Qian Liu;Jun Chen
This article presents a Ag nanoparticles (NPs)/MoS2 quantum dots (QDs)/single-walled carbon nanotubes (SWCNTs)/Si photodetector operating in the near-infrared (NIR) region. The QDs and metal NPs are combined on the prepared photodetector to improve the performance through the optical absorption enhancement of QDs and the local surface plasmon resonance effect of metal NPs. Meanwhile, the speed of the device is improved by the high mobility of SWCNTs. At −1 V, the responsivity of the photodetector is 454.7 mA/W for the 808-nm laser and 347.5 mA/W for the 1064-nm laser. The detectivity of this photograph detector reaches $2.75times 10^{{11}}$ Jones at 808 nm and $2.12times 10^{{11}}$ Jones at 1064 nm. It also has a good response time under high-frequency illumination, with a rise time of $2.5~mu $ s and a fall time of $62~mu $ s. Such Ag NPs/MoS2 QDs/SWCNTs/Si heterostructured photodetectors have high performance and can be widely used for NIR photodetection.
{"title":"High-Performance Ag NPs/MoS₂ QDs/SWCNTs/Si Near-Infrared Photodetector","authors":"Qian Liu;Jun Chen","doi":"10.1109/JSEN.2025.3526951","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3526951","url":null,"abstract":"This article presents a Ag nanoparticles (NPs)/MoS2 quantum dots (QDs)/single-walled carbon nanotubes (SWCNTs)/Si photodetector operating in the near-infrared (NIR) region. The QDs and metal NPs are combined on the prepared photodetector to improve the performance through the optical absorption enhancement of QDs and the local surface plasmon resonance effect of metal NPs. Meanwhile, the speed of the device is improved by the high mobility of SWCNTs. At −1 V, the responsivity of the photodetector is 454.7 mA/W for the 808-nm laser and 347.5 mA/W for the 1064-nm laser. The detectivity of this photograph detector reaches <inline-formula> <tex-math>$2.75times 10^{{11}}$ </tex-math></inline-formula> Jones at 808 nm and <inline-formula> <tex-math>$2.12times 10^{{11}}$ </tex-math></inline-formula> Jones at 1064 nm. It also has a good response time under high-frequency illumination, with a rise time of <inline-formula> <tex-math>$2.5~mu $ </tex-math></inline-formula>s and a fall time of <inline-formula> <tex-math>$62~mu $ </tex-math></inline-formula>s. Such Ag NPs/MoS2 QDs/SWCNTs/Si heterostructured photodetectors have high performance and can be widely used for NIR photodetection.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6283-6289"},"PeriodicalIF":4.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430546","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 : 2025-01-14DOI: 10.1109/JSEN.2025.3527150
Xiaozhu Yu;Yiqing Shen
Green tea from different origins develops unique qualities and flavors due to varying environmental factors, such as climate, soil, and water quality. Unfortunately, lower quality green tea is sometimes misrepresented as coming from prestigious origins. This study presents a fast, objective, and effective gas detection method combined with deep learning to assess green tea quality from different origins. First, gas information from green tea of six renowned Chinese origins is captured using an electronic nose (e-nose) system. Next, we introduce an adaptive gas features calculation module (AGFCM) that integrates deep gas features through two methods: multiscales convolution calculations and adaptive attention mechanisms. Finally, we propose an adaptive gas features classification network (AGFC-Net) to classify the gas information from different origins. Following structural optimizations, ablation studies, and comparison across classification methods, AGFC-Net achieves the best results, with 98.42% accuracy, 98.56% ${F}_{{1}}$ -score, and 98.62% kappa coefficient. Overall, this e-nose-based gas detection technology, combined with AGFC-Net, enables effective and rapid identification of green tea quality variations, offering technical support for quality assurance and market safety.
{"title":"Traceability of Green Tea Origin: An Adaptive Gas Features Classification Network Coupled With an Electronic Nose","authors":"Xiaozhu Yu;Yiqing Shen","doi":"10.1109/JSEN.2025.3527150","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3527150","url":null,"abstract":"Green tea from different origins develops unique qualities and flavors due to varying environmental factors, such as climate, soil, and water quality. Unfortunately, lower quality green tea is sometimes misrepresented as coming from prestigious origins. This study presents a fast, objective, and effective gas detection method combined with deep learning to assess green tea quality from different origins. First, gas information from green tea of six renowned Chinese origins is captured using an electronic nose (e-nose) system. Next, we introduce an adaptive gas features calculation module (AGFCM) that integrates deep gas features through two methods: multiscales convolution calculations and adaptive attention mechanisms. Finally, we propose an adaptive gas features classification network (AGFC-Net) to classify the gas information from different origins. Following structural optimizations, ablation studies, and comparison across classification methods, AGFC-Net achieves the best results, with 98.42% accuracy, 98.56% <inline-formula> <tex-math>${F}_{{1}}$ </tex-math></inline-formula>-score, and 98.62% kappa coefficient. Overall, this e-nose-based gas detection technology, combined with AGFC-Net, enables effective and rapid identification of green tea quality variations, offering technical support for quality assurance and market safety.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7708-7715"},"PeriodicalIF":4.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438352","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 : 2025-01-14DOI: 10.1109/JSEN.2024.3523477
Yanyan Hu;Xufeng Lin
In practical sensor networks, especially heterogeneous sensor networks, sensor nodes may have distinct sampling periods and initial sampling time instants, probably their observations are also nonuniform. However, the distributed consensus state estimation problem for continuous-time linear systems in such asynchronous sensor networks has not been addressed in the literature. To solve this problem, this article proposes a consensus filtering algorithm for distributed state estimation over asynchronous sensor networks based on the weighted average consensus strategy. First, asynchronous measurements at each sensor node within a given filtering interval are transformed to the consensus filtering time instant according to the continuous-time system dynamics. Statistical characteristics of converted measurement noises are carefully exploited as well as their cross-correlations induced by the synchronization procedure. It is also discovered that the converted measurement noises are one-step correlated with the discretized process noise. Second, measurements after synchronization are used to update local estimates at sensor nodes with the above correlations taken into account and weighted average consensus iterations are performed based on information interactions of sensor nodes with their neighbors. Finally, the estimation error of the proposed asynchronous consensus filtering algorithm is proved to be exponential mean-square bounded, and its effectiveness is evaluated by a simulation example.
{"title":"Weighted Average Consensus Filtering for Continuous-Time Linear Systems With Asynchronous Sensor Measurements","authors":"Yanyan Hu;Xufeng Lin","doi":"10.1109/JSEN.2024.3523477","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3523477","url":null,"abstract":"In practical sensor networks, especially heterogeneous sensor networks, sensor nodes may have distinct sampling periods and initial sampling time instants, probably their observations are also nonuniform. However, the distributed consensus state estimation problem for continuous-time linear systems in such asynchronous sensor networks has not been addressed in the literature. To solve this problem, this article proposes a consensus filtering algorithm for distributed state estimation over asynchronous sensor networks based on the weighted average consensus strategy. First, asynchronous measurements at each sensor node within a given filtering interval are transformed to the consensus filtering time instant according to the continuous-time system dynamics. Statistical characteristics of converted measurement noises are carefully exploited as well as their cross-correlations induced by the synchronization procedure. It is also discovered that the converted measurement noises are one-step correlated with the discretized process noise. Second, measurements after synchronization are used to update local estimates at sensor nodes with the above correlations taken into account and weighted average consensus iterations are performed based on information interactions of sensor nodes with their neighbors. Finally, the estimation error of the proposed asynchronous consensus filtering algorithm is proved to be exponential mean-square bounded, and its effectiveness is evaluated by a simulation example.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6881-6893"},"PeriodicalIF":4.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446260","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 : 2025-01-14DOI: 10.1109/JSEN.2025.3526997
Fang Ye;Han Zeng;Jinhui Cai
The microseismic monitoring system relies on multiple geophones to detect seismic events, while other methods to determine the operation status of geophones mainly rely on manual inspection of each geophone or comparison of significant changes in observation data, resulting in lower efficiency and accuracy. To address these limitations, we propose an innovative online detection method based on geophone spatiotemporal correlation and data augmentation to continuously monitor geophone status. The proposed extracts time–frequency and energy distribution features from observation data by applying a 230-Hz low-pass filter to preserve the main frequency band and decompose multiple frequency bands. To enhance the dataset, we use Monte Carlo method to generate additional samples of energy distribution features and extend the time–frequency features from 96 samples to 1000 samples using a generative adversarial network (GAN) model. In addition, a dualstream spatiotemporal network model is established for detecting geophone states, which utilizes the spatiotemporal correlation between geophones to improve detection accuracy. The accuracy of the model on the simulated dataset is 98.67%, with an F1-score of 0.9834. Using bootstrap to estimate the performance of the model on real datasets, the average accuracy is 98.99%, with a 95% confidence interval of [0.9688, 1.0000]. The experimental results verify that this method can detect geophone anomalies online, reducing the need for manual intervention. In addition to microseismic monitoring, our method also has potential applications in the detection and maintenance of operational sensors.
{"title":"A Method for Online Detection of the Operating Status of Geophones in the Microseismic System Using Data Augmentation and Spatiotemporal Neural Networks","authors":"Fang Ye;Han Zeng;Jinhui Cai","doi":"10.1109/JSEN.2025.3526997","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3526997","url":null,"abstract":"The microseismic monitoring system relies on multiple geophones to detect seismic events, while other methods to determine the operation status of geophones mainly rely on manual inspection of each geophone or comparison of significant changes in observation data, resulting in lower efficiency and accuracy. To address these limitations, we propose an innovative online detection method based on geophone spatiotemporal correlation and data augmentation to continuously monitor geophone status. The proposed extracts time–frequency and energy distribution features from observation data by applying a 230-Hz low-pass filter to preserve the main frequency band and decompose multiple frequency bands. To enhance the dataset, we use Monte Carlo method to generate additional samples of energy distribution features and extend the time–frequency features from 96 samples to 1000 samples using a generative adversarial network (GAN) model. In addition, a dualstream spatiotemporal network model is established for detecting geophone states, which utilizes the spatiotemporal correlation between geophones to improve detection accuracy. The accuracy of the model on the simulated dataset is 98.67%, with an F1-score of 0.9834. Using bootstrap to estimate the performance of the model on real datasets, the average accuracy is 98.99%, with a 95% confidence interval of [0.9688, 1.0000]. The experimental results verify that this method can detect geophone anomalies online, reducing the need for manual intervention. In addition to microseismic monitoring, our method also has potential applications in the detection and maintenance of operational sensors.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6473-6485"},"PeriodicalIF":4.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446248","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}
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