Pub Date : 2025-01-08DOI: 10.1109/JSEN.2024.3500136
Guangwu Chen;Xin Zhou;Yongbo Si
At present, integrating the Global Navigation Satellite System (GNSS), microelectromechanical system (MEMS), and odometer (OD) is the most practical and low-cost vehicle multifusion navigation system. However, time-varying noise due to satellite signal rejection can lead to serious degradation or even inaccurate positioning accuracy of the system. To overcome this problem, an adaptive spherical simplex unscented Kalman filter (SSUKF), which optimizes the distribution entropy of the innovation based on the Akaike information criterion, is proposed. Initially, the algorithm optimizes the distribution entropy of the SSUKF innovation sequences by considering the Akaike information criterion. Subsequently, it constructs a dynamic equation of the sliding window using the residual and innovative sequences based on covariance matching. Furthermore, the algorithm estimates and adjusts the statistical characteristics of the systematic process and measurement noise online and improves the adaptive ability of the SSUKF. The algorithm overcomes the problem of degradation and dispersion of the filtration accuracy of the SSUKF when there are unknown, inaccurate, or uncertain noise statistics. Finally, simulation and integrated navigation of actual tests were performed. The test outcomes indicate that the proposed algorithm reduces the errors of the east and north velocities by 67.76% and 70.29%, respectively, with root mean square error (RMSE) values of 0.1449 and 0.1308 m/s, respectively. Additionally, when compared to the SSUKF, the proposed algorithm reduces the errors of the latitude and longitude by 56.55% and 81.78%, respectively, with RMSE values of 3.1072 and 1.6076 m, respectively.
{"title":"An Adaptive SSUKF Based on Akaike Information Criterion to Optimize the Distribution Entropy of the Innovation","authors":"Guangwu Chen;Xin Zhou;Yongbo Si","doi":"10.1109/JSEN.2024.3500136","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3500136","url":null,"abstract":"At present, integrating the Global Navigation Satellite System (GNSS), microelectromechanical system (MEMS), and odometer (OD) is the most practical and low-cost vehicle multifusion navigation system. However, time-varying noise due to satellite signal rejection can lead to serious degradation or even inaccurate positioning accuracy of the system. To overcome this problem, an adaptive spherical simplex unscented Kalman filter (SSUKF), which optimizes the distribution entropy of the innovation based on the Akaike information criterion, is proposed. Initially, the algorithm optimizes the distribution entropy of the SSUKF innovation sequences by considering the Akaike information criterion. Subsequently, it constructs a dynamic equation of the sliding window using the residual and innovative sequences based on covariance matching. Furthermore, the algorithm estimates and adjusts the statistical characteristics of the systematic process and measurement noise online and improves the adaptive ability of the SSUKF. The algorithm overcomes the problem of degradation and dispersion of the filtration accuracy of the SSUKF when there are unknown, inaccurate, or uncertain noise statistics. Finally, simulation and integrated navigation of actual tests were performed. The test outcomes indicate that the proposed algorithm reduces the errors of the east and north velocities by 67.76% and 70.29%, respectively, with root mean square error (RMSE) values of 0.1449 and 0.1308 m/s, respectively. Additionally, when compared to the SSUKF, the proposed algorithm reduces the errors of the latitude and longitude by 56.55% and 81.78%, respectively, with RMSE values of 3.1072 and 1.6076 m, respectively.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6055-6066"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422907","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 : 2025-01-08DOI: 10.1109/JSEN.2024.3524757
Zhaohan Liu;Yunan Han;Bo Zhou;Xianbo Qiu
This article presents an improved cylindrical cavity sensor combined with machine learning techniques for the measurement of moisture and drug content (DC) in capsules. The sensor consists of a cylindrical cavity, two probe pins, and a transparent plastic tube that enables capsule passage. The cylindrical cavity, crafted with copper gilding, features inner dimensions of $phi ~100times 12$ mm, resulting in a minimum resonant frequency of 2.3 GHz. The proposed measurement method demonstrated an average sensitivity of 17 MHz per percentage of relative moisture content (MC). Two machine learning methods, namely, principal component analysis (PCA) and the Naive Bayes (NB) algorithms are applied to separate capsules with different DCs. Performing the ${S} _{{21}}$ amplitude and phase parameters analysis at 13.19–13.21 GHz, the proposed testing method combined with these two machine learning methods achieved 100% classification accuracy of capsules with different DCs in a single measurement. Furthermore, the classification accuracy of capsules with different DCs in five measurements reached 94%. This methodology offers a microwave sensor designed for the concurrent and accurate assessment of moisture and mass content in items such as cigarettes and coffee beans that can traverse the plastic tube, encompassing, but not restricted to capsules.
{"title":"Cylindrical Cavity Resonating Sensor for Testing Moisture and Drug Content in Capsule Based on Machine Learning","authors":"Zhaohan Liu;Yunan Han;Bo Zhou;Xianbo Qiu","doi":"10.1109/JSEN.2024.3524757","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524757","url":null,"abstract":"This article presents an improved cylindrical cavity sensor combined with machine learning techniques for the measurement of moisture and drug content (DC) in capsules. The sensor consists of a cylindrical cavity, two probe pins, and a transparent plastic tube that enables capsule passage. The cylindrical cavity, crafted with copper gilding, features inner dimensions of <inline-formula> <tex-math>$phi ~100times 12$ </tex-math></inline-formula> mm, resulting in a minimum resonant frequency of 2.3 GHz. The proposed measurement method demonstrated an average sensitivity of 17 MHz per percentage of relative moisture content (MC). Two machine learning methods, namely, principal component analysis (PCA) and the Naive Bayes (NB) algorithms are applied to separate capsules with different DCs. Performing the <inline-formula> <tex-math>${S} _{{21}}$ </tex-math></inline-formula> amplitude and phase parameters analysis at 13.19–13.21 GHz, the proposed testing method combined with these two machine learning methods achieved 100% classification accuracy of capsules with different DCs in a single measurement. Furthermore, the classification accuracy of capsules with different DCs in five measurements reached 94%. This methodology offers a microwave sensor designed for the concurrent and accurate assessment of moisture and mass content in items such as cigarettes and coffee beans that can traverse the plastic tube, encompassing, but not restricted to capsules.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6290-6300"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430489","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 : 2025-01-08DOI: 10.1109/JSEN.2024.3521896
Tao Sun;Tian Tan;Dongxuan Li;Bernd Markert;Peter B. Shull;Franz Bamer
Data diversity and quantity are crucial for training deep-learning models. However, the impact of dataset diversity and size on biomechanical variable estimation models has not been explicitly investigated during drop landings. This work investigates the impact of the number of subjects and the number of trials per subject on the performance of wearable inertial measurement unit (IMU)-driven deep-learning models for knee moment and ground reaction force estimation during drop-landing tasks. An investigation dataset with 16 subjects and 25 trials per subject was collected in a biomechanical laboratory. The impact of subject and trial quantification was explored under different model complexity and types, as well as data augmentation methods using the investigation dataset. The deep-learning models were implemented by a feature extractor and an estimator realized by several fully connected (FC) layers. The feature extractor was independently evaluated with FC neural networks, convolutional neural network (CNN), long short-term memory (LSTM) model, and transformer model. Three transformation-based data augmentation methods were proposed and compared with the measured dataset (MD). The results showed that the minimum required number of subjects and trials for the models to achieve an estimation performance of 0.85 of R-squared, 0.4 body weight $times $ body height of root mean square error (RMSE), and 0.1 of relative RMSE (rRMSE) is five subjects and five trials. Intriguingly, adding more subjects to the dataset improved the estimation performance while adding more trials did not. In addition, the proposed data augmentation can alleviate the data scarcity issue when the number of trials is small.
{"title":"Influence of Number of Subjects and Number of Trials on Biomechanical Variable Estimation via Deep-Learning Models and Wearable IMUs During Drop Landings","authors":"Tao Sun;Tian Tan;Dongxuan Li;Bernd Markert;Peter B. Shull;Franz Bamer","doi":"10.1109/JSEN.2024.3521896","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3521896","url":null,"abstract":"Data diversity and quantity are crucial for training deep-learning models. However, the impact of dataset diversity and size on biomechanical variable estimation models has not been explicitly investigated during drop landings. This work investigates the impact of the number of subjects and the number of trials per subject on the performance of wearable inertial measurement unit (IMU)-driven deep-learning models for knee moment and ground reaction force estimation during drop-landing tasks. An investigation dataset with 16 subjects and 25 trials per subject was collected in a biomechanical laboratory. The impact of subject and trial quantification was explored under different model complexity and types, as well as data augmentation methods using the investigation dataset. The deep-learning models were implemented by a feature extractor and an estimator realized by several fully connected (FC) layers. The feature extractor was independently evaluated with FC neural networks, convolutional neural network (CNN), long short-term memory (LSTM) model, and transformer model. Three transformation-based data augmentation methods were proposed and compared with the measured dataset (MD). The results showed that the minimum required number of subjects and trials for the models to achieve an estimation performance of 0.85 of R-squared, 0.4 body weight <inline-formula> <tex-math>$times $ </tex-math></inline-formula> body height of root mean square error (RMSE), and 0.1 of relative RMSE (rRMSE) is five subjects and five trials. Intriguingly, adding more subjects to the dataset improved the estimation performance while adding more trials did not. In addition, the proposed data augmentation can alleviate the data scarcity issue when the number of trials is small.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7532-7543"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446173","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 : 2025-01-08DOI: 10.1109/JSEN.2024.3523849
John A. Berkebile;Asim H. Gazi;Michael Chan;Tyler D. Albarran;Christopher J. Rozell;Omer T. Inan;Paul A. Beach
In neurodegenerative conditions like Parkinson’s disease (PD) and multiple system atrophy (MSA), cardiovascular autonomic dysfunction (CVAD) is associated with several poor long-term health outcomes. CVAD commonly manifests as orthostatic hypotension (OH), a sustained drop in blood pressure (BP) upon standing that can cause syncope and falls. Conventional screening methods for OH are suboptimal and formal autonomic testing is limited to specialized centers. This study explores a multimodal wearable sensing patch for remote monitoring of CVAD. We collected waveform data during clinical autonomic testing and a 24-h period at home from 20 participants with synucleinopathies (12 with OH) and six healthy controls. We developed an automated posture detection pipeline that identified 103 at-home orthostatic events. Then, physiomarkers related to heart rate variability (HRV), cardiac mechanics, and vasomotor function were derived during the supine and standing periods associated with clinical and at-home orthostatic transitions. Comparisons of baroreflex-related supine physiomarkers revealed significant differences between those with and without OH. We characterized cardiovascular autonomic dynamics while standing, leveraging low-dimensional representations, and found marked differences in the aggregate responses between groups. We also observed significantly higher within-subject similarity between the at-home responses of the OH group. Finally, we examined the discriminative power of the patch’s physiomarkers and demonstrated accurate classification of persons with OH during the clinical stand testing (${F} 1=0.83$ ). This study is the first to couple orthostatic event detection with machine learning (ML) analysis of wearable-derived physiomarkers, illustrating that wearable sensing can accurately classify OH and provide novel insights into CVAD outside the clinic.
{"title":"Remote Monitoring of Cardiovascular Autonomic Dysfunction in Synucleinopathies With a Wearable Chest Patch","authors":"John A. Berkebile;Asim H. Gazi;Michael Chan;Tyler D. Albarran;Christopher J. Rozell;Omer T. Inan;Paul A. Beach","doi":"10.1109/JSEN.2024.3523849","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3523849","url":null,"abstract":"In neurodegenerative conditions like Parkinson’s disease (PD) and multiple system atrophy (MSA), cardiovascular autonomic dysfunction (CVAD) is associated with several poor long-term health outcomes. CVAD commonly manifests as orthostatic hypotension (OH), a sustained drop in blood pressure (BP) upon standing that can cause syncope and falls. Conventional screening methods for OH are suboptimal and formal autonomic testing is limited to specialized centers. This study explores a multimodal wearable sensing patch for remote monitoring of CVAD. We collected waveform data during clinical autonomic testing and a 24-h period at home from 20 participants with synucleinopathies (12 with OH) and six healthy controls. We developed an automated posture detection pipeline that identified 103 at-home orthostatic events. Then, physiomarkers related to heart rate variability (HRV), cardiac mechanics, and vasomotor function were derived during the supine and standing periods associated with clinical and at-home orthostatic transitions. Comparisons of baroreflex-related supine physiomarkers revealed significant differences between those with and without OH. We characterized cardiovascular autonomic dynamics while standing, leveraging low-dimensional representations, and found marked differences in the aggregate responses between groups. We also observed significantly higher within-subject similarity between the at-home responses of the OH group. Finally, we examined the discriminative power of the patch’s physiomarkers and demonstrated accurate classification of persons with OH during the clinical stand testing (<inline-formula> <tex-math>${F} 1=0.83$ </tex-math></inline-formula>). This study is the first to couple orthostatic event detection with machine learning (ML) analysis of wearable-derived physiomarkers, illustrating that wearable sensing can accurately classify OH and provide novel insights into CVAD outside the clinic.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7250-7262"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430585","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 : 2025-01-08DOI: 10.1109/JSEN.2024.3524909
S. López-Soriano;P. Brunet;Mohammed A. Alsultan;Joan Melià-Seguí
Remote characterization of liquids can be beneficial in various industry sectors such as food and oil industries, medical diagnostics, agriculture, or waste management. However, current wireless solutions are often expensive and labor-intensive. Antenna-based sensors (ABSs) can potentially decrease the complexity and cost of current solutions. Ultrahigh-frequency (UHF) radio frequency identification (RFID) sensors for liquid characterization have the potential to provide remote monitoring while fulfilling the previous requirements. This work demonstrates the combined effects of the dielectric properties on the operation of RFID-based sensors and it presents an innovative approach for estimating the dielectric properties of a liquid under test (LUT) from the read range peak frequency and magnitude variations of a UHF RFID tag. The tag antenna consists of a patch-like antenna with an absorbent embedded into its substrate. Filling the absorbent with different LUTs modifies the dielectric properties of the substrate which has a measurable effect on the tag read range. Measurements show that the proposed method together with the specific sensor design enables the dielectric characterization of liquids using an energy-efficient and low-cost solution achieving an accuracy over 93.5% and 84% in the estimation of the LUT relative permittivity and the loss tangent, respectively, compared to the transmission line (TL) method.
{"title":"Remote Identification of Liquids Using Absorbent Materials: A Passive UHF RFID-Based Method","authors":"S. López-Soriano;P. Brunet;Mohammed A. Alsultan;Joan Melià-Seguí","doi":"10.1109/JSEN.2024.3524909","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524909","url":null,"abstract":"Remote characterization of liquids can be beneficial in various industry sectors such as food and oil industries, medical diagnostics, agriculture, or waste management. However, current wireless solutions are often expensive and labor-intensive. Antenna-based sensors (ABSs) can potentially decrease the complexity and cost of current solutions. Ultrahigh-frequency (UHF) radio frequency identification (RFID) sensors for liquid characterization have the potential to provide remote monitoring while fulfilling the previous requirements. This work demonstrates the combined effects of the dielectric properties on the operation of RFID-based sensors and it presents an innovative approach for estimating the dielectric properties of a liquid under test (LUT) from the read range peak frequency and magnitude variations of a UHF RFID tag. The tag antenna consists of a patch-like antenna with an absorbent embedded into its substrate. Filling the absorbent with different LUTs modifies the dielectric properties of the substrate which has a measurable effect on the tag read range. Measurements show that the proposed method together with the specific sensor design enables the dielectric characterization of liquids using an energy-efficient and low-cost solution achieving an accuracy over 93.5% and 84% in the estimation of the LUT relative permittivity and the loss tangent, respectively, compared to the transmission line (TL) method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7301-7309"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446286","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 : 2025-01-08DOI: 10.1109/JSEN.2024.3524866
Hsin-Yuan Chang;Wei-En Chang;Wei-Ho Chung
Multisensory cooperative localization has emerged as a promising approach to enhance positioning accuracy in vehicular ad hoc networks (VANETs). This article proposes a sensor fusion localization algorithm that integrates global navigation satellite system (GNSS), radar, and received signal strength indicator (RSSI) measurements to refine current localization using both present and historical measurements. To emphasize the differing levels of importance between historical and current measurements in cooperative localization, the proposed algorithm combines the capabilities of long short-term memory (LSTM) models for capturing temporal patterns, ensemble localization for enhancing neighboring estimations, and weighted attention mechanisms for effectively integrating information from both temporal and spatial domains. Extensive simulation results consistently demonstrate the superior localization performance of the proposed algorithm compared to state-of-the-art sensor fusion benchmark algorithms, including the derived Cramer-Rao lower bound (CRLB), when addressing a progressively increasing difficulty across two driving scenarios. The proposed cooperative localization algorithm improves localization error by at least 29% compared to original GNSS measurements.
{"title":"Spatial-Temporal Weighted Attention Model for Cooperative Vehicular Positioning System","authors":"Hsin-Yuan Chang;Wei-En Chang;Wei-Ho Chung","doi":"10.1109/JSEN.2024.3524866","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524866","url":null,"abstract":"Multisensory cooperative localization has emerged as a promising approach to enhance positioning accuracy in vehicular ad hoc networks (VANETs). This article proposes a sensor fusion localization algorithm that integrates global navigation satellite system (GNSS), radar, and received signal strength indicator (RSSI) measurements to refine current localization using both present and historical measurements. To emphasize the differing levels of importance between historical and current measurements in cooperative localization, the proposed algorithm combines the capabilities of long short-term memory (LSTM) models for capturing temporal patterns, ensemble localization for enhancing neighboring estimations, and weighted attention mechanisms for effectively integrating information from both temporal and spatial domains. Extensive simulation results consistently demonstrate the superior localization performance of the proposed algorithm compared to state-of-the-art sensor fusion benchmark algorithms, including the derived Cramer-Rao lower bound (CRLB), when addressing a progressively increasing difficulty across two driving scenarios. The proposed cooperative localization algorithm improves localization error by at least 29% compared to original GNSS measurements.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7655-7666"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438357","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 : 2025-01-07DOI: 10.1109/JSEN.2024.3524279
Abdullah Baihan;Mohammed Amoon;Torki Altameem;Mohammed Hashem
Contagious diseases such as COVID have significantly increased the need for personal health-monitoring systems through wearable devices. This article presents a model based on wearable devices for a health-monitoring system that aims to assist Hajj and Umrah pilgrims with tracking their vitals and providing them with safety advice. Using an advance deep reinforcement learning (DRL) model called deep Q network (DQN) helps to make adaptive decisions on alerts based on the context and historical data records of individual health records and mitigates false alarms. Wearable wristbands equipped with different types of sensors such as temperature, SPO2, time of flight (ToF), and heart rate sensors are employed. The data accumulated from sensors are analyzed periodically for density estimation and safety classification. The classification is based on the level of a threshold. The threshold level is determined based on the distance between persons and the number of persons. The analysis of the sensed data recommends a safe distance (SD) for person-to-person interaction and provides self-assisted health monitoring. The system is evaluated in different intervals between 5 and 60 min. Results reveal that the proposed model effectively improves the data analysis rate by 14.58%, density detection by 16.12%, recommendations by 15.79%, and distortion error by 11.57%.
{"title":"Pioneering Wearable Sensor-Driven Health-Monitoring System for Contagious Disease Prevention With Intelligent Crowd-Counting Models","authors":"Abdullah Baihan;Mohammed Amoon;Torki Altameem;Mohammed Hashem","doi":"10.1109/JSEN.2024.3524279","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524279","url":null,"abstract":"Contagious diseases such as COVID have significantly increased the need for personal health-monitoring systems through wearable devices. This article presents a model based on wearable devices for a health-monitoring system that aims to assist Hajj and Umrah pilgrims with tracking their vitals and providing them with safety advice. Using an advance deep reinforcement learning (DRL) model called deep Q network (DQN) helps to make adaptive decisions on alerts based on the context and historical data records of individual health records and mitigates false alarms. Wearable wristbands equipped with different types of sensors such as temperature, SPO2, time of flight (ToF), and heart rate sensors are employed. The data accumulated from sensors are analyzed periodically for density estimation and safety classification. The classification is based on the level of a threshold. The threshold level is determined based on the distance between persons and the number of persons. The analysis of the sensed data recommends a safe distance (SD) for person-to-person interaction and provides self-assisted health monitoring. The system is evaluated in different intervals between 5 and 60 min. Results reveal that the proposed model effectively improves the data analysis rate by 14.58%, density detection by 16.12%, recommendations by 15.79%, and distortion error by 11.57%.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7403-7416"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446175","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}
The charge quantity is a fundamental physical parameter that reflects the electrical state of an object. Accurately estimating the charge of an object facilitates the assessment of electrostatic discharge risks and aids in preventing accidents. Measuring the charge of a moving object has long posed a technical challenge in this field. This article proposes a noncontact method for estimating the charge of a moving object by using the electrostatic signals generated by the object’s motion and its motion data. First, a noncontact charge measurement model based on a mutual capacitance matrix was developed using the image charge method in electrostatics. The accuracy of the model was verified through simulations of the charge on the sensing electrode. Next, a correction method for charge calculation was further proposed to reduce measurement errors caused by parasitic capacitance from the experimental setup. Finally, a verification experiment was conducted, wherein an electrometer measured the charge of the object in a stationary state, providing a reference to validate the proposed method. The experimental results demonstrated a strong correlation (${r}~gt 0.96$ ) and consistency (within the 95% confidence interval) between the measured and reference values across various conditions. The absolute error of the measurements was within ±1 nC (mean ± standard deviation: $- 0.04~pm ~0.4$ nC), with a relative error of approximately ±10%. This study contributes to the prevention of electrostatic discharge accidents involving moving objects and presents novel insights and technological approaches for electrostatic detection.
{"title":"A Noncontact Method for Measuring the Charge of a Moving Object Based on Mutual Capacitance Matrix","authors":"Zhongzheng He;Sichao Qin;Juan Wu;Yu Qiao;Pengfei Li;Xi Chen","doi":"10.1109/JSEN.2024.3524277","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524277","url":null,"abstract":"The charge quantity is a fundamental physical parameter that reflects the electrical state of an object. Accurately estimating the charge of an object facilitates the assessment of electrostatic discharge risks and aids in preventing accidents. Measuring the charge of a moving object has long posed a technical challenge in this field. This article proposes a noncontact method for estimating the charge of a moving object by using the electrostatic signals generated by the object’s motion and its motion data. First, a noncontact charge measurement model based on a mutual capacitance matrix was developed using the image charge method in electrostatics. The accuracy of the model was verified through simulations of the charge on the sensing electrode. Next, a correction method for charge calculation was further proposed to reduce measurement errors caused by parasitic capacitance from the experimental setup. Finally, a verification experiment was conducted, wherein an electrometer measured the charge of the object in a stationary state, providing a reference to validate the proposed method. The experimental results demonstrated a strong correlation (<inline-formula> <tex-math>${r}~gt 0.96$ </tex-math></inline-formula>) and consistency (within the 95% confidence interval) between the measured and reference values across various conditions. The absolute error of the measurements was within ±1 nC (mean ± standard deviation: <inline-formula> <tex-math>$- 0.04~pm ~0.4$ </tex-math></inline-formula> nC), with a relative error of approximately ±10%. This study contributes to the prevention of electrostatic discharge accidents involving moving objects and presents novel insights and technological approaches for electrostatic detection.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6940-6951"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446266","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 : 2025-01-07DOI: 10.1109/JSEN.2024.3524001
Md. Bakey Billa;Touhidul Alam;Mohammad Tariqul Islam
The widespread issue of honey adulteration poses significant health risks and economic losses, necessitating more efficient and reliable detection methods. Traditional techniques are often time-consuming, expensive, and require sophisticated equipment. Moreover, the substrates traditionally used in metamaterial-based sensors present challenges such as rigidity, limited sensitivity, and selectivity. This study aims to address this problem by preparing Mg0.75Co0.15Ni0.1Fe2O4 nanoparticles and evaluating their performance in a flexible metamaterial sensor for honey adulteration detection. The dielectric property of the substrate is measured using a dielectric assessment kit (DAK)-3.5, with dielectric constants found to be 1.71. The proposed sensor fabricated on a Mg-Co ferrite substrate with a modified maze-shaped structure. The metamaterial exhibits $mu $ -negative characteristics within the frequency range of 7.6–8 GHz both simulated and measured, making it suitable for sensing applications. To optimize sensor performance, a circuit model is developed in Advanced Design System (ADS) and verified with CST microwave studio simulations, showing improved real-time efficiency. The sensor’s performance is evaluated using pure honey and honey adulterated with 5% and 10% saccharine and sugar. The dielectric constant increased with adulterant concentration, from 12.5 for pure honey to 15 for honey with 10% saccharine. The corresponding resonant frequency shifts increased from 230 to 480 MHz. Sensitivity ranged from 20 to 60 MHz/adulterant both simulated and measured. The relative error between simulated and measured data remained below 0.4%, confirming the sensor’s accuracy. The linear relationship between the effective dielectric constant and the resonant frequency shift, documented in the study’s figures, demonstrates a predictable method to determine honey adulteration levels, enhancing the practical applicability of this sensor in industrial food quality control.
{"title":"Preparation and Performance Analysis of Mg0.75Co0.15Ni0.1Fe2O4 Nanoparticle-Based Flexible Metamaterial for Honey Adulteration Detection","authors":"Md. Bakey Billa;Touhidul Alam;Mohammad Tariqul Islam","doi":"10.1109/JSEN.2024.3524001","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524001","url":null,"abstract":"The widespread issue of honey adulteration poses significant health risks and economic losses, necessitating more efficient and reliable detection methods. Traditional techniques are often time-consuming, expensive, and require sophisticated equipment. Moreover, the substrates traditionally used in metamaterial-based sensors present challenges such as rigidity, limited sensitivity, and selectivity. This study aims to address this problem by preparing Mg0.75Co0.15Ni0.1Fe2O4 nanoparticles and evaluating their performance in a flexible metamaterial sensor for honey adulteration detection. The dielectric property of the substrate is measured using a dielectric assessment kit (DAK)-3.5, with dielectric constants found to be 1.71. The proposed sensor fabricated on a Mg-Co ferrite substrate with a modified maze-shaped structure. The metamaterial exhibits <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-negative characteristics within the frequency range of 7.6–8 GHz both simulated and measured, making it suitable for sensing applications. To optimize sensor performance, a circuit model is developed in Advanced Design System (ADS) and verified with CST microwave studio simulations, showing improved real-time efficiency. The sensor’s performance is evaluated using pure honey and honey adulterated with 5% and 10% saccharine and sugar. The dielectric constant increased with adulterant concentration, from 12.5 for pure honey to 15 for honey with 10% saccharine. The corresponding resonant frequency shifts increased from 230 to 480 MHz. Sensitivity ranged from 20 to 60 MHz/adulterant both simulated and measured. The relative error between simulated and measured data remained below 0.4%, confirming the sensor’s accuracy. The linear relationship between the effective dielectric constant and the resonant frequency shift, documented in the study’s figures, demonstrates a predictable method to determine honey adulteration levels, enhancing the practical applicability of this sensor in industrial food quality control.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7135-7144"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430509","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 : 2025-01-07DOI: 10.1109/JSEN.2024.3524451
Liang Xiao;Jiahui Yuan;Jinfeng Xie
In magnetic resonance imaging (MRI), the method of acquiring signals close to the receiving coil and transferring the acquisition data via optical fibers can avoid electromagnetic (EM) interference and crosstalk in signal transmission to the greatest extent possible. The challenge lies in determining how to realize data transmission cost-effectively and maintain phase coherence between the radio frequency (RF) generator and the signal receiver. This article presents a design for an optical fiber spectrometer that is based on an optical fiber transmission scheme with lightweight resource consumption. The proposed spectrometer is composed of a main unit and an acquisition unit with four receiving channels. The acquisition data are uploaded to the main unit using the SerialLite II protocol, which has a very high transmission rate and is simple to implement in a field-programmable gate array (FPGA) device. The parameters and instructions are sent to the acquisition unit based on the use of a self-defined packing and conventional 8b/10b encoding, and a 60 MHz clock is also transmitted for decoding and signal sampling. To maintain phase coherence, specific timing information is appended to the downloaded initialization instruction for the digital local oscillator (LO) of the digital down converter (DDC) to ensure time alignment of the initialization events. Test results show that the transmission rate for the acquisition data reaches approximately 1440 Mb/s, and phase coherence is maintained reliably. Imaging experiments in a 0.35 T MRI system achieved satisfactory image quality.
{"title":"A Compact MRI Spectrometer Using Optical Fiber Transmission for Multichannel Signal Acquisition","authors":"Liang Xiao;Jiahui Yuan;Jinfeng Xie","doi":"10.1109/JSEN.2024.3524451","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524451","url":null,"abstract":"In magnetic resonance imaging (MRI), the method of acquiring signals close to the receiving coil and transferring the acquisition data via optical fibers can avoid electromagnetic (EM) interference and crosstalk in signal transmission to the greatest extent possible. The challenge lies in determining how to realize data transmission cost-effectively and maintain phase coherence between the radio frequency (RF) generator and the signal receiver. This article presents a design for an optical fiber spectrometer that is based on an optical fiber transmission scheme with lightweight resource consumption. The proposed spectrometer is composed of a main unit and an acquisition unit with four receiving channels. The acquisition data are uploaded to the main unit using the SerialLite II protocol, which has a very high transmission rate and is simple to implement in a field-programmable gate array (FPGA) device. The parameters and instructions are sent to the acquisition unit based on the use of a self-defined packing and conventional 8b/10b encoding, and a 60 MHz clock is also transmitted for decoding and signal sampling. To maintain phase coherence, specific timing information is appended to the downloaded initialization instruction for the digital local oscillator (LO) of the digital down converter (DDC) to ensure time alignment of the initialization events. Test results show that the transmission rate for the acquisition data reaches approximately 1440 Mb/s, and phase coherence is maintained reliably. Imaging experiments in a 0.35 T MRI system achieved satisfactory image quality.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7276-7290"},"PeriodicalIF":4.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446221","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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