Pub Date : 2024-04-26DOI: 10.1109/JRFID.2024.3394063
Anish Kumar Gupta;Punitkumar Bhavsar
This article proposes incorporation of reconfigurable intelligent surfaces (RIS) with ambient backscatter (AmBc) communication. The aim is to strengthen the radio links between ambient source to backscatter device (BD) and from BD to cooperative receiver (CR). The analysis considers correlated Rician channels and incorporates hardware imperfection (HWI) modeled by von-Mises distribution. We derive a closed form expression of the outage probability which is validated through Monte Carlo (MC) simulations. The findings show improvement in outage probability for a deliberate selection of parameters of the proposed RIS assisted AmBc communication system. In addition, the importance of correlated-channel behavior is considered and analyzed for its effect on the outage probability performance of the proposed system.
{"title":"RIS Assisted AmBc Communication Over Spatially Correlated Channels","authors":"Anish Kumar Gupta;Punitkumar Bhavsar","doi":"10.1109/JRFID.2024.3394063","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3394063","url":null,"abstract":"This article proposes incorporation of reconfigurable intelligent surfaces (RIS) with ambient backscatter (AmBc) communication. The aim is to strengthen the radio links between ambient source to backscatter device (BD) and from BD to cooperative receiver (CR). The analysis considers correlated Rician channels and incorporates hardware imperfection (HWI) modeled by von-Mises distribution. We derive a closed form expression of the outage probability which is validated through Monte Carlo (MC) simulations. The findings show improvement in outage probability for a deliberate selection of parameters of the proposed RIS assisted AmBc communication system. In addition, the importance of correlated-channel behavior is considered and analyzed for its effect on the outage probability performance of the proposed system.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"262-269"},"PeriodicalIF":0.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-24DOI: 10.1109/JRFID.2024.3392943
Sheng Liu;Xiaotian Zhuang;Liang Yan;Yu Wang;Shengnan Wu;Yisheng Lv;Fenghua Zhu;Fei-Yue Wang
In order to solve the problems of package delivery delay and package loss caused by the sudden increase of package transportation demand during large-scale promotion activities such as 11.11 and 6.18, this paper proposes a parallel logistic network method, aiming at the logistic network of large logistic enterprises, establishes its equivalent virtual logistic network, senses the package transportation demand and network state of the actual logistic network, circularly simulates the operation in advance, finds the delayed and lost packages, analyzes the reasons, and adjust the parameters of network nodes and transportation lines to ensure that packages are delivered on time at low cost. Then the adjusted virtual network parameters are fed back to the actual logistic network, so as to realize the efficient operation of logistic enterprises. A simulation engine ensures that the simulation of 400 million package distribution in 30 days can be completed in half an hour on a personal computer. The application results show that the parallel logistic network reduces the package transportation time by about 10%. The transportation distance is reduced by 7%. Reduce transportation costs by 15%.
{"title":"A Parallel Logistic Network Simulation Method and System to Improve Logistics Efficiency","authors":"Sheng Liu;Xiaotian Zhuang;Liang Yan;Yu Wang;Shengnan Wu;Yisheng Lv;Fenghua Zhu;Fei-Yue Wang","doi":"10.1109/JRFID.2024.3392943","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3392943","url":null,"abstract":"In order to solve the problems of package delivery delay and package loss caused by the sudden increase of package transportation demand during large-scale promotion activities such as 11.11 and 6.18, this paper proposes a parallel logistic network method, aiming at the logistic network of large logistic enterprises, establishes its equivalent virtual logistic network, senses the package transportation demand and network state of the actual logistic network, circularly simulates the operation in advance, finds the delayed and lost packages, analyzes the reasons, and adjust the parameters of network nodes and transportation lines to ensure that packages are delivered on time at low cost. Then the adjusted virtual network parameters are fed back to the actual logistic network, so as to realize the efficient operation of logistic enterprises. A simulation engine ensures that the simulation of 400 million package distribution in 30 days can be completed in half an hour on a personal computer. The application results show that the parallel logistic network reduces the package transportation time by about 10%. The transportation distance is reduced by 7%. Reduce transportation costs by 15%.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"580-591"},"PeriodicalIF":2.3,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141453461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-24DOI: 10.1109/JRFID.2024.3393242
Kai Huang;Yongtao Ma;Yicheng Chu;Zemin Wang
As the global population ages, the prevalence of elderly individuals living independently has risen. As one of the main threats to the health of the elderly, falling seriously reduces the happiness of the elderly and imposes a burden on the medical system. Therefore, the exploration of automatic fall detection systems is crucial. However, proposed fall detection systems exhibit varying degrees of shortcomings. In this paper, we propose a new fall detection method utilizing Doppler shift with RFID passive tags. The motion of the passive tag induces a Doppler shift in the reflected signal. This method is the first to use Doppler frequency shift for fall detection in RFID. Additionally, a velocity-position iteration algorithm is applied to ascertain the tag’s position and velocity over time. The combination of velocity and position for fall detection yields higher accuracy compared to individual parameters. The proposed method demonstrates the capability to differentiate between sudden and soft falls, aiding medical professionals in identifying the cause of a user’s fall. The experimental results demonstrate that the system achieves an accuracy rate of 91.7% in detecting sudden falls, and this accuracy remains at 86.8% even after incorporating soft falls into the analysis. Consequently, the proposed method proves to be an effective and reliable approach for fall detection.
随着全球人口老龄化的加剧，独立生活的老年人越来越多。作为老年人健康的主要威胁之一，跌倒严重降低了老年人的幸福感，也给医疗系统带来了负担。因此，探索自动跌倒检测系统至关重要。然而，目前提出的跌倒检测系统都存在不同程度的缺陷。在本文中，我们提出了一种利用多普勒频移和 RFID 无源标签的新型跌倒检测方法。无源标签的运动会引起反射信号的多普勒频移。该方法首次将多普勒频移用于 RFID 的跌倒检测。此外，还采用了速度-位置迭代算法来确定标签随时间变化的位置和速度。与单个参数相比，结合速度和位置进行跌倒检测的准确度更高。所提出的方法证明了区分突然跌倒和软跌倒的能力，有助于医疗专业人员识别用户跌倒的原因。实验结果表明，该系统检测突然跌倒的准确率达到 91.7%，即使将软跌倒纳入分析，准确率也保持在 86.8%。因此，所提出的方法被证明是一种有效、可靠的跌倒检测方法。
{"title":"Tag-Fall: A Doppler Shift-Based Fall Detection Method Using RFID Passive Tags","authors":"Kai Huang;Yongtao Ma;Yicheng Chu;Zemin Wang","doi":"10.1109/JRFID.2024.3393242","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3393242","url":null,"abstract":"As the global population ages, the prevalence of elderly individuals living independently has risen. As one of the main threats to the health of the elderly, falling seriously reduces the happiness of the elderly and imposes a burden on the medical system. Therefore, the exploration of automatic fall detection systems is crucial. However, proposed fall detection systems exhibit varying degrees of shortcomings. In this paper, we propose a new fall detection method utilizing Doppler shift with RFID passive tags. The motion of the passive tag induces a Doppler shift in the reflected signal. This method is the first to use Doppler frequency shift for fall detection in RFID. Additionally, a velocity-position iteration algorithm is applied to ascertain the tag’s position and velocity over time. The combination of velocity and position for fall detection yields higher accuracy compared to individual parameters. The proposed method demonstrates the capability to differentiate between sudden and soft falls, aiding medical professionals in identifying the cause of a user’s fall. The experimental results demonstrate that the system achieves an accuracy rate of 91.7% in detecting sudden falls, and this accuracy remains at 86.8% even after incorporating soft falls into the analysis. Consequently, the proposed method proves to be an effective and reliable approach for fall detection.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"252-261"},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-23DOI: 10.1109/JRFID.2024.3392682
Chai Yang;Xiaoxuan Hu;Qingli Zhu;Qiang Tu;Hongyang Geng;Jing Xu;Zhenfeng Liu;Yanjun Wang;Jing Wang
Individual medical costs prediction refers to the process of estimating the expenses associated with a patient’s medical care. Effective medical costs prediction helps in budgeting, resource allocation, and financial planning in healthcare settings, making it a crucial tool for both healthcare providers and patients. This study introduces an advanced method for predicting medical consumables costs, leveraging clinical notes and diagnosis related groups (DRGs). The approach employs Bidirectional Encoder Representations from Transformers (BERT) for text vectorization to enhance disease diagnosis and surgical procedure prediction within DRGs using Light Gradient Boosting Machine (LightGBM), and Random Forest Regression for accurate medical costs prediction. It achieves over 91% accuracy in predicting disease diagnosis and surgical procedures, and a Mean Absolute Error (MAE) of 2281.20 and an R-squared value of 0.8557. These metrics indicate a high level of accuracy and reliability, showcasing the model’s efficacy in predicting medical costs in a healthcare setting. This method improves hospital resource management and costs estimation by integrating semantic information with machine learning algorithms.
{"title":"Individual Medical Costs Prediction Methods Based on Clinical Notes and DRGs","authors":"Chai Yang;Xiaoxuan Hu;Qingli Zhu;Qiang Tu;Hongyang Geng;Jing Xu;Zhenfeng Liu;Yanjun Wang;Jing Wang","doi":"10.1109/JRFID.2024.3392682","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3392682","url":null,"abstract":"Individual medical costs prediction refers to the process of estimating the expenses associated with a patient’s medical care. Effective medical costs prediction helps in budgeting, resource allocation, and financial planning in healthcare settings, making it a crucial tool for both healthcare providers and patients. This study introduces an advanced method for predicting medical consumables costs, leveraging clinical notes and diagnosis related groups (DRGs). The approach employs Bidirectional Encoder Representations from Transformers (BERT) for text vectorization to enhance disease diagnosis and surgical procedure prediction within DRGs using Light Gradient Boosting Machine (LightGBM), and Random Forest Regression for accurate medical costs prediction. It achieves over 91% accuracy in predicting disease diagnosis and surgical procedures, and a Mean Absolute Error (MAE) of 2281.20 and an R-squared value of 0.8557. These metrics indicate a high level of accuracy and reliability, showcasing the model’s efficacy in predicting medical costs in a healthcare setting. This method improves hospital resource management and costs estimation by integrating semantic information with machine learning algorithms.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"412-418"},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141068931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-22DOI: 10.1109/JRFID.2024.3392152
Songlin Bai;Yunzhe Wang;Zhiyao Luo;Yonglin Tian
Diverse and large-high-quality data are essential to the deep learning algorithms for autonomous driving. However, manual data collection in intricate traffic scenarios is expensive, time-consuming, and hard to meet the requirements of quantity and quality. Though some generative methods have been used for traffic image synthesis and editing to tackle the problem of manual data collection, the impact of object relationships on data diversity is frequently disregarded in these approaches. In this paper, we focus on the occluded pedestrians within complex driving scenes and propose an occupancy-aided augmentation method for occluded humans in autonomous driving denoted as “Drive-CP“, built upon the foundation of parallel vision. Due to the flourishing development of AI Content Generation (AIGC) technologies, it is possible to automate the generation of diverse 2D and 3D assets. Based on AIGC technologies, we can construct our human library automatically, significantly enhancing the diversity of the training data. We experimentally demonstrate that Drive-CP can generate diversified occluded pedestrians in real complex traffic scenes and demonstrate its effectiveness in enriching the training set in object detection tasks.
{"title":"DriveCP: Occupancy-Assisted Scenario Augmentation for Occluded Pedestrian Perception Based on Parallel Vision","authors":"Songlin Bai;Yunzhe Wang;Zhiyao Luo;Yonglin Tian","doi":"10.1109/JRFID.2024.3392152","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3392152","url":null,"abstract":"Diverse and large-high-quality data are essential to the deep learning algorithms for autonomous driving. However, manual data collection in intricate traffic scenarios is expensive, time-consuming, and hard to meet the requirements of quantity and quality. Though some generative methods have been used for traffic image synthesis and editing to tackle the problem of manual data collection, the impact of object relationships on data diversity is frequently disregarded in these approaches. In this paper, we focus on the occluded pedestrians within complex driving scenes and propose an occupancy-aided augmentation method for occluded humans in autonomous driving denoted as “Drive-CP“, built upon the foundation of parallel vision. Due to the flourishing development of AI Content Generation (AIGC) technologies, it is possible to automate the generation of diverse 2D and 3D assets. Based on AIGC technologies, we can construct our human library automatically, significantly enhancing the diversity of the training data. We experimentally demonstrate that Drive-CP can generate diversified occluded pedestrians in real complex traffic scenes and demonstrate its effectiveness in enriching the training set in object detection tasks.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"235-240"},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140902522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel heading angle detection and compensation method is presented with the aim of addressing the navigation and localization accuracy challenges that unmanned robots encounter in their daily inspection jobs, thereby significantly raising the bar for smart port building and promoting the development of ports of superior quality. The Extended Kalman Filter (EKF) algorithm and a Global Navigation Satellite System (GNSS) Inertial Navigation System (INS)/Magnetometer combination navigation technology form the basis of this strategy. The suggested deviation detection and compensating method greatly enhances the navigation system’s performance when compared to the conventional EKF algorithm. Furthermore, we improved the navigation system’s ability to adapt to complex surroundings and sudden changes by adding the Particle Swarm Optimization (PSO) algorithm to the process. This allowed us to further optimize the system parameters based on the original innovation. This development is critical to enhancing unmanned robot navigation accuracy at smart ports and providing robust technical support for the growth of port automation and intelligence.
{"title":"Application Research of Parameter Uncertainty Optimization Method in Steering Detection and Correction System","authors":"Jiahao Yang;Ming Xu;Longhua Ma;Fangle Chang;Wenxiang Wu","doi":"10.1109/JRFID.2024.3392444","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3392444","url":null,"abstract":"A novel heading angle detection and compensation method is presented with the aim of addressing the navigation and localization accuracy challenges that unmanned robots encounter in their daily inspection jobs, thereby significantly raising the bar for smart port building and promoting the development of ports of superior quality. The Extended Kalman Filter (EKF) algorithm and a Global Navigation Satellite System (GNSS) Inertial Navigation System (INS)/Magnetometer combination navigation technology form the basis of this strategy. The suggested deviation detection and compensating method greatly enhances the navigation system’s performance when compared to the conventional EKF algorithm. Furthermore, we improved the navigation system’s ability to adapt to complex surroundings and sudden changes by adding the Particle Swarm Optimization (PSO) algorithm to the process. This allowed us to further optimize the system parameters based on the original innovation. This development is critical to enhancing unmanned robot navigation accuracy at smart ports and providing robust technical support for the growth of port automation and intelligence.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"665-670"},"PeriodicalIF":2.3,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Federated learning is a distributed machine learning approach that achieves collaborative training while protecting data privacy. However, in distributed scenarios, the operational data of industrial equipment is dynamic and non-independently identically distributed (non-IID). This situation leads to poor performance of federated learning algorithms in industrial anomaly detection tasks. Personalized federated learning is a viable solution to the non-IID data problem, but it is not effective in responding to dynamic environmental changes. Implementing directed updates to the model, thereby effectively maintaining its stability, is one of the solutions for addressing dynamic challenges. In addition, even though federated learning has the ability to protect data privacy, it still has the risk of privacy leakage due to differential privacy attacks. In this paper, we propose a personalized federated learning based on hypernetwork and credible directed update of models to generate stable personalized models for clients with non-IID data in a dynamic environment. Furthermore, we propose a parameter-varying differential privacy mechanism to mitigate compromised differential attacks. We evaluate the capability of the proposed method to perform the anomaly detection task using real air conditioning datasets from three distinct factories. The results demonstrate that our framework outperforms existing personalized federated learning methods with an average accuracy improvement of 11.32%. Additionally, experimental results demonstrate that the framework can withstand differential attacks while maintaining high accuracy.
{"title":"A Personalized and Differentially Private Federated Learning for Anomaly Detection of Industrial Equipment","authors":"Zhen Zhang;Weishan Zhang;Zhicheng Bao;Yifan Miao;Yuru Liu;Yikang Zhao;Rui Zhang;Wenyin Zhu","doi":"10.1109/JRFID.2024.3390142","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3390142","url":null,"abstract":"Federated learning is a distributed machine learning approach that achieves collaborative training while protecting data privacy. However, in distributed scenarios, the operational data of industrial equipment is dynamic and non-independently identically distributed (non-IID). This situation leads to poor performance of federated learning algorithms in industrial anomaly detection tasks. Personalized federated learning is a viable solution to the non-IID data problem, but it is not effective in responding to dynamic environmental changes. Implementing directed updates to the model, thereby effectively maintaining its stability, is one of the solutions for addressing dynamic challenges. In addition, even though federated learning has the ability to protect data privacy, it still has the risk of privacy leakage due to differential privacy attacks. In this paper, we propose a personalized federated learning based on hypernetwork and credible directed update of models to generate stable personalized models for clients with non-IID data in a dynamic environment. Furthermore, we propose a parameter-varying differential privacy mechanism to mitigate compromised differential attacks. We evaluate the capability of the proposed method to perform the anomaly detection task using real air conditioning datasets from three distinct factories. The results demonstrate that our framework outperforms existing personalized federated learning methods with an average accuracy improvement of 11.32%. Additionally, experimental results demonstrate that the framework can withstand differential attacks while maintaining high accuracy.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"468-475"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1109/JRFID.2024.3390624
Francesco Lestini;Gaetano Marrocco;Cecilia Occhiuzzi
Modern wireless communication systems are becoming increasingly necessary, emphasizing the need for electromagnetic devices that can flexibly operate under different conditions, e.g., under power constraints or in hostile environments where scattering objects randomly modify coverage areas and communication links. Due to their ability to dynamically change operating frequency, radiation pattern, bandwidth characteristics, and polarization, reconfigurable objects (especially antennas and backscattering surfaces) have received significant attention in this context. Electromagnetic features can be electronically selected by controlling the bias voltage of tunable elements adequately integrated into the layout. Usually, this is done by employing external programmable controllers that need power sources and wired connections, leading to unusable configurations for several scenarios. Thus, exploring alternative electronic tuning mechanisms becomes essential. This paper proposes RFID-Based Reconfigurable Electromagnetic Devices as a wireless, cost-effective, and low-power solution. The system’s operating principle, potential architectures, and applicability in practical scenarios are presented. Theoretical and experimental analysis validate the proposed architecture, whose capabilities are finally demonstrated by prototyping and testing two reconfigurable antenna arrays.
现代无线通信系统变得越来越必要，这强调了对能够在不同条件下灵活运行的电磁设备的需求，例如，在功率受限或散射物体随机改变覆盖区域和通信链路的恶劣环境中。可重构物体（尤其是天线和后向散射表面）能够动态改变工作频率、辐射模式、带宽特性和极化，因此在这方面受到极大关注。通过控制已充分集成到布局中的可调元件的偏置电压，可对电磁特性进行电子选择。通常情况下，这是通过采用外部可编程控制器来实现的，而外部控制器需要电源和有线连接，从而导致在多种情况下无法使用配置。因此，探索其他电子调谐机制变得至关重要。本文提出了基于 RFID 的可重构电磁设备，作为一种无线、经济、低功耗的解决方案。本文介绍了该系统的工作原理、潜在架构以及在实际场景中的适用性。理论和实验分析验证了所提出的架构，并通过两个可重构天线阵列的原型设计和测试，最终证明了该架构的能力。
{"title":"RFID-Based Reconfigurable Electromagnetic Devices","authors":"Francesco Lestini;Gaetano Marrocco;Cecilia Occhiuzzi","doi":"10.1109/JRFID.2024.3390624","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3390624","url":null,"abstract":"Modern wireless communication systems are becoming increasingly necessary, emphasizing the need for electromagnetic devices that can flexibly operate under different conditions, e.g., under power constraints or in hostile environments where scattering objects randomly modify coverage areas and communication links. Due to their ability to dynamically change operating frequency, radiation pattern, bandwidth characteristics, and polarization, reconfigurable objects (especially antennas and backscattering surfaces) have received significant attention in this context. Electromagnetic features can be electronically selected by controlling the bias voltage of tunable elements adequately integrated into the layout. Usually, this is done by employing external programmable controllers that need power sources and wired connections, leading to unusable configurations for several scenarios. Thus, exploring alternative electronic tuning mechanisms becomes essential. This paper proposes RFID-Based Reconfigurable Electromagnetic Devices as a wireless, cost-effective, and low-power solution. The system’s operating principle, potential architectures, and applicability in practical scenarios are presented. Theoretical and experimental analysis validate the proposed architecture, whose capabilities are finally demonstrated by prototyping and testing two reconfigurable antenna arrays.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"226-234"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140902543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a 2.45/5.8 GHz circularly polarized RFID antenna backed with a dual-band artificial magnetic conductor (AMC) is presented for tagged-object detection in Internet of things (IoT) networks. RFID systems require a long-range reader to maintain energy-efficient communication with the tagged devices. An efficient reader antenna is presented to increase the interrogation distance of the reader, and decreasing the uncertainty of tagged-object detection. The proposed RFID antenna consists of two dipole pairs, are printed on both sides of the substrate to obtain 2.45 GHz and 5.8 GHz bands, connected by feed delay lines in a cross-dipole arrangement. To increase the range of the reader, the cross-dipole antenna is supported by a $5times5$ AMC surface placed $0.042{lambda }_{0}$ beneath the antenna, where ${lambda }_{0}$ is the wavelength calculated at the lowest resonant frequency. In measurement, the metasurface backing results in gain enhancements of 4.1 dBi in the 2.45 GHz band and 4.6 dBi in the 5.8 GHz band, which improves the read range of the reader. The measurement also shows an improvement in RFID band coverage in terms of impedance bandwidth, 2.32–2.57 GHz in the 2.45 GHz band and 5.48-6 GHz in the 5.8 GHz band, reducing tag-detection error. The axial ratio bandwidth of the reader antenna configuration is 2.385–2.485 GHz and 5.77–5.88 GHz in the 2.45 GHz and 5.8 GHz bands, respectively.
{"title":"Directional Energy-Efficient Metasurface-Backed RFID Reader Antenna for Minimizing Tag-Detection Uncertainty in IoT Networks","authors":"Chandni Bajaj;Dharmendra Kumar Upadhyay;Sachin Kumar;Binod Kumar Kanaujia","doi":"10.1109/JRFID.2024.3389737","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3389737","url":null,"abstract":"In this paper, a 2.45/5.8 GHz circularly polarized RFID antenna backed with a dual-band artificial magnetic conductor (AMC) is presented for tagged-object detection in Internet of things (IoT) networks. RFID systems require a long-range reader to maintain energy-efficient communication with the tagged devices. An efficient reader antenna is presented to increase the interrogation distance of the reader, and decreasing the uncertainty of tagged-object detection. The proposed RFID antenna consists of two dipole pairs, are printed on both sides of the substrate to obtain 2.45 GHz and 5.8 GHz bands, connected by feed delay lines in a cross-dipole arrangement. To increase the range of the reader, the cross-dipole antenna is supported by a \u0000<inline-formula> <tex-math>$5times5$ </tex-math></inline-formula>\u0000 AMC surface placed \u0000<inline-formula> <tex-math>$0.042{lambda }_{0}$ </tex-math></inline-formula>\u0000 beneath the antenna, where \u0000<inline-formula> <tex-math>${lambda }_{0}$ </tex-math></inline-formula>\u0000 is the wavelength calculated at the lowest resonant frequency. In measurement, the metasurface backing results in gain enhancements of 4.1 dBi in the 2.45 GHz band and 4.6 dBi in the 5.8 GHz band, which improves the read range of the reader. The measurement also shows an improvement in RFID band coverage in terms of impedance bandwidth, 2.32–2.57 GHz in the 2.45 GHz band and 5.48-6 GHz in the 5.8 GHz band, reducing tag-detection error. The axial ratio bandwidth of the reader antenna configuration is 2.385–2.485 GHz and 5.77–5.88 GHz in the 2.45 GHz and 5.8 GHz bands, respectively.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"88-97"},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-16DOI: 10.1109/JRFID.2024.3389870
Giulio Maria Bianco;Gaetano Marrocco
Microfluidic has been an enabling technology for over a decade, particularly in the field of medical and wearable devices, allowing for the manipulation of small amounts of fluid in confined spaces. Micro-channels can also be used for wireless sensing thanks to the variations in antenna properties when the fluid flows near it. However, up to now, microfluidic channels and sensing antennas have always been designed separately; instead, since the liquid flow and the antenna geometry both contribute to the overall performance, they should be considered simultaneously when optimizing the antenna-microfluidic system. In this paper, the joint design of the antenna and microfluidic channels is investigated for liquid quantification. Self-tuning RFID microchips are exploited to minimize communication degradation due to the increase of lossy liquid amount over the sensing antenna while digitalizing the impedance mismatch itself. To experimentally corroborate the joint design technique, two different geometries are obtained and prototyped starting from a given antenna-microfluidic layout by setting different goals for an optimization function. The two flexible RFID prototypes returned performance in agreement with the simulated ones, achieving a maximum sensitivity of about 20 units of the digital metric per milligram increase of water.
十多年来，微流体技术一直是一种赋能技术，尤其是在医疗和可穿戴设备领域，它允许在密闭空间内操作少量流体。微通道还可用于无线传感，这要归功于流体在其附近流动时天线特性的变化。然而，迄今为止，微流体通道和传感天线一直是分开设计的；相反，由于液体流动和天线几何形状都会影响整体性能，因此在优化天线-微流体系统时应同时考虑这两个因素。本文针对液体定量研究了天线和微流控通道的联合设计。在对阻抗失配本身进行数字化的同时，利用自调谐射频识别（RFID）微芯片最大限度地降低了因传感天线上方有损液体量增加而导致的通信质量下降。为了在实验中证实联合设计技术，通过为优化函数设定不同的目标，从给定的天线-微流体布局开始，获得了两种不同的几何形状并制作了原型。两个柔性 RFID 原型的性能与模拟的一致，每增加一毫克水的最大灵敏度约为 20 个数字度量单位。
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