Pub Date : 2024-02-29DOI: 10.1109/JRFID.2024.3371877
Yasin Khan;Aaqib Afzal;Ankit Dubey;Alok Saxena
This paper investigates the secrecy performance of a Backscatter communication (BackCom) network considering a practical non-linear energy harvesting model with various tag selection schemes. The tag circuit reflects the signal by finding the optimal dynamic reflection coefficient that maximizes the backscattered signal power while satisfying the tag energy constraint. The channel gains of all links follow Rayleigh fading model. Accounting performance and complexity trade-offs, two computationally efficient sub-optimal and a random selection schemes are proposed and analyzed. The performance is analyzed by obtaining the secrecy outage probability (SOP) and probability of intercept (POI) in closed-form. To gain further insights, expressions for the asymptotic SOP and POI are also derived for all selection schemes. We compare the proposed selection schemes with optimal selection scheme and conclude that proposed schemes significantly reduce the complexity and performs satisfactorily in terms of SOP and POI.
本文研究了背向散射通信(BackCom)网络的保密性能,考虑了一个实用的非线性能量采集模型和各种标签选择方案。标签电路通过寻找最优动态反射系数来反射信号,从而在满足标签能量约束的同时使反向散射信号功率最大化。所有链路的信道增益都遵循瑞利衰减模型。考虑到性能和复杂性的权衡,提出并分析了两种计算效率较高的次优方案和随机选择方案。通过闭合形式获得保密中断概率(SOP)和截获概率(POI),对性能进行了分析。为了进一步深入了解,还推导出了所有选择方案的渐近 SOP 和 POI 表达式。我们将提出的选择方案与最优选择方案进行了比较,得出的结论是,提出的方案大大降低了复杂性,在 SOP 和 POI 方面的表现令人满意。
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Multi-pedestrian attribute recognition (Multi-PAR) is a vital task for smart city surveillance applications, which requires identifying various attributes of multiple pedestrians in a single image. However, most existing methods are limited by the complex backgrounds and the time-consuming pedestrian detection preprocessing work in real-world scenarios, and cannot achieve satisfactory accuracy and efficiency. In this paper, we present a novel end-to-end solution, named Adaptive Multi-Task Network (AMTN), which jointly performs multiple tasks and leverages an adaptive feature re-extraction (AFRE) module to optimize them. Specially, We integrate pedestrian detection into AMTN to perform PAR preprocessing, and incorporate a person re-identification (ReID) task branch to track pedestrians in video streams, thereby selecting the clearest video frames for analysis instead of every video frame to improve analysis efficiency and recognition accuracy. Moreover, we design a dynamic weight fitting loss (DWFL) function to prevent gradient explosions and balance tasks during training. We conduct extensive experiments to evaluate the accuracy and efficiency of our approach, and compare it with the state-of-the-art methods. The experimental results demonstrate that our method outperforms other state-of-the-art algorithms, achieving 1.5%-4.9% improvement in accuracy on Multi-PAR. The experiments also show that the AMTN can greatly improve the efficiency of preprocessing by saving the computation of feature extraction through basic features sharing. Compared with the state-of-the-art detection algorithm Yolov5s, it can improve the efficiency by 42%.
{"title":"Adaptive Multi-Task Learning for Multi-PAR in Real World","authors":"Haoyun Sun;Hongwei Zhao;Weishan Zhang;Liang Xu;Hongqing Guan","doi":"10.1109/JRFID.2024.3371881","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3371881","url":null,"abstract":"Multi-pedestrian attribute recognition (Multi-PAR) is a vital task for smart city surveillance applications, which requires identifying various attributes of multiple pedestrians in a single image. However, most existing methods are limited by the complex backgrounds and the time-consuming pedestrian detection preprocessing work in real-world scenarios, and cannot achieve satisfactory accuracy and efficiency. In this paper, we present a novel end-to-end solution, named Adaptive Multi-Task Network (AMTN), which jointly performs multiple tasks and leverages an adaptive feature re-extraction (AFRE) module to optimize them. Specially, We integrate pedestrian detection into AMTN to perform PAR preprocessing, and incorporate a person re-identification (ReID) task branch to track pedestrians in video streams, thereby selecting the clearest video frames for analysis instead of every video frame to improve analysis efficiency and recognition accuracy. Moreover, we design a dynamic weight fitting loss (DWFL) function to prevent gradient explosions and balance tasks during training. We conduct extensive experiments to evaluate the accuracy and efficiency of our approach, and compare it with the state-of-the-art methods. The experimental results demonstrate that our method outperforms other state-of-the-art algorithms, achieving 1.5%-4.9% improvement in accuracy on Multi-PAR. The experiments also show that the AMTN can greatly improve the efficiency of preprocessing by saving the computation of feature extraction through basic features sharing. Compared with the state-of-the-art detection algorithm Yolov5s, it can improve the efficiency by 42%.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140924704","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 : 2024-02-23DOI: 10.1109/JRFID.2024.3369470
Andrea Motroni;Salvatore D’Avella;Alice Buffi;Paolo Tripicchio;Matteo Unetti;Glauco Cecchi;Paolo Nepa
This paper presents the development and testing of a robot designed for automated inventory and 3D localization of items in high-density shelving systems. To enhance accuracy in locating UHF-RFID tagged objects by using synthetic aperture-based methods, rotating antennas are installed on the robot. This allows more degrees of freedom in antenna trajectory, by reaching large synthetic apertures that may deliver high-performance localization. The robot can navigate complex environments by utilizing a depth camera and a visual odometry system, and it can effectively avoid obstacles with the help of a Laser Imaging Detection And Ranging system. Extensive testing is conducted in a realistic shop-like scenario where tagged products are placed on either wooden shelves or hung on metal racks. The robot navigation capabilities are verified together with its inventory and localization performance for different visibility conditions of the tagged items.
{"title":"Advanced RFID-Robot With Rotating Antennas for Smart Inventory in High-Density Shelving Systems","authors":"Andrea Motroni;Salvatore D’Avella;Alice Buffi;Paolo Tripicchio;Matteo Unetti;Glauco Cecchi;Paolo Nepa","doi":"10.1109/JRFID.2024.3369470","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3369470","url":null,"abstract":"This paper presents the development and testing of a robot designed for automated inventory and 3D localization of items in high-density shelving systems. To enhance accuracy in locating UHF-RFID tagged objects by using synthetic aperture-based methods, rotating antennas are installed on the robot. This allows more degrees of freedom in antenna trajectory, by reaching large synthetic apertures that may deliver high-performance localization. The robot can navigate complex environments by utilizing a depth camera and a visual odometry system, and it can effectively avoid obstacles with the help of a Laser Imaging Detection And Ranging system. Extensive testing is conducted in a realistic shop-like scenario where tagged products are placed on either wooden shelves or hung on metal racks. The robot navigation capabilities are verified together with its inventory and localization performance for different visibility conditions of the tagged items.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141422474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-22DOI: 10.1109/JRFID.2024.3368882
Somnath Mukherjee;Debidas Kundu;Ashis Khan
The deleterious effect of eddy current due to the presence of a metal in magnetically coupled resonant high-frequency radio frequency identification (HF RFID) systems is addressed, and remediation using a resonant loop is discussed. The proposed solution is much more economic than conventional ferrite-based shielding. The resonant loop, which is referred to as auxiliary coil in this paper, does not increase the profile of the existing HF RFID tag. Analysis is carried out using full-wave numerical simulation resulting in an equivalent circuit model, and validated with measurements. The technique is illustrated through applications like HF RFID tags in close proximity to a metal seal required for induction sealing, as well for extending read range of readers compelled to operate near metallic environment.
{"title":"Mitigation of Metal Body Proximity Effect in Magnetically Coupled HF RFID Systems","authors":"Somnath Mukherjee;Debidas Kundu;Ashis Khan","doi":"10.1109/JRFID.2024.3368882","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3368882","url":null,"abstract":"The deleterious effect of eddy current due to the presence of a metal in magnetically coupled resonant high-frequency radio frequency identification (HF RFID) systems is addressed, and remediation using a resonant loop is discussed. The proposed solution is much more economic than conventional ferrite-based shielding. The resonant loop, which is referred to as auxiliary coil in this paper, does not increase the profile of the existing HF RFID tag. Analysis is carried out using full-wave numerical simulation resulting in an equivalent circuit model, and validated with measurements. The technique is illustrated through applications like HF RFID tags in close proximity to a metal seal required for induction sealing, as well for extending read range of readers compelled to operate near metallic environment.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141073591","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 : 2024-02-21DOI: 10.1109/JRFID.2024.3368226
Leilei Xie;Zheng Li;Fenghua Zhu
Aiming at the problem of false and missed detection due to the varied and dense target scale changes, the presence of occlusion, and insufficient light in the target detection task of complex road scenes for self-driving vehicles, an improved model YOLOv8-AUT based on YOLOv8 complex road target detection is proposed. Firstly, the MFR module is designed based on the multi-gradient flow residual structure of the attention mechanism, and the parallel gradient flow branches are added to the module to enrich the gradient flow of the model, so as to enhance the ability to extract the detailed information, and to improve the omission and misdetection of the small targets on the road. Secondly, the spatial pyramid network structure is improved using full-dimensional dynamic convolution to increase the sensory field of the model and improve the model’s ability to detect targets of different scales in complex backgrounds. Finally, the soft-NMS suppression algorithm is introduced to solve the problem of severe target leakage detection in obstacle-target dense regions. The experimental data show that on the BDD100K dataset, the improved algorithm improves the average accuracy mean by 7.7% compared with the original algorithm, mAP@0.5:0.9 by 5.7%, which proves that YOLOv8-AUT can better satisfy the demand for target detection in complex road scenarios of autonomous driving.
{"title":"Multi-Class Road Target Detection Based on Multi-Gradient Flow Residual Structure","authors":"Leilei Xie;Zheng Li;Fenghua Zhu","doi":"10.1109/JRFID.2024.3368226","DOIUrl":"https://doi.org/10.1109/JRFID.2024.3368226","url":null,"abstract":"Aiming at the problem of false and missed detection due to the varied and dense target scale changes, the presence of occlusion, and insufficient light in the target detection task of complex road scenes for self-driving vehicles, an improved model YOLOv8-AUT based on YOLOv8 complex road target detection is proposed. Firstly, the MFR module is designed based on the multi-gradient flow residual structure of the attention mechanism, and the parallel gradient flow branches are added to the module to enrich the gradient flow of the model, so as to enhance the ability to extract the detailed information, and to improve the omission and misdetection of the small targets on the road. Secondly, the spatial pyramid network structure is improved using full-dimensional dynamic convolution to increase the sensory field of the model and improve the model’s ability to detect targets of different scales in complex backgrounds. Finally, the soft-NMS suppression algorithm is introduced to solve the problem of severe target leakage detection in obstacle-target dense regions. The experimental data show that on the BDD100K dataset, the improved algorithm improves the average accuracy mean by 7.7% compared with the original algorithm, mAP@0.5:0.9 by 5.7%, which proves that YOLOv8-AUT can better satisfy the demand for target detection in complex road scenarios of autonomous driving.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140924705","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 : 2024-02-20DOI: 10.1109/JRFID.2024.3368005
Yann Houeix;Francisco J. Romero;Francisco G. Ruiz;Diego P. Morales;Noel Rodriguez;Darine Kaddour
This study presents a pioneering approach to fabricating single-layer Frequency Selective Surfaces (FSS) using Laser-Induced Graphene (LIG). The FSS structure proposed consists of periodic resistive patterns of LIG synthesized through a one-step laser photothermal process directly on the surface of a thin polyimide substrate. The structural and electrical properties of LIG were thoroughly investigated to develop an electrical model aiming at optimizing the design and absorbing properties. After that, a 12 mm thick LIG-FSS microwave absorber prototype was fabricated and tested under real conditions, demonstrating over 90% absorption in the frequency band from 1.69 to 2.91 GHz with a thickness of only 0.068 times the maximum wavelength $(lambda _{mathrm{ max}})$