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关键词： Radiofrequency identification   Virtual reality   User experience   RFID tags   Libraries   Market research   Computer architecture   RFID   virtual reality   mixed reality   extended reality   augmented virtuality   user experience design  

摘要： Recent technical advancement has driven the boundary between the physical reality and digital virtuality to diminish significantly. As part of the emerging trend, existing research leverages a synergized use of Radio Frequency IDentification (RFID) and virtual reality (VR) to create compelling hybrid user experience. However, current state-of-the-art literature indicates a lack of coherent architecture for seamlessly integrating these two siloed technology stacks, thus hindering full-fledged mixed and extended reality applications. In this article, we first conducted a comprehensive literature review and identified key design themes and different technical affordances of RFID within VR context;in reflection of our findings, we hence proposed an overarching architecture to facilitate swift and flexible composition of RFID and VR;Three use cases were further established using the proposed architecture to both demonstrate its technical feasibility and qualitatively assess RFID's augmentation over conventional VR applications. This exploratory research intends to offer some preliminary design knowledge and insights for designing and developing RFID-augmented VR applications, open up opportunities for further discussion and research interest in this area, thus ultimately contributing to more immersive, interactive and informative user experience.

关键词： 射频识别   标签   标签天线   抗金属   滚动型  

摘要： 随着射频识别（RFID）和传感器芯片技术的发展,标签的应用场景也随之多样化,标签天线作为RFID系统的重要组成部分,其性能优劣直接影响整个系统的工作性能及其适应复杂应用环境的能力。通常的抗金属RFID标签天线仅具备单面抗金属能力,限制了RFID系统在一些严苛环境下的应用。本论文针对全金属烤炉环境背景,研究了可在金属表面滚动放置的抗金属RFID标签天线,主要研究内容包括: 1.研究了一种带有U形槽的方柱形四臂滚动型抗金属RFID标签天线。该天线基于平面倒F天线结构,天线臂呈柱状布局,在臂上均开有U形槽,并通过前端一组十字交叉的短路墙相连接,采用同轴线与芯片基板相连接。天线整体尺寸为11.2×11.2×70.8 mm3,天线在金属表面滚动时存在三种典型接触状态,实测结果表明,三种状态中心频点分别为904MHz、924MHz、910MHz,中心频点变化范围为±10MHz,阻抗带宽分别为4.5MHz、4 MHz、6.5MHz,实测辐射效率分别为58%、30%、52%,具有可滚动放置、抗金属、低频偏等优势。 2.研究了一种加载辅助面的四臂滚动型抗金属RFID标签天线。该天线由倒F天线演变而成,两组四个天线臂呈柱状结构布局,通过前端“十”字形短路墙相连接,天线臂中心有一开矩形槽的辅助面,采用同轴线与芯片基板相连接。天线整体尺寸为11.3×11.3×77.5mm3,天线在金属表面滚动时存在三种典型接触状态,实测结果表明,三种状态中心频点分别为897MHz、910MHz、932MHz,中心频点变化范围为±17.5MHz,阻抗带宽分别为8MHz、8MHz、3.5MHz,具有在金属表面滚动放置的能力,实测辐射效率最高达到78%,具有较高的效率。 3.研究了一种带有辅助臂的六臂滚动型抗金属小型化高效率RFID标签天线。在对前两个天线研究分析的基础上,根据平面倒F天线电场分布特性,采用增加辅助臂的设计方法,三组六个天线臂呈柱状布局,通过前端两种不同类型的短路墙相连接,采用同轴线与芯片基板相连接。天线的整体尺寸为8×9×76.5mm3,小型化优势明显,天线在金属表面滚动时存在四种典型接触状态,实测结果表明,四种状态中心频点分别为907MHz、922MHz、922MHz、911MHz,中心频点变化范围为±7.5MHz,具有较低频偏,阻抗带宽分别为7MHz、5MHz、6MHz、5MHz,具有可滚动放置、抗金属的能力,实测辐射效率分别为89%、85%、78%、82%,高效率优势显著。

关键词： RFID   UHF   低增益   极化可重构   宽波束  

摘要： 物流仓储应用中的RFID技术是当下研究的热点,复杂金属环境是物流仓储应用中最常见的场景。复杂金属环境一方面对天线的读取范围提出了更严格的要求,另一方面金属环境中电磁波反射引起的高误读率也严重影响RFID系统的正常工作。本文主要针对复杂金属环境中UHF频段RFID读写器天线进行研究,主要的研究内容和创新点如下:针对已有的近场读写器天线读取面积有限而无法满足金属货架中大面积读取要求的问题,提出了具有读取区域切换功能的近场读写器天线阵列。对1×4天线阵列进行轮询馈电,使天线具有读取区域切换功能。经测试,天线阵列的最大读取范围达到了 200×830×70 mm3。针对已有的近场读写器天线读取距离近,很难覆盖较高的金属货架,而远场天线读取距离难以控制的问题,提出了一款低增益极化可重构读写器天线阵列。通过减小微带天线宽度获得低增益天线单元,实现对读取距离的控制。利用该单元设计2×2天线阵列,读取距离达到300mm,读取面积400×300 mm2,增益为-10.2dBi。同时,采用PIN二极管作为射频开关设计了可重构馈电网络,天线阵列实现了两个极化方向的可重构,提高了标签摆放的灵活性。针对已有的读写器天线应用在金属货架中,电磁波反射导致货架缓冲区外部误读率过高的问题,提出了一款低增益宽波束读写器天线阵列。通过差分馈电,2×2天线阵列获得了 3 dB波瓣宽度126°的宽波束效果。在金属货架中应用时,设计的宽波束天线产生的电磁波可以垂直入射到金属板表面,避免了斜入射导致的电磁波溢出,能够有效抑制RFID系统的误读效应。经测试,该天线可以实现960×800×970mm3金属货架内100%读取率和货架缓冲区外的0误读率。

关键词： RFID   YOLOv2   neural network   GRNN  

摘要： Effective recognition of tags in the dynamic measurement system would significantly improve the reading performance of the tag group, but the blurred outline and appearance of tag images captured in motion seriously limit the effectiveness of the existing tag group recognition. Thus, this paper proposes passive tag group recognition in the dynamic environment based on motion blur estimation and improved YOLOv2. Firstly, blur angles are estimated with a Gabor filter, and blur lengths are estimated through nonlinear modelling of a Generalized Regression Neural Network (GRNN). Secondly, tag recognition based on YOLOv2 improved by a Gaussian algorithm is proposed. The features of the tag group are analyzed by the Gaussian algorithm, the region of interest of the dynamic tag is effectively framed, and the tag foreground is extracted;Secondly, the data set of tag groups are trained by the end-to-end YOLOv2 algorithm for secondary screening and recognition, and finally the specific locations of tags are framed to meet the effective identification of tag groups in different scenes. A considerable number of experiments illustrate that the fusion algorithm can significantly improve recognition accuracy. Combined with the reading distance, the research presented in this paper can more accurately optimize the three-dimensional structure of the tag group, improve the reading performance of the tag group, and avoid the interference and collision of tags in the communication channel. Compared with the previous template matching algorithm, the tag group recognition ability put forward in this paper is improved by at least 13.9%, and its reading performance is improved by at least 6.2% as shown in many experiments.

关键词： 节电节水灌溉   电力计量及保护   射频识别   模糊控制   无线通信  

摘要： 水与电是重要资源,农业用水约占总用水量的70%以上,不合理的灌溉方式造成水与电资源的大量浪费,通过技术手段节约农业灌溉用水与用电具有重要意义。由国家发展改革委及水利部印发的《国家节水行动方案》中,提出将大力推动节水灌溉技术,开展农业用水精细化管理,加强农田土壤墒情监测,实现测墒灌溉。本文以电力计量、射频识别、模糊控制和无线通信等技术为抓手,在充分调研需求的基础上,设计出一款农业灌溉用水控制器,具体工作如下:(1)依据相关政策和实际应用场景需要,在需求调研的基础上确定了控制器相关的性能指标,给出了控制器的总体设计方案。控制器以墒情传感器监测、电量计量、流量计量等信息输入,磁保持继电器、继电器模块等部件作为输出执行机构,在核心软件与相关控制算法的控制下,通过无线通信实现了控制器的联网远程管理,人机与刷卡接口实现了控制器的现场使用交互,具有节水、节电和灌溉设备保护等特点,能够满足国家农业水价、用电改革要求。(2)依据总体设计方案,对控制器涉及的硬件电路进行了具体设计。硬件电路采用模块化结构,主要包括主控模块、电源电路、人机交互模块、实时时钟模块、数据存储模块、电量计量模块、射频IC刷卡模块、无线通讯4G模块、数据采集模块和控制执行模块等。(3)根据控制器功能需求,对控制器涉及的控制算法和软件进行了设计。主要包括控制器电量计量程序、射频IC卡读写程序、无线通讯程序、水量和墒情数据采集程序、模糊控制算法程序、控制执行模块程序、阶梯水价程序以及远程通信规约等。(4)对本文设计的农业灌溉用水控制器软硬件进行了联调测试。搭建了测试环境,建立了测试内容及标准,按照设计要求与指标,对电量参数进行了校准,对控制器的整体功能进行了测试。结果表明农业灌溉用水控制器的性能满足设计要求。

关键词： Big data  

摘要： The era people live in is the era of big data, and massive data carry a large amount of information. This study aims to analyze RFID data based on big data and clustering algorithms. In this study, a RFID data extraction technology based on joint Kalman filter fusion is proposed. In the system, the proposed data extraction technology can effectively read RFID tags. The data are recorded, and the KM-KL clustering algorithm is proposed for RFID data, which combines the advantages of the K-means algorithm. The improved KM-KL clustering algorithm can effectively analyze and evaluate RFID data. The experimental results of this study prove that the recognition error rate of the RFID data extraction technology based on the joint Kalman filter fusion is only 2.7%. The improved KM-KL clustering algorithm also has better performance than the traditional algorithm. © 2022 Lihua Lv.

关键词： UHF RFID   indoor localization   BP neural networks   SVR  

摘要： With the widespread of Internet of Things (IoT), Radio Frequency Identification (RFID) technology is used in various fields. In the complex indoor environment, the traditional RFID indoor localization algorithm cannot give superior performance. Because a large number of antennas cannot be deployed in practical applications, the dimension of the signal strength feature vector obtained is relatively low, and it is not easy to fully describe the information in the environmental field. Thus, must adopt the appropriate antenna placement structure, the phase difference information can be effectively used as the feature. In this paper, a method of indoor localization algorithm based on Back Propagation-Support Vector Regression (BP-SVR) is proposed, which takes the signal strength and phase difference of the RFID tag as the feature inputs and uses the hidden layer of the neural network to enhance the dimensionality of the data. Moreover, the Sequential Minimal Optimization (SMO) algorithm is used to increase the rate of model learning. The method was valid in a 2D scene. Experimental results show that the method has an average positioning error of 9.5cm in the indoor area of 6m x 8m.

关键词： Metals   Steel   Recycling   Europe   Industries   Automotive engineering   Memory   Radiofrequency identification   tags   data storage   RFID   end-of-life   EOL   exploitation of metal   second life material   value chain   circular economy   remanufacturing   refurbish   reuse  

摘要： Steel is one of the most important technical resources in the world and already consists of a large proportion of recycled scrap metal. The automotive recycling industry also contributes to this material recycling or the circular economy and has so far run as a high energy process chain starting with dismantling and shredding and ending with the sorting of heavy and light fractions. Different physical principles like magnetic, mechanical, aerodynamic or optical separation are used. The high energy and logistics costs of material recycling offer potential for optimization in terms of the CO2 balance. The manufacturing chain of metallic vehicle components produces such high-quality products that questions inevitably arise about alternative component utilization such as reuse, refurbish and remanufacturing. Based on this key question, the following article is about novel technologies for exploiting steel from end-of-life vehicles. In detail, the fabrication of brake covers from roofs of end-of-life vehicles and the brazing and desoldering of structural joints in the vehicle body were explored. In addition to the technical requirements and feasibility of the innovative manufacturing technologies, the need for information and data from product manufacturing for product utilization was also investigated. The use of special RFID data storage devices that can provide utilization processes with important parameters and properties of the original product was examined. The use of RFID data storage has been identified as a necessity for future "second-life-material" processes, especially because of its ability to increase efficiency and economy of processes.

关键词： Analysis   Attendance monitoring   Face recognition   High-definition camera   Real-time information system   RFID   Statistics   Web-based system  

摘要： In the academic environment, checking attendance can help lecturers better evaluate students’ performance in university. Traditional attendance checking has some disadvantages, which are wasting time and effort. The automatic attendance monitoring system, on the other hand, not only can help us solve these drawbacks but also bring a high-accuracy result compared to manually checking. The method uses ultra-high-frequency (UHF) RFID technology with four circularly polarized antennas, combined with the high-definition camera system used for face recognition that allows the system to recognize students’ faces. The system will check the attendance of students in offline classes through an RFID reader and camera which are set up in classrooms. In the case of online study in which students learn from home, the system can use students’ cameras directly from their laptops and smartphones to recognize their faces and check attendance. The web-based information system has real-time updates with attendance monitoring that allows the lecturer to review or determine the student’s attendance status. In the event of unexpected issues on the student side, the system enables lecturers to check attendance manually after receiving the student’s request. Our system, furthermore, can automatically generate a weekly report about student’s learning status in each class and provide the overall proportion of students’ commitment to attending classes for the lecturer. This paper brings some initial simulations of the system to give a more detailed picture of how the new system works and interacts. Besides, this manuscript provides a detailed performance analysis about the system with RFID and camera, then has an evaluation based on class’ learning outcome. The time, precision, and accuracy of our system are considered. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： RFID   防碰撞算法   深度Q网络   策略梯度算法  

摘要： 射频识别技术(RFID)是物联网领域的关键读写技术,RFID技术具有低成本、识别精确高效、数据处理快等特点,广泛应用于仓储、物流、溯源等领域。然而由于应用系统规模越来越大,需要使用多个阅读器支持应用系统的高读写任务。多个阅读器密集排列且同时进行识别时,构成了一个密集阅读器环境(DRE)。在DRE的互相干扰范围内,若多个阅读器同时选择了同一个信道资源,电磁波信号会互相发生干扰碰撞,严重影响应用系统的读写效率。为了更好地解决上述问题,本文将强化学习算法应用于RFID防碰撞算法,优化DRE中的信道碰撞问题。 本文建立了一个密集RFID阅读器环境的马尔可夫决策过程模型,分别定义了信道分配的状态空间、动作空间、奖励函数、阅读器拓扑关系以及环境的出口。提出了一种基于值函数近似算法的密集阅读器RFID防碰撞算法。算法通过深度Q网络近似传统Q学习的表格学习策略,并设计了一种线性退火的探索策略,将策略熵优化方案引入探索策略中进行对比。同时实现了一种带有优先度排序的优先采样经验池,相对于传统经验池有更优异的效果。实验显示使用本算法的信道碰撞率相较于随机分配降低了 34.46%,相较于传统Q-learning算法降低了 11.94%,拥有更好的防碰撞性能。 本文提出了一种基于深度确定性策略梯度方法的密集阅读器RFID防碰撞算法。算法分别建立决策网络和评估网络学习策略,并通过重参数技巧以一种可微、分的方式在一个离散的分布里做近似采样,将网络输出映射到离散的信道分配动作空间。同时使用了奥恩斯坦-乌伦贝克和因式高斯噪声增加代理对动作空间的探索。实验结果表明,在动作空间较大的环境中,本算法拥有更好的性能,在阅读器数量为49信道数量为25的大型RFID环境中,碰撞率只有深度Q网络的50%,相比随机分配的策略,碰撞率降低了 78%。 本文结合了强化学习算法与RFID防碰撞算法,对比实验及性能分析证明了本文算法在密集阅读器环境中能有效的降低信道碰撞率,具有良好的现实意义。