A Wireless Low-power System for Digital Identification of Examinees (Including Covid-19 Checks)

D. Nunes, Klaus Volbert
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Abstract

Indoor localization has been, for the past decade, a subject under intense development. There is, however, no currently available solution that covers all possible scenarios. Received Signal Strength Indicator (RSSI) based methods, although the most widely researched, still suffer from problems due to environment noise. In this paper, we present a system using Bluetooth Low Energy (BLE) beacons attached to the desks to localize students in exam rooms and, at the same time, automatically register them for the given exam. By using Kalman Filters (KFs) and discretizing the location task, the presented solution is capable of achieving 100% accuracy within a distance of 45cm from the center of the desk. As the pandemic gets more controlled, with our lives slowly transitioning back to normal, there are still sanitary measures being applied. An example being the necessity to show a certification of vaccination or previous disease. Those certifications need to be manually checked for everyone entering the university's building, which requires time and staff. With that in mind, the automatic check for Covid certificates feature is also built into our system.
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一种用于考生数字识别(含新冠肺炎检测)的无线低功耗系统
在过去的十年里,室内定位一直是一个蓬勃发展的课题。然而,目前还没有涵盖所有可能场景的可用解决方案。基于接收信号强度指示器(RSSI)的方法虽然研究得最为广泛,但仍然存在环境噪声的问题。在本文中,我们提出了一个系统,使用蓝牙低功耗(BLE)信标连接在桌子上,以定位学生在考场,同时,自动注册他们为给定的考试。通过使用卡尔曼滤波器(KFs)和离散定位任务,该解决方案能够在距离桌面中心45厘米的距离内实现100%的精度。随着疫情得到控制,我们的生活逐渐恢复正常,卫生措施仍在实施。例如,必须出示疫苗接种证明或以前的疾病。每个进入学校大楼的人都需要手工检查这些证书,这需要时间和人力。考虑到这一点,自动检查Covid证书功能也内置在我们的系统中。
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