Wireless localization is essential for tracking objects in indoor
environments. Internet of Things (IoT) enables localization through its diverse
wireless communication protocols. In this paper, a hybrid section-based indoor
localization method using a developed Radio Frequency Identification (RFID)
tracking device and multiple IoT wireless technologies is proposed. In order to
reduce the cost of the RFID tags, the tags are installed only on the borders of
each section. The RFID tracking device identifies the section, and the proposed
wireless hybrid method finds the location of the object inside the section. The
proposed hybrid method is analytically driven by linear location estimates
obtained from different IoT wireless technologies. The experimental results
using developed RFID tracking device and RSSI-based localization for Bluetooth,
WiFi and ZigBee technologies verifies the analytical results.

通过使用发展的射频识别（RFID）追踪装置和多种物联网无线技术，本文提出了一种基于分区的混合室内定位方法。这种混合方法通过线性位置估计从不同的物联网无线技术获得的实验结果验证了 RFID 追踪装置和蓝牙、WiFi 和 ZigBee 技术的基于 RSSI 的定位结果。

基于 RFID 的混合无线数据融合室内定位

RFID-Assisted Indoor Localization Using Hybrid Wireless Data Fusion

Open-Domain Question Answering (ODQA) systems necessitate a reader model
capable of generating answers by simultaneously referring to multiple passages.
Although representative models like Fusion-in-Decoder (FiD) have been proposed
to address this challenge, these systems can inadvertently rely on spurious
features instead of genuine causal relationships between the question and the
passages to generate answers. To counter this problem, we introduce the
Rational Fusion-in-Decoder (RFiD) model. Our model leverages the encoders of
FiD to differentiate between causal relationships and spurious features,
subsequently guiding the decoder to generate answers informed by this
discernment. Experimental results on two ODQA datasets, Natural Questions (NQ)
and TriviaQA (TQ), demonstrate that our model surpasses previous methods,
achieving improvements of up to 1.5 and 0.7 in Exact Match scores on NQ, and
exhibits an enhanced ability to identify causal relationships.

介绍一种用于 Open-Domain Question Answering 系统的 Rational Fusion-in-Decoder (RFiD) 模型，该模型利用 FiD 的编码器来区分因果关系和虚假特征，从而指导解码器生成基于此判断的答案，并在两个 ODQA 数据集上实现了最高 1.5 和 0.7 的精确匹配得分提高

RFiD：用于开放领域问答的理性融合解码器

RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question  Answering

People spend a significant amount of time in indoor spaces (e.g., office
buildings, subway systems, etc.) in their daily lives. Therefore, it is
important to develop efficient indoor spatial query algorithms for supporting
various location-based applications. However, indoor spaces differ from outdoor
spaces because users have to follow the indoor floor plan for their movements.
In addition, positioning in indoor environments is mainly based on sensing
devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot
apply existing spatial query evaluation techniques devised for outdoor
environments for this new challenge. Because Bayesian filtering techniques can
be employed to estimate the state of a system that changes over time using a
sequence of noisy measurements made on the system, in this research, we propose
the Bayesian filtering-based location inference methods as the basis for
evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two
novel models, indoor walking graph model and anchor point indexing model, are
created for tracking object locations in indoor environments. Based on the
inference method and tracking models, we develop innovative indoor range and k
nearest neighbor (kNN) query algorithms. We validate our solution through use
of both synthetic data and real-world data. Our experimental results show that
the proposed algorithms can evaluate indoor spatial queries effectively and
efficiently. We open-source the code, data, and floor plan at
this https URL.

本研究提出了基于贝叶斯滤波理论的室内数据定位算法和两种跟踪模型（室内步行图模型和锚定点索引模型），以评估采用 RFID 原始数据的室内空间查询，最终验证了方法的有效性和可行性。