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research-article

RF-ray: Joint RF and Linguistics Domain Learning for Object Recognition

Published: 14 September 2021 Publication History

Abstract

This paper presents a non-invasive design, namely RF-ray, to recognize the shape and material of an object simultaneously. RF-ray puts the object approximate to an RFID tag array, and explores the propagation effect as well as coupling effect between RFIDs and the object for sensing. In contrast to prior proposals, RF-ray is capable to recognize unseen objects, including unseen shape-material pairs and unseen materials within a certain container. To make it real, RF-ray introduces a sensing capability enhancement module and leverages a two-branch neural network for shape profiling and material identification respectively. Furthermore, we incorporate a Zero-Shot Learning based embedding module that incorporates the well-learned linguistic features to generalize RF-ray to recognize unseen materials. We build a prototype of RF-ray using commodity RFID devices. Comprehensive real-world experiments demonstrate our system can achieve high object recognition performance.

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Cited By

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  • (2024)A Wireless Self-Service System for Library Using Commodity RFID DevicesIEEE Internet of Things Journal10.1109/JIOT.2023.330146211:3(4998-5010)Online publication date: 1-Feb-2024
  • (2023)GC-LocProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694956:4(1-27)Online publication date: 11-Jan-2023
  • (2023)RSSI Fluctuation Analysis for RFID Tagged and Untagged Liquid Object2023 4th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)10.1109/ISPDS58840.2023.10235597(662-665)Online publication date: 14-Jul-2023
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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 5, Issue 3
Sept 2021
1443 pages
EISSN:2474-9567
DOI:10.1145/3486621
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 September 2021
Published in IMWUT Volume 5, Issue 3

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Author Tags

  1. RFIDs
  2. object recognition
  3. zero-shot learning

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Cited By

View all
  • (2024)A Wireless Self-Service System for Library Using Commodity RFID DevicesIEEE Internet of Things Journal10.1109/JIOT.2023.330146211:3(4998-5010)Online publication date: 1-Feb-2024
  • (2023)GC-LocProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694956:4(1-27)Online publication date: 11-Jan-2023
  • (2023)RSSI Fluctuation Analysis for RFID Tagged and Untagged Liquid Object2023 4th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)10.1109/ISPDS58840.2023.10235597(662-665)Online publication date: 14-Jul-2023
  • (2022)Sensing with EarablesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35503146:3(1-57)Online publication date: 7-Sep-2022
  • (2022)Towards Fair Workload Assessment via Homogeneous Order Grouping in Last-mile DeliveryProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557132(3361-3370)Online publication date: 17-Oct-2022
  • (2022)Utilizing Tag Interference for Refined Localization of Passive RFIDIEEE Internet of Things Journal10.1109/JIOT.2021.31379689:14(12656-12672)Online publication date: 15-Jul-2022

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