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    Air-writing via Receiver Array Based Ultrasonic Source Localization

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    Name:
    Air_writing_TIM.pdf
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    3.071Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Chen, Hui cc
    Ballal, Tarig
    Muqaibel, Ali H.
    Zhang, Xiangliang cc
    Al-Naffouri, Tareq Y. cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/662701
    
    Metadata
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    Abstract
    Air-writing systems have recently been proposed as tools for human-machine interaction where instructions can be represented using letters or digits written in the air. Different technologies have been used to realize air-writing systems. In this paper, we propose an air-writing system using acoustic waves. The proposed system consists of two components: a motion tracking component, and a text recognition component. For motion tracking, we utilize direction-of-arrival (DOA) information. An ultrasonic receiver array tracks the motion of a wearable ultrasonic transmitter by observing the change in the DOA of the signals. We propose a novel 2-D DOA estimation algorithm that can track the change in the direction of the transmitter using measured phase-differences between the receiver array elements. The proposed phase-difference projection (PDP) algorithm can provide accurate tracking with a 3-sensor receiver array. The motion tracking information is passed next for text recognition. To this end, and in order to strike the desired balance between flexibility, processing speed, and accuracy, a training-free order-restricted matching (ORM) classifier is designed. The proposed air-writing system, which combines the proposed DOA estimation and text recognition algorithms, achieves a letter classification accuracy of 96.31%. The utility, processing time, and classification accuracy are compared with four training-free classifiers and two machine learning classifiers to demonstrate the efficiency of the proposed system.
    Citation
    Chen, H., Ballal, T., Muqaibel, A. H., Zhang, X., & Al-Naffouri, T. Y. (2020). Air-writing via Receiver Array Based Ultrasonic Source Localization. IEEE Transactions on Instrumentation and Measurement, 1–1. doi:10.1109/tim.2020.2991573
    Sponsors
    The authors would like to thank KAUST Visualization Core Lab for facilitating part of the experimental tests.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Instrumentation and Measurement
    DOI
    10.1109/TIM.2020.2991573
    Additional Links
    https://ieeexplore.ieee.org/document/9082625/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082625
    ae974a485f413a2113503eed53cd6c53
    10.1109/TIM.2020.2991573
    Scopus Count
    Collections
    Articles; Computer Science Program; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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