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  • Optimized FPGA based continuous wavelet transform

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    QassimPUB3.pdf (425.0Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Qassim, Yahya T
    Cutmore, Tim RH
    Rowlands, David D
    Griffith University Author(s)
    Rowlands, David D.
    Year published
    2016
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    Abstract
    A memory efficient field programmable gate array (FPGA) method is described that facilitates the processing of the continuous wavelet transform (CWT) arithmetic operations. The CWT computations were performed in Fourier space and implemented on FPGA following several optimization schemes. First, the adapted wavelet function was stored in a lookup table instead of computing the equation each time. Second, the utilization of FPGA memory was highly optimized by only storing the nonzero values of the wavelet function. This reduces 89% of the memory storage and allows fitting the entire design into the FPGA. Third, the design ...
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    A memory efficient field programmable gate array (FPGA) method is described that facilitates the processing of the continuous wavelet transform (CWT) arithmetic operations. The CWT computations were performed in Fourier space and implemented on FPGA following several optimization schemes. First, the adapted wavelet function was stored in a lookup table instead of computing the equation each time. Second, the utilization of FPGA memory was highly optimized by only storing the nonzero values of the wavelet function. This reduces 89% of the memory storage and allows fitting the entire design into the FPGA. Third, the design decreases the number of multiplications and shortens the time to produce the CWT coefficients. The proposed design was tested using EEG data and demonstrated to be suitable for extracting features from the event related potentials. Fourth, wavelet function scales were eliminated which saves further resources. The achieved computation speed allows for real time CWT application. Keywords: CWT; EEG; ERP; FPGA; Optimization
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    Journal Title
    Computers and Electrical Engineering
    Volume
    49
    DOI
    https://doi.org/10.1016/j.compeleceng.2014.11.012
    Copyright Statement
    © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Software engineering
    Biomedical engineering not elsewhere classified
    Electrical engineering
    Publication URI
    http://hdl.handle.net/10072/150930
    Collection
    • Journal articles

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