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    Variable Fractional Delay FIR Filter Design with a Bicriteria and Coefficient Relationship

    196978_196978.pdf (200.1Kb)
    Access Status
    Open access
    Authors
    Dam, Hai Huyen Heidi
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Dam, Hai Huyen. 2014. Variable Fractional Delay FIR Filter Design with a Bicriteria and Coefficient Relationship. IEEE Transactions on Circuits and Systems II: Express Briefs. 61 (1): pp. 36-40.
    Source Title
    IEEE Transactions on Circuits and Systems II: Express Briefs
    DOI
    10.1109/tcsii.2013.2291063
    ISSN
    1549-7747
    Remarks

    Copyright © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/45502
    Collection
    • Curtin Research Publications
    Abstract

    This brief investigates a tradeoff between the integral squared error and the peak deviation error for a variable fractional delay (VFD) filter with a coefficient relationship. The integral squared error is minimized subject to additional constraints on the peak deviation error. The problem is solved by utilizing second-order cone programming. In addition, the performance of the VFD filter with discrete coefficients is investigated, in which the filter coefficients are expressed as the sum of power-of-two terms to reduce the filter operations to shifts and adds. Design examples show that the peak deviation error can be significantly reduced from the least squares solution while maintaining approximately the same integral squared error. Similarly, the integral squared error can be significantly reduced from the minimax solution while maintaining approximately the same peak deviation error. Furthermore, the tradeoff filters are less sensitive with respect to quantization than the least squares and minimax solutions.

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