Search Results - "Bolstad, Andrew K."

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  1. 1

    Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive by Webb, Joe L, Moe, Simon M, Bolstad, Andrew K, McNeill, Elizabeth M

    Published in PloS one (11-08-2021)
    “…Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging…”
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    Journal Article
  2. 2

    Beamforming Phased-Array-Fed Lenses With0.5λ-Spaced Elements by Wang, Wei, Estes, Nicholas, Garcia, Nicolas C, Roddy, Matthew, Bolstad, Andrew K, Chisum, Jonathan D

    “…We propose a phased-array-fed lens (PAFL) antenna that is capable of beamforming like a phased array but with array elements spaced beyond [Formula Omitted]…”
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    Journal Article
  3. 3

    Identification of Generalized Memory Polynomials Using Two-Tone Signals by Bolstad, Andrew K.

    Published in IEEE transactions on signal processing (15-08-2018)
    “…This paper shows that the coefficients of a generalized memory polynomial model of a nonlinear device can be estimated by examining the output when the input…”
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    Journal Article
  4. 4

    A Redirected Learning Architecture for Non-linear Digital Pre-distortion by Ramsey, Aaron F., Bolstad, Andrew K.

    “…This paper introduces the redirected learning architecture (RLA) for estimating non-linear digital pre-distortion models for non-linear devices such as power…”
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    Conference Proceeding
  5. 5

    Beamforming Phased-Array-Fed Lenses With >0.5λ-Spaced Elements by Wang, Wei, Estes, Nicholas, Garcia, Nicolas C., Roddy, Matthew, Bolstad, Andrew K., Chisum, Jonathan D.

    “…We propose a phased-array-fed lens (PAFL) antenna that is capable of beamforming like a phased array but with array elements spaced beyond <inline-formula>…”
    Get full text
    Journal Article
  6. 6

    Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive by Joe L. Webb, Simon M. Moe, Andrew K. Bolstad, Elizabeth M. McNeill

    Published in PloS one (01-01-2021)
    “…Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging…”
    Get full text
    Journal Article
  7. 7

    Exact Inverse Models for Evaluating Nonlinear Digital Predistortion Techniques by Fonseca, Aaron J., Bolstad, Andrew K.

    “…Digital predistortion aims to negate distortion introduced by a power amplifier by estimating a preinverse system. Several algorithms for parameter estimation…”
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    Conference Proceeding
  8. 8

    Practical sub-Nyquist sampling via array-based compressed sensing receiver architecture by Bolstad, Andrew K., Vian, James E., Chisum, Jonathan D., Youngho Suh

    “…This paper introduces the Array-based Compressed sensing Receiver Architecture (ACRA). ACRA allows digital receiver arrays to operate at dramatically larger…”
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    Conference Proceeding
  9. 9

    Reducing the Computational Complexity of the Volterra Series Using Permutation/Product Maps by Fonseca, Aaron J., Bolstad, Andrew K., Dickerson, Julie A.

    “…The Volterra series is often used to model nonlinear systems in the fields of system identification and adaptive filtering. One means of computing the response…”
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    Conference Proceeding
  10. 10

    Group sparse techniques for neurological data processing by Bolstad, Andrew K

    Published 01-01-2009
    “…The behavior of the human brain has long puzzled researchers in diverse fields including biology, psychology, and engineering. The emerging fields of…”
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    Dissertation
  11. 11

    An expectation-maximization algorithm for space-time sparsity regularization of the MEG inverse problem by Bolstad, Andrew K., Van Veen, Barry D., Nowak, Robert D., Wakai, Ronald T.

    Published in International Congress series (01-06-2007)
    “…We present a framework for “space-time sparsity” (STS) regularization in the MEG inverse problem via maximization of an appropriately penalized likelihood…”
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    Journal Article
  12. 12

    SPACE-TIME SPARSITY REGULARIZATION FOR THE MAGNETOENCEPHALOGRAPHY INVERSE PROBLEM by Bolstad, A.K., Van Veen, B.D., Nowak, R.D.

    “…The concept of "space-time sparsity" (STS) penalization is introduced for solving the magnetoencephalography (MEG) inverse problem. The STS approach assumes…”
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    Conference Proceeding
  13. 13

    Group sparse techniques for neurological data processing by Bolstad, Andrew K

    “…The behavior of the human brain has long puzzled researchers in diverse fields including biology, psychology, and engineering. The emerging fields of…”
    Get full text
    Dissertation
  14. 14

    An array-based compressed sensing receiver architecture by Bolstad, Andrew K., Vian, James E., Chisum, Jonathan D., Youngho Suh

    “…This paper describes an Array-based Compressed sensing Receiver Architecutre (ACRA) which allows a digital receiver array to drastically increase its…”
    Get full text
    Conference Proceeding
  15. 15

    Biquad implementation of an IIR filter for IQ mismatch correction in an SoC RF receiver by Gettings, Karen M. G. V., Bolstad, Andrew K., Ericson, Michael N., Xiao Wang

    “…This paper presents an IQ mismatch correction design and implementation that is part of a system-on-chip (SoC) that also includes a homodyne RF receiver and a…”
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    Conference Proceeding