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    Machine learning techniques for energy optimization in mobile embedded systems

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    Citable Link(s)
    http://hdl.handle.net/10217/68004
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    Donohoo_colostate_0053N_11114.pdf (3.552Mb)
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    Abstract
    Mobile smartphones and other portable battery operated embedded systems (PDAs, tablets) are pervasive computing devices that have emerged in recent years as essential instruments for communication, business, and social interactions. While performance, capabilities, and design are all important considerations when purchasing a mobile device, a long battery lifetime is one of the most desirable attributes. Battery technology and capacity has improved over the years, but it still cannot keep pace with the power consumption demands of today's mobile devices. This key limiter has led to a strong ...
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    Author(s)
    Donohoo, Brad Kyoshi

    Advisor(s)
    Pasricha, Sudeep

    Date Issued
    2012
    Format
    born digital; masters theses
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    • 2000-2019 - CSU Theses and Dissertations
    • Theses and Dissertations - Department of Electrical and Computer Engineering

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