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<div class="" style="word-wrap:break-word; line-break:after-white-space">This is an announcement of Pouya Mahdi Gholami's MS Presentation<br class="">
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Candidate: Pouya Mahdi Gholami<br class="">
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Date: Wednesday, December 14, 2022<br class="">
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Time: 11 am CST<br class="">
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Location: JCL 298
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<div class="">Remote Location: <a href="https://uchicago.zoom.us/j/95759025362?pwd=bmkwZVFubVlYSlNoVEFkL00zRU5PZz09" id="LPlnk913518" class="" style="font-family:"Segoe UI","Segoe UI Web (West European)","Helvetica Neue",sans-serif; font-size:16px; background-color:rgb(255,255,255)">https://uchicago.zoom.us/j/95759025362?pwd=bmkwZVFubVlYSlNoVEFkL00zRU5PZz09</a><br class="">
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M.S. Paper Title: SEASONS: Signal and Energy Aware Sensing on iNtermittent Systems<br class="">
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Abstract: Both energy-aware, batteryless intermittent systems and signal-aware adaptive sampling algorithms (ASA) aim to minimize sensor data accuracy under energy constraints in edge devices. Intuitively, combining both into a signal- & energy-aware solution
would yield even better accuracy. Unfortunately, ASAs and intermittent systems rely on conflicting energy availability assumptions. So, a straightforward combination cannot achieve their combined benefits. Therefore, we propose SEASONS, the first framework
for signal- and energy-aware intermittent systems. SEASONS buffers signal data in time, monitoring queue dynamics to ensure the data is reported within a user-specified latency constraint. SEASONS improves sensor data accuracy by 31% without increasing energy
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Advisors: Hank Hoffmann<br class="">
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Committee: Hank Hoffman, Pedro Lopes, and Shan Lu
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