Sub-Nyquist Sampling While Listening to my Girlfriend

Posted byB McGrawPosted inArticlesTags:, , , , , , , , , , ,

Dr. Chad Broman1

1 Department of of Applied Psychological Machine Learning, Cranberry-Lemon University, Pittsburgh, PA, USA

Abstract

The issue of listening to my girlfriend Tiffany while playing Dark Souls games is a long established problem which has yet to find a permanent solution [1]. With the release of the Elden Ring, the need for an efficient sampling scheme in order to maintain enough concentration to defeat the multitude of tough bosses throughout the game is paramount. In order to make an initial play through of Sekiro: Shadow’s Die Twice, I got stuck at the Guardian Ape until I estimated the minimum Nyquist sampling rate while listening to Tiffany and managed to save enough mental bandwidth to finally make it through the third zombie monkey stage of the battle [2]. Likewise, Elden Ring has shown to be filled with boss battles so tough that a sub-Nyquist sampling scheme must be developed to retain all information while listening to my Girlfriend while maintaining enough bandwidth to recognize certain enemy attacks in a split second to effectively dodge. This paper adapts, implements, and tests the Xampling methodology [3] to create a mixed Modulated Wideband Converter (MWC) so that I only listen to the words I absolutely need to hear. 

Keywords: Digital Signal Processing, Relationship Advice, Elden Ring, Speech Processing, Margit the Fell Omen

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Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

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