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  <title><![CDATA[PhD Defense by Coleman DeLude]]></title>
  <body><![CDATA[<p>Dear faculty and fellow students,</p><p>&nbsp;</p><p>You are cordially&nbsp;invited to my thesis defense on October 21st.</p><p>&nbsp;</p><p><strong>Title:&nbsp;</strong>Slepian Array Processing: Concepts and Practical Considerations</p><p>&nbsp;</p><p><strong>Date:&nbsp;</strong>Monday, October 21st</p><p><strong>Time:&nbsp;</strong>11:00 am</p><p>Location: Coda C1215 Midtown or <a href="https://teams.microsoft.com/l/meetup-join/19%3ameeting_NGM2YWY2ZjctYWUyOS00M2RhLTk2OTQtNzI3MWUxNWExY2U4%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2250304488-841a-44b1-ba48-7435b47f3678%22%7d" title="https://teams.microsoft.com/l/meetup-join/19%3ameeting_NGM2YWY2ZjctYWUyOS00M2RhLTk2OTQtNzI3MWUxNWExY2U4%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2250304488-841a-44b1-ba48-7435b47f3678%22%7d">Link</a></p><p>&nbsp;</p><p>&nbsp;</p><p><strong>Coleman DeLude</strong></p><p>Machine Learning&nbsp;PhD&nbsp;Student</p><p>Electrical &amp; Computer Engineering<br>Georgia Institute of Technology</p><p>&nbsp;</p><p><strong>Committee</strong></p><p>1 Dr. Justin Romberg (Advisor), Electrical and Computer Engineering, Georgia Tech</p><p>2 Dr. Mark Davenport, Electrical and Computer Engineering, Georgia Tech</p><p>3 Dr. Saibal Mukhopadhyay, Electrical and Computer Engineering, Georgia Tech</p><p>4 Dr. Matthieu Bloch, Electrical and Computer Engineering, Georgia Tech</p><p>5 Dr. Lisa Osadciw, Fellow, Lockheed Martin Corporation</p><p>&nbsp;</p><p><strong>Abstract</strong></p><p>There has been an immense effort in recent years to modernize approaches to classic problems in multi-sensor array processing. In support of this initiative, this thesis explores new mathematical frameworks for performing broadband source localization, broadband beamforming, array dimensionality reduction, and environmental emulation with an emphasis on applying these methods to radio frequency (RF) scenarios. In particular, we focus on how to leverage Slepian spaces in several of these applications. We show how each of these new approaches outperform existing methods in metrics such as computational complexity, power efficiency, dynamic range, and/or scalability. Additionally, we present multiple practical implementations of these concepts, which was achieved through a strong collaboration with several hardware/firmware/software engineering laboratories.</p>]]></body>
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