Durham School of Architectural Engineering and Construction
First Advisor
Fadi Alsaleem
Date of this Version
Summer 7-24-2023
Abstract
The rising complexity of machine learning algorithms and Artificial Intelligence in many applications, such as smart building, has prompted the development of alternate computing options. Because of their compact size, low power consumption, and diverse functionality, microelectromechanical systems (MEMS) have emerged as a possible candidate. This thesis focuses on using MEMS networks as computing units to classify a simple signal classification task using neural network methodology. The study intends to show the potential of using MEMS as an analog computing unit by discussing the advantage of the bi-stability pull-in behavior and hysteresis to create an accurate classifier of these waveforms. Modeling and simulation are being conducted to assess the MEMS-based computer units performance. The results reveal that the proposed methodology performs the required classification without requiring a digital computer. Furthermore, This study adds to the field of analog computing with MEMS by providing insights into the feasibility and potential of using MEMS networks for more complex classification tasks such as those related to smart building applications.
Adviser: Fadi Alsaleem
Comments
A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Architectural Engineering, Under the Supervision of Professor Fadi Alsaleem. Lincoln, Nebraska: July 2023
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