Full metadata
Title
Accelerometer Test Time Reduction with Machine Learning
Description
With the steady advancement of neural network research, new applications are continuously emerging. As a tool for test time reduction, neural networks provide a reliable method of identifying and applying correlations in datasets to speed data processing. By leveraging the power of a deep neural net, it is possible to record the motion of an accelerometer in response to an electrical stimulus and correlate the response with a trim code to reduce the total test time for such sensors. This reduction can be achieved by replacing traditional trimming methods such as physical shaking or mathematical models with a neural net that is able to process raw sensor data collected with the help of a microcontroller. With enough data, the neural net can process the raw responses in real time to predict the correct trim codes without requiring any additional information. Though not yet a complete replacement, the method shows promise given more extensive datasets and industry-level testing and has the potential to disrupt the current state of testing.
Date Created
2019
Contributors
- Debeurre, Nicholas (Author)
- Ozev, Sule (Thesis advisor)
- Vrudhula, Sarma (Thesis advisor)
- Kniffin, Margaret (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
31 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.55575
Level of coding
minimal
Note
Masters Thesis Computer Science 2019
System Created
- 2020-01-14 09:16:24
System Modified
- 2021-08-26 09:47:01
- 3 years ago
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