abstract: Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments. / Dissertation/Thesis / Masters Thesis Computer Science 2018
| Identifer | oai:union.ndltd.org:asu.edu/item:51679 |
| Date | January 2018 |
| Contributors | De La Rosa, Matthew Lee (Author), Chen, Yinong (Advisor), Collofello, James (Committee member), Huang, Dijiang (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 66 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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