Hur, B. (2025, March), Machine learning and Vision Based Embedded Linux System Education  Paper presented at 2025 ASEE -GSW Annual Conference, Arlington, TX, Texas. 10.18260/1-2--55068

\bibitem{asee_peer_55068}
  Hur, B. (2025, March), \emph{Machine learning and Vision Based Embedded Linux System Education}
  Paper presented at 2025 ASEE -GSW Annual Conference, Arlington, TX, Texas.
  10.18260/1-2--55068

Byul Hur.  "Machine learning and Vision Based Embedded Linux System Education".  2025 ASEE -GSW Annual Conference, Arlington, TX, Texas, 2025, March.  ASEE Conferences, 2025.  https://peer.asee.org/55068 Internet.  16 May, 2025

\bibitem{asee_peer_55068}
  Byul Hur.
  "Machine learning and Vision Based Embedded Linux System Education".
  \emph{2025 ASEE -GSW Annual Conference, Arlington, TX, Texas, 2025, March}.
  ASEE Conferences, 2025.
  https://peer.asee.org/55068 Internet.  16 May, 2025

@INPROCEEDINGS{asee_peer_55068
author = "Byul Hur"
title = "Machine learning and Vision Based Embedded Linux System Education"
booktitle = "2025 ASEE -GSW Annual Conference"
year = "2025"
month = "March"
address = "Arlington, TX, Texas"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/55068}
number = {10.18260/1-2--55068}
}

TY  - CPAPER
AB  - A course with practical applications of machine learning and vision processing can be stacked with the traditional embedded system course curriculum. A traditional embedded system curriculum covers learning about microcontroller architecture and hardware and software aspects of microcontrollers and applications. In order to prepare students for more complex tasks required in embedded systems and teach advanced topics of embedded systems, an Embedded Intelligent System Design course was created and initially offered in Fall 2019. Moreover, this course was also offered in Fall 2022 and Spring 2024. This paper introduces the topics of the course and practice session and term project content in this embedded intelligent system design course. Course topics included Search algorithms NumPy, Pandas, Sci-kit Learn, TensorFlow, Embedded Linux, and OpenCV. The primary language was Python in this course. Besides, C and assembly languages were also used in this course. The adopted single-board computers in this course were Raspberry Pi boards. Students studied various machine learning Python tools such as Numpy, Pandas, Sci-kit learn, and TensorFlow. Next, students studied OpenCV for image and video progressing in a Linux-embedded systems environment. In this paper, the lessons learned through this advanced embedded system graduate course will be presented and discussed.
AU  - Byul Hur
CY  - Arlington, TX, Texas
DA  - 2025/03/09
PB  - ASEE Conferences
TI  - Machine learning and Vision Based Embedded Linux System Education
UR  - https://peer.asee.org/55068
DO  - 10.18260/1-2--55068
ER  -