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This software engineering project involves development of machine learning algorithms for embedded applications. High speed 32-bit hardware devices such as Raspberry Pi and ARM microcontrollers has become inexpensive and readily available. Machine learning algorithms for applications such as image processing and image recognition are computationally intensive. But with the availability of low cost 32-bit embedded computing devices, it is now feasible to implement then on embedded hardware. This project will explore embedded applications of machine learning algorithms by following a software engineering design and test approach.


This poster was presented at the 31st Semi-Annual Honors and Undergraduate Research Scholars Poster Presentation at New York City College of Technology, Dec. 4, 2019. Mentor: Professor Farrukh Zia (Computer Engineering Technology).


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