Skip to main content
Open Access Publications from the University of California

A Pattern Recognition Framework for Embedded Systems

  • Author(s): Salehian, Shayan
  • Advisor(s): Vahid, Frank
  • et al.

Embedded systems are small computers dedicated to performing a specific task and can be designed as simple as a temperature controller to a complex medical imaging system. Embedded systems are ubiquitous having diverse applications in areas such as personal devices, factory automation, military, and medicine. A particular need in many embedded systems is recognizing patterns from available information to achieve a goal, such as determining the kind of fruit passing on a conveyor belt. Pattern recognition is a mature field that studies algorithms for learning patterns in data. However, many embedded systems designers do not have the expertise in the pattern recognition domain which imposes a challenge on employing these algorithms in their system designs. In this study, we introduce a pattern recognition framework for embedded systems that enables developers to use an interactive environment, tutorial, and reference code to develop K-nearest neighbors classification algorithm, which is a robust model in pattern recognition. To validate benefits of the proposed framework, we conducted an experiment on 66 students to evaluate their performance in terms of the code quality and development speed when the framework is used, compared to when it is not. The results demonstrate a considerable gain in the development experience using our framework.

Main Content
Current View