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MIEL 2025 Lecture - Comparison of Different TinyML Implementations for an Industrial Control System
In industrial control and robotic systems, the widespread adoption of machine learning solutions is recently experiencing a clear implementation shift from cloud computing to edge computing due to requirements regarding safety, reliability and others. In this context, the emerging concepts of Edge Artificial Intelligence and Tiny Machine Learning become essential for implementing machine learning based industrial control solutions using resources constrained devices. In this paper Tiny Machine Learning is used to solve the industrial control problem of apple classification using two small edge devices. Several versions are compared while presented experimental results and comparisons with our previous results demonstrate that Tiny Machine Learning can provide a viable solution even for complex industrial grade control problems in the food industry.
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- Faculty of Mechanical Engineering Nis
- Aleksandra Medvedeva 14
- Nis, Serbia & Montenegro
- Serbia & Montenegro 18000
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- Co-sponsored by Faculty of Electronic Engineering, University of Nis, Serbia
- Starts 13 October 2025 06:00 AM UTC
- Ends 13 October 2025 08:00 AM UTC
- No Admission Charge