Special Session 1

Machine Learning for Sensing Systems

SESSION SYNOPSIS

Traditionally, sensing systems have been providing raw sensor data that were either analyzed manually or fed to control systems for actuation purposes. In recent years, machine learning (ML) has become a popular method to process raw data collected in sensing systems to provide more intelligible information in an automated manner. This enables new applications in domains such as smart healthcare, smart industries, and smart cities, where complex data sets are being collected and valuable information is extracted.

This special session provides a forum for researchers to share and discuss their latest results on applying machine learning methods in the context of sensing systems and applications. Topics of interest include but are not limited to applications of ML in sensing systems, development and evaluation of novel ML methods, and ML on embedded systems (EmbeddedML or TinyML).

SESSION ORGANIZERS

  • Department of Computer and Electrical Engineering, Mid Sweden University, Sweden

  • Department of Electric, Electronic and Information Engineering, University of Catania, Italy

  • Canada Research Chair (Tier 2), Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada

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