Developing and Deploying Machine Learning Solutions for Embedded Applications

Machine learning is a powerful tool for solving complex modeling problems across a broad range of industries. The benefits of machine learning are being realized in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance. However, developing predictive models for signals obtained from sensors is not a trivial task. Moreover, there is an increasing need for developing smart sensor signal processing algorithms, which can be either deployed on edge nodes and embedded devices or on the cloud, depending on the application. MATLAB^® and Simulink^® provide a platform for exploring and analyzing time-series data and a unified workflow for the development of embedded software by providing a workflow from prototyping to production, including C code generation, processor-in-the-loop testing, and rapid prototyping on popular hardware platforms.

In this this talk, you will learn about:

• Time-frequency feature extraction techniques for machine learning workflows such as wavelets
• Automatic C code generation for preprocessing, feature extraction, and machine learning algorithms
• Rapid prototyping on embedded hardware such as Raspberry Pi™ and Android™