AI Techniques for Sensor Signal Processing: From Prototype to Production


An increasing number of applications require the joint use of signal processing and AI techniques on time series and sensor data. The benefits are being realized in applications everywhere, including predictive maintenance, health monitoring, and smart consumer products. 

However, developing AI models for signals obtained from sensors is not a trivial task. Moreover, there is a growing need to develop smart sensor signal processing algorithms that can be either deployed on embedded devices or on the cloud. MATLAB accelerates the development of data analytics and sensor processing systems by providing a full range of modelling and implementation capabilities within a single user-friendly environment.

In this presentation we will demonstrate end-to-end workflows of the latest machine and deep learning techniques in MATLAB.  Classification algorithms using physiological signals will be used as the basis of both workflows, but the techniques demonstrated can be applied to sensor signals in general.  You will see how easy it is to perform machine and deep learning in MATLAB with little prior experience. We will also discuss other useful capabilities for sensor signal processing such as data acquisition, preprocessing of time series data, and sensor fusion and tracking.


  • Develop predictive models for signals using machine and deep learning workflows
  • Automatically generate code from predictive models to deploy onto embedded systems 
  • Use interactive Apps to simplify common AI workflow tasks

Who Should Attend

  • Engineers/scientists working with sensor data
  • Engineers/scientists working on AI applications 

About the Presenter

Daryl Ning – Applications Engineer, MathWorks 

Daryl Ning is a principal applications engineer with MathWorks Australia. For over a decade, Daryl has supported MATLAB users from diverse industries working in the areas of data analytics, signal/image processing, and computer vision. He received both his Ph.D. and B. Eng. in electrical engineering from the Queensland University of Technology, where he also spent a year working as a research assistant. Daryl's former research was primarily in the field of speech and audio processing for biometrics and compression.


Time Title

Registration and breakfast

Breakfast will be provided. For dietary requirements, please contact

09:00am Welcome and Keynote
09:30am Essential Tools for Machine Learning
10:15am Break
10:45am Deep Learning for Time Series Signals
11:30am Other Useful Tools for Sensor Signal Processing 
12:00pm Event end

Product Focus

Registration closed