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.