Building AI Applications for Signals and Time Series Data

AI techniques can be applied to signal and time series data to classify signals, identify events of interest and anomalies, and make intelligent decisions at edge computing nodes. In this session, you will see how to use MATLAB^® to build robust real-world applications for communications, digital health, and machine health monitoring.

You’ll also learn tips and tricks to speed up data preparation, improve network accuracy and performance, and work with less training data. You’ll see the latest features for:

• Data synthesis through apps, simulation, and GANs
• Automated signal labeling techniques using the Signal Labeler app
• Advanced preprocessing and feature extraction techniques to improve deep networks, including automated feature extraction techniques and single line deep learning
• Deployment to embedded devices and GPUs