Objective: Equip the team with skills to use AI for training models and analyzing signals in future systems applications.
Agenda:
Introduction to AI for Signal Analysis (15 minutes)
Overview of AI capabilities in signal processing and analysis.
Examples of AI applications in signal analysis.
AI Tools for Signal Analysis (15 minutes)
Introduction to various AI tools for signal processing (e.g., MATLAB, Python libraries like SciPy and NumPy, TensorFlow).
Demonstration of a selected tool.
Hands-On Exercise: Signal Data Preprocessing (30 minutes)
Practical exercise: Use an AI tool to preprocess and clean a signal dataset.
Techniques for noise reduction and signal enhancement.
Training AI Models for Signal Analysis (30 minutes)
Using AI to train models for specific signal analysis tasks (e.g., classification, anomaly detection).
Practical exercise: Train a neural network to classify signals using TensorFlow or PyTorch.
Q&A and Discussion (30 minutes)
Addressing questions and discussing best practices for using AI in signal analysis.
Exploring potential applications in the team's projects.
Materials Needed:
Laptops with internet access.
Access to AI tools for signal processing (e.g., MATLAB, TensorFlow, PyTorch).
Sample signal datasets.