«You’ve probably seen AI in action — identifying faces in photos, recognizing handwritten digits, or detecting objects in videos. At the heart of these breakthroughs are Convolutional Neural Networks (CNNs) — powerful deep learning models that help machines “see” and understand images. But what if we told you that CNNs could also “see” invisible signals, like the ones your phone, Wi-Fi, or satellites use to communicate?

In this project, you’ll first learn how CNNs work — from detecting cats in photos to recognizing digits with datasets like CIFAR-10. Then, you’ll dive deeper into a fascinating application: using CNNs to recognize radio signal types — a process called Automatic Modulation Recognition (AMR). This is a key technology behind cognitive radios, 5G, and intelligent communication systems.

You’ll build and train your own CNN models using MATLAB or Python, explore real signal data, and test your models under different noise conditions — just like real engineers. You’ll also get a glimpse into our research lab, where we work with FPGAs, oscilloscopes, antennas, and robotics.

If you’re curious about AI, signals, or just want to see how machines learn to recognize the world — visible or invisible — this project is for you.»