Aliasing is one of those concepts that shows up everywhere - from audio and imaging to radar and communications - but it’s often misunderstood or oversimplified. In this video, we break down exactly what aliasing is, why it happens when sampling signals, and how it leads to distorted or misleading results if you’re not careful. We’ll start with the core idea of how sampling works, why the Nyquist limit exists, and what it actually means to sample “too slowly.” Then we’ll walk through intuitive visualizations and concrete examples to show how high-frequency signals can appear to be completely different - sometimes even disappearing altogether. Whether you're working in DSP, RF, or just curious about signal processing, this video gives you a foundation to understand aliasing and how to avoid it. There’s also a link in the description to a companion Python notebook where you can explore the concepts interactively. -------------------------------------------------- - Channel Memberships: - Merch: - Website: -------------------------------------------------- Resources: - Python notebook where you can play around with these concepts ( ) - - All code for this video - -------------------------------------------------- References: 1. Discrete-Time Signal Processing - Oppenheim (book) - 2. Robert Mailloux, Phased Array Antenna Handbook, Third Edition, Artech, 2017. (book) - --------------------------------------------------- All animations shown were created using Manim Community ( ) - a Python animation library written by 3Blue1Brown ( @3blue1brown) and maintained by the community. Huge thanks to everyone working on this incredible project! -------------------------------------------------- Timestamps: 0:00 - Sampling Recap 0:51 - Time Domain Sampling 4:13 - Frequency Spectrum 10:40 - An Infinite Number of Possibilities 11:27 - The Nyquist Zone Boundary...