An interactive demonstration of the Kalman Filter in action. Tweak sample rates, noise parameters, and other settings to see how sensor data is processed over time — watch the filter refine its estimates and respond to changing conditions in real time.
An interactive demonstration of K-means clustering powered by Lloyd’s algorithm. Add data points to watch the algorithm dynamically assign points to the nearest centroid and recalculate cluster centers until the clusters stabilize—or shift as new points are added.
Parallelizing compute shaders in Unity for beautiful renders.
Custom-designed hardware, instruction set, and assembler for Hamming forward error correction.