Explore topological data analysis and signal processing in this comprehensive lecture by Harish Chintakunta. Delve into Takens' embedding, trigonometric polynomials, and the effects of embedding dimensions. Examine experimental results, including the impact of higher-order harmonics and sample sizes. Investigate sampling noise and compare classification performance of Short-Time Fourier Transform (STFT) versus other methods. Learn about predicting epileptic seizures using sample time signals and cyclicity response. Discover applications in detecting oscillations in power grids and conduct comparative studies on synthetic signals. Gain insights into Prony analysis and its relevance to signal processing techniques.
Harish Chintakunta - Topological Data Analysis and Signal Processing
Applied Algebraic Topology Network via YouTube
Overview
Syllabus
Intro
Takens' embedding
Trigonometric polynomials
Effect of embedding dimension
Experiment
Effect of higher order harmonics
Effect of number of points
Sampling noise
Classification performance of STFT VS
Predicting epileptic seizures
Sample time signals
Cyclicity response vs STFT
Detecting oscillations in the power grid
Comparative studies
Synthetic Signals
Prony analysis
Taught by
Applied Algebraic Topology Network
