Fast DFT Computation for Signals with Structured Support

This thesis focuses on enhancing the efficiency of computing the Discrete Fourier Transform (DFT) for signals with known patterns. It proposes algorithms that significantly reduce computational complexity, when the support is known. These advancements enable faster and more efficient data processing, ultimately improving the performance and capabilities of technology across various scientific and engineering applications.