ELEM018 Advanced Transform Methods (Level: M Credits: 15)

This module introduces transform and sub-band techniques as a pre-cursor to compression and other applications. It is the first step beyond the fundamentals of Digital Signal Processing. The module highlights the time-frequency trade-off and students will learn to understand the relative merits of different time to frequency mappings. Students will also be a exposed to joint time-frequency transforms and will learn to utilize and develop skills in high performance mathematical visualization software.

Topics covered will include:

Inside the FFT; Basis functions; STFT, Spectrogram; Smoothing/averaging FFT spectra; Multi-rate filtering; Sub-band filtering; Discrete Cosine Transform; Wavelet Transform; Wigner-Ville distribution; Karhunen-Loeve Transform and PCA; 2D, 3D transforms; Applications.