Digital signal processing is the enabling technology for the generation, transformation, extraction, and interpretation of digital information. This course is designed to provide insights into these processes from both theoretical and practical perspectives. It aims to foster a thorough understanding of the underlying mathematical and statistical modelling techniques for processing and learning from signals. Selected applications from relevant fields such as speech, image, audio, wireless communication, and control systems are introduced to give context and guide further studies and research directions. The topics covered are packaged into two integrated modules:
Module 1 Discrete-Time Signal ProcessingSignal and system representations: sampling and quantisation, complex exponentials, linear time-invariant systems, discrete-time Fourier transform, z-transform, fast Fourier transform.Digital filter design: FIR filter, IIR filter, windowing, bilinear transform, phase and group delay, filter stability.
Module 2 Random Signal ProcessingProbability concepts: probability measures, probability density function (PDF), cumulative distribution function (CDF), random variables, expected values, functions of random variables, correlation and covariance.Stochastic processes: ensembles, stationarity, ergodicity, power spectral density.