ACS6103   Signal Processing and Estimation   (15 credits)

Description

The aim is to introduce students to signal processing and system identification techniques. System identification uses observations of inputs and outputs from physical systems and estimates dynamical models directly. The theoretical framework and the computational algorithms are explored using synthetic and real problems to show how models can be estimated and validated for future use. Digital signal processing concepts are introduced, including sampling and analysis of digital signals, design of digital filters and the introduction of digital image processing. Discrete signals and systems are studied, with an emphasis on the frequency-domain theory necessary for the analysis of discrete signals and design of digital filters. The concepts associated with digital images and some basic digital image processing operations are also covered.

Reading List