To introduce a number of mathematical and computer aided tools for analysing signals and systems in the time and frequency domains, such that students will develop a sound knowledge and understanding of linear transform theory for signal processing, and to apply it to correlation and filtering of signals, in analogue and digital domains.

Signal Classification: pulse waveforms, periodic waveforms, sine waves and phasors, signal symmetry. Fourier Series and Fourier Transform. Sampling, replication, and aliases. Finite Fourier Series and the DFT. Correlation and Convolution, digital and analogue. Introduction to Digital Filters and the DtFT. Windowing of signals, aspects of A/D and D/A conversion. Discrete-time systems and the z-transform. Elementary FIR filter design. LP, BP and HP filters. Simple IIR filters, intuitive design methods.

* Recognise, develop and write expressions and mathematical models that describe periodic, aperiodic, continuous, discrete and sampled signals in both time and frequency domains * Classify, analyse and synthesise signals and systems in the time and frequency domains. * Represent continuous and discrete linear time-invariant (LTI) systems in time by using differential and difference equations and block/flow diagrams * Recognise and apply z-domain descriptions of signals and systems in solving difference equations, in determining systems causality and stability, in performing convolution sums, and in qualitative assessment of system frequency response. * Characterise an LTI system by determining its impulse response and/or its transfer function using such analytical methods as convolution, the z-transform, and the input/output difference equation describing the system. * Apply Fourier transforms to specified signal descriptions, both continuous and discrete, for a dual-domain examination of such signals * Conceptualise and apply LTI system definitions, stability criteria, convolution, filtering, and the sampling theorem

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* Apply computer-aided tools for examining, analysing and simulation of continuous and discrete time signals and systems.

The module is delivered via 2 lecture hours and 2 laboratory hours per week over 12 teaching weeks. Assessment is based on 30% coursework and 70% final exam. Coursework comprises a number of lab-based exercises and assignments using Matlab environment (20%), and a mid-term test (10%).