BEng Mechatronic Engineering / Course details

This unit will cover the following:

Introduction to DSP and linear systems.

Convolution and correlation. Using impulse function to represent discrete signals; description of convolution using linear superposition; Fourier interpretation of convolution; simple filtering using convolution; auto-correlation and cross-correlation; cross-correlation, matched filters and signal-to-noise ratio enhancement; temporal smearing and pseudo random bit sequences.

Fourier analysis. The continuous trigonometric Fourier series for periodic signals; data representation and graphing; the continuous trigonometric Fourier series for aperiodic signals; observations on the continuous Fourier series; exponential representation of the Fourier series; the continuous Fourier transform; discrete Fourier analysis; introduction to the fast Fourier transform.

Discrete Fourier properties and processing. Window functions; spectral leakage; representation of spectral data; considerations of phase; key properties of the discrete Fourier transform; discrete Fourier transform signal processing.

The Laplace transform. Its use in differential equation; the s-plane; circuit analysis; analogue filter design.

The z-transform and digital filter design. Definitions and properties; digital filters, diagrams and the z transfer function; filter design using pole-zero placement; FIR and IIR filters: merits and disadvantages.

Signal sampling. The process of sampling; signal digitisation; principles of analogue to digital and digital to analogue conversion; ADCs and DACs in system.

Design of FIR filters. The window method; phase linearity; the frequency sampling method; software for arbitrary FIR design; inverse filtering and signal reconstruction.

Design of IIR filters. The bilinear z-transform; the BZT and 2nd order passive systems; digital Butterworth and Chebyshev IIR filters; pole-zero placement revisited; biquad algorithm design strategies; FIR expression of IIR responses.

Adaptive filters. Brief theory of adaptive FIR filters; the least mean square (LMS) adaptive FIR algorithm; use of the adaptive filter in system modelling; delayed (single) input adaptive LMS filters for noise removal; the true (dual input) adaptive LMS filter.

Real time DSP: the DSP563xx design. System architecture; assembly code programming; real time system design; peripheral interfacing; FIR, IIR and adaptive filters in real time.

The course unit aims to:

• Provide a thorough and complete introduction to the subject of modern digital signal processing;
• Emphasise the links between the theoretical foundations of the subject and the essentially practical nature of its realisation;
• Encourage and understand through the use of algorithms and real world examples;
• Provide useful skills through detailed practical laboratories, which explore both off-line and real-time DSP software and hardware

On the successful completion of the course, students will be able to:

ILO 1 Demonstrate a mastery and detailed knowledge of the founding principles of DSP, and understand how the various fundamental equations both operate and are constructed.

ILO 2 Recognise the different classes of problem in digital signal processing, and to decide upon appropriate methodologies in their solution.

ILO 3 Code and test off-line and real-time DSP algorithms, both using PCs and dedicated DSP hardware.

ILO 4 Design, from system level, a complete DSP engineering solution (hardware and software specification) intended for real-time use.

ILO 5 Use an Eclipse-based development and debugging framework.

The above ILOs are developed and assessed.

Coursework (20% of total unit assessment):

Laboratory in real-time DSP. This comprises six programming tasks (team based) and one design report (individual)

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Digital Signal Processing: A Practical Approach, Emmanuel C. Ifeachor and B Jervis

Foundations of Digital Signal Processing: Theory, algorithms and hardware design, Patrick Gaydecki