Course overview
Signal processing is a critical part of modern engineering systems, thanks to advances in computer hardware. Its versatility and high performance relative to cost have made them indispensable in a diverse range of applications. This course will provide students with an advanced understanding of signal processing principles and develop skills suitable for a wide range of applications. It is an elective course for both Electrical and Electronic specialisations of the Master of Engineering program. Students will learn the mathematical concepts underpinning signal processing techniques and apply them to solve problems in applications such as spectrum estimation, adaptive and optimum filtering, time-frequency analysis, multi-rate systems and wavelets. The course is delivered on campus, with an in-person component centred around workshops, and supported by tutorials. Learning activities at the scheduled sessions include group discussions and Matlab exercises. Assessment activities include workshop and tutorial participation, individual assignments and tests. The set of assessments is designed to allow students to demonstrate their ability to apply signal processing techniques to solve real engineering problems. Upon completion of the course, students will be able to recommend and implement signal processing algorithms suitable for a wide range of engineering applications. These skills will allow students to work effectively as electrical and electronic engineers, or to pursue higher degrees by research.
Course learning outcomes
- Describe and explain the basic models that underpin digital signal processing methods including discrete time linear systems and signals, digital filters and random processes
- Choose and apply suitlable technique(s) to estimate the spectrum from a signal's time series
- Explain the concept of an optimal (MMSE) filter, design and implement optimal filters for the problem of linear prediction
- Articulate the motivation for adaptive filtering, and produce practical solutions such as Wiener and LMS filters
- Describe the concept of multi-rate signal processing, its practical significance, incorporating aspects such as decimation and interpolation, multi-rate filters, perfect reconstruction, wavelet signal representations
- Implement algorithms in Matlab and undertake computer-based experiments involving simulated and real data