Course overview
Discrete time (DT) signals and systems are commonplace in engineering. It is essential for practicing electronic engineers to have a sound foundation in DT concepts and fluency in linear processing of DT signals. It is a core course in the Electronic specialisation, and an elective in the Electrical specialisation of the Master of Engineering program. Students will learn the fundamental principles and techniques of DT signals and systems, apply linear processing to solve practical problems in electronic, computer and information engineering. These skills will be developed concurrently in both time and transform (z and Fourier) Fourier domains. The course is delivered on campus, with an in-person component centred around workshops, and supported by tutorials. Learning activities include group discussions and individual written and Matlab exercises. Assessment activities include workshop and tutorial participation, individual assignments, tests and a final exam. The set of assessments is designed to allow students to demonstrate their knowledge in DSP and skills in solving problems with DSP techniques. Upon completion of the course, students will be able to apply linear digital processing methods to engineering analysis and problem solving. It will allow students to work or pursue further studies in areas including, but not limited to: control, telecommunications, biomedical engineering and machine learning.
Course learning outcomes
- Describe the process of sampling mathematically and articulate its benefits and limitations in modern engineering applications
- Use and manipulate representations of discrete-time signals in both the time and frequency domains
- Analyse and design discrete-time, linear shift-invariant (LSI) systems to manipulate discrete-time signals
- Apply various techniques underpinned by z- and Fourier transforms for signal processing applications
- Choose the most appropriate domain to perform processing, and switch fluidly between different domains
- Describe the characteristics of stochastic signals and processes using statistical measures, and apply them to model real-world signals
- Perform basic statistical spectrum analysis and apply them to the analysis of synthetic and real-world data in MATLAB
- Write MATLAB code to perform signal processing functions in a team environment, to produce a high level product for real-world use