This course introduces the concepts and techniques of digital signal processing (DSP) and demonstrates some interesting and useful practical applications of DSP.  It also provides practical experience in using Python in analysis and design of DSP systems and algorithms.

2 hours of flipped classroom teaching per week: Online videos and extensive lab session with problem based learning.

None.

None.

50% Examination

50% Assignment (3 assignments at 12.5%, 12.5% and 25%)

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. It is not possible to offer reassessment of the coursework and the mark achieved will be counted towards the final course grade for both the main exam and the resit.

It is not practicable to offer reassessment in any aspect of this course that requires practical or group work.

This class is heavily lab based. The assignments are based on the principle of problem based learning.

The aims of this course are to:

■ Introduce the concepts and techniques of digital signal processing (DSP)

■ Demonstrate some interesting and useful practical applications of DSP;

■ Provide practical experience in using DSP software in analysis and design of DSP systems and algorithms

■ Integrate interdisciplinary knowledge and skills into DSP such as audio, medical and communications

By the end of this course students will be able to:

■ use the Fourier transform to filter signals, such as audio, biomedical and communication signals

■ design FIR filters from a desired frequency response arising from interdisciplinary projects

■ design IIR filters on the basis of an analogue design and apply them to low latency applications

■ design matched filters for biomedical, communication and other interdisciplinary problems

■ optimise filters for best performance given a certain problem

■ use Python as a filter design tool

■ write object oriented DSP processing code in Python

■ able to understand interdisciplinary problems and provide a solution to the problem

Students must attend the degree examination and submit at least 75% by weight of the other components of the course's summative assessment.

Students must attend the timetabled laboratory classes.

Students should attend at least 75% of the timetabled classes of the course.

Note that these are minimum requirements: good students will achieve far higher participation/submission rates.  Any student who misses an assessment or a significant number of classes because of illness or other good cause should report this by completing a MyCampus absence report.