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.

Two lectures per week. Plus supporting Tutorials and Laboratory sessions.

None

None

70% Written Exam

30% Lab report

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.

■ design, implement, critically evaluate and benchmark an interdisciplinary DSP task

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

■ use the Fourier transform to filter signals from different application domain and critically evaluate them in the context of the application

■ design FIR filters from a desired frequency response and evaluate their performance in the light of the intended application

■ design IIR filters for low latency applications and evaluate them in terms of stability and latency introduced in the specific application.

■ design matched filters for medical and communication situations and being able to benchmark the filters for their given application

■ design adaptive filters

■ optimise filters for best performance;

■ use Python as a filter design tool and knowing about its limitations and risks

■ write object oriented DSP filter code in Python which can be used in production

■ acquire interdisciplinary knowledge to provide a solution to a DSP problem and able to critically evaluate it

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.