Postgraduate taught 

Sensor & Imaging Systems MSc

Digital Signal Processing ENG5027

  • Academic Session: 2018-19
  • School: School of Engineering
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes

Short Description

This course introduces the basic 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.

Timetable

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

Requirements of Entry

Mandatory Entry Requirements

None

Recommended Entry Requirements

None

Excluded Courses

None

Co-requisites

None

Assessment

70% Written Exam

30% Written Assignment

Main Assessment In: December

Course Aims

The aims of this course are to:

■ introduce the basic 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

Intended Learning Outcomes of Course

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

■ 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

Minimum Requirement for Award of Credits

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.