FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING - 2023/4
Module code: EEE3008
Module Overview
Expected prior learning: Knowledge of basic properties of signals and related methods, including Fourier series, Fourier transforms, Laplace transforms, and convolution. BEng/MEng students might have acquired this through study of module EEE2035 Engineering Mathematics III and/or module EEE2033 Circuits, Control and Communications. MSc students might have acquired this through study of an undergraduate module on “Signals and Systems” or through independent study.
Module purpose: This introductory course in Digital Signal Processing explores mathematical tools used to represent, analyse and design basic DSP systems. This module underpins many key areas of digital systems, including audio-visual technology, digital communications, control systems, and computer vision. This topic is therefore of paramount importance to any electronic engineer.
This module EEE3008 provides the expected prior learning for the following module EEEM030 Speech & Audio Processing and Recognition
Taking module EEE3008 may also contribute to a deeper understanding of the following modules EEE3032 Computer Vision and Pattern Recognition, EEE3042 Audio and Video Processing and EEEM071 Advanced Topics in Computer Vision and Deep Learning
Module provider
Computer Science and Electronic Eng
Module Leader
PLUMBLEY Mark (CS & EE)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 6
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 90
Lecture Hours: 22
Tutorial Hours: 10
Laboratory Hours: 8
Guided Learning: 10
Captured Content: 10
Module Availability
Semester 1
Prerequisites / Co-requisites
None. (But see: Overview: Expected prior learning)
Module content
This module initially revisits relevant areas of signals and systems, as well as mathematics, while introducing the fundamentals of digital signal processing. This is followed by introduction of methods for filter design and frequency analysis.
Indicative content includes the following:
Introduction to DSP, discrete signals and their properties, normalised frequency;
Sampling, aliasing, sampling theorem, quantisation, ideal signal reconstruction;
Discrete systems, linear time-invariant systems, convolution theorem;
The Z transforms and its properties, analysis and design of simple discrete systems;
Discrete Fourier series, discrete Fourier transform, circular and linear convolution;
Complexity of DFT, decimation in time and frequency, fast Fourier transform;
Truncation of Fourier series, design of FIR digital filters: linear phase filter design, types of windowing functions, FIR filter types;
Analogue filter design, including Butterworth filters. Design of IIR digital filters, including bilinear transform method.
Quantisation noise, decimation and interpolation, multirate signal processing.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Coursework | Coursework | 20 |
| Examination | Written Exam - 2hrs | 80 |
Alternative Assessment
N/A
Assessment Strategy
The assessment strategy for this module is designed to provide students with the opportunity to demonstrate the skills and knowledge as described in the learning outcomes. The written examination will assess the knowledge of terminology, concepts and theory of digital signal processing, as well as the ability to analyse problems and apply mathematical models of signal processing to solve and predict effects. The laboratory experiments will evaluate the acquired technical skills and expertise required to apply these methods to practical signal processing tasks.
Thus, the summative assessment for this module consists of the following:
Coursework: Laboratory experiments concerned with analysis and design of signal processing systems
Exam: Written examination.
Formative assessment and feedback
For the module, students will receive formative assessment/feedback in the following ways:
During lectures, by question and answer sessions;
During tutorials/tutorial classes;
During supervised laboratory sessions;
During meetings with lecturers and tutor.
Module aims
- Equip students with a thorough understanding of core DSP concepts and methods,
- Enable students to analyse discrete signals,
- Enable students to design discrete systems and
- Enable students to analyse more complex signal processing applications in the future.
- The module also aims to provide opportunities for students to learn about the Surrey Pillars listed below.
Learning outcomes
| Attributes Developed | Ref | ||
|---|---|---|---|
| 001 | Describe the terminology and concepts of core areas in digital signal processing. | K | C1 |
| 002 | Explain theory behind basic digital signal processing methods. | KC | C2 |
| 003 | Apply introduced methods in the design of basic signal processing applications. | KCT | C3 |
| 004 | Analyse and explain behaviour of basic digital signal processing systems. | KCPT | C6, C16, C17 |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to achieve the specified learning outcomes by teaching the module syllabus in lectures, and supporting the assimilation and understanding of the taught material via tutorial classes. The practical design and technical skills related to the subject are acquired through coursework involving the investigation and application of digital signal processing methods via laboratory experiments.
Learning and teaching methods include:
Lectures
Tutorials
Lab sessions
Revision tutorials
Captured content
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: EEE3008
Other information
Surrey's Curriculum Framework is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills and capabilities in the following areas:
Digital capabilities: Students will develop skills in digital signal processing, a key digital technology in electronic engineering. They will gain practical digital signal processing experience in the lab sessions.
Employability: This module provides foundational skills in digital signal processing, an important topic for a wide range of industry applications such as image processing, speech processing, and digital communications.
Sustainability: This module includes consideration of issues of efficiency in signal processing, important for sustainable use of computing resources.
Resourcefulness and Resilience: This module develops student skills in using the digital signal processing theory they have learned in lecture material to solve exercises in tutorial sheets and to solve problems in computer labs.
Programmes this module appears in
| Programme | Semester | Classification | Qualifying conditions |
|---|---|---|---|
| Computer and Internet Engineering MEng | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Computer and Internet Engineering BEng (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Electronic Engineering BEng (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Electrical and Electronic Engineering BEng (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Electronic Engineering with Nanotechnology BEng (Hons) | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Electronic Engineering with Nanotechnology MEng | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Electrical and Electronic Engineering MEng | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Electronic Engineering MEng | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Computer Vision, Robotics and Machine Learning MSc | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Electronic Engineering MSc | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Communications Networks and Software MSc | 1 | Optional | A weighted aggregate mark of 40% is required to pass the module |
| Electronic Engineering with Computer Systems MEng | 1 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
| Electronic Engineering with Computer Systems BEng (Hons) | 1 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.