FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING - 2018/9

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.

Module provider

Electrical and Electronic Engineering

Module Leader

PLUMBLEY MD Prof (Elec Elec En)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

JACs code: H610

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None.

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

Not applicable: students failing a unit of assessment resit the assessment in its original format.

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:

Exam: 2-hour, closed-book written examination.

Coursework: Laboratory experiments concerned with analysis and design of signal processing systems, typically 5 x 2-hour sessions.

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.

Learning outcomes

Attributes Developed
1 Describe the terminology and concepts of core areas in digital signal processing. KT
2 Explain theory behind basic digital signal processing methods.   KT
3 Apply introduced methods in the design of basic signal processing applications.   KP
4 Analyse and explain behaviour of basic digital signal processing systems.   KC

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Overall student workload

Independent Study Hours: 107

Lecture Hours: 33

Laboratory Hours: 10

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 (2 hours per week for 10 weeks)

Tutorials (1 hours per week for 10 weeks)

Lab sessions (approx. 5 x 2-hour sessions)

Revision tutorials (2 hours in the last week)

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

Reading list for FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING : http://aspire.surrey.ac.uk/modules/eee3008

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Computer Vision, Robotics and Machine Learning (EuroMasters) MSc 1 Optional A weighted aggregate mark of 40% is required to pass the module
Computer Vision, Robotics and Machine Learning (EuroMasters) MSc 1 Optional Each unit of assessment must be passed at 50% 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
Electrical and Electronic Engineering BEng (Hons) 1 Optional A weighted aggregate mark of 40% is required to pass the module
Electronic Engineering (by short course) MSc 1 Optional A weighted aggregate mark of 40% is required to pass the module
Communication Systems BEng (Hons) 1 Optional A weighted aggregate mark of 40% is required to pass the module
Communication Systems MEng 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
Electronic Engineering with Computer Systems BEng (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Computer and Internet Engineering BEng (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Computer and Internet Engineering MEng 1 Compulsory 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
Electronic Engineering with Computer Systems MEng 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Mobile Media Communications MSc 1 Optional A weighted aggregate mark of 40% is required to pass the module
Electronic Engineering with Audio-Visual Systems BEng (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Electronic Engineering (EuroMasters) 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
Computer Vision, Robotics and Machine Learning MSc 1 Optional A weighted aggregate mark of 40% is required to pass the module
Mobile Media Communications (EuroMasters) MSc 1 Optional A weighted aggregate mark of 50% is required to pass the module
Electronic Engineering with Communications MEng 1 Optional A weighted aggregate mark of 40% is required to pass the module
Electronic Engineering with Communications BEng (Hons) 1 Optional A weighted aggregate mark of 40% is required to pass the module
Electronic Engineering with Audio-Visual Systems MEng 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 2018/9 academic year.