FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING - 2023/4

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

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

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

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

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.