Course Title: Signals and Systems 2

Part A: Course Overview

Course Title: Signals and Systems 2

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


125H Electrical & Computer Engineering


Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 1 2015,
Sem 1 2016


City Campus


172H School of Engineering


Sem 1 2018,
Sem 1 2019,
Sem 1 2020


RMIT University Vietnam


172H School of Engineering


Viet2 2018,
Viet3 2019

Course Coordinator: Dr Margaret Lech

Course Coordinator Phone: +61 3 9925 1028

Course Coordinator Email:

Course Coordinator Location: 12.08.17

Course Coordinator Availability: Email for appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

You are required to have successfully completed EEET2248 Engineering Methods and EEET2369 Signals and Systems or other equivalent studies (these are not enforced pre-requisites).

Assumed knowledge and capabilities:

  • Being able to solve fundamental problems in calculus and algebra,
  • Being familiar with complex numbers, Fourier transform, Laplace transform, integration and differentiation and partial fraction expansion;
  • Having basic Matlab and/or C/C++ programming skills.

Course Description

This course will introduce you to the fundamentals, implementation and applications of digital signal processing (DSP) techniques as applied to practical, real world problems.

The course will extend your knowledge acquired in Signals and Systems (EEET2369) and Engineering Methods (EEET2248). You will learn how to formulate practical engineering problems in terms of signal processing tasks, and how to solve these problems using signal processing methods.

The laboratory assignments included in this course were designed in consultation with local industry to give you practical knowledge of current industry practice in sequential signal processing, data windowing, time and frequency analysis, filter design, adaptive filtering and signal separation.

Particular topics to be investigated include:
  • Analog to digital conversion, sampling and reconstruction
  • Amplitude quantization
  • Time domain representation of discrete time signals and systems
  • Frequency domain representation of discrete time signals and systems
  • Correlation and cross--correlation
  • Random signals
  • Signals in noise
  • Matched filters
  • Finite impulse response and infinite impulse response filters
  • Sequential processing and windowing
  • Adaptive filtering
  • Signal separation using independent component analysis
  • Speech production, modelling and analysis
  • Machine learning
  • Deep learning

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onward. See the WAM information web page for more information.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes of the Bachelor of Engineering (Honours):

     1.3 In-depth understanding of specialist bodies of knowledge within the engineering discipline.
     2.1 Application of established engineering methods to complex engineering problem solving.
     2.2 Fluent application of engineering techniques, tools and resources.


On completion of this course you should be able to:

  1. Explain basic concepts of signal and system analysis and principles of time and frequency domain signal and system representation
  2. Design and test DSP algorithms and digital filters
  3. Design and apply matched filters
  4. Apply principles of adaptive filtering and signal separation to a range of engineering problems
  5. Describe the production of speech, model speech production and analyse speech using DSP techniques
  6. Implement sequential signal processing
  7. Explain effects of windowing on spectral characteristics of signals
  8. Communicate your designs and test findings through written reports.

Overview of Learning Activities

Key concepts and their application will be explained in lectorials, with fully worked out examples demonstrating possible solutions.

The weekly homework exercises implemented as online blackboard self-tests will help you to develop problem solving skills and provide systematic feedback on your progress.

Laboratory assignments will enhance your communication skills through written and oral reports and verification methodology.

Overview of Learning Resources

All learning resources for this course are available on the Canvas university online system.

These resources include:

  • Weekly step by step guidance how to proceed with your study
  • Theoretical module including power point slides, video recordings explaining important topics and ideas
  • Self-testing module including hand written examples with solutions and self-tests with feedback on your study progress
  • Laboratory assignment instruction module including written explanations and video recorded instructions
  • Access to laboratory with complete Matlab software package
  • Access to Matlab software that you can install on your own computer

Overview of Assessment

☒ This course has no hurdle requirements.
☐ All hurdle requirements for this course are indicated clearly in the assessment regime that follows, against the relevant assessment task(s) and all have been approved by the College Deputy Pro Vice-Chancellor (Leaning & Teaching).

Assessment tasks:

Assessment Task 1:Progress test in Week 4

Weighting 25%

This assessment task supports CLOs 1 to 8


Assessment Task 2: Progress test in Week 8

Weighting 25%

This assessment task supports CLOs 1 to 8


Assessment Task 3: Progress test in Week 12

Weighting 20%

This assessment task supports CLOs 1 to 8


Assessment Task 42: Laboratory work and reports

Weighting 30%

This assessment task supports CLOs 2 to 6 and 8