Course Title: Signals and Systems 2

Part A: Course Overview

Course Title: Signals and Systems 2

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

EEET2113

City Campus

Undergraduate

125H Electrical & Computer Engineering

Face-to-Face

 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

EEET2113

City Campus

Undergraduate

172H School of Engineering

Face-to-Face

 Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2022,
Sem 1 2023,
Sem 1 2024

EEET2477

RMIT University Vietnam

Undergraduate

172H School of Engineering

Face-to-Face

 Viet2 2018,
Viet3 2019,
Viet3 2020,
Viet3 2021,
Viet2 2022,
Viet2 2023,
Viet2 2024

Course Coordinator: Prof Margaret Lech

Course Coordinator Phone: +61 3 9925 1028

Course Coordinator Email: margaret.lech@rmit.edu.au

Course Coordinator Location: 12.11.11

Course Coordinator Availability: Email for appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study:
You are expected to have completed EEET2369 Signals and Systems 1 or other equivalent studies.

Assumed Knowledge:
You should be able to write MATLAB applications to solve typical signal processing or electrical/electronic engineering problems.

  • Being able to solve fundamental problems in calculus and algebra,
  • Familiarity with complex numbers, Fourier transform, Laplace transform, integration and differentiation and partial fraction expansion;
  • Having basic MATLAB, Python 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 1 (EEET2369). 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 signal processing, machine learning and data analysis.

Particular topics to be investigated include:

  • Correlation
  • Random signals
  • Signals in noise
  • Matched filters
  • Algorithms machine learning and supervised classification
  • Algorithms for machine learning and unsupervised clustering
  • Basic Neural Networks
  • Numerical techniques for Deep Learning and Convolutional Neural Networks
  • Deep transfer learning
  • Autoencoders

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:

     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 digital signals and system analysis.
  2. Understand the basics of correlation analysis.
  3. Understand the concepts of random signals.
  4. Apply numerical correlation techniques for signal detection in noise.
  5. Design and apply matched filters.
  6. Explain effects of noise on signal characteristics.
  7. Understand the principles of digital techniques for machine learning.
  8. Apply supervised classification algorithms to predict data categories.
  9. Apply unsupervised clustering algorithms to analyse data.
  10. Understand the principles of Deep Learning Neural Network algorithms.
  11. Implement basic pre-trained Neural Networks to solve engineering problems.
  12. Communicate your designs and test findings through written reports.


Overview of Learning Activities

Key concepts and their applications will be explained in pre-recorded lectures and lectorials, and practice-test examples demonstrating possible solutions.

The weekly homework exercises implemented as online practice 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 verification methodology.


Overview of Learning Resources

All learning resources for this course are available on Canvas, the University Learning Management System.

These resources include:

  • Weekly modules providing step by step guidance how to proceed with your study.
  • Weekly modules including Power-point slides, and video recordings explaining course topics, and ideas.
  • Practice self-tests with feedback on your study progress included in weekly modules.
  • Laboratory assignment instructions within weekly modules, including written explanations and video-recorded instructions.
  • Access to the complete MATLAB software that you can install on your own computer to work in the lab or from home.


Overview of Assessment

This course has no hurdle requirements.
Assessment Tasks:

Schedule A
Assessment Task 1: Individual Home Assignment (not timed)
Weighting: 20%
It is an online, open-book test with short-answer questions.
This assessment task supports CLOs: 1 - 3. 

Assessment Task 2: Individual Lectorial Test (timed and timetabled)
Weighting: 15%
It is an on-paper, closed-book test with a mixture of multiple-choice, and short-answer questions. 
This assessment task supports CLOs 4 - 6.
This assessment is a timed and timetabled assessment of less than 2 hours duration that students must attend on campus.

Assessment Task 3: Individual Final Assessment (timed and timetabled)
Weighting: 25%
It is an on-paper, closed-book test with short-answer questions. 
This assessment task supports CLOs 7 - 10.
This assessment is a timed and timetabled assessment of less than 2 hours duration that students must attend on campus.

Assessment Task 4: Group Design Project Assessment
Weighting: 40% total, comprising:
Part 1 - Mid-Semester Progress Report
Weighting: 15%
Online submission is required
Part 2 - Final Report
Weighting: 25%
Online submission is required
This assessment task supports CLOs 11 - 12. 

Note: If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.

Schedule B
Assessment Task 1: Individual Written Assignment
Weighting: 25%
This assessment task supports CLOs 1-6.

Assessment Task 2: Lectorial Tests
Weighting: 30% (2 x 15%)
This assessment task supports CLOs 7-9

Assessment Task 3: Laboratory Group Projects Reports
Weighting: 20%
This assessment task supports CLOs 4,8,9 and 12

Assessment Task 4: Individual Final Timed and Timetabled Assessment
Weighting: 25%
This assessment task supports CLOs 10-12
This assessment is a timed and timetabled assessment that students must attend on campus except for international students who are outside Australia.