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

You should have satisfactorily completed or received credit for the following course/s before you commence this course

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

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in above course/s you may be eligible to apply for credit transfer. Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the abo course/s you may be eligible for recognition of prior learning. Please follow the link for further information on how to apply for credit for prior study or experience.

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:

PLO 1. Demonstrate a coherent and advanced understanding of   scientific theories, principles and concepts and engineering fundamentals within the engineering discipline​
PLO 2. Demonstrate a coherent and advanced body of knowledge within the engineering discipline
PLO 4. Apply knowledge of established engineering methods to the solution of complex problems in the engineering discipline
PLO 5. Utilise mathematics, software, tools and techniques, referencing appropriate engineering standards and codes of practice, in the design of complex engineering systems 
PLO 6. Use a systems engineering approach to synthesize and apply procedures for design, prototyping and testing to manage complex engineering projects. 
PLO 8. Communicate engineering designs and solutions respectfully and effectively, employing a range of advanced communication methods, in an individual or team environment, to diverse audiences.​​
PLO 11. Collaborate and contribute as an effective team member or leader in diverse, multi-disciplinary teams, with commitment to First Nations peoples and/or globally inclusive perspectives and participation in an engineering context.​

For more information on the program learning outcomes for your program, please see the program guide.


On completion of this course, you will be able to:

CLO1 Describe theoretical principles and concepts of digital signals and system analysis using machine learning.
CLO2 Apply classical machine learning algorithms for data analysis and prediction.
CLO3 Design and apply deep learning Neural Network algorithms to solve problems in security systems. 
CLO4 Analyse and critically evaluate the performance of machine learning solutions in security systems.
CLO5 Collaborate and contribute as an effective team member in the process of engineering design, implementation and evaluation of machine learning solutions to security systems. 
CLO6 Produce team project reports and proposals that align with project and specialist audience needs.


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:

Assessment Task 1: Assignment, 20%, CLO1 and CLO2
Assessment Task 2: Test, 15%, CLO2 and CLO3
Assessment Task 3: Test, 25%, CLO2, CLO3 and CLO4
Assessment Task 4: Design Project (Group), 40%, CLO2, CLO3, CLO4, CLO5 and CLO6

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.