Course Title: Signals and Systems 2
Part A: Course Overview
Course Title: Signals and Systems 2
Credit Points: 12.00
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
172H School of Engineering
|Sem 1 2017,
Sem 2 2017,
Sem 1 2018
Course Coordinator: Dr Margaret Lech
Course Coordinator Phone: +61 3 9925 1028
Course Coordinator Email: email@example.com
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.
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:
- Linear systems
- Time domain representation of continuous time signals and systems
- Frequency domain representation of continuous time signals and systems
- 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
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:
- Explain basic concepts of signal and system analysis and principles of time and frequency domain signal and system representation
- Design and test DSP algorithms and digital filters
- Design and apply matched filters
- Apply principles of adaptive filtering and signal separation to a range of engineering problems
- Describe the production of speech, model speech production and analyse speech using DSP techniques
- Implement sequential signal processing
- Explain effects of windowing on spectral characteristics of signals
- 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 blackboard 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 Task 1: Weekly progress online tests (weeks 2 - 12)
This assessment task supports CLOs 1 to 8
Assessment Task 2: Laboratory work and reports
This assessment task supports CLOs 2 to 6 and 8