Course Title: Digital Signal Processing
Part A: Course Overview
Course Title: Digital Signal Processing
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
EEET1123 |
City Campus |
Postgraduate |
125H Electrical & Computer Engineering |
Face-to-Face |
Sem 1 2006, Sem 1 2008, Sem 1 2009, Sem 1 2010, Sem 1 2012, Sem 1 2013, Sem 1 2014, Sem 1 2015, Sem 1 2016 |
EEET1123 |
City Campus |
Postgraduate |
172H School of Engineering |
Face-to-Face |
Sem 1 2017, Sem 1 2018, Sem 1 2020, Sem 1 2024 |
Course Coordinator: Dr Saman Atapattu
Course Coordinator Phone: -
Course Coordinator Email: saman.atapattu@rmit.edu.au
Course Coordinator Location: -
Course Coordinator Availability: By appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
Assumed Knowledge
You are expected to have sound knowledge on the following topics: signal description and analysis in time and frequency domains, Fourier/spectrum analysis, analog and digital filters, z-transforms, fundamentals of communication systems, digital modulation techniques, and signal analysis using the software tool Matlab. You are also expected to have basic computer programming skills to work on the lab exercises.
Course Description
Digital signal processing is an important aspect in the current era of communication engineering especially in wireless and mobile communication. Though deterministic signals are transmitted to communicate information in reality what we receive actually are corrupted signals that are random in nature. In this course therefore we cover random signal theories and its applications to communication engineering in order to understand the behavior of communication signals and improve the performance of communication systems by performing intelligent signal processing.
The topics covered in this course are;
- A quick recap of Discrete Fourier Transform (DFT), spectral analysis, filter design and windowing functions
- Fundamentals of random signal theory and analysis, and LTI system response to random signals
- Modelling communication signals as random processes
- Baseband signal processing, signal synthesis and filter design for communication
- Statistical signal processing in communication for signal detection and synchronization
Objectives/Learning Outcomes/Capability Development
Program Learning Outcomes
This course contributes to the program learning outcomes for the following program(s):
PLO 1. Demonstrate an advanced and integrated understanding of engineering theories, principles and concepts within multi-disciplinary engineering practice
PLO 2. Demonstrate an advanced and integrated understanding of specialist bodies of knowledge within the engineering discipline
PLO 4. Apply advanced knowledge of established engineering methods in the analysis of complex problems in the engineering discipline
PLO 5. Utilise advanced mathematics, software, tools and techniques, in the conduct of research into the design and analysis of complex engineering systems
PLO 6. Use a systems engineering approach to synthesize and apply procedures for design, prototyping and testing to develop creative, sustainable and integrated solutions to complex engineering problems.
PLO 7. Apply advanced contemporary engineering technologies and practices and research principles and methods, taking into account risk and economic, social, environmental and global context, to plan and execute complex projects.
PLO 8. Communicate engineering designs and solutions respectfully and effectively, employing a range of advanced communication methods in interpreting and transmitting knowledge, in an individual or team environment, to diverse audiences.
PLO 10. Critically analyse, evaluate, and transform information, while exercising professional expert judgement in a dynamic environment in the absence of complete data, in an engineering context.
PLO 11. Collaborate and contribute as an effective team member or leader in diverse specialist and multi-disciplinary teams, with commitment to First Nations peoples and/or globally inclusive perspectives and participation in an engineering context."
Upon successful completion of this course, you will be able to:
CLO1 Analyse deterministic and random discrete-time signals utilising advanced theoretical concepts and applied methods.
CLO2 Collaboratively devise a digital signal processing device that meets specified performance criteria for advanced digital communication systems.
CLO3 Simulate filtering of communication signals integrating advanced engineering concepts and software tools.
CLO4 Estimate parameters of signals in noise by integrating advanced knowledge of established signal processing tools and probability theories.
CLO5 Evaluate the implementation of discrete-time signal processing algorithms in communication applications using specialised tools and simulation techniques.
Overview of Learning Activities
You will be actively engaged in a range of learning activities such as pre-recorded lectures, lectorials, tutorials, and laboratories. Delivery may be face to face, online or a mix of both.
You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, andthrough links and material specific to this course that is available through myRMIT Studies Course.
The key concepts, theories, and their practical applications will be thoroughly explained in pre-recorded lecture videos. These pre-recorded lectures will encompass various case studies in the field of communication engineering.
The laboratory exercises will train students to apply the random signal theories in practical scenarios and develop skills to simulate signals and write algorithms to process signals to improve the performance of communication systems.
Overview of Learning Resources
RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.
There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.
Overview of Assessment
Assessment Tasks
Assessment Task 1: Lab assignment (group) 20% CLO1, CLO2 and CLO5
Assessment Task 2: Mid-semester test 20% CLO1 and CLO2
Assessment Task 3: Project assignment 30% CLOS CLO1, CLO2, CLO3, CLO4 and CLO5
Assessment Task 4: End of semester written test 30% CLO1, CLO3 and CLO4"
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 your program Manager or Equitable Learning Services if you would like to find out more.