- Code ENGN8538
- Unit Value 6 units
- Offered by School of Engineering
- ANU College ANU College of Engineering Computing & Cybernetics
- Course subject Engineering
- Areas of interest Mathematics, Engineering
- Work Integrated Learning Projects
- Academic career PGRD
- Mode of delivery In Person
First Semester 2024
See Future Offerings
- STEM Course
In 2023, this course is on campus with remote adjustments only for participants with unavoidable travel restrictions/visa delays.
The objective of ENGN8538 is to provide the fundamentals and advanced concepts of probability theory and random process to support graduate coursework and research in electrical, electronic and computer engineering. The required mathematical foundations will be studied at a fairly rigorous level and the applications of the probability theory and random processes to engineering problems will be emphasised. The simulation techniques will also be studied and MATLAB will be used as a software tool for bridging the probability theory and engineering applications.
• Overview of elementary probability;
• Discrete and continuous random variables and their statistical properties;
• Important random variables and their applications;
• Functions of random variables;
• Statistical properties of multiple random variables;
• Random processes: Classification and characterisation;
• Properties of random processes: Stationarity, correlation function, power spectral density, spectral analysis;
• Special processes: such as Gaussian and Poisson;
• Overview of Markov process and applications;
• Estimation theory, MMSE estimation, performance comparison of estimators;
• Overview of detection theory;
• Simulation techniques: generation of random variable/process in MATLAB;
• Examples of applications from signal processing (e.g., Wiener filter), digital communications (e.g., analysis and simulation of coded digital communication system) and mechatronic systems (e.g. estimation in simultaneous localisation and mapping).
Upon successful completion, students will have the knowledge and skills to:
- Apply the specialised knowledge in probability theory and random processes to solve practical engineering problems.
- Gain advanced and integrated understanding of the fundamentals of and interrelationship between discrete and continuous random variables and between deterministic and stochastic processes.
- Apply the fundamentals of probability theory and random processes to practical engineering problems, and identify and interpret the key parameters that underlie the random nature of the problems.
- Use the top-down approach to translate engineering system requirements into practical design problems.
- Create mathematical models for practical design problems and determine theoretical solutions to the created models.
- Analyse the performance in terms of probabilities and distributions achieved by the determined solutions.
- Apply research skills to develop a thorough understanding of emerging engineering research problems beyond the scope of the course materials and critically analyse the recent research outcomes.
- Professionally interpret and disseminate the design and results of engineering research problems to the audiences with different levels of background knowledge.
Work Integrated Learning
This course provides the fundamentals and advanced concepts of probability theory and random process to support graduate coursework and research in electrical, electronic and computer engineering.
This course provides you with the opportunity to deeply engage with the course content, drive your leaning, build skills and confidence,
and perform the learned tasks more effectively.
Professional Skills Mapping:
- Assignments (15) [LO 1,2,3,4,5,6,7,8]
- Computer Labs (6) [LO 1,2,3,4,5,6]
- Term Project (9) [LO 1,2,3,4,7,8]
- Midterm Exam (20) [LO 1,2,3,4,5,6]
- Final Exam (50) [LO 1,2,3,4,5,6]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
WorkloadStandard workload (approx. 10 hours a week). 2 lectures (1 x 2-hour lecture and 1 x 1-hour lecture) per week, up to 1 x 1-hour tutorial per week, up to 1 x 3-hour computer laboratories per week, approximately 6 hours independent study per week.
Requisite and Incompatibility
Prescribed TextsThe prescribed text for this course is:
- J. A. Gubner, Probability and Random Processes for Electrical and Computer Engineers, Cambridge University Press, 2006.
- S. L. Miller and D. Childers, Probability and Random Processes: With Applications to Signal Processing and Communications. (Online reserve: http://www.sciencedirect.com/science/book/9780121726515 )
- A. Papoulis and S.U. Pillai, Probability, Random Variables, and Stochastic Processes.
- H. Stark and J. Woods, Probability, Random Processes, and Estimation Theory for Engineers.
- G. R. Grimmett and D. R. Stirzaker, Probability and Random processes.
- ENGN2228 Signal Processing or equivalent (Suggested)
- Assume understanding of basic fundamentals of probability theory and linear algebra
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
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Offerings, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|2325||19 Feb 2024||26 Feb 2024||31 Mar 2024||24 May 2024||In Person||N/A|