- Code ENGN8538
- Unit Value 6 units
- Offered by Research School of Engineering
- ANU College ANU College of Engineering and Computer Science
- Course subject Engineering
- Areas of interest Mathematics, Engineering
- Academic career PGRD
- Dr Nan Yang
- Mode of delivery In Person
First Semester 2016
See Future Offerings
Upon successful completion, students will have the knowledge and skills to:
On successful completion of the course, students should be able to:
- Explain fundamentals of probability theory, random variables and random processes.
- Understand the mathematical concepts related to probability theory and random processes.
- Understand the characterization of random processes and their properties.
- Formulate and solve the engineering problems involving random processes.
- Analyze the given probabilistic model of the problem.
- Make precise statements about random processes.
- Use computational techniques to generate simulation results.
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;
• Sequence of random variables, random vectors, notions of convergence;
• Random processes: Classification and characterisation;
• Properties of random processes: Stationarity, correlation function, power spectral density, spectral analysis;
• Special processes: Gaussian, Poisson and Wiener;
• 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 (Wiener filter) and digital communications (simulation of coded digital communication system).
Computer Labs (CLabs) 6%
Term Project 6%
Midterm Exam 20%
Final Exam 50%
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Standard workload (approx. 10 hours a week). Weekly lectures and laboratories
Requisite and Incompatibility
Prescribed TextsJ. 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.
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are an undergraduate student and 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). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
- Domestic fee paying students
- International fee paying students
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|
|3547||15 Feb 2016||26 Feb 2016||31 Mar 2016||27 May 2016||In Person||N/A|