- Code ENGN6537
- Unit Value 6 units
- Offered by RS Electrical, Energy and Materials Engineering
- ANU College ANU College of Engineering and Computer Science
- Course subject Engineering
- Areas of interest Engineering, Electronics
Digital Signal Processing (DSP) has become over the years an important tool with applications in Electrical and Mechanical Engineering fields. DSP has penetrated many domains of applications, such as digital communications, medical imaging, audio & video systems, consumer electronics, robotics, remote sensing, finance, etc.
The Discrete-Time Signal Processing paradigm is a convenient setting to analyse the basic principles of DSP. At the end of this course, the students should be able to understand these basic principles, and apply fundamental algorithms and methods to analyse and design discrete- time systems for modern DSP applications. Though the course will focus on the study of theoretical concepts, methods and algorithms, the student will be confronted with application and implementation issues, through various examples and assignments requiring personal computer work including processing of real-world signals.
Upon successful completion, students will have the knowledge and skills to:
- Analyse and evaluate the properties of LTI systems in terms of its z-transforms.
- Understand the sampling theorem and perform sampling on continuous-time signals by applying advanced knowledge of the sampling theory (i.e., aliasing, quantisation errors, pre-filtering).
- Apply the concepts of all-pass and minimum-phase systems to analyse the LTI system and address complex design problems.
- Evaluate design problems related to frequency selective processing and design FIR/IIR filters.
- Construct systems for spectral estimation of real signals by applying advanced knowledge of Fourier techniques.
- Judge implementation aspects of modern DSP algorithms.
- Apply the relevant theoretical knowledge to design and analyse a practical discrete-time signal system, such as a radar or audio system, using Matlab platform.
- Investigate advanced signal processing techniques and implement complicated systems based on the course material.
- Weekly Quizzes (15) [LO 1,2,3,4]
- Mid-term Exam (20) [LO 1,2,3]
- Matlab Project (25) [LO 2,4,5,6,7,8]
- Final Exam (40) [LO 1,2,3,4,5,6]
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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Approximately 10 hours per week. The contact hours include 1 x 2 hrs lecture and 1 x 1 hr lecture, plus up to 1 x 1.5 hrs tutorial per week.
Requisite and Incompatibility
Tuition fees are for the academic year indicated at the top of the page.
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- Student Contribution Band:
- Unit value:
- 6 units
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|Class start date
|Last day to enrol
|Class end date
|Mode Of Delivery
|24 Feb 2020
|02 Mar 2020
|08 May 2020
|05 Jun 2020