• Offered by RS Electrical, Energy and Materials Engineering
  • ANU College ANU College of Engineering and Computer Science
  • Course subject Engineering
  • Areas of interest Engineering
  • Academic career UGRD
  • Course convener
    • Dr Prasanga Samarasinghe
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in First Semester 2019
    See Future Offerings

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.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

On successful completion of this course, students should have the skills and knowledge to:

  1. Analyse and evaluate the properties of LTI systems in terms of z-transforms.
  2. Understand the sampling theorem and perform sampling on continuous-time signals by applying advanced knowledge of sampling theory (i.e. aliasing, quantisation errors, pre-filtering).
  3. Apply the concepts of all-pass and minimum-phase systems to analyse LTI systems and address complex design problems.
  4. Evaluate design problems related to frequency selective processing and design FIR/IIR filters.
  5. Construct systems for spectral estimation of real signals by applying advanced knowledge of Fourier techniques.
  6. Judge implementation aspects of modern DSP algorithms.
  7. Apply the relevant theoretical knowledge to design and analyse a practical discrete-time signal system, such as a radar, image, speech, audio, bio-medical or wireless system.

Professional Skills Mapping:
Mapping of Learning Outcomes to Assessment and Professional Competencies 

Indicative Assessment

  1. Weekly Reports (15%); 
  2. Mid term Quiz (20%); 
  3. Matlab Project (25%); 
  4. Final Exam (40%)

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.

Requisite and Incompatibility

To enrol in this course you must have completed ENGN2228. Incompatible with ENGN6537

Majors

Minors

Fees

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:
2
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.

Units EFTSL
6.00 0.12500
Domestic fee paying students
Year Fee
2019 $4320
International fee paying students
Year Fee
2019 $5700
Note: Please note that fee information is for current year only.

Offerings and Dates

The list of offerings for future years is indicative only

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
2968 25 Feb 2019 04 Mar 2019 31 Mar 2019 31 May 2019 In Person N/A

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