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CECS Professional Skills Mapping

ENGN4537 — Digital Signal Processing

code: ENGN4537
name: Digital Signal Processing
unit value: 6
description: 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.
P&C: https://programsandcourses.anu.edu.au/course/ENGN4537
course learning outcomes:
  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.
assessment:
  1. Weekly Quizzes (15%)
  2. Mid-term Exam (20%)
  3. Matlab Project (25%)
  4. Final Exam (40%)

Mapped learning outcomes

learning outcome1. KNOWLEDGE AND SKILL BASE2. ENGINEERING APPLICATION ABILITY3. PROFESSIONAL AND PERSONAL ATTRIBUTESassessment tasks
1.11.21.31.41.51.62.12.22.32.43.13.23.33.43.53.61234
  1. Analyse and evaluate the properties of LTI systems in terms of z-transforms.
✓✓✓✓✓✓✓✓
  1. 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).
✓✓✓✓✓✓✓✓✓✓✓
  1. Apply the concepts of all-pass and minimum-phase systems to analyse LTI systems and address complex design problems.
✓✓✓✓✓✓✓✓
  1. Evaluate design problems related to frequency selective processing and design FIR/IIR filters.
✓✓✓✓✓✓✓✓✓✓✓✓✓✓✓
  1. Construct systems for spectral estimation of real signals by applying advanced knowledge of Fourier techniques.
✓✓✓✓✓✓✓✓✓✓✓✓✓✓
  1. Judge implementation aspects of modern DSP algorithms.
✓✓✓✓✓✓✓✓✓✓✓✓✓✓✓
  1. 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.
✓✓✓✓✓✓✓✓✓✓✓✓✓✓

Course contribution towards the Engineers Australia Stage 1 Competency Standard

This table depicts the relative contribution of this course towards the Engineers Australia Stage 1 Competency Standard. Note that this illustration is indicative only, and may not take into account any recent changes to the course. You are advised to review the official course page on P&C for current information..

1. KNOWLEDGE AND SKILL BASE
1.1
 
1.2
 
1.3
 
1.4
 
1.5
 
1.6
 
2. ENGINEERING APPLICATION ABILITY
2.1
 
2.2
 
2.3
 
2.4
 
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
3.1
3.2
 
3.3
 
3.4
 
3.5
 
3.6

Engineers Australia Stage 1 Competency Standard — summary

1. KNOWLEDGE AND SKILL BASE
✓1.1Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.
✓1.2Conceptual understanding of the, mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
✓1.3In depth understanding of specialist bodies of knowledge within the engineering discipline.
✓1.4Discernment of knowledge development and research directions within the engineering discipline.
✓1.5Knowledge of contextual factors impacting the engineering discipline.
✓1.6Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the engineering discipline.
2. ENGINEERING APPLICATION ABILITY
✓2.1Application of established engineering methods to complex engineering problem solving.
✓2.2Fluent application of engineering techniques, tools and resources.
✓2.3Application of systematic engineering synthesis and design processes.
✓2.4Application of systematic approaches to the conduct and management of engineering projects.
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
3.1Ethical conduct and professional accountability.
✓3.2Effective oral and written communication in professional and lay domains.
✓3.3Creative, innovative and pro-active demeanour.
✓3.4Professional use and management of information.
✓3.5Orderly management of self, and professional conduct.
3.6Effective team membership and team leadership.

Updated:  18 February 2021/ Responsible Officer:  Dean, CECS/ Page Contact:  CECS Academic Education Services