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Unit of study_

ELEC5308: Intelligent Information Engineering Practice

Semester 2, 2022 [Normal day] - Remote

This unit aims at the practising ability of students on utilizing intelligent information engineering techniques for solving practical problems in the latest applications of AI, e.g., Autopilot. Students will get programming skills for many tasks related to automatic driving, including lane detection, traffic sign detection, pedestrian detection, and path planning. Lane detection, traffic sign detection, pedestrian detection, and path planning are information processing techniques that will help students to learn how to use the Video Intelligence and Signal Understanding approaches for many practical problems. All students will be involved in designing mini-projects and a large project. The unit is project-oriented. Students will run their programs on simulated environment. The course will be taught through lectures mainly on how to accomplish the goal for the mini-project and the final project. A specific lab design will be provided to students for hands-on design. Communication skills will be tested through several project presentations. Some teaching will be provided by intelligent information engineers working in the industry.

Unit details and rules

Unit code ELEC5308
Academic unit Electrical and Information Engineering
Credit points 6
Assumed knowledge

Students must have a good understanding of Linear algebra and basic mathematics, Basic Programming skills in C, Python or Matlab

Available to study abroad and exchange students


Teaching staff

Coordinator Huaming Chen,
Lecturer(s) Charles Liu,
Type Description Weight Due Length
Assignment group assignment Mini project
Assessing the submitted code, report, and demo.
20% Week 08 2 weeks
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment
The answer consists of design and implementation.
40% Week 11
Due date: 23 Oct 2022 at 23:59
3 weeks
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Project
Assessing the submitted code, report, and demo.
40% Week 13 3-4 weeks
Outcomes assessed: LO1 LO2 LO3
group assignment = group assignment ?

Assessment summary

Two group projects:

   – one mini-project 

  – one larger project

One individual project.

The assessment approach of this unit covers both group and individual contributions, examining the technical merit, any extensive studies, completeness of documentation as well as the presentation of the solution.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result name Mark range Description
Mini project 0-100 The mini project will require students to develop some fundamental functionality in groups using the techniques covered thus far. The deliverable includes source code, software artefacts, documentation and demonstration.
Assignment 0-100 The assignment will consist of some design and implementation questions. The questions are often open-ended aiming at testing how far and wide the student has researched and studied in the related area. This assignment must be done independently. The answers are supposed to include solution designs in the form of text and graphs, (optionally) some pseudo or real code to indicate how things may work.
Final project 0-100 The final project will require students to develop more advanced functionality in groups using techniques covered in this unit and any extended studies in this area. The deliverable includes source
code, software artefacts, documentation and demonstration. 

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

5% per day.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Lecture 1: Introduction and Overview Laboratory: Overview Online class (3 hr) LO1 LO2 LO3
Week 02 Lecture 2: Technology review and basics of image acquisition & control Laboratory: Image acquisition & control Online class (3 hr) LO1 LO2 LO3
Week 03 Lecture 3: Lane detection – part 1 Laboratory: Lane detection – part 1 Online class (3 hr) LO1 LO2 LO3
Week 04 Lecture 4: Lane detection – part 2 Laboratory: Lane detection – part 2 & Mini project 1 Online class (3 hr) LO1 LO2 LO3
Week 05 Lecture 5: Mini project 1 – demo and presentation Laboratory: Mini project 1 – demo and presentation Online class (3 hr) LO1 LO2 LO3
Week 06 Lecture 6: Traffic sign recognition Laboratory: Traffic sign recognition Online class (3 hr) LO1 LO2 LO3
Week 07 Lecture 7: Obstacle recognition and control for avoiding obstacles Laboratory: Obstacle recognition Online class (3 hr) LO1 LO2 LO3
Week 08 Lecture 8: Pedestrian detection algorithm & design Laboratory: Pedestrian detection Online class (3 hr) LO1 LO2 LO3
Week 09 Lecture 9: Hosting and system design consideration Laboratory: Mini project 2 Online class (3 hr) LO1 LO2 LO3
Week 10 Lecture 10: Mini project 2 – demo and presentation Laboratory: Mini project 2 – demo and presentation Online class (3 hr) LO1 LO2 LO3
Week 11 Lecture 11: Date driven detection and Scene understanding – part 1 Laboratory: Date driven detection Online class (3 hr) LO1 LO2 LO3
Week 12 Lecture 12: Date driven detection and Scene understanding – part 2 Laboratory: Final project Online class (3 hr) LO1 LO2 LO3
Week 13 Lecture 13: Presentation and demo for the final project Laboratory: Presentation and demo for the final project Online class (3 hr) LO1 LO2 LO3

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. To learn basic intelligent information engineering techniques
  • LO2. To learn how to apply intelligent information engineering techniques to practical problems on a robot.
  • LO3. To apply programming skills for research and application tasks related to the intelligent information and signal processing, and automatic driving.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

The first time of this unit. No feedback from students.


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