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Course profile

Advanced Embedded Systems (CSSE4011)

Study period
Sem 1 2026
Location
St Lucia
Attendance mode
In Person

Course overview

Study period
Semester 1, 2026 (23/02/2026 - 20/06/2026)
Study level
Undergraduate
Location
St Lucia
Attendance mode
In Person
Units
2
Administrative campus
St Lucia
Coordinating unit
Elec Engineering & Comp Science School

Advanced topics in Embedded System, including wireless networks and wireless sensor networks.

The course provides a review of the theory and applications of advanced embedded systems and wireless sensor network applications. It also provides an opportunity to undertake a substantial group project in embedded systemᅠdevelopment.

Course requirements

Assumed background

The course involves advanced system programming, hardware design and operating system programming. In addition to CSSE3010, having done CSSE2310 or COMP3301 (or equivalent), would be advantageous.

Prerequisites

You'll need to complete the following courses before enrolling in this one:

CSSE3010

Incompatible

You can't enrol in this course if you've already completed the following:

CSSE4003 or CSSE7005 or CSSE7411

Course contact

Course staff

Lecturer

Dr Matthew D'Souza
Professor Michael Bruenig

Timetable

The timetable for this course is available on the UQ Public Timetable.

Aims and outcomes

The course aims to provide students with a theoretical understanding of the fundamentals of advanced embedded systems and Wireless Sensor Networks (WSN), and experience with programming advanced embedded systems/WSN systems in a major group project.

Learning outcomes

After successfully completing this course you should be able to:

LO1.

Explain and apply the fundamental background science and engineering behind advanced embedded systems.

LO2.

Apply advanced concepts of peripheral interfacing and communication protocols.

LO3.

Build embedded systems that utilise wireless communication networks.

LO4.

Create and execute a project plan for a complex embedded systems design.

LO5.

Work successfully as part of a team.

LO6.

Propose and analyse advanced embedded system design solutions using effective written and oral communication techniques.

LO7.

Demonstrate realtime operating system concepts used in advanced embedded systems.

LO8.

Use advanced hardware design principles for implementing embedded systems.

Assessment

Assessment summary

Category Assessment task Weight Due date
Computer Code, Paper/ Report/ Annotation, Notebook/ Logbook, Practical/ Demonstration Laboratory Practicals
  • Hurdle
  • Identity Verified
  • In-person
30%

Prac 0: 24/02/2026 - 25/02/2026

Prac 1: 3/03/2026 - 4/03/2026

Prac 2: 10/03/2026 - 11/03/2026

Prac 3: 17/03/2026 - 18/03/2026

Prac 4: 24/03/2026 - 25/03/2026

Prac 5: 31/03/2026 - 1/04/2026

All stated dates and times are AEST

Computer Code, Notebook/ Logbook Mini Project
  • Hurdle
  • Identity Verified
  • Team or group-based
  • In-person
20%

28/04/2026 - 29/04/2026

Computer Code, Practical/ Demonstration, Project Final Project Milestone
  • Identity Verified
  • Team or group-based
  • In-person
10%

12/05/2026 - 13/05/2026

The milestone presentation must be given by all group members in the assigned timeslot during the lab session. Before the presentation, all presentation slides must be uploaded to Blackboard, and the code and wiki must be committed to the git repository.

Computer Code, Practical/ Demonstration, Poster, Project Final Project
  • Hurdle
  • Identity Verified
  • Team or group-based
  • In-person
40%

26/05/2026 - 27/05/2026

A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.

Assessment details

Laboratory Practicals

  • Hurdle
  • Identity Verified
  • In-person
Mode
Activity/ Performance, Product/ Artefact/ Multimedia
Category
Computer Code, Paper/ Report/ Annotation, Notebook/ Logbook, Practical/ Demonstration
Weight
30%
Due date

Prac 0: 24/02/2026 - 25/02/2026

Prac 1: 3/03/2026 - 4/03/2026

Prac 2: 10/03/2026 - 11/03/2026

Prac 3: 17/03/2026 - 18/03/2026

Prac 4: 24/03/2026 - 25/03/2026

Prac 5: 31/03/2026 - 1/04/2026

All stated dates and times are AEST

Other conditions
Time limited, Secure.

See the conditions definitions

Learning outcomes
L01, L02, L03, L06, L07, L08

Task description

There are five assessed practicals (Prac 1 to 5) and one non-assessed prac (Prac 0). Pracs 1 to 5 are worth 7.5% each. Prac 0 is not assessed. The total prac mark is calculated as the sum of the four highest prac marks.

Each prac consists of preparation, demonstration, code repository, and report submission. The prac sessions are designed to build up the necessary knowledge and skills and develop software modules. The work in the pracs takes you from the basic functionality of software modules to advanced features.

For each prac session, the first 2 hours are for working on the prac and the last hour is for marking and feedback.

Demonstration (in-person)

The prac must be demonstrated (in person) at the end of the first 2 hours of your assigned prac session, in the same week as the prac is due. Failure to demonstrate your prac will result in no marks being recorded for the demonstration.

Report

Each prac requires a report to be completed and submitted at the end of the first 2 hours of your assigned prac session, in the same week as the prac is due. The report paper booklet must be submitted by giving it to the assessing teaching staff member in the prac venue (lab). The report paper booklet must not be removed by the student from the prac venue.

Use of AI

This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of the assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.

A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.

To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission, independent of AI and MT tools.

Hurdle requirements

You must obtain at least 50% on your combined prac and mini project marks to pass the course (i.e. obtain at least 25 out of 50 for the sum of your combined prac mark (out of 30) and your mini project mark (scaled by your project scaling factor (PSF), out of 20)).

Submission guidelines

Prac code must be submitted to the code repository at end of the first two hours of your prac session, using git push. Code submissions made with only git commit, will not be accepted. You must demonstrate your prac at the end of the first two hours of your prac session. The report paper booklet must also be submitted at the end of the first two hours of your prac session. The report paper booklet must be submitted by giving it to the assessing teaching staff member in the prac venue (lab). The report paper booklet must not be removed by the student from the prac venue.

Deferral or extension

You cannot defer or apply for an extension for this assessment.

As time is provided within the practical class to complete all work (demonstration and report), no extensions are permitted.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

As time is provided within the practical class to complete all work, no late submissions will be accepted and a 100% late penalty applies.

This has been approved by the Associate Dean (Academic)

Mini Project

  • Hurdle
  • Identity Verified
  • Team or group-based
  • In-person
Mode
Activity/ Performance, Product/ Artefact/ Multimedia
Category
Computer Code, Notebook/ Logbook
Weight
20%
Due date

28/04/2026 - 29/04/2026

Learning outcomes
L01, L02, L03, L04, L05, L07, L08

Task description

The mini project is done in a group of four and is demonstrated during the lab session in week 9. The project will involve designing and deploying an embedded system for a specified real-world application that involves:

  • Using a real-time operating system
  • Sensor/peripheral integration
  • Advanced networking features (e.g. Bluetooth)
  • IoT services

Peer Assessment

A peer assessment factor is applied to the mini project mark of each group member. The default Peer Assessment Factor (PAF), which will moderate the mini project mark, will be 1.0. If any members of the group wish to change this distribution, a moderation meeting will be required with the teaching staff. Students must indicate this intention by email to the course coordinator by 4pm Friday 1 May 2026.

A moderation meeting will require the attendance of all members and a brief presentation with the best evidence of their contribution to the project. Every opportunity will be given to the group to derive an acceptable distribution of PAFs. If no resolution within the meeting is possible, teaching staff will allocate PAFs based on the material presented, history of performance, staff feedback, and marked project demo.

A PAF of one indicates that you are making a satisfactory/expected contribution to the group. A PAF less than one indicates your contribution is less than expected. A PAF greater than one indicates you are contributing more than expected.

Your overall PAF will determine a Project Scaling Factor (PSF) as follows:

  • PSF = PAF if your PAF is <= 1 and PSF = (1+PAF)/2 if your PAF is > 1

Your mini project mark will be scaled (multiplied) by this PSF (and capped at the maximum possible project mark where applicable).

Use of AI

This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of the assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.

A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.

To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission, independent of AI and MT tools.

Hurdle requirements

You must obtain at least 50% on your combined prac and mini project marks to pass the course (i.e. obtain at least 25 out of 50 for the sum of your combined prac mark (out of 30) and your mini project mark (scaled by your project scaling factor (PSF), out of 20)).

Submission guidelines

The project code and the wiki must be submitted to the specified code repository by the due date using git push. Code submissions made with only git commit will not be accepted. The project poster must also be uploaded to BlackBoard. The group demonstration must be given in an assigned time slot during the lab session. In accordance with UQ Assessment Policy, your presentation will be recorded.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Extensions for groupwork are typically not available as this impacts on all members of the team. Students with valid extension requests either receive a team mark or will be required to undertake an alternative assessment.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

Final Project Milestone

  • Identity Verified
  • Team or group-based
  • In-person
Mode
Oral, Product/ Artefact/ Multimedia
Category
Computer Code, Practical/ Demonstration, Project
Weight
10%
Due date

12/05/2026 - 13/05/2026

The milestone presentation must be given by all group members in the assigned timeslot during the lab session. Before the presentation, all presentation slides must be uploaded to Blackboard, and the code and wiki must be committed to the git repository.

Learning outcomes
L01, L02, L04, L05, L06

Task description

The project proposal and progress will be assessed in the project milestone. The project milestone is assessed as an oral presentation that must involve all group members. The current project progress will be assessed in the lab session.

Use of AI

This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of the assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.

A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.

To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission, independent of AI and MT tools.

Submission guidelines

Milestone code and the wiki must be submitted to the specified code repository by the due date using git push. Code submissions made with only git commit will not be accepted. The milestone presentation slides must also be uploaded to BlackBoard. The milestone presentation must be given by all group members in the assigned time slot during the lab session. In accordance with UQ Assessment Policy, your presentation will be recorded.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Extensions for groupwork are typically not available as this impacts on all members of the team. Students with valid extension requests either receive a team mark or will be required to undertake an alternative assessment.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

Final Project

  • Hurdle
  • Identity Verified
  • Team or group-based
  • In-person
Mode
Oral, Product/ Artefact/ Multimedia
Category
Computer Code, Practical/ Demonstration, Poster, Project
Weight
40%
Due date

26/05/2026 - 27/05/2026

Other conditions
Secure.

See the conditions definitions

Learning outcomes
L01, L02, L03, L04, L05, L06, L07, L08

Task description

The project is done in a group and demonstrated during the lab session in week 13. The project will involve implementing an embedded system for a real-world application that involves:

  • Using a real-time operating system
  • Sensor/peripheral integration
  • Advanced networking features (e.g. Bluetooth, MQTT)
  • sensor data fusion techniques or embedded machine learning
  • Integrated software and hardware design approaches

Peer Assessment

A peer assessment factor is applied to the final project mark of each group member. The default Peer Assessment Factor (PAF), which will moderate the final project mark, will be 1.0. If any members of the group wish to change this distribution, a moderation meeting will be required with the teaching staff. Students must indicate this intention by email to the course coordinator by 4pm Friday 29 May 2026.

A moderation meeting will require the attendance of all members and a brief presentation with the best evidence of their contribution to the project. Every opportunity will be given to the group to derive an acceptable distribution of PAFs. If no resolution within the meeting is possible, teaching staff will allocate PAFs based on the material presented, history of performance, staff feedback, and marked project demo.

A PAF of one indicates that you are making a satisfactory/expected contribution to the group. A PAF less than one indicates your contribution is less than expected. A PAF greater than one indicates you are contributing more than expected.

Your overall PAF will determine a Project Scaling Factor (PSF) as follows:

  • PSF = PAF if your PAF is <= 1 and PSF = (1+PAF)/2 if your PAF is > 1

Your final project mark will be scaled (multiplied) by this PSF (and capped at the maximum possible project mark where applicable).

Use of AI

This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of the assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.

A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.

To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission, independent of AI and MT tools.

Hurdle requirements

You must achieve at least 50% of the final project mark (after the application of your product scaling factor) in order to pass the course.

Submission guidelines

The project code and the wiki must be submitted to the specified code repository by the due date using git push. Code submissions made with only git commit will not be accepted. The project poster must also be uploaded to Blackboard. The group demonstration must be given in an assigned time slot during the lab session. In accordance with UQ Assessment Policy, your presentation will be recorded.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Extensions for groupwork are typically not available as this impacts on all members of the team. Students with valid extension requests either receive a team mark or will be required to undertake an alternative assessment.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Grade Cut off Percent Description
1 (Low Fail) 0 - 19

Absence of evidence of achievement of course learning outcomes.

2 (Fail) 20 - 46

Minimal evidence of achievement of course learning outcomes.

3 (Marginal Fail) 47 - 49

Demonstrated evidence of developing achievement of course learning outcomes

4 (Pass) 50 - 64

Demonstrated evidence of functional achievement of course learning outcomes.

5 (Credit) 65 - 74

Demonstrated evidence of proficient achievement of course learning outcomes.

6 (Distinction) 75 - 84

Demonstrated evidence of advanced achievement of course learning outcomes.

7 (High Distinction) 85 - 100

Demonstrated evidence of exceptional achievement of course learning outcomes.

Additional course grading information

Your overall percentage will be the sum out of 100 of your assessment marks which is then rounded to the nearest whole percent and then possibly capped as described below. Assessment items will be weighted as described above.

  • If you achieve less than 50% on the Final Project, then your overall percentage will be capped at 49% and your final grade is capped at 3.
  • If you achieve less than 50% on your combined Practical and Mini Project marks, then your overall percentage will be capped at 49% and your final grade is capped at 3.
  • In other words, to pass the course, you must achieve:
  • at least 50% of the total course marks, and
  • at least 50% of the total of the combined Total Practical and Mini Project marks, and
  • at least 50% on the Final Project mark.

Supplementary assessment

Supplementary assessment is available for this course.

Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Additional learning resources information

Lecture recordings will be made available on Blackboard.ᅠ

All additional technical information required in this course will be supplied as copies of engineering papers, documentation, or source code.

A development kit is available for loan for each student. The development kit must be signed out, in-person during a lab session, during the first week. If you are unable to attend a lab session in the first week, contact the coordinator to make an alternate arrangement. The development kits can only be collected from the lab and in-person. The development kit must be returned at the end of the semester or if the student unenrolls in the course.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

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Learning period Activity type Topic
Multiple weeks

From Week 1 To Week 13
(23 Feb - 31 May)

Lecture

Weekly Lectures

Weekly Lectures cover the theoretical background to the course. The lectures are aligned as much as possible with laboratory experiments and the project.
The schedule of lectures and the topics is available on this course Blackboard site.

Learning outcomes: L01, L02, L07, L08

Multiple weeks

From Week 1 To Week 6
(23 Feb - 05 Apr)

Practical

Lab Sessions

Weekly laboratory development and consultation sessions with tasks specified according to the Practical specifications.

Learning outcomes: L01, L02, L03, L04, L07, L08

Multiple weeks

From Week 7 To Week 9
(13 Apr - 03 May)

Practical

Mini Project

Students will work in groups to undertake the development and analysis of a significant embedded system that solves a given challenge.

Learning outcomes: L01, L02, L03, L04, L05, L07, L08

Multiple weeks

From Week 10 To Week 13
(04 May - 31 May)

Practical

Final Project

Students will work in groups to propose and undertake the development of a significant embedded system for a realworld application

Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.

School guidelines

Your school has additional guidelines you'll need to follow for this course: