Course coordinator
Dr Matthew D'Souza
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- 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, Practical/ Demonstration |
Laboratory Practicals
|
50% Prac 1 is worth 10%. Prac 2 and 3 are each worth 20%. |
Practical 1 17/03/2025 1:00 pm Practical 2 7/04/2025 1:00 pm Practical 3 6/05/2025 1:00 pm
All stated dates and times are AEST |
| Computer Code, Practical/ Demonstration, Project |
Project Milestone
|
10% |
15/05/2025 - 16/05/2025
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 BB, and the code and wiki must be committed to the git repository. |
| Computer Code, Practical/ Demonstration, Poster, Project |
Project Submission
|
40% |
29/05/2025 - 30/05/2025
All stated dates and times are AEST |
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
- Category
- Computer Code, Practical/ Demonstration
- Weight
- 50% Prac 1 is worth 10%. Prac 2 and 3 are each worth 20%.
- Due date
Practical 1 17/03/2025 1:00 pm
Practical 2 7/04/2025 1:00 pm
Practical 3 6/05/2025 1:00 pm
All stated dates and times are AEST
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03, L06, L07, L08
Task description
There are three practicals (prac). Practicals will be done in groups of 3 with designated tasks for each group member. A group has partner A, partner B, and partner C. Each practical consists of a demonstration and code repository submission. Prac work will involve individual tasks for each partner. The lab sessions are designed to build up the necessary knowledge and skills and develop software modules. The work in pracs takes you from the basic functionality of software modules to advanced features.
All assessment tasks evaluate students' abilities, skills, and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI technologies to develop responses (e.g. generate code) is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Students may only use AI code libraries in completing an assessment task when directed to by the instructor (teaching staff) and only with the AI tools and libraries approved by the instructor. Students must clearly reference any use of AI (libraries and tools) in each instance.
Hurdle requirements
You must obtain at least 50% of the combined total of Practical marks (Prac 1 + Prac 2 + Prac 3) to pass the course.Submission guidelines
Practical code 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 practical must be demonstrated during your assigned lab session.
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.
If you have an approved extension, you must post a private note on the ED Discussion Board using the EXTENSION tag when your code has been uploaded to your repository. A staff member will respond to your note to state when your demo will be assessed.
If an extension has been granted for a group member, then the same extension duration may be applied to the whole group, depending on the circumstance. An affected group is required to check with the coordinator.
This course uses a progressive assessment approach, where feedback will be released to students within 14 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
You must post a private note on the ED Discussion Board, using the LATE tag, when your code has been uploaded to your repository. A staff member will respond to your note to state when your demo will be assessed. You will be expected to demo with your group.
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
15/05/2025 - 16/05/2025
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 BB, 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.
All assessment tasks evaluate students' abilities, skills, and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI technologies to develop responses (e.g. generate code) is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Students may only use AI code libraries in completing an assessment task when directed to by the instructor (teaching staff) and only with the AI tools and libraries approved by the instructor. Students must clearly reference any use of AI (libraries and tools) in each instance.
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.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
If there is a situation that affects multiple members of the group and the oral presentation cannot be given by all group members, then an extension may be granted. The affected group must consult with the course coordinator before the group's assigned presentation time slot during the lab session.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Project Submission
- 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
29/05/2025 - 30/05/2025
All stated dates and times are AEST
- 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 30 May 2025.
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).
All assessment tasks evaluate students' abilities, skills, and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI technologies to develop responses (e.g. generate code) is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Students may only use AI code libraries in completing an assessment task when directed to by the instructor (teaching staff) and only with the AI tools and libraries approved by the instructor. Students must clearly reference any use of AI (libraries and tools) in each instance.
Hurdle requirements
Must achieve at least 50% of the total project marks, 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.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
If there is a situation that affects multiple members of the group and the demonstration cannot be given by all group members, then an extension may be granted. The group must consult with the course coordinator before the group's assigned demonstration time slot during the lab session.
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 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 combined Practical marks, and
- - at least 50% on the Final Project mark.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Use of AI Tools
All assessment tasks evaluate students' abilities, skills, and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI technologies to develop responses (e.g. generate code) is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Students may only use AI code libraries in completing an assessment task when directed to by the instructor (teaching staff) and only with the AI tools and libraries approved by the instructor. Students must clearly reference any use of AI (libraries and tools) in each instance.
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 complete development kit and software environment is available for loan. 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. The development kit must be returned at the end of the semester or if theﾠstudent 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.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 12 |
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. Learning outcomes: L01, L02, L07, L08 |
Multiple weeks From Week 1 To Week 9 |
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 10 To Week 13 |
Practical |
Project Students will work in groups to undertake the development of a significant embedded systems 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:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
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: