Course coordinator
Dr Lei Guo
For in-person consultation, please visit 78-539. The available time is 2:30 pm-5:30 pm, Friday.
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
This course covers the modelling of electromagnetic phenomena, in particular, the Method of Moments (MoM), the finite difference Time domain (FDTD) and ray based high frequency methods. At the conclusion of the course, students should be able to understand the concepts and models used in Computational Electromagnetics and also be able to apply these to advanced engineering problems.
Computational electromagnetics (CEM) deals with theﾠmodelling of the interaction of electromagnetic waves with physical objects and the environment. CEM plays anﾠimportant role inﾠthe designﾠand modelling of antennas, mobile phones andﾠotherﾠcommunication systems, medical imaging devices,ﾠphotonic componentsﾠand high-s siliconﾠelectronics,ﾠamong other applications. In this course, students will be exposed to various aspects of numerical solutions of electromagnetic equations in the time and frequency domains, learn the programming skills that are widely used in CEM algorithms, and compare their developed CEM algorithmsﾠwith the up-to-date industry-level simulators.ﾠ
In response to previous student feedback, the assignments are improved to have closer relationship with practical applications.
Course requirements
Assumed background
A basic undergraduate-level background in engineering mathematics, electromagneticsﾠand computational techniques is assumed. In addition, it is assumed that the student has a basic understanding of MATLAB/Python programming.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ELEC3100 or ELEC7101
Course contact
Course staff
Lecturer
Dr Lei Guo
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
In this course, studentsﾠwill learn basic theory and computational techniques for numerical analysisﾠof electromagnetics problems, including the finite difference andﾠmoment methods. This course will particularly teach students how to developﾠcomputer codes for the simulation of electromagnetic fields in engineering problems and compare their results with those from the up-to-date industry-level electromagnetic simulators.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Identify applications in engineering where computational electromagnetics techniques can be applied
LO2.
Interpret the basic knowledge about electromagnetic for numerical computation
LO3.
Explain a number of key algorithms in computational electromagnetics in detail
LO4.
Interpret the theoretical foundations of computational electromagnetics (including frequency and time domain methods)
LO5.
Implement programming codes for computational techniques in MATLAB or Python with a high level of proficiency
LO6.
Summarize and analyse information and data from journal articles, technical reports and industry documents about the up-to-date computational electromagnetic techniques.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation | Assignment 1 | 20% |
22/08/2024 2:00 pm |
| Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set | Assignment 2 | 25% |
4/10/2024 2:00 pm |
| Paper/ Report/ Annotation, Tutorial/ Problem Set | Assignment 3 | 25% |
18/10/2024 2:00 pm |
| Examination |
Final Exam
|
30% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
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
Assignment 1
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 20%
- Due date
22/08/2024 2:00 pm
- Learning outcomes
- L01, L02, L04, L05
Task description
The assignments will be designed to develop skills in problem solving based on the theory and techniques presented in the first part of the lectures (static electromagnetics and one-dimensional finite-difference-time-domain (1D-FDTD) technique), often using MATLAB or Python.
Submission guidelines
Submit the assignment report and code via the Blackboard Turnitin assignment submission system.
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.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 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.
Assignment 2
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set
- Weight
- 25%
- Due date
4/10/2024 2:00 pm
- Learning outcomes
- L01, L02, L04, L05
Task description
The assignments will be designed to develop skills in problem solving based on the theory and techniques presented in the second part of the lectures (two-dimensional finite-difference-time-domain (2D-FDTD) technique), often using MATLAB or Python.
Submission guidelines
Submit the assignment report and code via the Blackboard Turnitin assignment submission system.
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.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 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.
Assignment 3
- Mode
- Product/ Artefact/ Multimedia
- Category
- Paper/ Report/ Annotation, Tutorial/ Problem Set
- Weight
- 25%
- Due date
18/10/2024 2:00 pm
- Learning outcomes
- L03, L06
Task description
This assignment will be designed to run an electromagnetic simulation by using an industry-level simulator (CST Microwave Studio Suite). The simulated model is similar to that used in Assignment 2 so the simulated results will be compared with those generated from Assignment 2.
Submission guidelines
Submit the assignment report via the Blackboard Turnitin assignment submission system.
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.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 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.
Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04
Task description
The final exam will contain short-answer and problem-solving questions.
Hurdle requirements
You must achieve at least 40% on this item to pass the course.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | Casio FX82 series calculator only |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
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 - 44 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 45 - 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
Marks are rounded to the nearest integer before calculating the final grade. Half-integers are rounded up. At the discretion of the coordinator, final marks of the class may be scaled upwards but not downwards.
In order to achieve a Grade of 4 (Pass) or greater in the course, students must achieve at least 40% in the final exam. If students achieve 50% or more overall, but do not achieve at least 40% in the final exam, they will be awarded a grade of 3.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help. Speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Artificial Intelligence (AI) or Machine Translation (MT)
This assessment task evaluates 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 is strictly prohibited and may constitute student misconduct under the Student Code of Conduct
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.
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 13 |
Lecture |
Fundamentals of Computational Electromagnetics This lecture series will provide background and context for classical and modern computational electromagnetics. Learning outcomes: L01, L02, L03, L04, L05, L06 |
Multiple weeks From Week 2 To Week 13 |
Practical |
Contact hours (1) We will guide students on how to develop high-quality CEM codes in MATLAB or Python. Learning outcomes: L01, L03, L05, L06 |
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: