EGB151 Systems Engineering for Intelligent Technologies

Unit Outline: Semester 2 2027, Gardens Point, Internal

Overview

This unit introduces students to systems engineering principles tailored for intelligent and autonomous technologies. It focuses on the design, integration, and lifecycle management of complex systems that incorporate machine learning, sensing, and control. You will learn how to model system requirements, architect intelligent components, and evaluate system performance across diverse engineering domains such as robotics, smart infrastructure, and industrial automation. 

Learning Outcomes

On successful completion of this unit you will be able to:

1. Engage stakeholders to capture needs, constraints, and success criteria; translate these into requirements and functional decompositions. 
2. Model system behaviour/architecture (SysML/UML), justify trade‑offs, and align design quality with contemporary ML‑enabled systems.
3. Plan verification/validation strategies and manage configuration/risk across the system lifecycle.
4. Demonstrate leadership and collaboration in team‑based systems design, showing initiative and creativity.
5. Communicate design decisions and implications (performance, safety, sustainability) to varied stakeholders. 

Content

Learning Approaches

Lectures will provide the foundational knowledge required for this unit, including real-world examples and engineering applications of systems engineering. Tutorial sessions will enable you to apply the concepts introduced in lectures through hands-on design, integration and problem-solving tasks using modern libraries/frameworks. 

 In this unit, you can expect the following timetabled activities: 

• Formal lectures provide insights into design, integration and management theory, practices and engineering applications. 
• Weekly tutorials, applying lecture materials to authentic engineering problems through exercises. 
• Hands-on projects involving design, integration, management and evaluation using state-of-the-art tools. 

To complement timetabled activities, you will be provided with: 

• Video demonstrations of design and implementation workflows. 
• Sample additional material for independent practice. 

 You are expected to: 

• Case studies: Review case studies in incorporating intelligent components into engineering systems. 

Feedback on Learning and Assessment

You will receive formative oral feedback on your progress in this unit during tutorials and discussions throughout the semester. You will receive written feedback on the unit's problem solving tasks as well as the tutorial and Matlab work. Formative practice and feedback on exam related concepts will also be covered in lectures and tutorials. Additional feedback on a group or individual basis will also be available through appointment with a member of the teaching team. 

Assessment

Overview

Assessment in the unit consists of a series of problem-solving tasks throughout the semester of increasing complexity involving signal processing systems and related components. This is reinforced with Matlab exercises to relate theory to practice and a final exam covering all major concepts and learning across the unit. 

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Assignment

You will design and analyse solutions to problems applying the theoretical concepts and present your results in a report. The design and analysis will rely on the use of Matlab. 

This assignment is eligible for the 48-hour late submission period and assignment extensions.

The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.

Assessment: Group Assignment

You will design and analyse solutions to a significant problem and present your results in a report. The design and analysis will rely on the use of Matlab. 

This assignment is eligible for the 48-hour late submission period and assignment extensions.

The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.

Assessment: Examination

This tests all of the major concepts covered in the unit including principles, problem-solving, and analysis of signals, systems, and transforms.  

The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.

Academic Integrity

Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.

The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.

Resources

Resource Materials

Reference book(s)

Title: Signals and systems using MATLAB, Second edition. 

Author: Chaparro, Luis F. 

Publisher: Oxford, UK; Waltham, MA: Academic Press 

Year: 2015

Risk Assessment Statement

You will undertake lectures and tutorials in the traditional classrooms and lecture theaters. As such,
there are no extraordinary workplace health and safety issues associated with these components of
the unit.

Course Learning Outcomes

EN01 Bachelor of Engineering (Honours)

1. Engage stakeholders professionally and communicate the outcomes of your work effectively to expert and non-expert audiences using appropriate modes.
   Relates to: ULO1, ULO5, Assignment, Group Assignment, Examination
2. Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
   Relates to: ULO4, Group Assignment
3. Manage projects to solve complex engineering problems, using appropriate information, engineering methods, and technologies.
   Relates to: ULO2, ULO3, Assignment, Group Assignment, Examination
4. Deploy appropriate approaches to engineering design and quality.
   Relates to: ULO2, Assignment, Group Assignment, Examination
5. Engage with and apply regulatory requirements relating to safety, risk management, and sustainability in professional engineering practice.
   Relates to: ULO3, ULO5, Assignment, Group Assignment, Examination