Outline

Overview

This unit introduces sensors and signals with examples from modern computational imaging. It covers the fundamentals of image formation including light, lenses, and pixels, and develops an understanding of multi-dimensional signals through frequency-domain analysis and the mathematics of multiplexing. It combines these to study classical computational imaging systems in which optics and algorithms are co-developed. Advanced topics draw from unconventional imaging modalities, radiance field representations, machine learning-based reconstruction, optimisation-driven design, and active imaging with time-of-flight and structured light systems. Hands-on, students construct and characterise computational imaging devices and associated algorithms through guided exercises. An open-ended project allows students to build a system of their choice, with examples including lensless cameras, relightable 3D models, visual heart-rate detection, and imaging around corners. By the end of the unit, students have a practical understanding of how physics, sensing, and signal processing combine to enable next-generation imaging technologies.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	MTRX3700 or MTRX3760
Corequisites ?	None
Prohibitions ?	MECH8720
Assumed knowledge ?	Strong MATLAB skills and some Electromagnetics;
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Donald Dansereau, donald.dansereau@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Supervised exam	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Practical skill	Laboratory Four group submissions submitted via canvas	40%	Multiple weeks	Two weeks per lab for four labs	AI allowed
Outcomes assessed:
Practical skill	Major Project Students research, design, construct, and characterise a computational imaging device	20%	Week 13	Three weeks	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Lab activities: Weekly group activities during which students assemble and characterise devices illustrating sensing and signal processing concepts. Each group submits a report communicating key findings. Labs are due 11:59 pm Friday at the ends of Weeks 3, 5, 7, and 10.

Major Project. In groups, students design, construct, and characterise a sensing device using the concepts taught in lecture. Groups submit a brief report communicating key design choices, device characterisation, and project outcomes, and present their work in an interactive presentation and Q&A session. The report is due 11:59 pm Friday of Week 13, and the oral presentation will be held during the lecture session of Week 13.

Final Exam. A supervised two-hour open-book exam.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Group marks will be moderated on the basis of individual effort and understanding, as perceived by the Lecturer and Tutor(s). Sparkplus may be used for self and peer feedback for group activities and marks may be adjusted based on Sparkplus results.

Result name	Mark range	Description
High distinction	85 - 100	When you demonstrate an exceptional grasp of computational imaging, designing and reasoning about optics–sensor–computation systems with insight, building and characterising imaging hardware rigorously, and producing computational reconstructions that reflect a deep, critical understanding of the underlying tradeoffs.
Distinction	75 - 84	When you demonstrate a very high standard of understanding across the co-design of optics, sensors, and computation, implementing imaging systems and reconstruction pipelines competently and analysing their performance and limitations with clear technical judgement.
Credit	65 - 74	When you demonstrate a good, solid understanding of computational imaging principles, build and operate imaging systems that work as intended, and carry out computational reconstruction and analysis correctly with only minor gaps.
Pass	50 - 64	When you meet the learning outcomes of the unit to an acceptable standard, showing a working understanding of the core concepts and completing the practical and computational tasks adequately.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction	Lecture (2 hr)
Week 01	Introduction to the labs, camera kit	Practical (3 hr)
Week 02	Physics of image formation	Lecture (2 hr)
Week 02	Classical cameras p1	Practical (3 hr)
Week 03	Multiplexing	Lecture (2 hr)
Week 03	Classical cameras p2	Practical (3 hr)
Week 04	Foundations of computational imaging	Lecture (2 hr)
Week 04	Multiplexing via light stage p1	Practical (3 hr)
Week 05	Coded aperture	Lecture (2 hr)
Week 05	Multiplexing via light stage p2	Practical (3 hr)
Week 06	Multi-dimensional signals, frequency domain analysis and filtering	Lecture (2 hr)
Week 06	Coded aperture p1	Practical (3 hr)
Week 07	Lensless imaging, inverse problems	Lecture (2 hr)
Week 07	Coded aperture p2	Practical (3 hr)
Week 08	Single-pixel imaging, compressive sensing; Modern sensing case study	Lecture (2 hr)
Week 08	Lensless imaging p1	Practical (3 hr)
Week 10	Light fields and radiance fields	Lecture (2 hr)
Week 10	Lensless imaging p2	Practical (3 hr)
Week 11	Commercial sensors: Lidar, radar, sonar; structured light, time of flight, stereo; Modern sensing case study	Lecture (2 hr)
Week 11	Major project	Practical (3 hr)
Week 12	Learned optics and task-specific imaging; Modern sensing case study	Lecture (2 hr)
Week 12	Major project	Practical (3 hr)
Week 13	Project lightning talks	Lecture (2 hr)
Week 13	Major project	Practical (3 hr)
Weekly	Students are expected to commit to at least 5 hours per week of independent study in addition to timetabled activities.	Self-directed learning (65 hr)

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

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.

Outcomes
Graduate qualities

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

LO1. understand computational imaging as the integration of optics, sensors, and computation to create novel imaging capabilities, and describe the range of design possibilities for such systems
LO2. describe examples of classical and modern computational imaging, including their sensing mechanisms, multiplexing strategies, and reconstruction algorithms
LO3. explain the fundamental performance limitations of classical and computational imaging systems arising from physical and computational constraints
LO4. apply mechanical, electrical and software engineering skills to build, test, and analyse the performance of computational imaging devices and algorithms
LO5. understand how different computational imaging techniques address practical imaging and sensing challenges in engineering
LO6. complete technical work and collaborate effectively in a shared laboratory environment

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
Learning outcomes

Responding to student feedback

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

The unit has been updated with more modern content, exploring sensors and signals with examples drawn from computational imaging.

Additional information

Work, health and safety

Students will be required to perform a lab induction.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

MECH5720: Sensors and Signals

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links