Outline

Overview

Syllabus Summary: This course starts by providing a background to the signals and transforms required to understand modern sensors. It goes on to provide an overview of the workings of typical active sensors (Radar, Lidar and Sonar). It provides insight into basic sensing methods as well as aspects of interfacing and signal processing. It includes both background material and a number of case studies. The course covers the following topics: a) SIGNALS: Convolution, The Fourier Transform, Modulation (FM, AM, FSK, PSK etc), Frequency shifting (mixing) b) PASSIVE SENSORS: Infrared Radiometers, Imaging Infrared, Passive Microwave Imaging, Visible Imaging and Image Intensifiers c) ACTIVE SENSORS THE BASICS: Operational Principles, Time of flight (TOF) Measurement and Imaging of Radar, Lidar and Sonar, Radio Tags and Transponders, Range Tacking, Doppler Measurement, Phase Measurement d) SENSORS AND THE ENVIRONMENT: Atmospheric Effects, Target Characteristics, Clutter Characteristics, Multipath e) ACTIVE SENSORS: ADVANCED TECHNIQUES: Probability of Detection, Angle Measurement and Tracking, Combined Range/Doppler and Angle Tracking, Frequency Modulation and the Fast Fourier Transform, High Range Resolution, Wide Aperture Methods, Synthetic Aperture Methods (SAR) Objectives: The course aims to provide students with a good practical knowledge of a broad range of sensor technologies, operational principles and relevant signal processing techniques. Expected Outcomes: A good understanding of active sensors, their outputs and applicable signal processing techniques. An appreciation of the basic sensors that are available to engineers and when they should be used.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Strong MATLAB skills
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Graham Brooker, graham.brooker@sydney.edu.au
Tutor(s)	Timothy Mitchell, timothy.mitchell@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam Type C	30%	Formal exam period	2 hours
Outcomes assessed:
Tutorial quiz	MATLAB tutorial Matlab-based analysis	20%	Multiple weeks	2 hrs in tutorial plus additional time
Outcomes assessed:
Tutorial quiz	Laboratory Quiz to be completed after each laboratory session.	22%	Multiple weeks	3 hrs in Lab plus 1 hr post-Lab
Outcomes assessed:
Small test	Post-lecture Quizzes Quiz to be completed after each lecture to assist with consolidation	13%	Multiple weeks	Typically 30 to 60 minutes
Outcomes assessed:
Creative assessment / demonstration	Assignment Research and design assignment ongoing	15%	Week 12 Due date: 28 Oct 2022 at 23:00	n/a
Outcomes assessed:
= hurdle task ? = Type C final exam ?

Assessment summary

MATLAB tutorial: A number of hands-on tutorials will be undertaken during which students are expected to apply and investigate what they have learned by developing models and software.
Quizzes: A quiz will be held after each lecture to ensure that students have understood the work covered so far.
Lab activities: Weekly individual in-person (on campus students) or online (off camuus students) activities during which students will be required to assemble sensing, processing, and actuation hardware that illustrates sensing and signal processing concepts.
Assignment: The individual (or group) design assignment will be based on ongoing work done by the students to develop ideas for a sensing device in stages throughout the first half of the semester as their knowledge and understanding of the subject develops.
Final exam: Open-book examination that will include a number of short-answer questions. Students are required to pass the exam to pass the unit.

Detailed information for each assessment task can be found on Canvas.

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.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
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.

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 (or part thereof)

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Modulation	Lecture (2 hr)
Week 01	Measure Foundry Introduction	Practical (3 hr)
Week 02	Filtering and Modulation	Lecture (2 hr)
Week 02	Measure Foundry - Graphs, Filtering & FFT	Practical (3 hr)
Week 03	Active Ranging Sensors	Lecture (2 hr)
	Modulation	Computer laboratory (1 hr)
	Measure Foundry - Pulsed Sonar	Practical (3 hr)
Week 04	Active Imaging Sensors	Lecture (2 hr)
	Modulation	Computer laboratory (1 hr)
	Measure Foundry - Tellurometer Sonar	Practical (3 hr)
Week 05	Signal Propagation	Lecture (2 hr)
	3D Imaging	Computer laboratory (1 hr)
	Measure Foundry - Attenuation	Practical (3 hr)
Week 06	Target Detection in Noise	Lecture (2 hr)
	3D Imaging	Computer laboratory (1 hr)
	Measure Foundry - Multipath	Practical (3 hr)
Week 07	Target and Clutter Characteristics	Lecture (2 hr)
	Radar Range Equation	Computer laboratory (1 hr)
	Measure Foundry - RCS with Angle	Practical (3 hr)
Week 08	Doppler Processing	Lecture (2 hr)
	Radar Range Equation	Computer laboratory (1 hr)
	Measure Foundry - RCS with Angle	Practical (3 hr)
Week 09	High Angular Resolution Sensors	Lecture (2 hr)
	Matched Filter & Doppler	Computer laboratory (1 hr)
	Measure Foundry - Phased Array Sonar	Practical (3 hr)
Week 10	High Range Resolution Sensors	Lecture (2 hr)
	Matched Filter & Doppler	Computer laboratory (1 hr)
	Measure Foundry - Phased Array Sonar	Practical (3 hr)
Week 11	Range and Angle Estimation	Lecture (2 hr)
	Phased Arrays	Computer laboratory (1 hr)
	Measure Foundry - Antenna Transfer Function and Tracking	Practical (3 hr)
Week 12	Tracking Moving Targets	Lecture (2 hr)
	Phased Arrays	Computer laboratory (1 hr)
	Measure Foundry - Antenna transfer Function and Tracking	Practical (3 hr)
Week 13	Radiometry	Lecture (2 hr)
Weekly	Students are expected to commit to at least 5 hours per week of independent study in addition to timetabled activities.	Independent study (65 hr)

Attendance and class requirements

Project work (own time): A design project will be undertaken by groups of students. This will take the form of research conducted by each student during the semester as their knowledge of the subject improves. Towards the end of semester each student will compile an individual design report which will be assessed by the lecturer.
Independent study: Depending on student competence and background, at least five hours of private study per week outside formal contact hours will be required to consolidate the work covered in class.
Laboratory: Student groups will assemble and measure the characteristics of various sensors. Some Measure Foundry code will be provided but students will be expected to develop additional code. Laboratory sessions may be in-person (on campus students) or online (off campus students).
Tutorial: A number of MATLAB tutorials will be undertaken, during which students are expected to develop code to model some sensing or signal processing application.

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.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Graham Brooker, Introduction to Sensors for Ranging and Imaging. Scitech Publishing, 2008. 9781891121746.

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. assimilate information regarding the myriad of possibilities for the design of a sensor, and to convey this information to ones colleagues
LO2. develop skills for efficient project management in a team environment
LO3. integrate incomplete information and make value judgements to solve a sensing problem by using engineering "gut feel", rather than a rigorous analytical approach
LO4. apply specialised engineering skills (mechanical, electrical, and software) to analyse the performance of a sensor
LO5. understand active sensors, their outputs, and applicable signal processing techniques, and demonstrate an appreciation of the basic sensors that are available to engineers, and when they should be used
LO6. describe a number of sensors
LO7. make a distinction between sensor performance, based on simulation and measurement.

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.

This unit is mature, so the only changes made have been in response to COVID-19. This has resulted in the development of some online labs.

Additional information

Work, health and safety

Details of the WHS requirements for in-person laboratory sessions during the COVID-19 pandemic are available on Canvas.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

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

Media

Student links

About us

Connect

Current students

MECH5720: Sensors and Signals

Semester 2, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

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

Media

Student links

About us

Connect