ELEC5516: Semester 1, 2020

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Units ELEC5516 Semester 1 2020 [Normal day]

Unit outline_

ELEC5516: Electrical and Optical Sensor Design

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

The course focuses on environmentally friendly, intelligent sensors for multiple parameters monitoring to be used in power network and broadband network. The concepts learnt in this unit will be heavily used in various engineering applications in power systems, fiber optic systems and health monitoring. These concepts include: 1) Theory, design and applications of optical fiber sensors. 2) Sensor technologies for the growth of smart grid in power engineering. 3) Actuators and motors for electrical sensor and its applications. 4) Wearable sensor technologies for ehealth monitoring.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Math Ext 1, fundamental concepts of signal and systems, fundamental electrical circuit theory and analysis
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Liwei Li, liwei.li@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam	Assignment	60%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Laboratory	20%	Multiple weeks	n/a
Outcomes assessed:
Assignment	Assignment	20%	Multiple weeks	n/a
Outcomes assessed:

Assessment summary

Detailed information for each assessment 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.

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	Introduction: sensors and applications	Lecture and tutorial (5 hr)
Week 02	Engineering measurement with sensors (general concepts, static and dynamic measurement)	Lecture and tutorial (5 hr)
Week 03	Signal conditioning (filters, noise reduction) and data acquisition	Lecture and tutorial (5 hr)
Week 04	Potentiometers, capacitive sensors and inductive sensors, Hall effect sensors	Lecture and tutorial (5 hr)
Week 05	Principles of electromagnetics	Lecture and tutorial (5 hr)
Week 06	Electromagnetic actuators (relays) and motors (AC/DC/Step motors)	Lecture and tutorial (5 hr)
Week 07	Optical sensor intruction	Lecture and tutorial (5 hr)
Week 08	Light sources and photodetectors	Lecture and tutorial (5 hr)
Week 09	Optical devices	Lecture and tutorial (5 hr)
Week 10	Optical strain and temperature sensor	Lecture and tutorial (5 hr)
Week 11	Optical sensors for power systems and guest lecture	Lecture and tutorial (5 hr)
Week 12	Biophotonic sensors	Lecture and tutorial (5 hr)
Week 13	Fiber optic sensor review	Lecture and tutorial (5 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.

Required readings

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

2nd Edition by P.P.L Regtien, Electronic Instrumentation. Delft University Press.
Edited by Shizhou Yin, Paul B Ruffin and Francis T.S. Yu , Fiber-optic sensors. CRC Press.

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. demonstrate fluency in communicating concisely and accurately using varied formats such as written and oral, to deliver specific engineering project information
LO2. work in a team and promote creative team interaction to encourage contribution from all members to deliver specific engineering projects and assignments
LO3. design, evaluate, and select the appropriate sensors according to practical engineering applications
LO4. analyze sensor performance and present an initial design for engineering applications
LO5. understand the basic principles and performance characteristics of common electrical and fiber sensor technologies
LO6. formulate analytical modeling and perform software simulation to determine the key characteristics of specified sensors
LO7. identify information needs and target information searches effectively and efficiently using varied sources such as internet, library databases and electronic publications as part of specific engineering projects
LO8. apply concepts, principles and techniques to analyse sensors
LO9. demonstrate an understanding of system concepts of sensor technologies
LO10. select and use maths/science models and tools for performance analysis of sensor devices.

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.

It has been a wonderful experience to lecturing ELEC5516 Electrical and Optical Sensor Design and working with so many young students this semester. I can see that the students have very positive feedback about the laboratories, as these activities are good prospects for students to really “see” and “manipulate” the “sensors” and “sensing network designs” in the lab environment, which intend to let the students to understand the concept better and easier. I would also like to take this great opportunity to sincerely thank you for your great effort and support for this unit of study throughout the whole period. If you have any further questions or you would like to discuss more about your future study plan, please feel free to contact me by email, phone or in person. Finally, wishing you an amazing adventure and all the very best for the future study and career.

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

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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