ELEC9103: Semester 2, 2022

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Units ELEC9103 Semester 2 2022 [Normal day]

Unit outline_

ELEC9103: Simulations and Numerical Solutions in Eng

Semester 2, 2022 [Normal day] - Remote

Overview

Objectives: How to apply the software package Matlab to achieve engineering solutions; Critical assessment of various computer numerical techniques; Professional project management, teamwork, ethics. This unit assumes an understanding of the fundamental concepts and building blocks of electrical and electronics circuits. As well as covering the specific topics described in the following paragraphs, it aims to develop skills in professional project management and teamwork and promote an understanding of ethics. Basic features of Matlab. The Matlab desktop. Interactive use with the command window. Performing arithmetic, using complex numbers and mathematical functions. Writing script and function m-files. Matrix manipulations. Control flow. Two dimensional graphics. Application of Matlab to simple problems from circuit theory, electronics, signals and systems and control. Investigation of the steady state and transient behaviour of LCR circuits. Matlab based numerical solutions applicable to numerical optimisation, ordinary differential equations, and data fitting. Introduction to symbolic mathematics in Matlab. Applications, including the derivation of network functions for simple problems in circuit analysis. Introduction to the use of Simulink for system modelling and simulation.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ELEC5723 OR ELEC2103 OR COSC1001 OR COSC1901
Assumed knowledge ?	ELEC9703. Understanding of the fundamental concepts and building blocks of electrical and electronics circuits and aspects of professional project management, teamwork, and ethics
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Phee Yeoh, phee.yeoh@sydney.edu.au
Tutor(s)	Phee Yeoh, phee.yeoh@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Lab Report tudents need to submit a report for each lab.	15%	Multiple weeks	n/a
Outcomes assessed:
Tutorial quiz	Mid-Semester Quiz Mid-semester quiz to cover Week 1 to 5 content.	30%	Week 06	2 hours
Outcomes assessed:
Assignment	Assignment Students work in groups to explore data analysis tools.	25%	Week 08	n/a
Outcomes assessed:
Tutorial quiz	End of Semester Quiz To cover week 6-12 Content.	30%	Week 12	2 hours
Outcomes assessed:
= group assignment ?

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 to Matlab	Lecture (1 hr)
Week 01	Lab	Tutorial (3 hr)
Week 02	Programming in Matlab 1	Lecture (1 hr)
Week 02	Tutorial 1	Tutorial (3 hr)
Week 03	Programming in Matlab 2	Lecture (1 hr)
Week 03	Tutorial 2	Tutorial (3 hr)
Week 04	1. Symbolic math; 2. Circuits 1	Lecture (1 hr)
Week 04	Tutorial 3	Tutorial (3 hr)
Week 05	1. Laplace transform; 2. Circuits 2	Lecture (1 hr)
Week 05	Tutorial 4	Tutorial (3 hr)
Week 06	Lag compensation	Lecture (1 hr)
Week 06	Tutorial 5	Tutorial (3 hr)
Week 07	1. Statistics; 2. Data files	Lecture (1 hr)
Week 07	Tutorial 6	Tutorial (3 hr)
Week 08	1. Numerical methods; 2. Matrices	Lecture (1 hr)
Week 08	Tutorial 7	Tutorial (3 hr)
Week 09	Differential equations	Lecture (1 hr)
Week 09	Tutorial 8	Tutorial (3 hr)
Week 10	Signals and linear systems	Lecture (1 hr)
Week 10	Tutorial 9	Tutorial (3 hr)
Week 11	1. Lag compensation; 2. Revision	Lecture (1 hr)
Week 11	Tutorial 10	Tutorial (3 hr)
Week 12	Laplace Transform and LTI systems	Lecture (1 hr)
Week 12	Tutorial 11	Science laboratory (3 hr)
Week 13	Review	Lecture (1 hr)
Week 13	Review and exam practice	Tutorial (3 hr)

Attendance and class requirements

Study commitment: Students are required to prepare the next class’ topic and come with questions in mind; read the textbook and make use of other information resources. Students are also required to carry out research and complete the assignment project and report in their own time.
Independent study: Prepare next class topic and come with questions in mind; read textbook and make use of other information resources. 4 hours of independent study is expected each week.
Project work (own time): Carry out research and complete the assignment project and report.

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.

William Palm III, Introduction to Matlab for Engineers (3rd). McGraw-Hill, 2011. 978-0-07-353487-9.
Sandeep Nagar, Introduction to MATLAB for Engineers and Scientists, Apress, 2017. 978-1-4842-3188-3.
Jamal T. Manassah, Elementary Mathematical and Computational Tools for Electrical and Computer Engineers Using Matlab (2nd). Taylor & Francis, 2007. 978-0-8493-7425-8.
David Houcque, Introduction to Matlab for Engineering Students, Northwestern University, 2005.

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. analyse and solve problems using Matlab in command mode and by writing m-files and displaying results in specific engineering problems
LO2. use Matlab proficiently for specific analysis, including LCR circuits, system analysis with Laplace transforms and other engineering specific applications
LO3. demonstrate an understanding of the concepts of applied mathematics in the context of specific engineering problems
LO4. select and synthesise information from various resources for specific engineering projects
LO5. communicate clearly and effectively in laboratory team tasks and written reports
LO6. communicate in written and computer-based format to deliver meaningful summaries of engineering project work
LO7. demonstrate an understanding of the engineering environment, professional and ethical standards to the limit of lectures, assignment, laboratory, group work, case studies and class discussion
LO8. work constructively in a team by clarifying collaborative duties and encouraging contribution from all members to achieve specific engineering project goals.

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 according to student feedback and comments.

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

Disclaimer

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