Unit Guide

General Information

Unit convenor and teaching staff	Unit convenor and teaching staff Mohsen Asadniaye Fard Jahromi mohsen.asadnia@mq.edu.au Stuart Jackson stuart.jackson@mq.edu.au
Credit points	Credit points 3
Prerequisites	Prerequisites COMP115 and ENGG150 and (MATH132 or MATH135)
Corequisites	Corequisites
Co-badged status	Co-badged status
Unit description	Unit description This unit provides knowledge and skills for further study in electrical and mechanical disciplines of engineering. Sound engineering relies on adequate modelling and visualisation of data. This unit develops the concept that engineering data consists of discrete and vectored quantities that is fit to a model of reality. Foundation knowledge and skill in computer programming, electrical and mechanical concepts, and mathematics is assumed. The concept of data as discrete numerical vectors is developed using computer controlled interfaces to acquire real electrical and mechanical measurements. Spreadsheets and numerical computing environments, are introduced and used to manipulate, process, view and present data. The adequacy of simple models of transient responses, temperature dependence, and other physical process is explored. Judgement is developed in the appropriate visualisation of data to best expose physical processes and check the design of experiment. The concepts developed in this unit are required in the advanced engineering units for treatment of modelling, simulation, optimisation, and computer aided design. The skills in this unit also will be used in subsequent engineering units dealing with presentation of data.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

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

Demonstrate a good understanding of different types of data and methods of data visualisation: To achieve the skill in different methods of data visualisation to present the information in the most understandable way.
To apply foundational scientific knowledge (mathematics, physics and engineering) in collecting and processing engineering data. This requires knowledge on data acquisition systems, design of experiments, noise and error analysis.
Develop students’ knowledge in engineering through the relationship between theoretical, mathematical and computational modelling to solve real world and complex engineering problems

General Assessment Information

Notifications

Formal notification of assessment tasks, grading rubrics and due dates will be posted on iLearn. Although all reasonable measures to ensure the information is accurate, the University reserves the right to make changes without notice. Each student is responsible for checking iLearn for changes and updates.

Weekly Plan

A weekly plan of lectures, assignments, tests, laboratory and workshop sessions will be posted on iLearn. Students are expected to be aware of possible minor variations.

Assignment Tasks

Assignment questions will be posted on iLearn. Assignment solutions will be posted within one to three days after the submission date. Submissions will not be accepted once the solution is posted.

All assignments must be submitted electronically through iLearn (in pdf format). Submissions are expected to be typed in a logical layout and sequence. Markers WILL NOT grade poorly organized or illegible scans or drafts. The expected workload includes preparation of final copies and clear diagrams. Resubmissions will be permitted up to due date.

All assignments should be prepared individually. It is expected that students consult tutors, lecturers or other students while learning the concepts, but copying assignments from others is not accepted. Students are expected to have read and understood the academic honesty policy.

Absences

Late notices or absences from tests, workshops and laboratories will be considered under extenuating circumstances upon lodgement and approval of a formal notice of disruption of studies.

Grading

To obtain a passing grade (P/CR/D/HD) a total mark of 50 more is required AND a mark of 50 or more is required for the final examination. The final exam is a hurdle requirement because it is the only reliable assessment of individual performance for this unit. A passing grade of 50% or more in the final examination is a condition of passing this unit. Students who make a serious attempt but fail to meet the hurdle requirement will be given one further opportunity to pass. A serious attempt is defined as achievement of a mark of 40% or greater.

Laboratory

Due: Weekly

Weighting: 20%

Practical sessions start in Week 2. They are comprised of laboratory or problem-solving workshop sessions linked to each learning outcome; and they are compulsory for all students. Students are expected to arrive on time and use the laboratory time efficiently. Students should enroll to one practical class at the beginning of the semester. Laboratory or workshop worksheets, required data and other necessary items will be posted on iLearn prior to the weekly sessions and it is compulsory for students to complete the preparatory work before coming to the session.

Practicals will be largely assessed in class but there will some "take-home assignment" for the laboratory sections. There will be one final exam from practical contents in week 13. More information will be available on iLearn.

In laboratory sessions, students learn how to utilize their data visualisation knowledge in processing real-world engineering problems. Students will work on different skills such as advance plotting in MATLAB and Excel, programming in MATLAB, data visualisation and programming in LabVIEW. Students do not need to bring their personal laptop to the laboratory session. Computers with required software (such as MATLAB, Excel, LabVIEW will be provided.

Each student must have a bound notebook to be used as a logbook (A4 size preferred, graph pages are not required). This logbook should be used for all practical work including preliminary and post (reflection) work. It should contain dates, calculations and results recorded during these sessions, in time order. On the completion of each session, logbook entries must be signed and dated by a tutor. Logbooks must be kept in good order for a final check at the end of the semester.

Food and drink are not permitted in the laboratory. Students will not be permitted to enter the laboratory without appropriate footwear. Thongs and sandals are not acceptable.

Assignments:

Assessment Tasks are relates to the following Learning Outcomes:

1. Demonstrate practical use of MATLAB and Excel in plotting different types of data, image and video processing

2. Demonstrate creativity and initiative in developing data processing

3. Demonstrate practical use of mathematical techniques in solving engineering questions

Assignment 1

Due: Week 4 or check iLearn

Weighting: 10%

Questions to be solved at home on the concepts learning outcome 1 and to be submitted electronically to iLearn.

This assessment task is related to being able to visualise different types of data using MALAB and Excel. It also evaluate student's understanding on different types of data and visualisation methods.

Assignment 2

Due: Week 8 or check iLearn Weighting: 10%

Questions to be solved at home on the concepts learning outcome 2 and to be submitted electronically to iLearn

This assignment task relates to data acquisition and noise analysis in engineering systems.

Assignment 3

Due: Week 12 or check iLearn Weighting: 10%

Questions to be solved at home on the concepts learning outcome 3 and to be submitted electronically to iLearn.

This assignment task is related to developing a simulation method to model and an engineering problem.

Final exam

Due: Will appear in exam calendar

Weighting: 50%

A close-book 3-hour exam will be conducted in the formal examination period

This Assessment Task relates to the following Learning Outcomes:

Demonstrate a good understanding of the different types of data confronting engineers and the methods of data visualisation.
To apply mathematics, physics and engineering principles to the analysis of measured data.
Demonstrate knowledge of data acquisition systems, noise and error analysis.
Demonstrate knowledge in engineering through the relationship between theoretical, mathematical and computational modelling to solve real world and dynamical problems.

Assessment Tasks

Name	Weighting	Hurdle	Due
Laboratory	20%	No	Weekly (starts in week 2)
Assignment 1	10%	No	Week 4
Assignment 2	10%	No	Week 8
Assignment 3	10%	No	week 12
Final exam	50%	Yes	Based on the exam calender