Thanks to:

Y. C. Kuang and Melanie Ooi 2006

J. A. Bennett 2007

J Zhang 2009-2010

E. Viterbo 2011-2014

ECE 2011

Signal Processing

2

ECE 2011 Signal Processing

• Unit Coordinator and Lecturer:

Dr Yi Hong – Office: Room 224/Bld.72 email: yi.hong@monash.edu

• Lecture notes available on Moodle

• Head of Tutors and Lab Demonstrators

• Mohammad Morshed email: mohammad.morshed@monash.edu

• Tutors and Lab Demonstrators

Dimuthu Jayasingha <dimuthu.jayasingha@monash.edu>,

Lin Yun Huang <huanglinyun18@gmail.com>,

Md Bhuiyan <shakil.bhuiyan@monash.edu>,

Emran Amin <emran.amin@monash.edu>,

Aminul Islam <aminul.islam@monash.edu>,

Anee Azim <rubayet.azim@monash.edu>,

Muhsiul Hassan <muhsiul.hassan@monash.edu>,

Omar Arafat <omar.arafat@monash.edu>,

3

Prescribed Text

Signal Processing First

McClellan, J., Schafer, R.

and Yoder, M.

Pearson Prentice Hall

2003

4

References

• Recommended Reading

– Linear System and Signal, Lathi

– Signal and Systems, Oppenheim

• Useful References

– Digital Signal Processing, Mitra

5

Course Attributes

• 6 credit points

• 12 x 3 lectures

• 11 x 1 hour tutorials

• 11 x 2 hours of laboratory works (Compulsory)

• Assessment: 3-hour examination: 70%

• Lab tasks verification: 10%

• Quiz: 10%

• Lab test: 10%

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Course Attributes

Assessment

Task

Val

ue

45% Pass Total pass Due Date

1. Lab tasks

verification

10

%

30%

>13.5/30

>50%

During and at the

end of each lab.

Week 2-12

2.Quiz 10

%

During and at the

end of each tutorial.

Week 2-12

3.Lab test 10

%

Week 12

4. Examination (3

hours)

70% >31.5/70

7

Course Attributes

All laboratory classes are compulsory even for repeating students. To

achieve a pass in the unit students must achieve a mark of 45% in each

of the assessment components and an overall mark of 50%.

Assessment Task 1: Lab tasks verification (Week 2- Week 11)

Details of task:

During each lab, upon completion of each task, students are required to

demonstrate their experiment results to the demonstrators before proceed

to the next lab task. At the end of each lab, students are required to ask

the lab demonstrators to verify their entire experiment results and sign off

Verification Form. Students must keep an individual lab logbook recording

the details of lab experiment.

Criteria for Assessment task 1:

As stated in the instruction of each lab/tutorial

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Course Attributes

Assessment Task 2: Quiz (Week 1 – Week 11)

Details of task: A weekly quiz will be based on MCQ type questions and

numerical problems.

Criteria for Assessment task 2:

Number of correct answers

9

Course Attributes

Assessment Task 3: Lab test (Week 12)

Details of task:

Computer based written test that covers all the labs over the semester.

Students are required to complete independently and individually a set of

tasks selected from the labs they performed over the semester. Students

are allowed to use their lab logbook in the test.

Criteria for Assessment task 3:

Use of correct concepts

MATLAB programming

Correct and accurate computation results

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Week1

• No Lab this week.

• Tutorials are on this week (Revision on

complex number, Phasors, etc)

• Quiz 1 is on.

• MULO recoding lecture

• http://www.mulo.monash.edu.au/ece2011

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Online Moodle Quizzes

• All questions carry equal marks.

• Three attempts will be allowed for each quiz every week.

• The best result out of three will be marked.

• Quizzes are open for attempt from Tuesday afternoon till

Monday 9am

• Correct answers will appear after a quiz is closed Monday 9am

• Mainly two types of questions.

– Multiple choice questions: Choose the correct answer from a set of answers

– Calculated answer questions: Calculate a numerical answer (Do not enter the

units with a numerical answer; i.e. if answer is 1000 Hz enter only 1000)

• Answers to questions need to be submitted

– Open attempts that are not submitted are automatically submitted when

the quiz period lapses.

• The best 7 quizzes scores out of 10 will form the final 10% mark

– First week is just to warm up, last week is for lab test

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To Ensure Good Results

• Attend all lectures then try quizzes

• Revise the lecture notes and read the

references within 2 days

• Attend all tutorials and participate

• Practice

• Practice

• Practice

Very

important !

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• Read up before attending lecture

• Attempt the tutorial questions and other

questions from books

• Miss a lecture ?

– At least 2 hours studying the textbook and

resolve any queries with lecturer ASAP

• Seek help early if there is any problem

– Last minute rush is a perfect recipe for

disaster

To Ensure Good Results

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Tutorials

• Group studies is recommended

– Form groups of 3 ~ 6

– Helping your friends will help to deepen your own

understanding of the subject

• Discuss your solution with your friends before

coming to tutorial and laboratory session

• Make sure you can do the tutorial questions using

pen and paper

• You are responsible for your own studies

in the university !

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• Consultation

– Tutorial session

• Activities during tutorial session:

1. Attempt tutorial questions

2. Feedback on coursework / test

3. Discussion of new concepts in the lecture

4. Discussion of upcoming laboratory

exercises

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Problem with Tutorial Problems

• First stop: Textbook / Supporting CD

– Study the examples

– Read the textbook again

• Second stop: Friends

– Discussion helps (a lot)

• Third stop: Yourself

– Reflect deeply on the topic

• Desperate: Helpdesk

– Don’t give up easily

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Laboratory/Tutorials

• Two-hour sessions per week Labs (check Allocate)

• One our session tutorial per week (check Allocate)

• Group of 2 or individual

• Within 12 weeks

– 10 laboratory exercises

• Most laboratory exercises come with some preliminary

works

– Some come with extra exercises and quizzes

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Preliminary Works

• Must be completed prior to the start of

the main exercises

• Submit at beginning of every laboratory

session

• Let lab demonstrator sign off lab tasks

on lab verification pages upon

completion of the tasks

• Sign attendance at the same time

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Laboratory Exercises

• Exercises after preliminary work are aimed

at assisting the understanding of difficult

concepts

– Normally time consuming

– Ideally should be done before attending the

laboratory sessions

– They are mostly interactive using MATLAB

• You might be quizzed to test your

understanding

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Lab Schedule

• Check your time in Allocate+, there might

be changes before Week 2.

• I cannot change your allocation, if you

have a timetable collision you need to

contact the Faculty Office building 72.

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Unit Objectives

• Understanding of continuous and discrete time signals

– Mathematical modeling & analysis of signals for signal and information processing

– Relevant to all fields of engineering, science and beyond

• Frequency domain representation and analysis

– Most powerful analytical tool to deal with signals and linear systems

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Objectives

• Properties of linear time invariant

systems

– Simple model of real systems

– Very useful first approximation

• Fourier Transforms

– Time frequency transformation

– Most widely used in applied science and

engineering

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Objectives

• To develop the ability to:

– Relate time domain and frequency domain analyses and interpret the results properly

– Aware of the presence of sampling errors, aliasing, windowing and other artifacts introduced into the data due to signal processing techniques

– Recognize the implications of signal processing technique and existence of man-made artifacts in the signal

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Relevance & Applications

• Measurement instrumentation, control feedback

– Oscilloscope, position measurement GPS, etc

• Communication

– Mobile phone, radar, television, etc.

• Medical imaging

– MRI, CT, Ultrasound, etc.

• Almost all scientific/engineering works

– Hardly any modern example does not need signal

processing technique

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Simple Linear Filtering

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Major Topics

1. Continuous & discrete signals

• Sinusoid and phasors

• General signals

• Sampling theory

• Spectrum

2. Transforms

• Fourier transform

• Z – transform

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Major Topics

3. Filters and linear systems

• Convolution

• FIR filters/systems

• IIR filters/systems

4. Applications

• Denoising

• Spectrum analysis

• Signal conditioning

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Prerequisites

• Integration

– Trigonometry and exponential functions

• Complex number

– Euler’s relation

• MATLAB programming

– Extensively used in the laboratory exercises

– Solving more realistic examples

32

Integration

Complex number

MATLAB programming

No excuses please