ME606 - Digital Signal Processing

Credit points: 20 credit points

Workload: 60 hours

Prerequisite: ME502 Digital Communication

Co-requisite: N/A

Aims & objectives

This is a core unit out of 12 units in the Master of Engineering (Telecommunications). This unit addresses the Master of Engineering (Telecommunications) course learning outcomes and complements other courses in a related field by developing students’ specialised knowledge in applied digital signal processing. It addresses critical skills in signal analysis and complements units which rely on telecommunication equipment such as antennas, microwaves, GPS and spectrum analyser. For further course information refer to: http://www.mit.edu.au/courses/master-engineering-telecommunications.
This unit covers aspects of signal processing which form the basis for modern transceivers and decision making in telecommunication systems. It discusses discrete signals, signal transformations, representation of signals in the z- and frequency domains, orthogonal transforms, spectrum estimation, digital filter design, multirate signal processing and applications of signal processing.
This unit will cover the following areas:
Mobile Communication Theory:

  • Classification of signals in the time and frequency domain
  • Review of Laplace and z-transforms
  • Signal transformations in the s, z and frequency domains
  • Fast Fourier transforms and orthogonal transforms
  • Frequency response
  • Spectrum estimation of deterministic and non-deterministic signals
  • Digital filters, designs and applications
  • Multirate signal processing and filterbanks
  • Discrete wavelet transforms
  •  Applications of digital signal processing in imaging and audio systems

Learning outcomes

At the completion of this unit students should be able to:
a. Development and implementation of signal processing algorithms in Matlab or Scilab
b. In-depth design of digital filters
c. Understand the design of multirate signal processing and their applications
d. Implementation and applications of FFT
e. Develop skills in spectral estimation for deterministic and non-deterministic signals
f. Apply discrete wavelet transforms for processing images and audio
g. Develop understanding of video and audio compression systems

Weekly Topics

This unit will cover the content below:

Week # Topic Laboratory/Tutorial Reading
1 Discrete time signals and sampling Introduction to Matlab/Tutorial Chapters 2, 6
2 Z-Transforms of Signals Audio capture and processing with Matlab/Tutorial Chapter 3
3 Frequency Analysis of Signals Signal Spectrum with Matlab/Tutorial Chapters 4, 5
4 DFT and Fast Fourier Transform and Frequency Response Image capture and processing with Matlab/Tutorial Chapters 7, 8
5 Power Spectrum estimation of deterministic and non-deterministic signals Implementing FFT using Matlab/Tutorial Chapter 14
6 Mid – Term Test Test Lectures 1 - 5
7 Digital filter design and applications Spectral Estimation of Signals in Matlab/Tutorial Chapters 9, 10
8 Multirate signal processing and filterbanks FIR & IIR filter designs using Matlab/Tutorial Chapter 11
9 Discrete wavelet transforms Image interpolation/decimation with Matlab/Tutorial Signal Processing with Scilab
10 Digital Audio Processing Wavelet transforms with Matlab/Tutorial Signal Processing with Scilab
11 Digital Image Processing Noise Filtering Using wavelet transform algorithms/Tutorial Signal Processing with Scilab
12 Sample Exam(s) 50% Review

Please note that the laboratories may be offered in a different order depending on availability. Signal processing software other than Matlab may also be used in the laboratories.

Assessment

Assessment Task Release Date Due Date A B Learning Outcomes Assessed
Laboratory and Problem Based Learning participation & submission Each week in the Laboratory Each week in the Laboratory 20% - a, b, d
Formative Assignment 1A
Assignment 1B (In class test, face-to-face, On Campus)		 Week 1 Week 3
Week 5		 5% 10% a-d
Assignment 2 Week 5 Week 10 25% - a-e
Final Examination (3 hours) (Face-to-face, On Campus) End of trimester - - 40% a-e
TOTALS   50% 50%    

Task Type: Type A: unsupervised, Type B: supervised.

Class Participation Assessment
This unit has class participation as an assessment. The assessment task and marking rubric will
follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/aboutus/
governance/institute-rules-policies-and-plans/policies-procedures-and guidelines/Guidelines_on_Assessing_Class_Participation). Further details will be provided in the assessment specification on the type of assessment tasks and the marking rubrics.

Textbook and reference materials

Text Book:
[1] John G. Proakis and Dimitris G. Monolakis, Digital Signal Processing: Principles, Algorithms and Applications, fourth edition, Pearson Prentice Hall, 2007, ISBN 0-13-187374-1.
[2] Shlomo Engelberg, Digital Signal Processing: an experimental approach, Springer, ISBN 978-1-84800-118-3, e-ISBN 978-1-84800-119-0; 2008
Further reading:
[1] Signal Processing with SCILAB, INRIA, France
[2] Signal Processing Toolbox (For Use with Matlab), User’s Guide, The MATH WORKS
Internet resources:
Adopted Reference Style: IEEE

Graduate attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and lifelong learning Ethics Analytical and problem solving Cultural and global awareness Team work Specialist knowledge of a field of study

Legend

Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.