The subject was introduced as a 2 semester core subject in 1991 for students enrolled in the course of Bachelor of Computing (Digital Technology) covering both analog and digital signal processing, which was subsequently revised in 1995, 1997 and 1999 as a 1 semester elective or core subject in the current course of Bachelor of Digital Systems (DGS for short). It has been run as a core subject for DGS students and an elective for Computer Science and Software Engineering students. Digital signal processing is an enabling technology. The theory, principles and techniques covered by the subject provide students with knowledge and skills which underpins various application areas including ICT (Information and Communications Technology), such as speech and data communications, intelligent systems, digital control and robotic systems, instrumentation, biomedical engineering, acoustics, sonar, radar, seismology, oil exploration, mechatronics, consumer electronics, etc. ?1?. The subject strengthens students' software and hardware knowledge and skills in programmable DSP based embedded systems and real-time processing systems.

It leads to advanced subjects and topics in honours, master by course work programmes, such as image processing, advanced digital signal processing, digital video coding and compression, neuro-fuzzy computing, digital communications, data and image compression, etc. offered by the Faculty of IT.

Reference ?1? A. Oppenheim and R. Schafer Discrete-Time Signal Processing'',

CSE3132 DSP as a level 3 subject is designed to equip students in digital systems, computer science and other information technology courses with fundamental concepts, principles and techniques in digital analysis and processing of analog and discrete-time signals problem solving skills by combining their knowledge learnt in mathematics, programming and physics in order to prepare them for advanced subjects in digital systems and computer engineering as well as their applications such as digital image processing, digital communications, multimedia signal processing and communications, biomedical imaging, automatic control and robotic systems, instrumentation, etc. It has a strong focus on real-time processing and embedded systems based on state-of-the-art programmable DSPs and fast algorithms and implementation for DSP applications. It is an integral part of the degree course for students to consolidate their theoretical, software and hardware skills in the discipline.

CSE3132 DSP is a unique subject that has no similar offerings in the Faculty of Information Technology.

CSE3132 DSP offers students in digital systems, computer science and other information technology courses a unique subject to develop their theoretical/analytical and problem solving skills in dealing with digital and computer systems for real-world applications. DSP as an enabling technology is everywhere and it is a must for students in IT and Computer Engineering in the area of technical computing.

It consolidates students knowledge and proficiency in mathematics and specialised and embedded system programming. It tests students knowledge and skills in real-time processing and dealing with theoretical and practical issues in computing such as fast algorithms and implementations, finite word length computation, Nyquist bound for analog and digital signal conversion, sampling rate conversion, and digital filter design. It challenges students ability in critical thinking. It assists students in developing their technical communications skills to describe a technical concept/principle/technique/matter in plain (e.g., English) language, rigorous mathematic language and visual presentation.

On completion of the subject, students should have

Scientific curiosity, systematic learning approach, analysing and investigating based on facts, importance of the conditions for knowledge to apply, values and limitations of subject matters studied under the subject, Monash University motto: "I'm still learning", i.e., life-time learning skills and abilities, applying knowledge to solve real-world problems.

In application of mathematical knowledge in definition or description, analysis and solving of problems; in using mathematical programming language, e.g., Matlab to deal with signals and systems problems such as signal analysis and digital filter and design; in real-time system programming and implementation for programmable DSP based embedded systems; and in both written and oral communications.

This subject, a continuation of<A>CSE2131</A>, addresses fundamental concepts, theory and techniques of digital signal processing (DSP); applications of DSP and their implementations; and an appreciation of specific computer architectures used in digital signal processors. It provides the basis for more advanced topics in the area, such as advanced DSP, neural networks, video coding and compression, digital communications, digital control, and advanced image and voice processing. The syllabus covers sampling of continuous-time signals and sampling rate conversion, digital signal processing systems, structures for discrete-time systems, digital filter design techniques, discrete Fourier transform (DFT) and computation of DFTs, discrete Hilbert transform and its applications, quantisation effect in digital signal processing; Fourier analysis of signals using the DFT, multirate digital signal processing, applications of DSP, and real-time DSP implementation using, for example, the TMS320C25/C30/C6x digital signal processor(s).

<HBPrescribedText></HBPrescribedText> Sanjit K. Mitra, Digital Signal Processing--A Computer-Based Approach", 2nd Ed., McGraw-Hill, 2001.

<HBRecommendedReading></HBRecommendedReading> A. Oppenheim and R. Schafer Discrete-Time Signal Processing'' Prentice-Hall, 1989.

E.C. Ifeachor and B.W. Jervis, Digital Signal Processing--

Lectures 2 hours per week; tutorials 1 hour per week; laboratory practical work 2 hours per week for 10 weeks.

Moed: On-campus.

Strategies of Teaching: lectures, tutorials, and laboratory practical work.

Teaching Methods in Relationship to Objectives:

Lectures: 1-18

Tutorial: 1-6, and communication skills

Lab: 1-6 and pracitcal skills

Examination (3 hours): 60% - Practical work: 40%

Prescribed texts: <HBPrescribed>Texts </HBPrescribed> Sanjit K. Mitra, Digital Signal Processing--A Computer-Based Approach", 2nd Ed., McGraw-Hill, 2001.

Examination (3 hours): 60% - Practical work: 40% <HBPrescribed>Texts </HBPrescribed> Sanjit K. Mitra, Digital Signal Processing--A Computer-Based Approach", 2nd Ed., McGraw-Hill, 2001.

6 Points

2 hrs lectures, 1 hr tutorial, 2 hr lab work and 7 hours private study time per week.

Theatre equipped with overhead projector(s).

Theatre or tutorial room equipped with overhead projector and black/white board.

TI TMSC6x DSP development systems in the DSP Lab of Department of Electrical and Computer Systems Engineering and software tools as well as Matlab.

1 Lecturer, 1 tutor, 1 lab demonstrator.

• Software Title:
• Vendor:
• Version:
• OS: MS Windows XP ( ) and/or LINUX ( )
• License Details: (A valid software license certificate/s or Vendor permission must be available upon request if the software is to be installed for teaching)
• Are we currently licensed: Yes ( ) or No ( ) (If unsure, please check with your local IT support )
• If (Yes), who is the contact person for this software?:
• If (No), will the software license be arranged by: Staff member requesting the software ( ) or Technical Services* ( ) ? *If the latter is selected, please discuss your requirement/s with myself ASAP, prior to finalising your request, as there could be financial or resourcing implications.
• Is the software to be installed and accessed from a: Client/Workstation ( ), or Server ( ) ?
• Type of license: Site License ( ), Freeware ( ), FOSS (Free and Open Source Software) ( ), Shareware ( ), Lab based ( ) # of Licenses needed ____
• In what week will you commence using the software in class? Semester 1 (Week ___ ) and/or Semester 2 (Week ___ ):
• Nominated Staff For Acceptance Testing Software: (Name) (Phone) (Email)
• Additional Information: Special Requirements, Software Configuration/Installation Requirements, etc:

Matlab, TI TMSC6x EVM development tools.

Refer to recommended reading

CSSE

The lab work is conducted in DSP teaching lab in Department of Electrical and Computer Systems Engineering, Faculty of Engineering. Booking of the lab for classes is required (currently done by Faculty Office).

CSE2131 FDSP or equivalent.

Refer to CSE2131 FDSP