COURSE OUTLINE
1. GENERAL
SCHOOL TECHNOLOGICAL APPLICATIONS
DEPARTMENT COMPUTER SYSTEMS ENGINEERING
LEVEL OF STUDY UNDERGRADUATE
SEMESTER OF STUDY 4th
COURSE UNIT CODE 244403
COURSE TITLE DIGITAL SIGNAL PROCESSING
COURSEWORK BREAKDOWN
Lectures
COURSE UNIT TYPE
PREREQUISITES :
LANGUAGE OF
INSTRUCTION/EXAMS:
COURSE DELIVERED TO
ERASMUS STUDENTS
MODULE WEB PAGE (URL)
TEACHING
WEEKLY HOURS
2
Laboratory
2
Total
4
ECTS
Credits
4
Compulsory
NONE
Greek
Yes in English
http://aveloni.daidalos.teipir.gr/dsp/
2. LEARNING OUTCOMES
Learning Outcomes
This course will develop digital signal processing (DSP) theory and methods with the
following objectives:
 Understand the significance of digital signal processing in multi-media technology,
storage and communications.
 Familiarity with fundamental concepts such as 'linearity' , 'time-invariance', 'impulse
response', 'convolution', 'frequency response', 'z-transforms' and the 'discrete time
Fourier transform'. as applied to signal processing systems.
 Knowledge of digital filters and their application to digitised sound and images.
 Understand how FIR and IIR type digital filters: may be designed and implanted in
software.
 Use the "MATLAB" language and "signal processing toolboxes" for designing,
implementing and simulating digital signal processing (DSP) operations as applied to
speech, music and images..
 Understand analogue/digital conversion as required for the digital processing of
analogue signals.
 Understand the discrete Fourier transform (DFT), its applications and its
implementation by FFT techniques. Gain some knowledge of the 2-D FFT and its
application to image processing and compression.
General Skills
Teamwork
3. COURSE CONTENTS
The following gives a list of topics, covered in the course :
Theory
1. Introduction: What is signal processing, history of the topic, application examples.
2. Discrete-time (DT) signals: the discrete-time complex exponential, and a computer
music synthesis example.
3. Digital Signal Processing and DSP Systems
4. Model of DSP Systems
5. Z Transform
6. Fourier Analysis: The discrete Fourier transform (DFT) and series (DFS).
7. The discrete-time Fourier transform (DTFT). Examples.
8. The fast Fourier transform algorithm (FFT).
9. Linear Filters: Linear time-invariant systems, convolution, ideal and realizable filters.
10. Filter design and implementation, examples.
11. Interpolation and Sampling: Continuous-time (CT) signals, interpolation, sampling.
12. The sampling theorem. Processing of CT signals in DT.
13. DSP, Tools, DSP and the Future
Laboratory
1.
2.
3.
4.
5.
6.
7.
8.
Discrete-Time Signals in the Time Domain
Discrete-Time Systems in the Time Domain
Discrete-Time Signals in the Frequency Domain
LTI Discrete-Time Systems in the Frequency Domain
Digital Processing of Continuous-Time Signals
Digital Filter Structures
Digital Filter Design
Digital Filter Implementation
4. TEACHING METHODS - ASSESSMENT
MODE OF DELIVERY Face to Face
USE OF INFORMATION AND
COMMUNICATION TECHNOLOGY
TEACHING METHODS
ASSESSMENT METHODS
Using computers to assist in teaching and learning
Method description
Lectures
Labs
Preparation for the lab
Semester Workload
26
26
10
Self study
38
Total
100
Final exam (40%)
Coursework 20%
Written lab exams (40%)
5. RESOURCES
- Recommended Book and Journal Article Resources:
1.
2.
3.
4.
Advanced Topics in Digital Signal Processing, by John G. Proakis, Charles Rader, and Fuyun
Ling, Prentice Hall, 1992
Analog and Digital Filter Design Using C (Book/Disk), 1/e , by Leslie D. Thede , Prentice Hall,
1996
Digital Signal Processing , by Oppenheim, Prentice Hall, 1988
Signals and Systems Laboratory With Matlab by Palamides A., Veloni A., CRC Press, 2010