Key words
signal processing
Objectives
To provide insight in basic techniques of digital signal processing with the main focus on digital filtering of multimedia signals.
During the practical sessions the acquired knowledge results in the implementation of digital processing of multimedia signals on DSP-processors.
Topics
Theory:
Introduction: DSP-systems and application examples
Linear time invariant systems
Statistical description of signals
Transforms:
o Fouriertransform (DFT, FFT)
o Z-Transform
o Supplements
Sampling:
o ADC, under- and oversampling
o DAC and sampling rate conversions
Digital filters:
o Transform analysis of systems
o Filter structures (FIR, IIR, Lattice structures)
o Design of FIR filters
o Design of IIR filters
Supplements:
o Adaptive filters
o Spectral estimation of audiosignals
Practical sessions:
Sampling, aliasing and resampling (MatLab)
Design of digital filters (MatLab)
Processing of multimedia signals in the frequency domain (MatLab)
Implementation of a FIR and a IIR filter on DSP-processors
Implementation of an adaptive filter on DSP-processors
Prerequisites
See final objectives of Signalen en Systemen, Wiskunde III en Statistiek, Informatica II and Gegevensverwerking.
Final Objectives
To be able to analyse, design and implement signal processing systems and multimedia applications
To be able to analyse, design and implement datacommunication and telecommunication systems
To be able to analyse, design and make abstractions at system level
Materials used
Syllabus: Digital Signal Processing - Monson H. Hayes (Schaum's outlines, ISBN: 0-07-027389-8)
Supplementary information on the electronic study environment Dokeos.
Study costs
Cost for the course material (30 €)
Study guidance
Individual guidance is provided by contacting the responible lecturer / lab responsible.
Teaching Methods
Ex cathedra colleges (theory) and practical sessions.
Assessment
Theory: oral evaluation after written preparation
Practical sessions: continuous evaluation and written test
A weighted average is used to compute the final score for a training item.
However, if a student gains a score of 7 or less on 20 on one of the different courses
(parts of training items) , he proves that his skill for certain subcompetencies is
insufficient. Consequently, one can turn from the arithmetical calculation of the final
assignment of quotas of a training item and the new marks can be awarded on consensus.
Of course, the examiners can judge that the arithmetic regulations mentioned in the
study index card can also be used for 7 or less.
For each deviation a detailed motivation ought to be drawn up. In that case one should
point out that the skill for this subcompetency is proven to be insufficient,
if the student didn’t pass the partim that is considered to be important
for certain subcompetencies.
Lecturer(s)
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