Studyinfo subject ACIT4710 2020 HØST
ACIT4710 Digital Signal and Image Processing Course description
- Course name in Norwegian
- Digital Signal and Image Processing
- Study programme
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Master's Programme in Applied Computer and Information Technology
- Weight
- 10.0 ECTS
- Year of study
- 2020/2021
- Curriculum
-
FALL
2020
- Schedule
- Programme description
- Course history
-
Introduction
Signal processing defines the mathematical tools used to analyze, model and operate on measured data from physical signals and their sources. In digital signal processing (DSP) the following topics are lectured: the different domains for describing discrete signals and linear time invariant systems, digital filters and frequency analysis. Images are two-dimensional signals and the same analytic methods apply. Reconstruction of medical images will be used as practical application.
Recommended preliminary courses
Signal Processing or knowledge of the Fourier and Laplace transforms. Some knowledge of Matlab progamming.
Required preliminary courses
No formal requirements over and above the admission requirements.
Learning outcomes
On completion of this course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
Knowledge
The course will give the students in depth knowledge on the following topics
- Sampling, digitalization and reconstruction.
- How to obtain the signal spectrum of digital and analog signals
- How to obtain the frequency response of digital and analogue systems
- Filtering
- Detection techniques, correlation and deconvolution
- Reconstruction of images from measured data by inverse methods
Skills
The student will know how to:
- Describe digital signals and systems mathematically in the time domain, the frequency domain and the Z-domain
- Describe digital images mathematically in real space and K-space
- Describe linear time invariant systems using difference equations, impulse responses, point spread functions and transfer functions
- Analyze time discrete systems in the frequency domain and discrete images in K- space
- Use analog filters and the digitized versions of analog filters: FIR and IIR
- Apply post processing of images for filtering
- Apply inverse methods for image reconstruction from measured data
General competence
The student will have general competence on:
- Spectrums, impulse responses, point spread functions, frequency responses, K- space, correlation, convolution and modulation
- Fourier-series (FS), Fourier transform (FT).
- Sampling, reconstruction and aliasing
- Implementation of DSP-filters and inverse methods on a computer.
Content
This course will contain the following topics:
- Sampling, reconstruction and aliasing
- Impulse response, Point spread function
- Fourier series and Fourier transform
- Frequency analysis and k-space analysis
- Frequency response, filters and transfer functions
- Detection methods, correlation, convolution and modulation
- Inverse methods for image reconstruction
Teaching and learning methods
Lectures.
Practical training
Computer exercises.
Course requirements
Mandatory computer exercises, individual or in groups (6 hours per week).
Assessment
Exam autumn 2020 due to Covid-19:
Individual digital home exam, 3 hours.
The exam grade can be appealed.
In the event of a resit or rescheduled exam, an oral examination may be used instead. In such case, the grade cannot be appealed.
[Exam earlier:]
Individual written exam, 3 hours.
The exam grade can be appealed.
In the event of a resit or rescheduled exam, an oral examination may be used instead. In such case, the grade cannot be appealed.
Permitted exam materials and equipment
Aids autumn 2020:
All aids allowed, except communication with others
[Aids earlier:]
Calculator that cannot be used to communicate with others.
Grading scale
For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.
Examiners
One internal examiners. External sensor is used periodically. If oral, two internal examiners.