ACIT4710 Digital Signal and Image Processing Emneplan

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 similar analytic methods apply. 

Signal Processing or knowledge of the Fourier and Laplace transforms. Some knowledge of Matlab progamming.

No formal requirements over and above the admission requirements.

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 systems 
• Methods of filtering discrete signals
• Digital image formats

Skills

The student will know how to: 

• Describe digital signals and systems mathematically in the time domain
• Describe discrete signals and systems in the frequency domain and the Z-domain 
• Describe digital images mathematically in real space and frequency space 
• Describe linear time invariant systems using difference equations, impulse responses, and transfer functions 
• Analyze time discrete systems in the frequency domain  
• Use discrete filters and the digitized versions of analog filters: FIR and IIR 
• Apply post processing of images for filtering, noise reduction, edge detection and presentation.

General competence

The student will have general competence on:

• Frequency spectrums, impulse responses, frequency responses,  convolution and modulation 
• Fourier-series (FS), Fourier transform (FT). 
• Sampling, reconstruction and aliasing 
• Implementation of DSP-filters
• Improving image presentation

This course will contain the following topics: 

• Sampling, reconstruction and aliasing 
• Impulse response and difference equations
• Fourier series and Fourier transform 
• Frequency analysis and frequency response
• Transfer functions, filter design of FIR and IIR filters 
• Image presentation and processing

Lectures.

Exercises 

Computer exercises.

None

Exam autumn 2021 due to Covid-19:

Individual digital home exam, 3 hours and 30 min to upload/scan.

The exam grade can be appealed.

 New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

In the event of a postponed examination in this course the exam may be held as an oral exam. Oral exams cannot be appealed.

[Exam earlier:]

Individual written exam, 3 hours.

The exam grade can be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

In the event of a postponed examination in this course the exam may be held as an oral exam. Oral exams cannot be appealed.

All written material, but no communication will be allowed.

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.

Two internal examiners. External examiner is used periodically.

Associate Professor Nils Sponheim