EPN-V2

ACIT4710 Digital Signal and Image Processing Emneplan

Engelsk emnenavn
Digital Signal and Image Processing
Studieprogram
Master's Programme in Applied Computer and Information Technology
Omfang
10.0 stp.
Studieår
2020/2021
Emnehistorikk

Innledning

No formal requirements over and above the admission requirements.

Anbefalte forkunnskaper

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

Forkunnskapskrav

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

Læringsutbytte

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.

Innhold

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

Arbeids- og undervisningsformer

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.

Arbeidskrav og obligatoriske aktiviteter

All written material, but no communication will be allowed.

Vurdering og eksamen

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.

Hjelpemidler ved eksamen

Two internal examiners. External examiner is used periodically.

Vurderingsuttrykk

Associate Professor Nils Sponheim

Sensorordning

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