Courses

ET2546 Multidimensional Signal Processing

Single subject course, 7,5 Higher education credits, Second cycle, autumn semester 2017

It is no longer possible to apply to this option

The course aims at making the student expand her/his knowledge within digital signal processing to multidimensional signals and systems, e.g. analysis and construction of multidimensional filters and spectral analysis of multidimensional signals. The applications in the course mainly deal with two-dimensional signal processing, i.e. image processing.

Facts

  • Type of instruction: On campus, day, part-time 50%
  • Study period: 2017-October-30 until 2018-January-14
  • Education level: A1N
  • Application: It is no longer possible to apply to this option
  • Language of instruction:
  • Location: Karlskrona
  • Main field of study: Electrical Engineering
  • Course syllabus: Download
  • Welcome letter: Download
  • Entry requirements: For admission to the course the following course is required: Signal Processing II, ET1303 7,5 credit points

Content

Central items of the course are:
Signals, systems, Fourier- and Z-transform
  • Two-dimensional signals and linear time-invariant systems
  • The Fourier transform and the frequency

concept for two-dimensional signals, e.g.
images
  • The sampling theorem for two dimensions
  • Two-dimensional Z-transform, convergence,

pole surfaces and stability
  • Two-dimensional difference equations,

recursive countability and masks
  • Two-dimensional DFT and FFT
  • The discrete cosinus transform

Multidimensional digital filters
  • FIR filters: zero-phase filter, the window method, the frequency sampling method, the frequency transformation method
  • Optimal filter design
  • IIR-filter: Design in spatial domain
  • Design in frequency domain
  • Implementation
  • Stabilization

Spectral estimation
  • Two-dimensional stochastic processes
  • Correlation and spectral density
  • The Wiener filter
  • Methods for spectral estimation based on the Fourier transform
  • High-definition methods, the Maximum

Likelihood Method, the Maximum Entropy Method
  • Autoregressive signal modeling

Image processing
  • Bases for image processing
  • Representation of color images
  • Image enhancement: contrast amplification, histogrammodification, spatial noise reduction, high-pass filtration
  • Homomorphic image processing
  • Low-pass filtration
  • Median filtration
  • Edge detection
  • Motion estimation
  • Image reconstruction: Wiener filtration
  • Spectral subtraction

Learning outcomes

After completion of the course the student will:
  • be able to understand and apply the concept multidimensional signal processing.
  • be able to understand and use relevant frequency transformations in various dimensions, e.g. the Z-transform, the Fourier transform.
  • be able to design and use filters according to given specifications in various dimensions.
  • be able to estimate effect spectra according to classical methods.
  • have a basic understanding of digital processing of images, and be able to make use of ordinary linear and non-linear filter structures.

Generic Skills

Course literature and other teaching material

Course literature and other teaching material

Learning methods

The teaching comprises lectures, laboratory work, project work and exercises. During the arithmetical exercises the theory is applied to signal processing problems.
In order to further explain the theory and its applications compulsory laboratory work assignments form part of the course. The laboratory work assignments are based on programming assignments where program packages for signal and image processing are used. The laboratory work assignments can be done individually or in a group. The project assignment consists of the student making an in-depth study of one of the image processing methods that are brought up in the course. The laboratory work assignments and the project assignment are compulsory and will be solved individually or in a group.

Work placement

No work placement is included in the planned learning activities. BTH is aiming for a close contact with the surrounding community when developing courses and programmes.

Teachers

Examiner
  1. Benny Lövström
Course Manager
  1. Irina Gertsovich

Planned learning activities

Lectures, exercises, laboratory sessions and projects.

Time allocation

On average, a student should study 200 hours to reach the learning outcomes.
This time includes all the various available learning activities (lectures, self studies, examinations, etc.).
This estimation is based on the fact that one academic year counts as 60 ECTS credits,
corresponding to an average student workload of 1 600 hours. This may vary individually.

Assessments

Component examinations for the course
Code Title ECTS credits Grade
1310 Exam 1 6 A-F
1320 Laboration 1.5 G-U
  1. 1Determines the final grade for the course, which will only be issued when all components have been approved.

Grading

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Insufficient, supplementation required, F Fail.

Future exams

No upcoming, centrally coordinated, examinations for this course were found.

To participate in a centrally coordinated examination, you must enroll in Student's Portal, no later than fifteen days before the examination.


Time and location for the examination will be published about 5 days in advance.


There might be other scheduled examinations. Information concerning these examinations are available in It's Learning or at other places that the person who is responsible of the course will refer to.

Course Evaluation

The course manager is responsible for the views of students on the course being systematically and regularly gathered and that the results of the evaluations in various forms affect the form and development of the course.