EPN-V2

ACIT4710 Digital Signal and Image Processing Course description

Course name in Norwegian
Digital Signal and Image Processing
Study programme
Master's Programme in Applied Computer and Information Technology
Weight
10.0 ECTS
Year of study
2021/2022
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 similar analytic methods apply.

Recommended preliminary courses

Bachelor's or master's degree in engineering or science.

Required preliminary courses

This course covers contemporary topics in smart energy systems such as smart power grid, smart buildings, vehicle-to-grid (V2G) and communication technologies for and network security in smart energy systems, including emerging approaches towards energy intelligence such as machine learning and blockchain.

The course will be offered once a year, provided 5 or more students sign up for the course. If less than 5 students sign up for a course, the course will be cancelled for that year

Learning outcomes

None.

Content

The course is divided into three modules.

The first module covers lectures on economic interactions for the energy market, focusing mainly on applications such as demand response management (DRM), and vehicle-to-grid (V2G), etc.

The second module consists of lectures on current and emerging approaches such as machine learning and blockchain for energy intelligence and network security.

The third module will be a seminar which will include a hands-on session on tools such as optimisation and machine learning for solving specific problems in future energy information networks, and will conclude with a project assignment to be submitted by a given deadline.

Teaching and learning methods

Lectures.

Exercises

Computer exercises.

Course requirements

Module 1 and 2 will take the form of a series of lectures. Module 3 will be a combination of hands-on sessions along with the project assignment.

Practical training

The students will solve specific problems using optimisation or machine learning techniques. The students will submit a brief report with results for the problem in the assignment, also describing the process they used for solving the assignment, including the code.

Assessment

None.

Permitted exam materials and equipment

The results for the project assignment, process description, and the code will be assessed by the course leader. The exam can be appealed.

Grading scale

All aids are permitted.

Examiners

Pass or fail.

Course contact person

One examiner. External examiner is used periodically.