ACIT4710 Digital Signal and Image Processing Emneplan

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:

Knowledge

The student

• has a thorough technical understanding of the functioning of Internet og computer networks
• has an overview of the most important principles in pervasive computing, and the Internet of Things, which include wearable devices, context aware computing, health monitoring, smart houses, crowd sensing, smart grids and ambient intelligence
• understands the basic technical principles behind different algorithms for autonomous control in the Internet of Things
• has a deep understanding of how the Internet of Things and pervasive computing affect security and the protection of privacy in our society
• has good knowledge of the scientific advances and technology leaps that enabled the Internet of Things

Skills

The student

• masters basic concepts and has an overview of algorithms used on the internet and in data communication
• is capable of conceptualising architecture for solutions based on the Internet of Things and pervasive computing
• is able to contrast and discuss IoT-related designs relative to their own field of study

General competence

The student

• is capable of designing solutions based on the principles behind the Internet of Things and pervasive computing
• is capable of communicating aspects of IoT in relationship to their own field of study

Professor Lothar Fritsch

Students are expected to have the following learning outcomes in terms of knowledge, skills and general competence.

Knowledge

On successful completion of the course, the students have:

• an overview on different perspectives, history and future of AI and Computational Intelligence (CI) fields.
• familiarity with the essential terminologies, concepts, ideas, elements and principles in the three pillar fields of CI.
• an in-depth understanding of state-of-the-art CI methods (fuzzy systems, neural networks, evolutionary computation, deep learning, and hybrid AI techniques).
• knowledge and understanding of open problems and future challenges and opportunities in the AI and CI field.

Skills

On successful completion of the course, the students can: 

• determine when to use and deploy the CI methods learned for real-world applications.
• apply appropriate CI models and algorithms to address modeling and optimization problems in real-world applications.
• analyze complex and uncertain datasets with CI algorithms.

General competence

On successful completion of the course, the students can:

• program the CI models/algorithms.
• deploy CI systems/models in real-world applications.
• solve complex search, optimization or decision-making problems using evolutionary algorithms.

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

The course consists of lectures (theory), labs (practical exercises and computer simulations/experiments), group discussions, Q&As, as well as group projects. The group projects will be assigned from a list of the suggested topics/areas. The students will work in groups and finally submit the project report as well as the code. 

Practical exercises: Lab and Q&A sessions.

The following coursework is compulsory and must be approved before the student can take the exam:

Two compulsory assignments:

• One project proposal, outlining the rationale and plan for the project. Between 500 and 1000 words.
• One project report, documenting the project and its results. Between 2500 and 5000 words.

The deadlines for submitting the compulsory assignments and other details are stipulated in the teaching plan made available at the start of the semester.

The following two group assignments must be approved before the student can take the final exam:

• 1 - Group report and presentation: Group written report and oral presentation on the assigned topic.
• 2 - Group project proposal: A group project proposal (1000 - 1200 words) on the assigned topic, containing  project description, the available dataset(s), method/algorithm to be employed, and references (including several most recent journal publications).

The final exam consists of two parts:

• Part 1 - Group project report with code: A group (2-4 students) project implementation, including a project report (5000 - 7000 words, excluding references) and code as an appendix (counts 50% towards the final grade). Both the code and the report will be evaluated. The comprehensiveness of the code is evaluated under the assumption that each member of the group has worked on the project for 60 hours. 
• Part 2 - Individual written exam: An individual, closed-book, written exam (3 hours) (counts 50% towards the final grade)

Both parts must be passed in order to pass the course (i.e., if a student fails in one part, he or she would automatically fail the course). 

The exam results 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 registering 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.

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.

Grade scale: A-F.