The course is based upon ongoing research in the department or relevant requirements of industry. The course will deepen the competence and expertise of a student within a focused application- or research area. Individually or in small groups, students specialize in topics that are approved by members of the academic staff. The topics should be relevant to the specialization profile that the students have chosen. The student may be required to present scientific papers.
The student can select only one of the pre-approved topics/direction and pursue that for the semester. A successful participation in a topic/direction within this course might depend on the participation in corresponding seminars in previous semesters. Additional topics / directions are subject of negotiation between the student, a topic expert from the academic staff, and the study programme manager.
Pre-approved course topics / directions include:
Overview of this direction
The students will deepen their competence, understanding and practical experience in the application of technologies like sensor-based data collection / IoT, data representation / ontologies, machine learning, natural language processing (NLP), and more, for intelligent eHealth applications and services. Examples are clinical decision support systems and personalized wellness coaching.
Following some project-specific teaching / repetition of needed technologies (minor course part), the students will carry out an advanced project including literature study / research, prototype development, testing and evaluation (major course part), accompanied with regular or on-demand supervision. The topic of the project(s) and the deadline will be negotiated with the student group(s) at the beginning of the course.
As the target project(s) will depend to a high degree on Artificial Intelligence (AI) technologies, students should have participated the "Artificial Intelligence - Learning Systems" direction in IKT440-G (ICT Seminar 1).
Furthermore, the project builds on top of eHealth specific competence lectured in the "eHealth topic/direction" in IKT441-G (ICT Seminar 2).
(2) Database Management
Overview of this direction
This is an advanced graduate course on database management. The focus of this course is on distributed and unstructured database management, as well as on core relationship between data warehousing, information integration, and information fusion technologies. We will elaborate on challenges in developing an infrastructure that facilitates efficient information consolidation. In particular, we will consider how concepts of information linkage and information fusion accelerate novel research directions in advanced data management. We will explore synergy between these areas under scenarios of large-scale data integration and warehousing. Information fusion deals with reconstructing objects from multiple, possibly incomplete and inconsistent observations. The task of scalable information fusion is critical for interdisciplinary research where a comprehensive picture of the subject requires large amounts of data from disparate data sources. In this context, we will explore cutting-edge database management technologies and their applicability limits. In particular, we will elaborate on No-SQL solutions and their applicability to the task of scalable information consolidation.
No single textbook will be used for this class. Students will be required to read a selection of papers and book chapters on relevant topics, and be prepared to discuss them in class. Most of the class materials will be posted for electronic download.
Project and Report
A project will be carried out in groups and has to be documented by a written group report. A working implementation of the system has to be built, tested and demonstrated by each group. The groups are "self-policed''. Each group will work on a different data-intensive application as explained in project descriptions that will be made available at the beginning of the semester in the LMS (CANVAS). The final report has to be handed in for grading at the due date that will be published also at the beginning of the semester.
(3) Embedded Systems
A selection of project topics from the area of "Embedded Systems" will be announced at the beginning of the semester in the LMS (CANVAS).
A final report should be handed in for grading at the end of the term. The report must be formatted according to IEEE guide lines for conferences and journals.
The project requires a working implementation of the system to be built, tested, and demonstrated. The schedule and deadlines will be determined at the beginning of the semester.
(4) Cisco Certification (CCNA/CCNP or other relevant certification)
This direction has the same procedure as Cisco Certification Associate CCNA in ICT Seminar 1 (IKT440-G).
(5) Selected Topics in Beyond 5G and IoT Networks
Together with the tutor, a student who selects this direction is expected to identify a topic which is targeted at advancing the state-of-the-art techniques beyond 5G and IoT. Depending on the interest and expertise of the candidate, the topic could be either theoretical or practical, or a combination of them. The potential topics will very likely be relevant to the ongoing research activities at the Dept. of ICT, UiA.
No lectures are planned for this direction, but a weekly meeting with the tutor is expected. As the outcome of this semester project, a technical report in a scientific paper style needs to be submitted. A student who selects this direction may consider this semester project as a pre-project for his/her Master thesis.
To select or to get further information about a topic / direction or to select a direction, please send an email to the instructors of the direction that you are interested in, and copy to Martin Gerdes (firstname.lastname@example.org).
Be aware that not all topics in the seminar courses might be available each semester.
This course will be carried out as a combination of lectures, independent theoretical or practical assignments, paper studies, guided or independent lab work, and report writing. The tasks are done individually or in small groups with group supervision.
The work load for the average student is approximately 200 hours.
Faculty of Engineering and Science