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About course

FEO3290 Selected Topics on Emerging Information Technologies for Industrial Digitalization 8.0 credits

Administer About course

Do you want to widen your horizon on key information technologies for practical industrial transformation? Do you want to be inspired your research with industrial insights? This cousre may be right for you. In the course, we will study the key information technologies for emerging industry digitalization (ID). The main objective of the course is to train and update students with the newest information technologies for industry digitalization, especially those related to vertical applications. The preliminary topics include, high-performance wireless networks (including 5G) for ID, distributed machine learning for ID, digital twin, global coverage (satellite based communications and computing), industry and function security and guest lectures. The course will be in the form of lectures by teachers and student presentations.

Course offering missing for current semester as well as for previous and coming semesters
Headings with content from the Course syllabus FEO3290 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Lecture 1. Course information and introduction of industry transformation

Lecture 2. IP-based Convergence and Interoperability of Industrial IoT Standards

Lecture 3. High performance wireless networks Part 1, requirements, enabling technologies and theories

Lecture 4. High performance wireless networks, Part 2, Vectrical Applications and experinments.

Lecture 5. Physical Layer Security for Industry Wireless Networks

Lecture 6. Functional Safety for Industry Systems

Lecture 7. Industrial AI, Part 1, Requirements and Algorithms

Lecture 8. Industrial AI, Part 2, Vertical Applications and experinments

Lecture 9. Digital Twin

Lecture 10. Global Coverage, Satellite Based Communications and Computing

Lecture 11. Guest Lectures on Industrial Robots etc. 

Lecture 12. High-accuracy industrial positioning 

Lecture 13. Industrial Cloud/edge computing

Lecture 14. Reflection on Information Technologies for Sustainable Industries

Intended learning outcomes

With the development of various enabling technologies, e.g., AI, 5G and beyond, industry security, digital twin, global coverage and edge computing, the digitalization of industries has attracted lots of research efforts and started to be deployed in various industry scenarios. Though some courses may involve a part of those topics, a comprehensive and vertical-application oriential course have not been developed. Moreover, a systematic study on requirements, challenges and development of information technologies for industry digitalization has not been developed yet. This course aims to address these problems by developing systematic and vertical-application oriented course for industry digitalization. The main objectives of the course are to train the students on the key information technologies for industry digital transformation and to inspire the students for potential new research topics. After the course, the students should: 

  1. Know an overview of and the technical requirements for industry digitalization.
  2. Know the information technologies for sustainable industries. 
  3. Understand key requirements and enabling technologies of information technologies (both theories and practices) for industry digitalization, including e.g., wireless networks, AI, security, digital twin and global coverage etc.
  4. Understand how the information technologies are applied in vertical use cases for industry digitalization.
  5. Understand recent development and existing challenges of information technologies for industry digitalization. 

Course disposition

The course includes lectures by teachers and presentations by students. To pass the course, the students should do following tasks (1), present (about 20+10 minutes for one selected topic) in the class; (2), An approved survey report (minimum 5 page) on one selected topic (Technology or Vertical Applications); (3) Attend 80% lectures (about 11 out of 14).

The course will be given once per two years.

Student work load: Lecture hours, 20 hours; Presentation: about 25 hours (preparation+oral presentation); Home work 60 hours (literature reading for different topics);  Survey report (students) may take up to 92 hours; Peer-grading of final report: 16 hours.  Total time for each student is about 213 working hours.

Literature and preparations

Specific prerequisites

Knowledge of digital communications, corresponding to EQ2410 or similar is required.

Recommended prerequisites

  1. Basic knowledge of 5G and beyond is recommended.
  2. Knowledge of machine learning, corresponding to EQ2421 Machine Learning or similar is recommended.

Equipment

Potentially via ZOOM, if classroom is not feasible due to pandemic.

Literature

  1. X, Wang and L. Gao, “When 5G Meets Industry 4.0,” Springer, 2020.
  2. A. Ustundag and E. Cevikcan, “Industry 4.0: Managing The Digital Transformation,” Springer, 2018.
  3. S. V. Nath, A. Dunkin, M. Chowdhary and N. Patel, “Industrial Digital Transformation,” Packt Publishing, 2020.
  4. Related papers/publications, available in KTH library in an electronic version. 

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F

Examination

  • EXA1 - Examination, 8.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

  1. Oral presentation on one selected topic.
  2. Approved survey reports on select topic, the report should be comprehensive and can be combined with student’s own research. The minimum length should be 5 pages including references.
  3. Attendence higher than 80% (attending 11 or more of 14 lectures/meetings).
  4. Peer-reviewing at least one report.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Profile picture Ming Xiao

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FEO3290

Offered by

EECS/Information Science and Engineering

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted

Contact

Ming Xiao (mingx@kth.se), Zhibo Pang (zhibo@kth.se)

Supplementary information

The course content is new and clearly different from existing courses. For instance, compared to “FEO 3260 Machine-to-Machine Communication”, our wireless networks mainly concentrate on physical layer (theories and pratices) and vertical applications (e.g., digital factory). FEO 3260 is mainly on the upper layer (network or application layers) and various wireless protocols. Similarly, “FEL3245 Principles of Wireless Sensor Networks” also mainly considers MAC layer and network optimization. Moreover, other topics, e.g., AI for industry transformation, global coverage (satellicate based communications and computing), and edge computing, high accuracy position has not been covered by other courses at KTH, to our knowledge. Furthermore, our angle is from technologies for industry transferation, which may be different from other aspects.

Postgraduate course

Postgraduate courses at EECS/Information Science and Engineering