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

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.

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

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. 

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

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

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

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. 

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

• 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.

Ming Xiao

• 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.

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.