Course language is English. 6 ECTS. The module is a M.Sc. course and will be offered in summer semester. For further information, please contact Carlos Chillon Geck (carlos.chillon.geck(at)tuhh(dot)de).
The students will be taught principles and practices of smart monitoring, including structural health monitoring (SHM), environmental monitoring, and related areas. The students will be able to design decentralized smart systems to be applied for continuous (remote) monitoring of systems in the built and in the natural environment. In addition, the students will learn to design and to implement intelligent sensor systems using state-of-the-art data analysis techniques, modern software design concepts, and embedded computing methodologies. Autonomous software and decentralized data processing are further crucial parts of the course, including concepts of the Internet of Things, Industry 4.0 and cyber-physical systems. Besides lectures, project work is an integral part of this module. In small groups, the students will design smart monitoring systems that integrate a number of “intelligent” sensors to be implemented by the students. Specific focus will be put on the application of machine learning techniques. The smart monitoring systems will be mounted on real-world (built or natural) systems, such as bridges or slopes, or on scaled lab structures for validation purposes. The outcome of every group will be documented in a paper. All students of this module will “automatically” participate with their smart monitoring system in the annual "SMART MONITORING" competition. The written papers and oral examinations form the final grades. The module will be taught in English. Limited enrollment.
Basic knowledge in scientific writing and good English skills. Interest in modern research and teaching areas, such as Internet of Things, Industry 4.0 and cyber-physical systems, as well as the will to deepen skills of scientific working. Basic knowledge or interest in object-oriented modeling, programming, and sensor technologies are helpful.
Examples of what students have achieved in this course in previous semesters