In collaboration with Dr. Beata Zima (Gdańsk University of Technology, Poland) a manuscript on guided wave propagation in complex structures has been accepted for publication in Measurement journal: Abstract: Guided wave-based techniques are particularly effective for corrosion assessment due to their sensitivity to geometric variations and ability to propagate over long distances. However, most existing...Read More

A joint work from the BMBF project KIMono has been accepted for publication in Structural Health Monitoring journal. Abstract: In recent years, the development of machine learning techniques has led to significant progress in the field of structural health monitoring with ultrasonic guided waves. However, a number of challenges still need to be resolved for...Read More

A paper describing the main results of the DROSERA project has been accepted for publication in Structural Health Monitoring journal. Abstract: Delivery drones have become increasingly important in recent years. It is advantageous for commercialization that suppliers are able to deliver orders autonomously and directly to their customers via air transport. However, the safety aspect...Read More

A research paper with Jannik Henkmann (Goethe University Frankfurt) and Dr. Vittorio Memmolo (University of Naples Federico II, Italy) on tiny machine learning for ultrasonic structural health monitoring has been accepted for publication in Sensors. Abstract: This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI)...Read More

A joint paper, coordinated by Anastasiia Volovikova from KIT, has been accepted for publication in the Research and Review Journal of Nondestructive Testing (ReJNDT). The authors work together in the DFG network “Towards a holistic quality assessment for guided wave-based SHM”. Abstract: The continuous monitoring of structural integrity is crucial, as imperceptible damage may appear...Read More

The project “Acoustic population monitoring in remote locations using edge AI and satellite communication (SAT-AND-SOUND)” has been selected for funding. The project partners are: IMST GmbH (Kamp-Lintfort) University of Siegen (Siegen) Funding number: 16GM105902Read More