Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning
J. Gerritzen, A. Hornig, P. Winkler, M. Gude, in: ECCM21 - Proceedings of the 21st European Conference on Composite Materials, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259.

Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing
I. Gräßler, M. Hieb, in: Lectures, AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp. 253–524.

Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses
S. Lange, D. Schröder, C. Hedayat, H. Kuhn, U. Hilleringmann, in: 22nd IEEE International Conference on Industrial Technology (ICIT), IEEE,  Valencia, Spain , 2021.

Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters
S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, p. I.

Automatic generation of phonetic regression class trees for MLLR adaptation
R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001) 299–302.