Kimotho, J. K., Meyer, T., &; Sextro, W. (2014). PEM fuel cell prognostics using particle filter with model parameter adaptation. In Prognostics and Health Management (PHM), 2014 IEEE Conference on (pp. 1–6).https://doi.org/10.1109/ICPHM.2014.7036406 

Kimotho, J. K., &; Sextro, W. (2014). An approach for feature extraction and selection from non-trending data for machinery prognosis. In Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014 (Vol. 5).

Unger, A., Sextro, W., Althoff, S., Eichwald, P., Meyer, T., Eacock, F., &; Brökelmann, M. (2014). Experimental and Numerical Simulation Study of Pre-Deformed Heavy Copper Wire Wedge Bonds. In Proceedings of the 47th International Symposium on Microelectronics (IMAPS) (pp. 289–294). San Diego, CA, US.

Drude, L., Chinaev, A., Tran Vu, D. H., &; Haeb-Umbach, R. (2014). Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models. In 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014) (pp. 213–217).

Leutnant, V., Krueger, A., &; Haeb-Umbach, R. (2014). A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(1), 95–109.https://doi.org/10.1109/TASLP.2013.2285480 

Li, J., Deng, L., Gong, Y., &; Haeb-Umbach, R. (2014). An Overview of Noise-Robust Automatic Speech Recognition. IEEE Transactions on Audio, Speech and Language Processing, 22(4), 745–777.https://doi.org/10.1109/TASLP.2014.2304637 

Heinrichs, N., Hess, D., Homburg, C., Lorenz, M., &; Sievers, S. (2013). Extended dividend, cash flow, and residual income valuation models: Accounting for deviations from ideal conditions. Contemporary Accounting Research (VHB-JOURQUAL 3 Ranking A), 30(1), 42–79.https://doi.org/10.2139/ssrn.1145201 

Klobucnik, J., &; Sievers, S. (2013). Valuing high technology growth firms. Journal of Business Economics (VHB-JOURQUAL 3 Ranking B), 83(9), 947–984.https://doi.org/10.1007/s11573-013-0684-2 

Chinaev, A., &; Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations. In 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) (pp. 3352–3356).https://doi.org/10.1109/ICASSP.2013.6638279 

Leutnant, V., Krueger, A., &; Haeb-Umbach, R. (2013). Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition. IEEE Transactions on Audio, Speech, and Language Processing, 21(8), 1640–1652.https://doi.org/10.1109/TASL.2013.2258013 

Vu, D. H. T., &; Haeb-Umbach, R. (2013). Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) (pp. 863–867).https://doi.org/10.1109/ICASSP.2013.6637771 

Becker, M., Luckey, M., &; Becker, S. (2012). Model-driven Performance Engineering of Self-adaptive Systems: A Survey. In Proceedings of the 8th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA) (pp. 117–122). New York, NY, USA: ACM.https://doi.org/10.1145/2304696.2304716 

Hunstig, M., Hemsel, T., &; Sextro, W. (2012). An efficient simulation technique for high-frequency piezoelectric inertia motors. In Ultrasonics Symposium (IUS), 2012 IEEE International (pp. 277–280).https://doi.org/10.1109/ULTSYM.2012.0068 

Leutnant, V., Krueger, A., &; Haeb-Umbach, R. (2012). A Statistical Observation Model For Noisy Reverberant Speech Features and its Application to Robust ASR. In Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference on.

Krueger, A., &; Haeb-Umbach, R. (2010). Model-Based Feature Enhancement for Reverberant Speech Recognition. IEEE Transactions on Audio, Speech, and Language Processing, 18(7), 1692–1707.https://doi.org/10.1109/TASL.2010.2049684 

Tran Vu, D. H., &; Haeb-Umbach, R. (2010). Blind speech separation employing directional statistics in an Expectation Maximization framework. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010) (pp. 241–244).https://doi.org/10.1109/ICASSP.2010.5495994 

Krupp, A., &; Müller, W. (2010). A Systematic Approach to Combined HW/SW System Test. Proceedings of DATE’10. Design, Automation &; Test in Europe Conference &; Exhibition (DATE 2010), Dresden.https://doi.org/10.1109/DATE.2010.5457186 

Defo, G. B., Müller, W., &; Kuznik, C. (2010). Verification of a CAN Bus Model in SystemC with Functional Coverage. Proceedings of SIES 2010. International Symposium on Industrial Embedded System (SIES),https://doi.org/10.1109/SIES.2010.5551379__Trento, Italy.https://doi.org/10.1109/SIES.2010.5551379 

Bevermeier, M., Peschke, S., &; Haeb-Umbach, R. (2009). Robust vehicle localization based on multi-level sensor fusion and online parameter estimation. In 6th Workshop on Positioning Navigation and Communication (WPNC 2009) (pp. 235–242).https://doi.org/10.1109/WPNC.2009.4907833 

Bevermeier, M., Peschke, S., &; Haeb-Umbach, R. (2009). Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning. In IEEE 69th Vehicular Technology Conference (VTC 2009 Spring) (pp. 1–5).https://doi.org/10.1109/VETECS.2009.5073634