Reverberant Speech Recognition
A. Krueger, R. Haeb-Umbach, in: Techniques for Noise Robustness in Automatic Speech Recognition, Wiley, 2012.

Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data
A. Krueger, O. Walter, V. Leutnant, R. Haeb-Umbach, in: Proc. Interspeech, Portland, USA, 2012.

Investigations Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech
V. Leutnant, A. Krueger, R. Haeb-Umbach, Speech Communication; 10. ITG Symposium; Proceedings Of (2012) 1–4.

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

Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain
V. Leutnant, A. Krueger, R. Haeb-Umbach, Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain, 2012.

Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov Models
D.H. Tran Vu, R. Haeb-Umbach, in: International Workshop on Acoustic Signal Enhancement (IWAEN12), 2012.

Cooperative Awareness at Low Vehicle Densities: How Parked Cars Can Help See through Buildings
D. Eckhoff, C. Sommer, R. German, F. Dressler, in: 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011, 2012.

On the Necessity of Accurate IEEE 802.11P Models for IVC Protocol Simulation
D. Eckhoff, C. Sommer, F. Dressler, in: 2012 IEEE 75th Vehicular Technology Conference (VTC Spring), 2012.

Comparing apples and oranges?
S. Joerer, F. Dressler, C. Sommer, in: Proceedings of the Ninth ACM International Workshop on Vehicular Inter-Networking, Systems, and Applications - VANET ’12, 2012.

Toward reproducibility and comparability of IVC simulation studies: a literature survey
S. Joerer, C. Sommer, F. Dressler, IEEE Communications Magazine (2012) 82–88.

Content downloading in vehicular networks: Bringing parked cars into the picture
F. Malandrino, C. Casetti, C.F. Chiasserini, C. Sommer, F. Dressler, in: 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC), 2012.

mLearning. Akzeptanz von Mobile Learning. Chancen und Probleme in der betrieblichen Bildung
M. Beutner, R. Pechuel, in: F. Siepmann, P. Müller (Eds.), Jahrbuch eLearning und Wissensmanagement 2013. Die Zukunft der Bildung und die Rolle der digitalen Medien, Siepmann Media, Albstedt, 2012, pp. 30–34.

Built-in Self-Diagnosis Targeting Arbitrary Defects with Partial Pseudo-Exhaustive Test
A. Cook, S. Hellebrand, M. E. Imhof, A. Mumtaz, H.-J. Wunderlich, in: 13th IEEE Latin American Test Workshop (LATW’12), IEEE, Quito, Ecuador, 2012, pp. 1–4.

Built-in Self-Diagnosis Exploiting Strong Diagnostic Windows in Mixed-Mode Test
A. Cook, S. Hellebrand, H.-J. Wunderlich, in: 17th IEEE European Test Symposium (ETS’12), IEEE, Annecy, France, 2012, pp. 1–6.

GPU-based cloud computing for comparing the structure of protein binding sites
M. Leinweber, L. Baumgärtner, M. Mernberger, T. Fober, E. Hüllermeier, G. Klebe, B. Freisleben, in: In Proceedings IEEE Conference on Digital Ecosystem Technologies-Complex Environment Engineering Campione D`Italia, Italy, 2012.

Efficient learning of classifiers based on the 2-additive Choquet integral
E. Hüllermeier, A. Fallah Tehrani, in: C. Moewes, A. Nürnberger (Eds.), Computational Intelligence in Intelligent Data Analysis, Springer, 2012, pp. 17–30.

Koli Calling 2009 and 2010
C. Schulte, J. Suhonen, Computer Science Education 22 (2012) 79–80.

Computer science at school/CS teacher education: Koli working-group report on CS at school
C. Schulte, M. Hornung, S. Sentance, V. Dagiene, T. Jevsikova, N. Thota, A. Eckerdal, A.-K. Peters, in: Koli Calling, ACM, 2012, pp. 29–38.

Uncovering structure behind function: the experiment as teaching method in computer science education
C. Schulte, in: WiPSCE, ACM, 2012, pp. 40–47.

Learning conditional lexicographic preference trees
M. Bräuning, E. Hüllermeier, in: In Workshops on Preference Learning at ECAI, European Conference on Artiticial Intelligence, Montpellier, France, 2012.