S. Ober-Blöbaum, M. Tao, M. Cheng, H. Owhadi, and J. E. Marsden, “Variational integrators for electric circuits,” Journal of Computational Physics, vol. 242, pp. 498–530, 2013.

M. Happe, E. Lübbers, and M. Platzner, “A Self-adaptive Heterogeneous Multi-core Architecture for Embedded Real-time Video Object Tracking,” International Journal of Real-time Image Processing, vol. 8, no. 1, pp. 95–110, 2013.

J. Anwer, S. Meisner, and M. Platzner, “Dynamic reliability management: Reconfiguring reliability-levels of hardware designs at runtime,” in Reconfigurable Computing and FPGAs (ReConFig), 2013 International Conference on, 2013, pp. 1–6.

C. Bick, Beschleunigung von Tiefenberechnung aus Stereobildern durch FPGA-basierte Datenflussrechner. Paderborn University, 2013.

A. Boschmann, B. Nofen, and M. Platzner, “Improving transient state myoelectric signal recognition in hand movement classification using gyroscopes,” in Proc. IEEE Int. Conf. Eng. Med. Biolog. (EMBC), 2013.

A. Boschmann and M. Platzner, “Reducing the limb position effect in pattern recognition based myoelectric control using a high density electrode array,” in Proc. IEEE ISSNIP Biosignals and Biorobotics Conference (BRC), 2013.

K. Glette, P. Kaufmann, C. Assad, and M. Wolf, “Investigating Evolvable Hardware Classification for the BioSleeve Electromyographic Interface,” in IEEE Intl. Conf. on Evolvable Systems (ICES), 2013, vol. 1, pp. 1–1.

P. Kaufmann, Adapting Hardware Systems by Means of Multi-Objective Evolution. Berlin: Logos Verlag, 2013.

P. Kaufmann, K. Glette, T. Gruber, M. Platzner, J. Torresen, and B. Sick, “Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers,” IEEE Transactions on Evolutionary Computation, vol. 17, no. 1, pp. 46–63, 2013.

M. Knoop, Behavior Models for Electric Vehicles. IWES Kassel, 2013.