@article{GasparyanPoghossianVitusevichetal.2011, author = {Gasparyan, Ferdinand V. and Poghossian, Arshak and Vitusevich, Svetlana A. and Petrychuk, Mykhaylo V. and Sydoruk, Viktor A. and Siqueira, Jos{\´e} R. Jr. and Oliveira, Osvaldo N. Jr. and Offenh{\"a}usser, Andreas and Sch{\"o}ning, Michael Josef}, title = {Low-Frequency Noise in Field-Effect Devices Functionalized With Dendrimer/Carbon-Nanotube Multilayers}, series = {IEEE Sensors Journal. 11 (2011), H. 1}, journal = {IEEE Sensors Journal. 11 (2011), H. 1}, publisher = {IEEE}, address = {New York}, isbn = {1530-437X}, pages = {142 -- 149}, year = {2011}, language = {en} } @article{GriegerSchwabedalWendeletal.2021, author = {Grieger, Niklas and Schwabedal, Justus T. C. and Wendel, Stefanie and Ritze, Yvonne and Bialonski, Stephan}, title = {Automated scoring of pre-REM sleep in mice with deep learning}, series = {Scientific Reports}, volume = {11}, journal = {Scientific Reports}, number = {Art. 12245}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-021-91286-0}, year = {2021}, abstract = {Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.}, language = {en} } @article{HeuermannSchiek1997, author = {Heuermann, Holger and Schiek, Burkhard}, title = {Line Network Network (LNN): an alternative in-fixture calibration procedure}, series = {IEEE transactions on microwave theory and techniques : MTT ; a publication of the IEEE Microwave Theory and Techniques Society. 45 (1997), H. 3}, journal = {IEEE transactions on microwave theory and techniques : MTT ; a publication of the IEEE Microwave Theory and Techniques Society. 45 (1997), H. 3}, isbn = {0018-9480}, pages = {408 -- 413}, year = {1997}, language = {en} } @article{HeuermannPetersSchmitzetal.2005, author = {Heuermann, Holger and Peters, N. and Schmitz, T. and Sadeghfam, Arash}, title = {Concept of Balanced Antennas with Load-Invariant Base Impedance Using a Two Element LC-Coupler. 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