@inproceedings{DeyElsenFerreinetal.2021, author = {Dey, Thomas and Elsen, Ingo and Ferrein, Alexander and Frauenrath, Tobias and Reke, Michael and Schiffer, Stefan}, title = {CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom}, series = {PETRA '21: Proceedings of the 14th Pervasive Technologies Related to Assistive Environments Conference}, booktitle = {PETRA '21: Proceedings of the 14th Pervasive Technologies Related to Assistive Environments Conference}, editor = {Makedon, Fillia}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {9781450387927}, doi = {10.1145/3453892.3462697}, pages = {292 -- 299}, year = {2021}, abstract = {In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.}, language = {en} } @article{GermanMikuckiWelchetal.2021, author = {German, Laura and Mikucki, Jill A. and Welch, Susan A. and Welch, Kathleen A. and Lutton, Anthony and Dachwald, Bernd and Kowalski, Julia and Heinen, Dirk and Feldmann, Marco and Francke, Gero and Espe, Clemens and Lyons, W. Berry}, title = {Validation of sampling antarctic subglacial hypersaline waters with an electrothermal ice melting probe (IceMole) for environmental analytical geochemistry}, series = {International Journal of Environmental Analytical Chemistry}, volume = {101}, journal = {International Journal of Environmental Analytical Chemistry}, number = {15}, publisher = {Taylor \& Francis}, address = {London}, issn = {0306-7319}, doi = {10.1080/03067319.2019.1704750}, pages = {2654 -- 2667}, year = {2021}, abstract = {Geochemical characterisation of hypersaline waters is difficult as high concentrations of salts hinder the analysis of constituents at low concentrations, such as trace metals, and the collection of samples for trace metal analysis in natural waters can be easily contaminated. This is particularly the case if samples are collected by non-conventional techniques such as those required for aquatic subglacial environments. In this paper we present the first analysis of a subglacial brine from Taylor Valley, (~ 78°S), Antarctica for the trace metals: Ba, Co, Mo, Rb, Sr, V, and U. Samples were collected englacially using an electrothermal melting probe called the IceMole. This probe uses differential heating of a copper head as well as the probe's sidewalls and an ice screw at the melting head to move through glacier ice. Detailed blanks, meltwater, and subglacial brine samples were collected to evaluate the impact of the IceMole and the borehole pump, the melting and collection process, filtration, and storage on the geochemistry of the samples collected by this device. Comparisons between melt water profiles through the glacier ice and blank analysis, with published studies on ice geochemistry, suggest the potential for minor contributions of some species Rb, As, Co, Mn, Ni, NH4+, and NO2-+NO3- from the IceMole. The ability to conduct detailed chemical analyses of subglacial fluids collected with melting probes is critical for the future exploration of the hundreds of deep subglacial lakes in Antarctica.}, language = {en} } @inproceedings{ChajanSchulteTiggesRekeetal.2021, author = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas}, title = {GPU based model-predictive path control for self-driving vehicles}, series = {IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {New York, NY}, isbn = {978-1-7281-5394-0}, doi = {10.1109/IV48863.2021.9575619}, pages = {1243 -- 1248}, year = {2021}, abstract = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.}, language = {en} } @article{KoehlerRoepkeWolf2021, author = {K{\"o}hler, Klemens and R{\"o}pke, Ren{\´e} and Wolf, Martin R.}, title = {Through a mirror darkly - On the obscurity of teaching goals in game-based learning in IT security}, series = {ISAGA 2019: Simulation Gaming Through Times and Disciplines}, journal = {ISAGA 2019: Simulation Gaming Through Times and Disciplines}, publisher = {Springer}, address = {Cham}, doi = {10.1007/978-3-030-72132-9_6}, pages = {61 -- 73}, year = {2021}, abstract = {Teachers and instructors use very specific language communicating teaching goals. The most widely used frameworks of common reference are the Bloom's Taxonomy and the Revised Bloom's Taxonomy. The latter provides distinction of 209 different teaching goals which are connected to methods. In Competence Developing Games (CDGs - serious games to convey knowledge) and in IT security education, a two- or three level typology exists, reducing possible learning outcomes to awareness, training, and education. This study explores whether this much simpler framework succeeds in achieving the same range of learning outcomes. Method wise a keyword analysis was conducted. The results were threefold: 1. The words used to describe teaching goals in CDGs on IT security education do not reflect the whole range of learning outcomes. 2. The word choice is nevertheless different from common language, indicating an intentional use of language. 3. IT security CDGs use different sets of terms to describe learning outcomes, depending on whether they are awareness, training, or education games. The interpretation of the findings is that the reduction to just three types of CDGs reduces the capacity to communicate and think about learning outcomes and consequently reduces the outcomes that are intentionally achieved.}, language = {en} } @inproceedings{FerreinMeessenLimpertetal.2021, author = {Ferrein, Alexander and Meeßen, Marcus and Limpert, Nicolas and Schiffer, Stefan}, title = {Compiling ROS schooling curricula via contentual taxonomies}, series = {Robotics in Education}, booktitle = {Robotics in Education}, editor = {Lepuschitz, Wilfried}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-67411-3}, doi = {10.1007/978-3-030-67411-3_5}, pages = {49 -- 60}, year = {2021}, abstract = {The Robot Operating System (ROS) is the current de-facto standard in robot middlewares. The steadily increasing size of the user base results in a greater demand for training as well. User groups range from students in academia to industry professionals with a broad spectrum of developers in between. To deliver high quality training and education to any of these audiences, educators need to tailor individual curricula for any such training. In this paper, we present an approach to ease compiling curricula for ROS trainings based on a taxonomy of the teaching contents. The instructor can select a set of dedicated learning units and the system will automatically compile the teaching material based on the dependencies of the units selected and a set of parameters for a particular training. We walk through an example training to illustrate our work.}, language = {en} } @misc{JungMuellerStaat2021, author = {Jung, Alexander and M{\"u}ller, Wolfram and Staat, Manfred}, title = {Corrigendum to "Wind and fairness in ski jumping: A computer modelling analysis" [J. Biomech. 75 (2018) 147-153]}, series = {Journal of Biomechanics}, volume = {128}, journal = {Journal of Biomechanics}, number = {Article number: 110690}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0021-9290}, doi = {10.1016/j.jbiomech.2021.110690}, pages = {1 Seite}, year = {2021}, language = {en} } @article{JablonskiPoghossianKeusgenetal.2021, author = {Jablonski, Melanie and Poghossian, Arshak and Keusgen, Michael and Wege, Christina and Sch{\"o}ning, Michael Josef}, title = {Detection of plant virus particles with a capacitive field-effect sensor}, series = {Analytical and Bioanalytical Chemistry}, volume = {413}, journal = {Analytical and Bioanalytical Chemistry}, publisher = {Springer Nature}, address = {Cham}, issn = {1618-2650}, doi = {10.1007/s00216-021-03448-8}, pages = {5669 -- 5678}, year = {2021}, abstract = {Plant viruses are major contributors to crop losses and induce high economic costs worldwide. For reliable, on-site and early detection of plant viral diseases, portable biosensors are of great interest. In this study, a field-effect SiO2-gate electrolyte-insulator-semiconductor (EIS) sensor was utilized for the label-free electrostatic detection of tobacco mosaic virus (TMV) particles as a model plant pathogen. The capacitive EIS sensor has been characterized regarding its TMV sensitivity by means of constant-capacitance method. The EIS sensor was able to detect biotinylated TMV particles from a solution with a TMV concentration as low as 0.025 nM. A good correlation between the registered EIS sensor signal and the density of adsorbed TMV particles assessed from scanning electron microscopy images of the SiO2-gate chip surface was observed. Additionally, the isoelectric point of the biotinylated TMV particles was determined via zeta potential measurements and the influence of ionic strength of the measurement solution on the TMV-modified EIS sensor signal has been studied.}, language = {en} } @article{PoghossianSchoening2021, author = {Poghossian, Arshak and Sch{\"o}ning, Michael Josef}, title = {Recent progress in silicon-based biologically sensitive field-effect devices}, series = {Current Opinion in Electrochemistry}, journal = {Current Opinion in Electrochemistry}, number = {Article number: 100811}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2451-9103}, doi = {10.1016/j.coelec.2021.100811}, year = {2021}, abstract = {Biologically sensitive field-effect devices (BioFEDs) advantageously combine the electronic field-effect functionality with the (bio)chemical receptor's recognition ability for (bio)chemical sensing. In this review, basic and widely applied device concepts of silicon-based BioFEDs (ion-sensitive field-effect transistor, silicon nanowire transistor, electrolyte-insulator-semiconductor capacitor, light-addressable potentiometric sensor) are presented and recent progress (from 2019 to early 2021) is discussed. One of the main advantages of BioFEDs is the label-free sensing principle enabling to detect a large variety of biomolecules and bioparticles by their intrinsic charge. The review encompasses applications of BioFEDs for the label-free electrical detection of clinically relevant protein biomarkers, deoxyribonucleic acid molecules and viruses, enzyme-substrate reactions as well as recording of the cell acidification rate (as an indicator of cellular metabolism) and the extracellular potential.}, language = {en} } @inproceedings{HeuermannHarzheimMuehmel2021, author = {Heuermann, Holger and Harzheim, Thomas and M{\"u}hmel, Marc}, title = {A maritime harmonic radar search and rescue system using passive and active tags}, series = {2020 17th European Radar Conference (EuRAD)}, booktitle = {2020 17th European Radar Conference (EuRAD)}, publisher = {IEEE}, address = {New York, NY}, isbn = {978-2-87487-061-3}, doi = {10.1109/EuRAD48048.2021.00030}, pages = {73 -- 76}, year = {2021}, abstract = {This article introduces a new maritime search and rescue system based on S-band illumination harmonic radar (HR). Passive and active tags have been developed and tested attached to life jackets and a rescue boat. This system was able to detect and range the active tags up to a range of 5800 m in tests on the Baltic Sea with an antenna input power of only 100 W. All electronic GHz components of the system, excluding the S-band power amplifier, were custom developed for this purpose. Special attention is given to the performance and conceptual differences between passive and active tags used in the system and integration with a maritime X-band navigation radar is demonstrated.}, 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} }