@inproceedings{29767,
  author       = {{Abughannam, Saed and Scheytt, J. Christoph}},
  booktitle    = {{International Symposium on Circuits and Systems (ISCAS 2022)}},
  publisher    = {{IEEE Xplore}},
  title        = {{{Low-Power Low-Data-Rate Wireless PPM Receiver Based on 13-Bits Barker Coded SAW Correlator with Scalable Data-Rate and Sensitivity}}},
  year         = {{2022}},
}

@inproceedings{29839,
  abstract     = {{The development of business models is a challenging task that can be supported with software tools. Here, existing approaches and tools do not focus on the company’s situation in which the development takes place (e.g., financial resources, product type). To tackle this challenge, we used design science research to develop a situation-specific business model development approach that contains three stages: First, existing knowledge in terms of tasks to do (e.g., analyze competitive advantage), and decisions to be made (e.g., social media marketing) are stored in repositories. Second, the knowledge is used to compose a development method based on the company’s situation. Third, the development method is enacted to develop a business model. This demonstration paper presents a tool-support called Situational Business Model Developer that supports all stages of our approach. We release the tool under open-source and evaluate it with a case study on developing business models for mobile apps.}},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}},
  booktitle    = {{Proceedings of the 17th International Conference on Wirtschaftsinformatik}},
  keywords     = {{Business Model Development, Situational Method Engineering, Tool Support}},
  location     = {{Nuremberg}},
  publisher    = {{AIS}},
  title        = {{{Situational Business Model Developer: A Tool-support for Situation-specific Business Model Development}}},
  year         = {{2022}},
}

@inproceedings{29840,
  abstract     = {{Due to the proliferation of Virtual Reality (VR) technology, VR is finding new applications in various domains, such as stock trading. Here, traders invest in stocks intending to increase their profit. For this purpose, in conventional stock trading, traders usually make use of 2D applications on desktop or laptop devices. This leads to many drawbacks such as poor visibility due to limited 2D representation, complex interaction due to indirect interaction via mouse and keyboard, or restricted support for collaboration between traders. To overcome these issues, we have developed a novel collaborative, virtual environment for stock trading, which enables stock traders to view financial information and trade stocks with other collaborators. The main results of a usability study indicate that the VR environment, compared to conventional stock trading, shows no significant advantages concerning efficiency and effectiveness, however, we could observe an increased user satisfaction and better collaboration.}},
  author       = {{Yigitbas, Enes and Gottschalk, Sebastian and Nowosad, Alexander and Engels, Gregor}},
  booktitle    = {{Proceedings of the 17th International Conference on Wirtschaftsinformatik}},
  keywords     = {{virtual reality, stock trading, collaboration, usability}},
  location     = {{Nuremberg}},
  publisher    = {{AIS}},
  title        = {{{Development and Evaluation of a Collaborative Stock Trading Environment in Virtual Reality}}},
  year         = {{2022}},
}

@article{29843,
  author       = {{Castenow, Jannik and Kling, Peter and Knollmann, Till and Meyer auf der Heide, Friedhelm}},
  issn         = {{0890-5401}},
  journal      = {{Information and Computation}},
  keywords     = {{Computational Theory and Mathematics, Computer Science Applications, Information Systems, Theoretical Computer Science}},
  publisher    = {{Elsevier BV}},
  title        = {{{A Discrete and Continuous Study of the Max-Chain-Formation Problem}}},
  doi          = {{10.1016/j.ic.2022.104877}},
  year         = {{2022}},
}

@phdthesis{29672,
  author       = {{Schneider, Stefan Balthasar}},
  title        = {{{Network and Service Coordination: Conventional and Machine Learning Approaches"}}},
  doi          = {{10.17619/UNIPB/1-1276 }},
  year         = {{2022}},
}

@inproceedings{29380,
  abstract     = {{Cyber-physical systems generate and collect huge amounts of usage data during operation. Analyzing these data may enable manufacturing companies to identify weaknesses and learn about the users of their products. Such insights are valuable in the early phases of product development like product planning, as they facilitate decision-making for product improvement. The analysis and exploitation of usage data in product planning, however, is a new task for manufacturing companies. To reduce mistakes and improve the results, companies should build upon a suitable reference process model. Unfortunately, established models for analyzing data cannot be easily applied for product planning. In this paper, we propose a reference process model for usage data-driven product planning. It builds on three well-established models for analyzing data and addresses the unique characteristics of usage data-driven product planning. Finally, we customize the model for a manufacturing company and demonstrate how it could be implemented in practice.}},
  author       = {{Meyer, Maurice and Wiederkehr, Ingrid and Panzner, Melina and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the 55th Hawaii International Conference on System Sciences}},
  pages        = {{6105--6114}},
  title        = {{{A Reference Process Model for Usage Data-Driven Product Planning}}},
  year         = {{2022}},
}

@inproceedings{29927,
  author       = {{Yigitbas, Enes and Karakaya, Kadiray and Jovanovikj, Ivan and Engels, Gregor}},
  booktitle    = {{Software Engineering 2022, Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-25. Februar 2022, Virtuell}},
  editor       = {{Grunske, Lars and Siegmund, Janet and Vogelsang, Andreas}},
  pages        = {{95–96}},
  publisher    = {{Gesellschaft für Informatik e.V.}},
  title        = {{{Enhancing Human-in-the-Loop Adaptive Systems through Digital Twins and VR Interfaces}}},
  doi          = {{10.18420/se2022-ws-033}},
  volume       = {{{P-320}}},
  year         = {{2022}},
}

@inproceedings{29926,
  author       = {{Yigitbas, Enes and Gorissen, Simon and Weidmann, Nils and Engels, Gregor}},
  booktitle    = {{Software Engineering 2022, Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-25. Februar 2022, Virtuell}},
  editor       = {{Grunske, Lars and Siegmund, Janet and Vogelsang, Andreas}},
  pages        = {{93–94}},
  publisher    = {{Gesellschaft für Informatik e.V.}},
  title        = {{{Collaborative Software Modeling in Virtual Reality}}},
  doi          = {{10.18420/se2022-ws-032}},
  volume       = {{{P-320}}},
  year         = {{2022}},
}

@inproceedings{29945,
  author       = {{Witschen, Linus Matthias and Wiersema, Tobias and Reuter, Lucas David and Platzner, Marco}},
  booktitle    = {{2022 59th ACM/IEEE Design Automation Conference (DAC)}},
  location     = {{San Francisco, USA}},
  title        = {{{Search Space Characterization for Approximate Logic Synthesis }}},
  year         = {{2022}},
}

@inproceedings{29865,
  author       = {{Witschen, Linus Matthias and Wiersema, Tobias and Artmann, Matthias and Platzner, Marco}},
  booktitle    = {{Design, Automation and Test in Europe (DATE)}},
  location     = {{Online}},
  title        = {{{MUSCAT: MUS-based Circuit Approximation Technique}}},
  year         = {{2022}},
}

@article{30012,
  abstract     = {{The growing demand for bandwidth and energy efficiency requires new solutions for signal detection and processing. We demonstrate a concept for high-bandwidth signal detection with low-speed photodetectors and electronics. The method is based on the parallel optical sampling of a high-bandwidth signal with sinc-pulse sequences provided by a Mach-Zehnder modulator. For the electronic detection and processing this parallel sampling enables to divide the high-bandwidth optical signal with the bandwidth B into N electrical signals with the baseband bandwidth of B/(2N) . In proof-of-concept experiments with N=3 , we present the detection of 24 GHz optical signals by detectors with a bandwidth of only 4 GHz. For ideal components, the sampling and bandwidth down-conversion does not add an excess error to the signals and even for the non-ideal components of our proof-of-concept setup, it is below 1%. Thus, the rms error for the measurement of the 24 GHz signal was reduced by a factor of about 3.4 and the effective number of bits were increased by 1.8.}},
  author       = {{Meier, Janosch and Singh, Karanveer and Misra, Arijit and Preussler, Stefan and Scheytt, Christoph and Schneider, Thomas}},
  issn         = {{1943-0655 }},
  journal      = {{IEEE Photonics Journal}},
  title        = {{{High-Bandwidth Arbitrary Signal Detection Using Low-Speed Electronics}}},
  doi          = {{10.1109/JPHOT.2022.3149389}},
  volume       = {{14}},
  year         = {{2022}},
}

@misc{30152,
  author       = {{Roopa, Rajanna}},
  title        = {{{Evaluation of Algorithms for the Node Capacitated Clique}}},
  year         = {{2022}},
}

@misc{30198,
  author       = {{Korzeczek, Sebastian}},
  title        = {{{Aufarbeitung und lmplementierung von DAG-Rider}}},
  year         = {{2022}},
}

@misc{30199,
  author       = {{Nachtigall, Marcel}},
  title        = {{{Hybrid Routing in Three Dimensions}}},
  year         = {{2022}},
}

@inproceedings{30236,
  abstract     = {{Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive
results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field.

To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of
wireless mobile networks.}},
  author       = {{Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}},
  booktitle    = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}},
  keywords     = {{wireless mobile networks, network management, continuous control, cognitive networks, autonomous coordination, reinforcement learning, gym environment, simulation, open source}},
  location     = {{Budapest}},
  publisher    = {{IEEE}},
  title        = {{{mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}}},
  year         = {{2022}},
}

@inbook{16296,
  abstract     = {{Multiobjective optimization plays an increasingly important role in modern
applications, where several objectives are often of equal importance. The task
in multiobjective optimization and multiobjective optimal control is therefore
to compute the set of optimal compromises (the Pareto set) between the
conflicting objectives. Since the Pareto set generally consists of an infinite
number of solutions, the computational effort can quickly become challenging
which is particularly problematic when the objectives are costly to evaluate as
is the case for models governed by partial differential equations (PDEs). To
decrease the numerical effort to an affordable amount, surrogate models can be
used to replace the expensive PDE evaluations. Existing multiobjective
optimization methods using model reduction are limited either to low parameter
dimensions or to few (ideally two) objectives. In this article, we present a
combination of the reduced basis model reduction method with a continuation
approach using inexact gradients. The resulting approach can handle an
arbitrary number of objectives while yielding a significant reduction in
computing time.}},
  author       = {{Banholzer, Stefan and Gebken, Bennet and Dellnitz, Michael and Peitz, Sebastian and Volkwein, Stefan}},
  booktitle    = {{Non-Smooth and Complementarity-Based Distributed Parameter Systems}},
  editor       = {{Michael, Hintermüller and Roland, Herzog and Christian, Kanzow and Michael, Ulbrich and Stefan, Ulbrich}},
  isbn         = {{978-3-030-79392-0}},
  pages        = {{43--76}},
  publisher    = {{Springer}},
  title        = {{{ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation}}},
  doi          = {{10.1007/978-3-030-79393-7_3}},
  year         = {{2022}},
}

@inbook{30294,
  abstract     = {{With the ever increasing capabilities of sensors and controllers, autonomous driving is quickly becoming a reality. This disruptive change in the automotive industry poses major challenges for manufacturers as well as suppliers as entirely new design and testing strategies have to be developed to remain competitive. Most importantly, the complexity of autonomously driving vehicles in a complex, uncertain, and safety-critical environment requires new testing procedures to cover the almost infinite range of potential scenarios.}},
  author       = {{Peitz, Sebastian and Dellnitz, Michael and Bannenberg, Sebastian}},
  booktitle    = {{German Success Stories in Industrial Mathematics}},
  editor       = {{Bock, H. G. and Küfer, K.-H. and Maas, P. and Milde, A. and Schulz, V.}},
  isbn         = {{9783030814540}},
  issn         = {{1612-3956}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Efficient Virtual Design and Testing of Autonomous Vehicles}}},
  doi          = {{10.1007/978-3-030-81455-7_23}},
  volume       = {{35}},
  year         = {{2022}},
}

@inproceedings{30347,
  author       = {{Schafmeister, Frank}},
  booktitle    = {{International Conference on Electric & Electronic in Hybrid and Electric Vehicles and Electric Energy Management (EEHE),}},
  location     = {{Bamberg, Germany}},
  title        = {{{Compensation of LF Common-Mode Noise by the internal DC/DC-Stage for transformerless On-Board Chargers at Three- and Single-Phase Operation}}},
  year         = {{2022}},
}

@inproceedings{30349,
  author       = {{Förster, Nikolas and Rehlaender, Philipp and Wallscheid, Oliver and Schafmeister, Frank and Böcker, Joachim}},
  booktitle    = {{Proc. 37th IEEE Applied Power Electronics Conference (APEC)}},
  location     = {{Houston, TX, USA}},
  publisher    = {{IEEE}},
  title        = {{{An Open-Source Transistor Database and Toolbox as an Unified Software Engineering Tool for Managing and Evaluating Power Transistors}}},
  year         = {{2022}},
}

@inproceedings{30350,
  author       = {{Keuck, Lukas and Schafmeister, Frank and Böcker, Joachim}},
  booktitle    = {{Proc. IEEE International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management (PCIM)}},
  location     = {{Nuremberg, Germany}},
  publisher    = {{IEEE}},
  title        = {{{Robust Hysteresis Control for LLC Resonant Converters Using a Fully Isolated Measurement Scheme}}},
  year         = {{2022}},
}