@inproceedings{ElgamalHeuermann2020,
  author    = {Elgamal, Abdelrahman and Heuermann, Holger},
  title     = {Design and Development of a Hot S-Parameter Measurement System for Plasma and Magnetron Applications},
  series = {Proceedings of the 2020 German Microwave Conference},
  booktitle = {Proceedings of the 2020 German Microwave Conference},
  publisher = {IEEE},
  address   = {New York, NY},
  isbn      = {978-3-9820397-1-8},
  pages     = {124 -- 127},
  year      = {2020},
  abstract  = {This paper presents the design, development and calibration procedures of a novel hot S-parameter measurement system for plasma and magnetron applications with power level up to 6 kW. Based on a vector network analyzer, a power amplifier and two directional couplers, the input matching hotS 11 and transmission hotS 21 of the device under test are measured at 2.45 GHz center frequency and 300MHz bandwidth, while the device is driven by the magnetron. This measurement system opens a new horizon to develop many new industrial applications such as microwave plasma jets, dryer systems, dryers and so forth. Furthermore, the developing, controlling and monitoring a 2kW 2.45GHz plasma jet and a dryer system using the measurement system are presented and explained.},
  language  = {en}
}
@inproceedings{DinghoferHartung2020,
  author    = {Dinghofer, Kai and Hartung, Frank},
  title     = {Analysis of Criteria for the Selection of Machine Learning Frameworks},
  series = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  publisher = {IEEE},
  address   = {New York, NY},
  doi       = {10.1109/ICNC47757.2020.9049650},
  pages     = {373 -- 377},
  year      = {2020},
  abstract  = {With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.},
  language  = {en}
}