TY - CHAP A1 - Hadinejad-Mahram, Hafez A1 - Gligorevic, Snjezana A1 - Ritscher, Matthias T1 - Joint iterative channel estimation and parallel interference cancellation for coded DS-CDMA T2 - Proceeding of the International Conference on Telecommunications 2002 : [Beijing, 23 - 26 June 2002]. Vol. 2 Y1 - 2002 SP - 566 EP - 570 ER - TY - CHAP A1 - Schneckenburger, N. A1 - Franzen, N. A1 - Gligorevic, Snjezana A1 - Schnell, M. T1 - L-band compatibility of LDACS1 T2 - IEEE/AIAA 30th Digital Avionics Systems Conference (DASC) : 16 - 20 Oct. 2011, Seattle, Wash. Y1 - 2011 SN - 978-1-61284-797-9 SN - 2155-7195 SP - 4C3-1 EP - 4C3-11 ER - TY - CHAP A1 - Gligorevic, Snjezana A1 - Schneckenburger, N. A1 - Franzen, N. A1 - Schnell, Michael A1 - Müller, S. A1 - Günzel, H. T1 - L-Band compatibility of LDACS1 T2 - 3rd CEAS Air and Space Conference, 24 - 28 October 2011, Venezia, Italy Y1 - 2011 SP - 1 EP - 8 ER - TY - CHAP A1 - Schnell, Michael A1 - Franzen, Nico A1 - Gligorevic, Snjezana T1 - L-DACS1 laboratory demonstrator development and compatibility measurement set-up T2 - IEEE/AIAA 29th Digital Avionics Systems Conference (DASC) : 3 - 7 Oct. 2010, Salt Lake City, Utah Y1 - 2010 SN - 9781424466160 ; 9781424466184 SP - 3E3-1 EP - 3E3-11 ER - TY - CHAP A1 - Ferrein, Alexander A1 - Niemüller, Tim A1 - Schiffer, Stefan A1 - Lakemeyer, Gerhard ED - Boots, Byron T1 - Lessons learnt from developing the embodied AI platform CAESAR for domestic service robotics T2 - Designing intelligent robots : reintegrating AI II ; papers from the AAAI spring symposium ; [held March 25 - 27, 2013 in Palo Alto, California, USA, on the campus of Stanford University]. (Technical Report / Association for the Advancement of Artificial Intelligence ; 2013,4) Y1 - 2013 SN - 9781577356011 SP - 21 EP - 26 ER - TY - CHAP A1 - Becker, Tim A1 - Bragard, Michael T1 - Low-Voltage DC Training Lab for Electric Drives - Optimizing the Balancing Act Between High Student Throughput and Individual Learning Speed T2 - 2024 IEEE Global Engineering Education Conference (EDUCON) N2 - After a brief introduction of conventional laboratory structures, this work focuses on an innovative and universal approach for a setup of a training laboratory for electric machines and drive systems. The novel approach employs a central 48 V DC bus, which forms the backbone of the structure. Several sets of DC machine, asynchronous machine and synchronous machine are connected to this bus. The advantages of the novel system structure are manifold, both from a didactic and a technical point of view: Student groups can work on their own performance level in a highly parallelized and at the same time individualized way. Additional training setups (similar or different) can easily be added. Only the total power dissipation has to be provided, i.e. the DC bus balances the power flow between the student groups. Comparative results of course evaluations of several cohorts of students are shown. KW - Synchronous machines KW - Power dissipation KW - Throughput KW - Low voltage KW - DC machines KW - Manifolds KW - Training Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578902 SN - 2165-9559 SN - 2165-9567 (eISSN) N1 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - https://doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Czarnecki, Christian A1 - Winkelmann, Axel A1 - Spiliopoulou, Myra ED - Pokorny, Jaroslav ED - Repa, Vaclav ED - Richta, Karel ED - Wojtkowski, Wita ED - Linger, Henry ED - Barry, Chris ED - Lang, Michael T1 - Making business systems in the telecommunication industry more customer-oriented T2 - Information Systems Development : Business Systems and Services: Modeling and Development N2 - Market changes have forced telecommunication companies to transform their business. Increased competition, short innovation cycles, changed usage patterns, increased customer expectations and cost reduction are the main drivers. Our objective is to analyze to what extend transformation projects have improved the orientation towards the end-customers. Therefore, we selected 38 real-life case studies that are dealing with customer orientation. Our analysis is based on a telecommunication-specific framework that aligns strategy, business processes and information systems. The result of our analysis shows the following: transformation projects that aim to improve the customer orientation are combined with clear goals on costs and revenue of the enterprise. These projects are usually directly linked to the customer touch points, but also to the development and provisioning of products. Furthermore, the analysis shows that customer orientation is not the sole trigger for transformation. There is no one-fits-all solution; rather, improved customer orientation needs aligned changes of business processes as well as information systems related to different parts of the company. KW - Business Process KW - Customer Orientation KW - Enterprise Architecture KW - Transformation Project KW - Telecommunication Industry Y1 - 2011 SN - 978-1-4419-9645-9 (Print) SN - 978-1-4419-9790-6 (Online) U6 - https://doi.org/10.1007/978-1-4419-9790-6_14 N1 - 19th International Conference on Information Systems Development held in Prague, Czech Republic, August 25 - 27, 2010. SP - 169 EP - 180 PB - Springer CY - New York ER - TY - CHAP A1 - Schütz, P. A1 - Breuer, M. A1 - Höfken, Hans-Wilhelm A1 - Schuba, Marko T1 - Malware proof on mobile phone exhibits based on GSM/GPRS traces T2 - The Second International Conference on Cyber Security, Cyber Peacefare and Digital Forensic (CyberSec 2013) : 04.03. - 06.03.2013, Kuala Lumpur, Malaysia Y1 - 2013 SN - 978-0-9853483-7-3 SP - 89 EP - 96 PB - The Society of Digital Information and Wireless Communication ER - TY - CHAP A1 - Detert, T. A1 - Gligorevic, Snjezana A1 - Haak, W. A1 - Sorger, Ulrich T1 - Maximum-likelihood channel estimation using the spreading matrix in fast time-variant frequency selective channels T2 - Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003 : 14 - 17 December 2003, Darmstadt, Germany Y1 - 2003 SN - 0-7803-8292-7 SP - 347 EP - 350 ER -