@phdthesis{62167,
  abstract     = {{Diese Dissertation befasst sich mit der Entwicklung eines induktiv-basierten Lokalisierungsverfahrens mittels planarer Spulen, das auf magnetischer Kopplung beruht. Grundlage ist die elektromagnetische Induktion, bei der sich die entstehende Spannung proportional zur Gegeninduktivität verhält. Das Verfahren arbeitet im physikalischen Nahfeld und im Frequenzbereich von einigen kHz bis MHz, um eine effiziente Kopplung ohne Ausbreitung elektromagnetischer Wellen zu gewährleisten. Im Vergleich zu etablierten Verfahren wie GPS oder Ultraschall zeigt die induktive Ortung Vorteile bei kurzer Reichweite, insbesondere durch hohe Genauigkeit im Zentimeterbereich und geringe Materialabhängigkeit. Zudem lässt sie sich in bestehende Technologien wie bei der drahtlosen Energieübertragung integrieren und durch Sensorfusion mit anderen Verfahren kombinieren. Zur Modellierung und Optimierung werden physikalische Eigenschaften von planaren Spulen und EM-Feldern analysiert und elektrische Ersatzschaltbilder eingesetzt. Die geometriebasierte Berechnung der Gegeninduktivität ermöglicht die Entwicklung und Bewertung geeigneter Ortungsalgorithmen. Stochastische Filterverfahren verbessern zusätzlich die Robustheit gegenüber Umgebungseinflüssen. Abschließend wird eine modulare Simulationsplattform vorgestellt, die als Grundlage für die Generierung von Trainingsdaten sowie zur Weiterentwicklung von Mess-, Ortungs- und Filtermethoden dient.}},
  author       = {{Lange, Sven}},
  pages        = {{247}},
  publisher    = {{Universitätsbibliothek Paderborn}},
  title        = {{{Analyse und Modellierung eines induktiven Ortungsprozesses unter Berücksichtigung der elektromagnetischen Wechselwirkungen planarer Spulensysteme}}},
  doi          = {{10.17619/UNIPB/1-2436}},
  year         = {{2025}},
}

@inproceedings{55453,
  author       = {{Namujju, Lillian Donna and Mwammenywa, Ibrahim and Kagarura, Geoffrey Mark and Hilleringmann, Ulrich and Hehenkamp, Burkhard}},
  booktitle    = {{2024 IEEE 8th Energy Conference (ENERGYCON)}},
  publisher    = {{IEEE}},
  title        = {{{Smart Metering and Choice Architecture in Demand-Side Management: A Power Resource-Constrained Perspective}}},
  doi          = {{10.1109/energycon58629.2024.10488738}},
  year         = {{2024}},
}

@inproceedings{55452,
  author       = {{Namujju, Lillian Donna and Mwammenywa, Ibrahim and Kagarura, Geoffrey Mark and Hilleringmann, Ulrich and Hehenkamp, Burkhard}},
  booktitle    = {{2024 IEEE 8th Energy Conference (ENERGYCON)}},
  publisher    = {{IEEE}},
  title        = {{{Smart Metering and Choice Architecture in Demand-Side Management: A Power Resource-Constrained Perspective}}},
  doi          = {{10.1109/energycon58629.2024.10488738}},
  year         = {{2024}},
}

@phdthesis{55455,
  author       = {{Mwammenywa, Ibrahim Abdallah}},
  title        = {{{A Novel Autonomous and Real-time Load Monitoring and Control System in Microgrids based on Fuzzy Logic Control and LoRa Wireless Communication}}},
  year         = {{2024}},
}

@inproceedings{59704,
  author       = {{Kagarura, Geoffrey Mark and Hilleringmann, Ulrich and Petrov, Dmitry}},
  booktitle    = {{2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)}},
  publisher    = {{IEEE}},
  title        = {{{A low cost weather monitoring, PV and prediction system in East Africa}}},
  doi          = {{10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00130}},
  year         = {{2024}},
}

@inproceedings{56782,
  author       = {{Lange, Sven and Olbrich, Marcel and Hemker, Dennis and Maalouly, Jad and Kutter, Jürgen and Schröder, Dominik and Hedayat, Christian and Kleinen, Michael and Grünwaldt, Andreas and Bärenfänger, Jörg and Mathis, Harald and Kuhn, Harald}},
  booktitle    = {{2024 International Symposium on Electromagnetic Compatibility – EMC Europe}},
  location     = {{Bruges, Belgium}},
  publisher    = {{IEEE}},
  title        = {{{A Hybrid Data Generation Approach for the Development of an AI-based EMC Interference Recognition Method}}},
  doi          = {{10.1109/emceurope59828.2024.10722681}},
  year         = {{2024}},
}

@inproceedings{56781,
  author       = {{Maalouly, Jad and Hemker, Dennis and Lange, Sven and Olbrich, Marcel and Hedayat, Christian and Kutter, Jürgen and Mathis, Harald}},
  booktitle    = {{2024 International Symposium on Electromagnetic Compatibility – EMC Europe}},
  location     = {{Brugge, Belgium }},
  publisher    = {{IEEE}},
  title        = {{{Evaluation of Simulated and Real Measurement Data for AI-based Interference Classification in EMC Applications}}},
  doi          = {{10.1109/emceurope59828.2024.10722094}},
  year         = {{2024}},
}

@inproceedings{56924,
  author       = {{Stiemer, Marcus and Lange, Sven and Schröder, Dominik and Hedayat, Christian and Maalouly, Jad and Hemker, Dennis and Mathis, Harald}},
  booktitle    = {{2024 Smart Systems Integration Conference and Exhibition (SSI)}},
  location     = {{Hamburg}},
  publisher    = {{IEEE}},
  title        = {{{Enhancing Information Extraction in EMC Measurements through Artificial Intelligence}}},
  doi          = {{10.1109/ssi63222.2024.10740546}},
  year         = {{2024}},
}

@article{57499,
  author       = {{Maalouly, J. and Hemker, D. and Hedayat, C. and Olbrich, M. and Lange, Sven and Mathis, H.}},
  journal      = {{Advances in Radio Science}},
  location     = {{Miltenberg}},
  pages        = {{53–59}},
  title        = {{{Using Autoencoders to Classify EMC Problems in Electronic System Development}}},
  doi          = {{10.5194/ars-22-53-2024}},
  volume       = {{22}},
  year         = {{2024}},
}

@inproceedings{55447,
  author       = {{Mwakijale, Joseph S. and Mwammenywa, Ibrahim and Hilleringmann, Ulrich}},
  booktitle    = {{2023 IEEE PES/IAS PowerAfrica}},
  publisher    = {{IEEE}},
  title        = {{{LoRa-based Swarm Grid Implementation for Rural Electrification in Africa}}},
  doi          = {{10.1109/powerafrica57932.2023.10363319}},
  year         = {{2023}},
}

@inproceedings{39359,
  author       = {{Mwammenywa, Ibrahim and Petrov, Dmitry and Holle, Philipp and Hilleringmann, Ulrich}},
  booktitle    = {{2022 International Conference on Engineering and Emerging Technologies (ICEET)}},
  publisher    = {{IEEE}},
  title        = {{{LoRa Transceiver for Load Monitoring and Control System in Microgrids}}},
  doi          = {{10.1109/iceet56468.2022.10007274}},
  year         = {{2023}},
}

@inproceedings{49890,
  abstract     = {{In this paper, the influence of the environment on an inductive location system is analyzed. In the inductive location method, high frequency magnetic fields generated by planar coils lead to induction in other coils, which is used for localization analysis. Magnetic fields are not affected by changes in the dielectric properties of the environment, which is an advantage over other localization methods. However, electrical material parameters can still affect the localization results by indirect effects. For this reason, in this publication the influence will be investigated using real material parameters and their effects on the localization will be considered, so that the robustness and the limits of the inductive localization can be evaluated.}},
  author       = {{Lange, Sven and Hilleringmann, Ulrich and Hedayat, Christian and Kuhn, Harald and Förstner, Jens}},
  booktitle    = {{2023 IEEE Conference on Antenna Measurements and Applications (CAMA)}},
  keywords     = {{Planar coils, inductive locating, magnetic fields, environmental influences, eddy currents, tet_topic_hf, tet_enas}},
  location     = {{Genoa, Italy }},
  publisher    = {{IEEE}},
  title        = {{{Characterization of Various Environmental Influences on the Inductive Localization}}},
  doi          = {{10.1109/cama57522.2023.10352780}},
  year         = {{2023}},
}

@inproceedings{34140,
  abstract     = {{In this paper, machine learning techniques will be used to classify different PCB layouts given their electromagnetic frequency spectra. These spectra result from a simulated near-field measurement of electric field strengths at different locations. Measured values consist of real and imaginary parts (amplitude and phase) in X, Y and Z directions. Training data was obtained in the time domain by varying transmission line geometries (size, distance and signaling). It was then transformed into the frequency domain and used as deep neural network input. Principal component analysis was applied to reduce the sample dimension. The results show that classifying different designs is possible with high accuracy based on synthetic data. Future work comprises measurements of real, custom-made PCB with varying parameters to adapt the simulation model and also test the neural network. Finally, the trained model could be used to give hints about the error’s cause when overshooting EMC limits.}},
  author       = {{Maalouly, Jad and Hemker, Dennis and Hedayat, Christian and Rückert, Christian and Kaufmann, Ivan and Olbrich, Marcel and Lange, Sven and Mathis, Harald}},
  booktitle    = {{2022 Kleinheubach Conference}},
  keywords     = {{emc, pcb, electronic system development, machine learning, neural network}},
  location     = {{Miltenberg, Germany}},
  publisher    = {{IEEE}},
  title        = {{{AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development}}},
  year         = {{2022}},
}

@inproceedings{33508,
  abstract     = {{In this work, methods will be evaluated to numerically calculate the passive electrical parameters of planar coils. These parameters can then be used to optimize inductive applications such as wireless power transmission. The focus here will be on inductive localization, which uses high-frequency magnetic fields and the resulting induced voltage to provide localization through the coupling parameter mutual inductance. To achieve localization with high accuracy and best possible operation (resonance, signal strength, etc.), the coil parameters need to be well known. For this reason, some numerical methods for the calculation of these quantities are presented and validated. In addition, the physical effects are thereby considered in more detail, allowing the localization procedure to be better optimized compared to simulative black-box methods. The goal should be a dedicated simulation platform for planar coils to be able to develop training data for neural networks and to test and optimize localization algorithms.}},
  author       = {{Lange, Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  keywords     = {{Simulation Environment, Inductive Localization, Coil Parameters, Inductive Applications, Near-Field}},
  location     = {{Grenoble, France}},
  publisher    = {{IEEE}},
  title        = {{{Modeling and Characterization of a 3D Environment for the Design of an Inductively Based Locating Method by Coil Couplings}}},
  doi          = {{10.1109/ssi56489.2022.9901416}},
  year         = {{2022}},
}

@inproceedings{33510,
  abstract     = {{In the manufacture of real wood products, defects can quickly occur during the production process. To quickly sort out these defects, a system is needed that finds damage in the irregularly structured surfaces of the product. The difficulty in this task is that each surface is visually different and no standard defects can be defined. Thus, damage detection using correlation does not work, so this paper will test different machine learning methods. To evaluate different machine learning methods, a data set is needed. For this reason, the available samples were recorded manually using a static fixed camera. Subsequently, the images were divided into sub-images, which resulted in a relatively small data set. Next, a convolutional neural network (CNN) was constructed to classify the images. However, this approach did not lead to a generalized solution, so the dataset was hashed using the a- and pHash. These hash values were then trained with a fully supervised system that will later serve as a reference model, in the semi-supervised learning procedures. To improve the supervised model and not have to label every data point, semi-supervised learning methods are used in the following. For this purpose, the CEAL method (wrapper method) is considered in the first and then the Π-Model (intrinsically semi-supervised).}},
  author       = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneiß, Volker and Hedayat, Christian and Kuhn, Harald}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  keywords     = {{Machine Learning, CNN, Hashing, semi-supervised learning}},
  location     = {{Grenoble, France}},
  publisher    = {{IEEE}},
  title        = {{{Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods}}},
  doi          = {{10.1109/ssi56489.2022.9901433}},
  year         = {{2022}},
}

@inproceedings{39376,
  author       = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneis, Volker and Hedayat, Christian and Kuhn, Harald}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  publisher    = {{IEEE}},
  title        = {{{Detection of Defects on Irregularly Structured Surfaces using Supervised and Semi-Supervised Learning Methods}}},
  doi          = {{10.1109/ssi56489.2022.9901433}},
  year         = {{2022}},
}

@inproceedings{39372,
  author       = {{Lange, Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  publisher    = {{IEEE}},
  title        = {{{Modeling and Characterization of a 3D Environment for the Design of an Inductively Based Locating Method by Coil Couplings}}},
  doi          = {{10.1109/ssi56489.2022.9901416}},
  year         = {{2022}},
}

@inproceedings{39373,
  author       = {{Lange, Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  publisher    = {{IEEE}},
  title        = {{{Modeling and Characterization of a 3D Environment for the Design of an Inductively Based Locating Method by Coil Couplings}}},
  doi          = {{10.1109/ssi56489.2022.9901416}},
  year         = {{2022}},
}

@inproceedings{39375,
  author       = {{Lange, Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}},
  booktitle    = {{2022 Smart Systems Integration (SSI)}},
  publisher    = {{IEEE}},
  title        = {{{Modeling and Characterization of a 3D Environment for the Design of an Inductively Based Locating Method by Coil Couplings}}},
  doi          = {{10.1109/ssi56489.2022.9901416}},
  year         = {{2022}},
}

@inproceedings{59758,
  author       = {{Mwammenywa, Ibrahim and Kagarura, Geoffrey Mark and Petrov, Dmitry and Holle, Philip and Hilleringmann, Ulrich}},
  booktitle    = {{2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)}},
  publisher    = {{IEEE}},
  title        = {{{LoRa-based Demand-side Load Monitoring and Management System for Microgrids in Africa}}},
  doi          = {{10.1109/icecet52533.2021.9698506}},
  year         = {{2022}},
}