Publikationen

Brans, P. & Witt, N. (2025):

Fraunhofer IIS Walks the Line for Edge AI

In: EE Times Europe Magazine

Manjunath, H., Heublein, L., Feigl, T., & Ott, F. (2025):

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization

In: arXiv preprint arXiv:2501.05079

Gaikwad, N. S., Heublein, L., Raichur, N. L., Feigl, T., Mutschler, C., & Ott, F. (2024):

Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification

In: arXiv preprint arXiv:2410.15681

Heublein, L., Feigl, T., Rügamer, A., & Ott, F. (2024):

Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling

In: arXiv preprint arXiv:2410.14686

Heublein, L., Feigl, T., Nowak, T., Rügamer, A., Mutschler, C., & Ott, F. (2024):

Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization 

In: arXiv preprint arXiv:2409.15114

Mutschler, C., Münzenmayer, C., Uhlmann, N., & Martin, A (2024): 

Unlocking Artificial Intelligence: From Theory to Applications

In: Springer

Ott, F., Heublein, L., Rügamer, D., Bischl, B., & Mutschler, C. (2024):

Fusing structure from motion and simulation-augmented pose regression from optical flow for challenging indoor environments

In: Journal of Visual Communication and Image Representation, 103, 104256

Raichur, N. L., Heublein, L., Feigl, T., Rügamer, A., Mutschler, C., & Ott, F. (2024):

Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning

In: arXiv preprint arXiv:2405.11067

Ott, J., Pirkl, J., Stahlke, M., Feigl, T., & Mutschler, C. (2024):

Radio Foundation Models: Pre-training Transformers for 5G-based Indoor Localization

In: 2024 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-6)

Göttl, Q., Asif, H., Mattick, A., Marzilger, R., & Plinge, A. (2024):

Automated Design in Hybrid Action Spaces by Reinforcement Learning and Differential Evolution

In: German Conference on Artificial Intelligence (Künstliche Intelligenz) (pp. 292-299)

Ott, F., Heublein, L., Raichur, N. L., Feigl, T., Hansen, J., Rügamer, A., & Mutschler, C. (2024):

Few-shot learning with uncertainty-based quadruplet selection for interference classification in GNSS data

In: 2024 International Conference on Localization and GNSS (ICL-GNSS) (pp. 1-7)

Heublein, L., Raichur, N. L., Feigl, T., Brieger, T., Heuer, F., Asbach, L., ... & Ott, F. (2024):

Evaluation of (Un-) Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies

In: Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024) (pp. 1260-1293)

Ott, F., Heublein, L., & Feigl, T. (2024):

Research Avenues for GNSS Interference Classification Robustness: Domain Adaptation, Continual Learning & Federated Learning

In: ECML PKDD Industry Track

Witt, N., Deutel, M., Schubert, J., Sobel, C., & Woller, P. (2024)

Energy-Efficient AI on the Edge

In: Unlocking Artificial Intelligence: From Theory to Applications (pp. 359-380)

Deutel, M., Hannig, F., Mutschler, C., & Teich, J (2024):

Fused-Layer CNNs for Memory-Efficient Inference on Microcontrollers

In: Workshop on Machine Learning and Compression, NeurIPS 2024

Deutel, M., Hannig, F., Mutschler, C., & Teich, J. (2024):

On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers

In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2024

Deutel, M., Mutschler, C., & Teich, J. (2024):

microYOLO: Towards Single-Shot Object Detection on Microcontrollers

In: arXiv preprint arXiv:2408.15865.

Herzog, B., Schubert, J., Rheinfels, T., Nickel, C., & Hönig, T. (2024):

GreenPipe: Energy-Efficient Data-Processing Pipelines for Resource-Constrained Systems.

In: Proceedings of the 21st International Conference on Embedded Wireless Systems and Networks (EWSN'24), ACM

Periyasamy, M., Plinge, A., Mutschler, C., Scherer, D. D., & Mauerer, W. (2024):

Guided-spsa: Simultaneous perturbation stochastic approximation assisted by the parameter shift rule

In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 1, pp. 1504-1515)

Rietsch, S., Dubey, A. Y., Ufrecht, C., Periyasamy, M., Plinge, A., Mutschler, C., & Scherer, D. D. (2024):

Unitary synthesis of clifford+t circuits with reinforcement learning

In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 1, pp. 824-835)

Meyer, N., Murauer, J., Popov, A., Ufrecht, C., Plinge, A., Mutschler, C., & Scherer, D. D. (2024):

Warm-start variational quantum policy iteration

In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 1, pp. 1458-1466)

Meyer, N., Ufrecht, C., Periyasamy, M., Plinge, A., Mutschler, C., Scherer, D. D., & Maier, A. (2024):

Qiskit-torch-module: Fast prototyping of quantum neural networks

In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 1, pp. 817-823)

Meyer, N., Röhn, M., Murauer, J., Plinge, A., Mutschler, C., & Scherer, D. D. (2024):

Comprehensive Library of Variational LSE Solvers

In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 2, pp. 1-4)

Seitz, P., Geiger, M., Ufrecht, C., Plinge, A., Mutschler, C., Scherer, D. D., & Mendl, C. B. (2024):

SCIM MILQ: an HPC quantum scheduler

In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 2, pp. 292-298)

Richter, M., Dubey, A. Y., Plinge, A., Mutschler, C., Scherer, D. D., & Hartmann, M. J. (2024):

Quantum Wasserstein Compilation: Unitary Compilation using the Quantum Earth Mover's Distance

In: arXiv preprint arXiv:2409.05849

Yammine, G., Kontes, G., Franke, N., Plinge, A., & Mutschler, C. (2023):

Efficient Beam Search for Initial Access Using Collaborative Filtering

In: 2023 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6)

Ufrecht, C., Periyasamy, M., Rietsch, S., Scherer, D. D., Plinge, A., & Mutschler, C. (2023):

Cutting multi-control quantum gates with ZX calculus

In: Quantum, 7, 1147

Meyer, N., Scherer, D. D., Plinge, A., Mutschler, C., & Hartmann, M. J. (2023):

Quantum natural policy gradients: Towards sample-efficient reinforcement learning

In: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 2, pp. 36-41)

Wiedmann, M., Hölle, M., Periyasamy, M., Meyer, N., Ufrecht, C., Scherer, D. D., ... & Mutschler, C. (2023):

An empirical comparison of optimizers for quantum machine learning with spsa-based gradients

In: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 1, pp. 450-456)

Franz, M., Wolf, L., Periyasamy, M., Ufrecht, C., Scherer, D. D., Plinge, A., ... & Mauerer, W. (2023):

Uncovering instabilities in variational-quantum deep q-networks

In: Journal of The Franklin Institute, 360(17), pp. 13822-13844

Meyer, N., Scherer, D., Plinge, A., Mutschler, C., & Hartmann, M. (2023):

Quantum policy gradient algorithm with optimized action decoding

In: International Conference on Machine Learning (pp. 24592-24613)

Rietsch, S., Huang, S. Y., Kontes, G., Plinge, A., & Mutschler, C. (2022)

Driver dojo: A benchmark for generalizable reinforcement learning for autonomous driving

In: arXiv preprint arXiv:2207.11432

Landgraf, D., Völz, A., Kontes, G., Mutschler, C., & Graichen, K. (2022):

Hierarchical learning for model predictive collision avoidance

In: IFAC-PapersOnLine, 55(20), 355-360

Schmidt, L. M., Brosig, J., Plinge, A., Eskofier, B. M., & Mutschler, C. (2022):

An introduction to multi-agent reinforcement learning and review of its application to autonomous mobility

In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 1342-1349)

Schmidt, L. M., Rietsch, S., Plinge, A., Eskofier, B. M., & Mutschler, C. (2022):

How to learn from risk: Explicit risk-utility reinforcement learning for efficient and safe driving strategies

In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 1913-1920)

Periyasamy, M., Meyer, N., Ufrecht, C., Scherer, D. D., Plinge, A., & Mutschler, C. (2022):

Incremental data-uploading for full-quantum classification

In: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (pp. 31-37)

Meyer, N., Ufrecht, C., Periyasamy, M., Scherer, D. D., Plinge, A., & Mutschler, C. (2022):

A survey on quantum reinforcement learning

In: arXiv preprint arXiv:2211.03464

Adler, R., Bunte, A., Burton, S., Großmann, J., Jaschke, A., Kleen, P., ... & Wrobel, S. (2022):

Deutsche Normungsroadmap Künstliche Intelligenz

In: Fraunhofer Publica

Ott, F., Rügamer, D., Heublein, L., Bischl, B., & Mutschler, C. (2022):

Joint classification and trajectory regression of online handwriting using a multi-task learning approach

In: Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 266-276)

Reeb, L. (2022):

Searching for Soccer Scenes using Siamese Neural Networks

In: Towards Data Science 2022

Löffler, C., & Mutschler, C. (2022):

IALE: imitating active learner ensembles

In: Journal of Machine Learning Research, 23(107), 1-29

Lukcin, I., Duong, P. B., Dietmayer, K., Ali, S. U., Kram, S., Seitz, J., & Felber, W. (2021):

A Combined Ray Tracing Simulation Environment for Hybrid 5G and GNSS Positioning

In: ICL-GNSS 2021 WiP Proceedings

Blauberger, P., Marzilger, R., & Lames, M. (2021):

Validation of player and ball tracking with a local positioning system

In: Sensors, 21(4), 1465

Potortì, F., Torres-Sospedra, J., Quezada-Gaibor, D., Jiménez, A. R., Seco, F., Pérez-Navarro, A., ... & Oh, H. L. (2021):

Off-line evaluation of indoor positioning systems in different scenarios: The experiences from IPIN 2020 competition

In: IEEE Sensors Journal, 22(6), 5011-5054

Stahlke, M., Kram, S., Ott, F., Feigl, T., & Mutschler, C. (2021):

Estimating TOA reliability with variational autoencoders

In: IEEE Sensors Journal, 22(6), 5133-5140

Schmidt, L. M., Kontes, G., Plinge, A., & Mutschler, C. (2021):

Can you trust your autonomous car? interpretable and verifiably safe reinforcement learning

In: 2021 IEEE Intelligent Vehicles Symposium (IV) (pp. 171-178)

Löffler, C., Reeb, L., Dzibela, D., Marzilger, R., Witt, N., Eskofier, B. M., & Mutschler, C. (2021):

Deep siamese metric learning: A highly scalable approach to searching unordered sets of trajectories

In: ACM Transactions on Intelligent Systems and Technology (TIST), 13(1), 1-23

Löffler, C., Nickel, C., Sobel, C., Dzibela, D., Braat, J., Gruhler, B., ... & Mutschler, C. (2021):

Automated quality assurance for hand-held tools via embedded classification and AutoML

In: Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V (pp. 532-535)

Ott, F., Wehbi, M., Hamann, T., Barth, J., Eskofier, B., & Mutschler, C. (2020):

The onhw dataset: Online handwriting recognition from imu-enhanced ballpoint pens with machine learning

In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1-20

Feigl, T., Kram, S., Woller, P., Siddiqui, R. H., Philippsen, M., & Mutschler, C. (2020):

RNN-aided human velocity estimation from a single IMU

In: Sensors, 20(13), 3656

Ott, F., Feigl, T., Loffler, C., & Mutschler, C. (2020):

ViPR: visual-odometry-aided pose regression for 6DoF camera localization

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 42-43)

Feigl, T., Porada, A., Steiner, S., Löffler, C., Mutschler, C., & Philippsen, M. (2020):

Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-scale Industry Environments

In: VISIGRAPP (1: GRAPP) (pp. 307-318)

Stahlke, M., Kram, S., Mutschler, C., & Mahr, T. (2020):

NLOS detection using UWB channel impulse responses and convolutional neural networks

In: 2020 International Conference on Localization and GNSS (ICL-GNSS) (pp. 1-6)

Feigl, T., Gruner, L., Mutschler, C., & Roth, D. (2020):

Real-time gait reconstruction for virtual reality using a single sensor

In: 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 84-89)

Redžepagić, A., Löffler, C., Feigl, T., & Mutschler, C. (2020):

A sense of quality for augmented reality assisted process guidance

In: 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 129-134)

Kontes, G. D., Scherer, D. D., Nisslbeck, T., Fischer, J., & Mutschler, C. (2020):

High-speed collision avoidance using deep reinforcement learning and domain randomization for autonomous vehicles

In: 2020 IEEE 23rd international conference on Intelligent Transportation Systems (ITSC) (pp. 1-8)

Mishra, A., Löffler, C., & Plinge, A. (2020):

Recipes for Post-training Quantization of Deep Neural Networks

In: Workshop on Energy Efficient Machine Learning and Cognitive Computing; 2020 Virtual 

Niitsoo, A., Edelhäußer, T., Eberlein, E., Hadaschik, N., & Mutschler, C. (2019):

A deep learning approach to position estimation from channel impulse responses

In: Sensors, 19(5), 1064

Feigl, T., Roth, D., Gradl, S., Wirth, M., Latoschik, M. E., Eskofier, B. M., ... & Mutschler, C. (2019):

Sick moves! motion parameters as indicators of simulator sickness

In: IEEE transactions on visualization and computer graphics, 25(11), 3146-3157

Feigl, T., Kram, S., Woller, P., Siddiqui, R. H., Philippsen, M., & Mutschler, C. (2019):

A bidirectional LSTM for estimating dynamic human velocities from a single IMU

In: 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-8)

Feigl, T., Kram, S., Woller, P., Siddiqui, R. H., Philippsen, M., & Mutschler, C. (2019):

A bidirectional LSTM for estimating dynamic human velocities from a single IMU

In: Computer Vision Foundation (CVF) (Eds.), Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM (pp. 42-43)

Butyrev, L., Edelhäußer, T., & Mutschler, C. (2019):

Deep reinforcement learning for motion planning of mobile robots

In: arXiv preprint arXiv:1912.09260

Kram, S., Stahlke, M., Feigl, T., Seitz, J., & Thielecke, J. (2019):

UWB channel impulse responses for positioning in complex environments: A detailed feature analysis

In: Sensors, 19(24), 5547

Ghimire, B., Seitz, J., & Mutschler, C. (2018):

Indoor positioning using OFDM-based visible light communication system

In: 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-8)

Löffler, C., Riechel, S., Fischer, J., & Mutschler, C. (2018):

Evaluation criteria for inside-out indoor positioning systems based on machine learning

In: 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-8)

Niitsoo, A., Edelhäußer, T., & Mutschler, C. (2018):

Convolutional neural networks for position estimation in tdoa-based locating systems

In: 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-8)

Nowak, T., Hartmann, M., Thielecke, J., Hadaschik, N., & Mutschler, C. (2018):

Super-resolution in RSS-based direction-of-arrival estimation

In: 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-8)

Feigl, T., Mutschler, C., & Philippsen, M. (2018):

Supervised learning for yaw orientation estimation

In: 2018 international conference on indoor positioning and indoor navigation (IPIN) (pp. 206-212)

Feigl, T., Nowak, T., Philippsen, M., Edelhäußer, T., & Mutschler, C. (2018):

Recurrent neural networks on drifting time-of-flight measurements

In: 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 206-212)

Feigl, T., Mutschler, C., & Philippsen, M. (2018):

Human compensation strategies for orientation drifts

In: 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 409-414)

Feigl, T., Mutschler, C., & Philippsen, M. (2018):

Head-to-body-pose classification in no-pose VR tracking systems

In: 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 1-2)

Roth, D., Klelnbeck, C., Feigl, T., Mutschler, C., & Latoschik, M. E. (2018):

Beyond replication: Augmenting social behaviors in multi-user virtual realities

In: 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 215-222)

Lugrin, J. L., Kern, F., Schmidt, R., Kleinbeck, C., Roth, D., Daxer, C., ... & Latoschik, M. E. (2018):

A location-based VR museum

In: 2018 10Th international conference on virtual worlds and games for serious applications (VS-games) (pp. 1-2)

Gorse, L., Löffler, C., Mutschler, C., & Philippsen, M. (2018):

Optical camera communication for active marker identification in camera-based positioning systems

In: 2018 15th Workshop on Positioning, Navigation and Communications (WPNC) (pp. 1-6)

Roth, D., Kleinbeck, C., Feigl, T., Mutschler, C., & Latoschik, M. E. (2017):

Social augmentations in multi-user virtual reality: A virtual museum experience

In: 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct) (pp. 42-43)

Feigl, T., Kõre, E., Mutschler, C., & Philippsen, M. (2017):

Acoustical manipulation for redirected walking

In: Proceedings of the 23rd ACM symposium on virtual reality software and technology (pp. 1-2)

Gradl, S., Eskofier, B. M., Eskofier, D., Mutschler, C., & Otto, S. (2016):

Virtual and augmented reality in sports: an overview and acceptance study

In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 885-888)

Alawieh, M., Hadaschik, N., Franke, N., & Mutschler, C. (2016):

Inter-satellite ranging in the low earth orbit

In: 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) (pp. 1-6)

Sackenreuter, B., Hadaschik, N., Faßbinder, M., & Mutschler, C. (2016):

Low-complexity PDoA-based localization

In: 2016 international conference on indoor positioning and indoor navigation (IPIN) (pp. 1-6)

Löffler, C., Mutschler, C., & Philippsen, M. (2015):

Approximative event processing on sensor data streams (Best Poster and Demostration Award)

In: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (pp. 360-363)

Mutschler, C., & Philippsen, M. (2014):

Adaptive speculative processing of out-of-order event streams

In: ACM Transactions on Internet Technology (TOIT), 14(1), 1-24

Mutschler, C. (2014):

Latency minimization of order-preserving distributed event-based systems

In: Dissertation. Dr. Hut Verlag. 229 Seiten. ISBN 978-3-8439-1472-7

Mutschler, C., Löffler, C., Witt, N., Edelhäußer, T., & Philippsen, M. (2014):

Predictive load management in smart grid environments

In: Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems (pp. 282-287)

Mutschler, C., Ziekow, H., & Jerzak, Z. (2013):

The DEBS 2013 grand challenge

In: Proceedings of the 7th ACM international conference on Distributed event-based systems (pp. 289-294)

Mutschler, C., Witt, N., & Philippsen, M. (2013):

Demo: do event-based systems have a passion for sports?

In: Proceedings of the 7th ACM international conference on Distributed event-based systems (pp. 331-332)

Mutschler, C., & Philippsen, M. (2013):

Reliable speculative processing of out-of-order event streams in generic publish/subscribe middlewares

In: Proceedings of the 7th ACM international conference on Distributed event-based systems (pp. 147-158)

Löffler, C., Mutschler, C., & Philippsen, M. (2013):

Evolutionary algorithms that use runtime migration of detector processes to reduce latency in event-based systems

In: 2013 NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013) (pp. 31-38)

Mutschler, C., & Philippsen, M. (2013):

Dynamic Low-Latency Distributed Event Processing of Sensor Data Streams

In: GI (Hrsg.): Proceedings of the 25th Workshop on Parallel Systems and Algorithms (PARS 2013) (25th Workshop on Parallel Systems and Algorithms (PARS 2013), Erlangen, Germany)

Mutschler, C., & Philippsen, M. (2013):

Runtime migration of stateful event detectors with low-latency ordering constraints

In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) (pp. 609-614)

Mutschler, C., & Philippsen, M. (2013):

Distributed low-latency out-of-order event processing for high data rate sensor streams

In: 2013 IEEE 27th International Symposium on Parallel and Distributed Processing (pp. 1133-1144)

Mutschler, C., & Philippsen, M. (2012):

Learning event detection rules with noise hidden markov models

In: 2012 NASA/ESA Conference on Adaptive Hardware and Systems (AHS) (pp. 159-166)