36 citations · 178 across the 30 of their papers we have counts for
10 papers · 1 filter
Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization
Lucas Heublein, Mark Deutel, Axel Plinge +1
Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw in-phase and quadrature (IQ)…
PDRNN: Modular Data-driven Pedestrian Dead Reckoning on Loosely Coupled Radio- and Inertial-Signalstreams
Peter Bauer, Andreas Porada, Felix Ott +2
Modern pedestrian dead reckoning (PDR) systems rely on fusing noisy and biased estimates of position, velocity, and calibrated orientation derived from loosely coupled sensors to d…
Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers
Lukas Schelenz, Shobha Rajanna, Denis Gosalci +6
Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as NBA players. This task poses si…
GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU
Thorben Wegner, Lucas Heublein, Tobias Feigl +3
Traditional methods for classifying global navigation satellite system (GNSS) jamming signals typically involve post-processing raw or spectral data streams, requiring complex and…
VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification
Lucas Heublein, Simon Kocher, Tobias Feigl +3
Distributed learning and Edge AI necessitate efficient data processing, low-latency communication, decentralized model training, and stringent data privacy to facilitate real-time…
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
Nishant S. Gaikwad, Lucas Heublein, Nisha L. Raichur +3
Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. Each device trains the model on its local server a…
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation
Yucheng Wang, Peiliang Gong, Min Wu +4
Time-Series (TS) data has grown in importance with the rise of Internet of Things devices like sensors, but its labeling remains costly and complex. While Unsupervised Domain Adapt…
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift
Felix Ott, David Rügamer, Lucas Heublein +2
The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. To mitigate this…
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition
Felix Ott, David Rügamer, Lucas Heublein +2
Cross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities. Cr…
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens
Felix Ott, David Rügamer, Lucas Heublein +4
Purpose. Handwriting is one of the most frequently occurring patterns in everyday life and with it come challenging applications such as handwriting recognition (HWR), writer ident…