6 papers
Resource-Constrained On-Device Learning by Dynamic Averaging
Lukas Heppe, Michael Kamp, Linara Adilova +3
The communication between data-generating devices is partially responsible for a growing portion of the world's power consumption. Thus reducing communication is vital, both, from…
The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting
Benjamin Sliwa, Niko Piatkowski, Christian Wietfeld
Ubiquitously deployed Internet of Things (IoT)- based automatic vehicle classification systems will catalyze data-driven traffic flow optimization in future smart cities and will t…
LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations
Benjamin Sliwa, Nico Piatkowski, Christian Wietfeld
Exploiting big data knowledge on small devices will pave the way for building truly cognitive Internet of Things (IoT) systems. Although machine learning has led to great advanceme…
The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization
Sibylle Hess, Nico Piatkowski, Katharina Morik
Boolean matrix factorization (BMF) is a popular and powerful technique for inferring knowledge from data. The mining result is the Boolean product of two matrices, approximating th…
The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization
Sibylle Hess, Katharina Morik, Nico Piatkowski
Mining and exploring databases should provide users with knowledge and new insights. Tiles of data strive to unveil true underlying structure and distinguish valuable information f…
Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction
Benjamin Sliwa, Robert Falkenberg, Thomas Liebig +2
The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. Ho…