Real-world datasets collected with sensor networks often contain incomplete and uncertain labels as well as artefacts arising from the system environment. To address these challenges, we propose to make use of system context information formalized in an information graph and embed it in the learning process via contrastive learning. Based on real-world data we show that this approach leads to an increased accuracy in case of weakly labeled data and leads to an increased robustness and transferability of the classifier to new sensor locations. Read more...
In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. Read more...
In this work, we present a systematic strategy to identify a multitude of external influence sources, characterize and quantify their impact and develop methods for automated identification in microseismic signals. We apply the strategy developed to a real-world, multi-sensor, multi-year microseismic monitoring experiment performed at the Matterhorn Hörnligrat (Switzerland). Read more...
Nyquist sinc-pulse shaping provides spectral efficiencies close to the theoretical limit. We worked on real-time Nyquist pulse shaping implementations using field programmable gate arrays (FPGA) along with high-speed digital-to-analog and analog-to-digital converters (DAC, ADC) within optical transceivers. Read more...
- AMLD2020: Monitoring Climate Change at the Edge of the Cloud
- ZHdK Environmental Listening Session: Unknown Soundscapes: Rivers, Soils, Rocks
- IPSN2019: Event-triggered Natural Hazard Monitoring with Convolutional Neural Networks on the Edge
- IPSN2019: How Many Climb the Matterhorn? (Demo)
- EGU2017: Data Exploration using Unsupervised Feature Extraction for Mixed Micro-Seismic Signals