Sub-100 μW Multispectral Riemannian Classification for EEG-Based Brain-Machine Interfaces.

Motor imagery (MI) brain-machine interfaces (BMIs) enable us to control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units (MCUs), for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost trade-off for embedded BMI solutions.

The relevance of rock shape over mass-implications for rockfall hazard assessments.

The mitigation of rapid mass movements involves a subtle interplay between field surveys, numerical modelling, and experience. Hazard engineers rely on a combination of best practices and, if available, historical facts as a vital prerequisite in establishing reproducible and accurate hazard zoning. Full-scale field tests have been performed to reinforce the physical understanding of debris flows and snow avalanches.

SmarTEG: An Autonomous Wireless Sensor Node for High Accuracy Accelerometer-Based Monitoring.

We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade's wear detection, and sends the report wirelessly.

Towards Edge-Aware Spatio-Temporal Filtering in Real-Time.

Spatio-temporal edge-aware (STEA) filtering methods have recently received increased attention due to their ability to efficiently solve or approximate important image-domain problems in a temporally consistent manner - which is a crucial property for video-processing applications. However, existing STEA methods are currently unsuited for real-time, embedded stream-processing settings due to their high processing latency, large memory, and bandwidth requirements, and the need for accurate optical flow to enable filtering along motion paths. To this end, we propose an efficient STEA filtering pipeline based on the recently proposed permeability filter (PF), which offers high quality and halo reduction capabilities.