The OWL-v2026 is a ~220 USD open-source logger that records six-axis IMU data at 208-416 Hz and GNSS data at 10 Hz with sub-10 ms UTC timestamp accuracy, validated for over 10 days of continuous operation at low power.
Ocean modelling , volume=
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An adaptive spatiotemporal clustering framework boosts deep learning reconstruction of global ocean subsurface temperature fields from surface data, delivering 12.4% to 27.2% RMSE improvements when paired with models such as DP-CNN, Attention U-Net, and ViT.
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OpenWaveLogger v2026 (OWL-v2026): an open source, low cost, easy to build, high performance logger for wave data measurements
The OWL-v2026 is a ~220 USD open-source logger that records six-axis IMU data at 208-416 Hz and GNSS data at 10 Hz with sub-10 ms UTC timestamp accuracy, validated for over 10 days of continuous operation at low power.
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An Adaptive Spatiotemporal Clustering Framework for 3D Ocean Subsurface Temperature Reconstruction
An adaptive spatiotemporal clustering framework boosts deep learning reconstruction of global ocean subsurface temperature fields from surface data, delivering 12.4% to 27.2% RMSE improvements when paired with models such as DP-CNN, Attention U-Net, and ViT.