Nebula Public Library

Recent studies have shown that remote sensing of floating marine plastic litter (MPL) is feasible from unmanned aerial systems, aircraft and satellite missions. However, in the infrared (IR) spectrum, water is a strong light absorber which makes the spectral detection and discrimination of plastics challenging. Additionally, MPL is often covered with living organisms and it is unclear what impact different thickness of biofouling may have on the spectral reflectance of floating plastic.

We aim to develop a novel control theory methodology using input-output process models for error mitigation in signal processing data-paths that operate in radiation prone environments.  In this approach, difference equations are used to characterize the process with perturbation corresponding to faulty digital circuits.

‘STARCOP’ is an initiative to use ML and multiple satellites of diverse detection capabilities to quickly detect methane leaks onboard and notify them in near-real-time. In this project we have developed ML models for automatic detection of methane in hyperspectral and multispectral imagery. Our hyperspectral model (HyperSTARCOP) is able to capture more than 90% of plumes in our test data while reducing the false positive rate by 39% when compared to state-of-the-art models.

In most in vitro cell models, cultures are made adhering to flat culture slides and the expected activation of specific cell markers is measured on cells. However, in this simplified condition, cell response is not representative of the in vivo response, which is based on cell interactions that occur in three-dimensional (3D) non-flat environments and between several cell populations.