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Nikolaos Salaris

Publications and source records attributed to Nikolaos Salaris.

2 recordsLinked to original sources
arXiv2026

Computer vision enabled oxygen sensing

Luminescence-based chemical sensors are almost universally read as point detectors with the signal inverted through a single calibration model. Here we reframe optical oxygen sensing as a computer vision problem, in which the sensing film acts as a spatially heterogeneous encoder and a pretrained Temporal Vision Transformer (TViT) as its decoder. The heterogeneity in film thickness, diffusion path length and illumination translate to pixels that provide complementary information on the underlying diffusion dynamics. We achieved up to 54% lower mean absolute error (MAE) by increasing the internal diversity in performance of a pixel group; a gain that linear models cannot reproduce. Using a low-cost platform comprising a Raspberry Pi camera, a UV LED and a porous PtOEP/polystyrene film in tandem with a TViT architecture yielded an MAE of ~6.7 μmol/L, a 96% reduction from the two-site Stern-Volmer (SV) model. We showed that this framework can computationally mitigate the fundamental trade-off between mechanical robustness and temporal response in diffusion limited oxygen sensing by reducing T90 response times by 91%, while exhibiting physically plausible dynamics under a Rauch-Tung-Striebel smoother (2.3% flag rate). The framework was applied across setups, environments and biofilm states, establishing an IoT-compatible paradigm for computationally compensated diffusion-limited chemical sensing.

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arXiv2026

Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring

The escalating climate crisis and ecosystem degradation demand intelligent, low-cost sensors capable of robust, long-term monitoring in real-world environments. Absolute dissolved oxygen (DO) concentration is a key parameter for predicting climate tipping points. Inexpensive optoelectronic sensors based on microstructured polymer films doped with phosphorescent dyes could be readily deployable; however, signal drift and marine biofouling remain major challenges. Here, we introduce a sensing paradigm that combines camera-based DO sensors with a visual transformer (ViT)-based physics-informed neural network (PINN) for high-fidelity sensing under biofouling conditions. Training and testing data were obtained from an algae-laden water tank over 14 days to capture accelerated biofouling. The ViT-PINN, which embeds the Stern-Volmer (SV) equation into the loss function, reduces mean average error (MAE) by 92% and 89% compared to classical statistical and ML approaches, achieving ~2 umol/L absolute error. A deep ensemble further quantifies predictive uncertainty, enabling self-diagnostic sensing.

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