Skip to content
Log in
Sponsored Have an interesting project or opportunity? → Hire me ↗
Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Hamed Majidiyan

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Jul 2026

UAS-Validated Comparison of Sentinel-2 Shoreline Extraction Techniques for Large-Lake Coastal Mapping

Reliable assessment of shorelines extracted from medium-resolution satellite imagery requires independent high-resolution reference data and statistical methods that account for spatial dependence. This study compared three conventional analyst-assisted shoreline-extraction workflows—histogram thresholding, band ratio, and the Normalised Difference Water Index (NDWI)—at Coronation Park, Lake Ontario, Canada, using Sentinel-2 Level-2A imagery. A manually digitised shoreline derived from a UAV-based orthomosaic acquired approximately 27 h before the Sentinel-2 scene served as the independent reference. The UAV-based reference and each Sentinel-2-derived shoreline were divided into 31 ordered segments. For each Sentinel-2-derived segment midpoint, the shortest planar Euclidean distance to the nearest UAV-based reference midpoint was calculated and used to derive mean absolute error (MAE) and root mean square error (RMSE). Residual spatial autocorrelation was assessed using Moran’s I with 9999 permutations. Because the paired differences departed from normality, the Friedman test was treated as the primary overall comparison, while contiguous spatial-block permutation tests across block sizes of two to eight shoreline locations assessed robustness to local spatial dependence. NDWI achieved the highest positional agreement (MAE = 5.645 m; RMSE = 6.429 m), followed by band ratio (MAE = 14.303 m; RMSE = 14.797 m) and histogram thresholding (MAE = 26.167 m; RMSE = 26.910 m). Significant positive residual spatial autocorrelation was identified for all three methods (Moran’s I = 0.587–0.832, all p < 0.001). The Friedman test confirmed a significant extraction-method effect, χ2(2) = 49.226, p < 0.001, Kendall’s W = 0.794, and the effect remained significant across all tested spatial-block sizes, with empirical p-values ranging from 0.000007 to 0.004630. Among the three conventional methods tested at this large-lake site, NDWI provided the highest positional agreement and therefore offers a defensible baseline for evaluating future Sentinel-2 image-enhancement approaches.

Mohamed M. Elmeligy, A. El-Rabbany, S. Abdelrahman et al. · 0 citations
Open access Jul 2026

Physics-informed jitter augmentation for hydrodynamic time series

Machine learning (ML) and deep learning (DL) have significantly advanced maritime engineering and hydrodynamics by enabling data-driven modelling and prediction. In hydrodynamics, high-fidelity surrogates offer precision, but low-fidelity surrogates are often preferred for generating large training sets due to their low computational cost. Time series records are a central form of data across seakeeping, structural analysis, sea-state estimation, ship-response prediction, control, and manoeuvring; they encode both the system’s response to excitation and key vessel characteristics. To improve robustness, coverage, and perturbation resilience, augmentation techniques, such as jittering, scaling, warping, and permutation, are commonly applied, but typically tuned by trial-and-error and rarely grounded in physics. To address this, we introduce a novel feature-engineering methodology that derives a physics-informed jitter variance from experiment–simulation discrepancies in time–frequency features, turning jittering from a heuristic into a principled step. The method is evaluated on a spherical floating model tested in regular and irregular unidirectional waves and compared against numerical simulations. Beyond PDF/PSD-based representational fidelity comparison, the augmented datasets are further assessed through downstream simulation-to-experiment classification across benchmark ML/DL models. Compared with raw simulation and RMS-matched standard Gaussian jittering, the proposed physics-informed augmentation improved experimental test accuracy and macro-F1 across most model families. These results demonstrate that the proposed method is not merely a spectral noise adder, but a physics-guided augmentation strategy that improves the ML usability of low-fidelity hydrodynamic time series.

Hamed Majidiyan, Hossein Enshaei, Damon Howe · 0 citations