Digital Incoherent Compressive Holography Using a Geometric Phase Metalens

We propose a compressive self-interference incoherent digital holography (SIDH) with a geometric phase metalens for section-wise holographic object reconstruction. We specify the details of the SIDH with a geometric phase metalens design that covers the visible wavelength band, analyze a spatial distortion problem in the SIDH and address a process of a compressive holographic section-wise reconstruction with analytic spatial calibration. The metalens allows us to realize a compressive SIDH system in the visible wavelength band using an image sensor with relatively low bandwidth. The operation of the proposed compressive SIDH is verified through numerical simulations.

Morphodynamic Modelling with Uncertain Geometry Input

For morphodynamic modelling, riverbed survey data are essential as the basis for the evaluation of temporal riverbed development, mesh creation, and model calibration. To study the effects of uncertain geometry input on these issues, datasets of different spatial resolutions were analysed. As a result, cross-profile data were derived from high-resolution survey data, which are available for a river reach in the Upper Danube in Bavaria for several periods. Finally, the prediction quality of simulations based on cross-profile and high-resolution spatial data was assessed. The analysis of both datasets shows continuous riverbed erosion but of different magnitudes. However, complex riverbed geometry due to, e.g., scours, is depicted poorly by cross-profile data. In more homogenously characterised reaches, cross-profile data significantly more closely represents the riverbed geometry than the high-resolution spatial data base. Local misinterpretation of riverbed geometry by cross-profile data leads to deviations of calibration parameters in the entire study area. Consequently, these deviations in calibration outcome effect the model predictions. In this case, cross-profile calibration generally induces higher transport capacities, leading to more erosion in the study area compared to the model based on high-resolution spatial calibration. The general shape of predicted riverbed geometries is found to be similar but with local deviations, which are not limited to areas with complex river geometry.

ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab

Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.

Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWAT-SVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWAT-SVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m3 s-1 and 30 m3 s-1, and its applicable spatial scale fell between 500 to 3000 km2. The overall performance of the model on yearly time series is “Satisfactory”. This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW.