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This article proposes model-based homogeneity (MBH) to extend compressed sensing (CS) for landmine-detection ground penetrating radar (GPR). Conventional CS methods have difficulty in distinguishing landmines from clutter, since it principally pays attention to signal magnitude. In contrast, our method visualizes landmines based on homogeneity of high-dimensional scattering features in a spatial model. It realizes both the exclusion of clutter and the reduction of measurement points. Experiments demonstrate that the total measurement and processing time are reduced to one-twentieth of a conventional dense measurement case. We also investigate the influence of model size and number of landmines on the performance. The proposed method is capable of visualizing any objects having respective shapes by configuring corresponding models. [ABSTRACT FROM AUTHOR]

In broad engineering fields, missing data is a common issue which often causes undesired bias and sparseness impeding rigorous data analyses. To tackle this problem, many imputation theories have been proposed and widely used. However, prior methods often require distributional assumptions and prior knowledge regarding data which may cause some difficulty for engineering research. Essentially, the fractional hot-deck imputation (FHDI) is an assumption-free imputation method, holding broad applicability in the engineering domains. FHDIs internal parameters and impact on statistical and machine learning methods, however, have been rarely understood. Thus, this study investigates the behavior and impacts of FHDI on prediction methods including generalized additive model, support vector machine, extremely randomized trees, and artificial neural network, for which four practical datasets (appliance energy, air quality, phenotypes, and weather) are used. Results show that FHDI performs better for improving the prediction accuracy compared to a simple naive method which cures missing data using the mean value of attributes, and FHDI has an asymptotically positive effect on prediction accuracy with decreasing response rates. Regarding an optimal setting, 30 to 35 is recommended for the FHDIs internal categorization number while 5 is recommended for the FHDI donors, which is aligned with Rubins recommendation. [ABSTRACT FROM AUTHOR]