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The use of UAVs for remote sensing is increasing. In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of Size, Weight, Power, and Computational constraints. These constraints hinder the deployment of rapidly evolving computer vision and robotics algorithms on UAVs, because they require intricate knowledge about the system and architecture to allow for effective implementation. We propose integrating computational monitoring techniques—profiling—with an industry standard specifying software quality—ISO 25000—and fusing both in a decision-making model—the analytic hierarchy process—to provide an informed decision basis for deploying embedded systems in the context of UAV-based remote sensing. One software package is combined in three software–hardware alternatives, which are profiled in hardware-in-the-loop simulations. Three objectives are used as inputs for the decision-making process. A Monte Carlo simulation provides insights into which decision-making parameters lead to which preferred alternative. Results indicate that local weights significantly influence the preference of an alternative. The approach enables relating complex parameters, leading to informed decisions about which hardware is deemed suitable for deployment in which case.

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This research was a step towards enabling Unmanned Aerial Vehicles to use semantic information for navigation. A navigation framework was established, a method to address constraints that UAVs face during early development stages was introduced and additional steps towards more effective grid maps for semantic navigation were taken by introducing a novel mapping approach and a map representation that respects UAV flying height. This thesis supports UAV autonomy by taking steps towards higher-level navigation using meaningful representations of environments.

For tissue engineering applications, accurate prediction of the effective mechanical properties of tissue scaffolds is critical. Open and closed cell modelling, mean-field homogenization theory, and finite element (FE) methods are theories and techniques currently used in conventional homogenization methods to estimate the equivalent mechanical properties of tissue-engineering scaffolds. This study aimed at developing a formulation to link the microscopic structure and macroscopic mechanics of a fibrous electrospun scaffold filled with a hydrogel for use as an epicardial patch for local support of the infarcted heart. The macroscopic elastic modulus of the scaffold was predicted to be 0.287 MPa with the FE method and 0.290 MPa with the closed-cell model for the realistic fibre structure of the scaffold, and 0.108 MPa and 0.540 MPa with mean-field homogenization for randomly oriented and completely aligned fibres. The homogenized constitutive description of the scaffold was implemented for an epicardial patch in a FE model of a human cardiac left ventricle to assess the effects of patching on myocardial mechanics and ventricular function in the presence of an infarct. Epicardial patching was predicted to reduce maximum myocardial stress in the infarcted LV from 19 kPa (no patch) to 9.5 kPa (patch) and to marginally improve the ventricular ejection fraction from 40% (no patch) to 43% (patch). This study demonstrates the feasibility of homogenization techniques to represent complex multiscale structural features in a simplified but meaningful and effective manner.