Data-driven techniques are finding their way into the calibration procedure of galvanometric setups. However, they bypass the underlying physical or mathematical model completely. Recent work has shown that a simple assumption about an underlying truth can improve the predictions: laser beams leaving the device follow a straight line. In this paper we take that approach a step further. Both the inputs (the pairs of rotations of the two mirrors) and outputs (the straight lines) lie on a manifold. We can incorporate this prior knowledge in the model via constraints built in the formulation of a covariance function. We propose two constrained models: one in which a linear constraint on the direction vector is written as a differential equation and one in which a quadratic constraint is imposed by a reparametrization of the line coordinates. We compare them to data-driven and unconstrained model-based approaches. We show that enforcing constraints improves the quality of the predictions significantly and thus the accuracy of the calibration. We validate our findings against real world data by predicting points on validation planes, calculating line segment distances, considering the training times for the models and assessing how much a predicted line resembles an actual straight line.

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This work presents our vision and work-in-progress on a new platform for immersive virtual and augmented reality (AR) training. ImmersiMed is aimed at medical educational and professional institutions for educating nurses, doctors, and other medical personnel. ImmersiMed is created with multi-platform support and extensibility in mind. By creating consistent experiences across different platforms and applications, ImmersiMed intends to increase simulation availability. Furthermore, it is expected to improve the quality of training and prepare students better for more advanced tasks and boost confidence in their abilities. Tools for educators are being provided so new scenarios can be added without the intervention of costly content creators or programmers. This article addresses how Immersive’s mixed platform approach can ease the transition from basic school training to real-world applications by starting from a virtual reality simulation and gradually let the student move on to guided AR in the real world. By explaining the idea of a single development platform for multiple applications using different technologies and by providing tools for educators to create their own scenarios, ImmersiMed will improve training quality and availability at a low training and simulation costs.

We address the challenge of determining a valid set of parameters for a dynamic line scan thermography setup. Traditionally, this optimization process is labor- and time-intensive work, even for an expert skilled in the art. Nowadays, simulations in software can reduce some of that burden. However, when faced with many parameters to optimize, all of which cover a large range of values, this is still a time-consuming endeavor. A large number of simulations are needed to adequately capture the underlying physical reality. We propose to emulate the simulator by means of a Gaussian process. This statistical model serves as a surrogate for the simulations. To some extent, this can be thought of as a “model of the model”. Once trained on a relative low amount of data points, this surrogate model can be queried to answer various engineering design questions. Moreover, the underlying model, a Gaussian process, is stochastic in nature. This allows for uncertainty quantification in the outcomes of the queried model, which plays an important role in decision making or risk assessment. We provide several real-world examples that demonstrate the usefulness of this method.