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This paper presents a class project that can be performed in a junior-level fluid mechanics course. Students gain experience in design and conducting experiments, and in reviewing the relevant technical literature. Experiments were conducted to determine the drag coefficient of a badminton shuttlecock. Two types of testing were conducted: wind tunnel tests of a full-scale model, and drop tests using a high-accuracy radar gun. The drag coefficients calculated from these measurements were then compared to the limited data available in the literature. The range of drag coefficients measured was from 0.55 to 0.65.

We consider the car key localization task using ultra-wideband (UWB) signal measurements. Given labeled data for a certain car, we train a deep classifier to make the prediction about the new points. However, due to the differences in car models and possible environmental effects that might alter the signal propagation, data collection requires considerable effort for each car. In particular, we consider a situation where the data for the new car is collected only in one environment, so we have to utilize the measurements in other environments from a different car. We propose a framework based on generative adversarial networks (GANs) to generate missing parts of the data and train the classifier on it, mitigating the necessity to collect the real data. We show that the model trained on the synthetic data performs better than the baseline trained on the collected measurements only. Furthermore, our model closes the gap to the level of performance achieved when we would have the information about the new car in multiple environments by 35%.