Multi-resolution methods in non-destructive testing of aerospace structures and in medicine

Thorough testing of a huge aerospace structures results in a large amount of data, and long processing time. To decrease the processing time, we use a "multi-resolution" technique, in which we first separate the data into data corresponding to different vibration modes, and then combine these data together. We show how a general methodology for choosing the optimal uncertainty representation can be used to find the optimal uncertainty representations for this particular problem. Namely, we show that the problem of finding the best approximation to the probability of detection (POD) curve can be solved similarly to the problem of finding the best activation function in neural networks. A similar approach can be used in detecting faults in medical images.