  This study concerns the effectiveness of several techniques and methods of
signals processing and data interpretation for the diagnosis of aerospace
structure defects. This is done by applying different known feature extraction
methods, in addition to a new CBIR-based one; and some soft computing
techniques including a recent HPC parallel implementation of the U-BRAIN
learning algorithm on Non Destructive Testing data. The performance of the
resulting detection systems are measured in terms of Accuracy, Sensitivity,
Specificity, and Precision. Their effectiveness is evaluated by the Matthews
correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments
are performed on a standard dataset of eddy current signal samples for aircraft
structures. Our experimental results evidence that the key to a successful
defect classifier is the feature extraction method - namely the novel
CBIR-based one outperforms all the competitors - and they illustrate the
greater effectiveness of the U-BRAIN algorithm and the MLP neural network among
the soft computing methods in this kind of application.
  Keywords- Non-destructive testing (NDT); Soft Computing; Feature Extraction;
Classification Algorithms; Content-Based Image Retrieval (CBIR); Eddy Currents
(EC).
