No abstract

This article focuses on the fusion of flaw indications from multi-sensor nondestructive materials testing. Because each testing method makes use of a different physical principle, a multi-method approach has the potential of effectively differentiating actual defect indications from the many false alarms, thus enhancing detection reliability. In this study, we propose a new technique for aggregating scattered two- or three-dimensional sensory data. Using a density-based approach, the proposed method explicitly addresses localization uncertainties such as registration errors. This feature marks one of the major of advantages of this approach over pixel-based image fusion techniques. We provide guidelines on how to set all the key parameters and demonstrate the technique’s robustness. Finally, we apply our fusion approach to experimental data and demonstrate its capability to locate small defects by substantially reducing false alarms under conditions where no single-sensor method is adequate.

Abstract. We present and compare two different approaches for NDT multi-sensor data fusion at signal (low) and decision (high) levels. Signal-level fusion is achieved by applying simple algebraic rules to strategically post-processed images. This is done in the original domain or in the domain of a suitable signal transform. The importance of signal normalization for low-level fusion applications is emphasized in regard to heterogeneous NDT data sets. For fusion at decision level, we develop a procedure based on assembling joint kernel density estimation (KDE). The procedure involves calculating KDEs for individual sensor detections and aggregating them by applying certain combination rules. The underlying idea is that if the detections from more than one sensor fall spatially close to one another, they are likely to result from the presence of a defect. On the other hand, single-senor detections are more likely to be structural noise or false alarm indications. To this end, we design the KDE combination rules such that it prevents single-sensor domination and allows data-driven scaling to account for the influence of individual sensors. We apply both fusion rules to a three-sensor dataset consisting in ET, MFL/GMR and TT data collected on a specimen with built-in surface discontinuities. The performance of the fusion rules in defect detection is quantitatively evaluated and compared against those of the individual sensors. Both classes of data fusion rules result in a fused image of fewer false alarms and thus improved defect detection. Finally, we discuss the advantages and disadvantages of low-level and high-level NDT data fusion with reference to our experimental results.

Electrophysiological signals such as the EEG, MEG, or LFPs have been extensively studied over the last decades, and elaborate signal processing algorithms have been developed for their analysis. Many of these methods are based on time-frequency decomposition to account for the signals' spectral properties while maintaining their temporal dynamics. However, the data typically exhibit intra- and interindividual variability. Existing algorithms often do not take into account this variability, for instance by using fixed frequency bands. This shortcoming has inspired us to develop a new robust and flexible method for time-frequency analysis and signal feature extraction using the novel smooth natural Gaussian extension (snaGe) model. The model is nonlinear, and its parameters are interpretable. We propose an algorithm to derive initial parameters based on dynamic programming for nonlinear fitting and describe an iterative refinement scheme to robustly fit high-order models. We further present distance functions to be able to compare different instances of our model. The method's functionality and robustness are demonstrated using simulated as well as real data. The snaGe model is a general tool allowing for a wide range of applications in biomedical data analysis.

No abstract