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We describe a neutron/gamma pulse shape discrimination (PSD) system that overcomes count rate limitations of previous methods for distinguishing neutrons from gammas in liquid scintillation detectors. Previous methods of PSD usually involve pulse shaping time constants that allow throughput of tens of thousands counts per second. Time correlated measurements require many millions of counts per second to accurately characterize nuclear material samples. To rapidly inspect many test articles, a highthroughput system is desired. To add neutron -gamma distinction to the analysis provides a much desired enhancement to the characterizations. However, if the PSD addition significantly slows down the inspection throughput, this PSD feature defeats any analysis advantage. Our goal for the fast PSD system is to provide sorted timing pulses to a fast, multi-channel, time-correlation processor at rates approaching several million counts per second enabling high throughput, enhanced inspection of nuclear materials.

This paper is concerned with the detection of physical flaws on pipe walls in gas pipelines. The sensor technology is EMAT (electromagnetic acoustic transducer), a non-contact ultrasonic technology. One EMAT is used as a transmitter, exciting an ultrasonic impulse into the pipe wall. Another EMAT located a few inches away from the first is used as a receiving transducer. This paper reports on the identification of flaw signatures in the receiver output. The first step in flaw characterization is to perform wavelet analysis of the signature. Being non-shift-invariant, an array of coefficients of a discrete wavelet transform of a signal is not directly suitable as a pattern recognition feature. However, comparing composite properties of the signal on different scales is useful, because the mode conversion caused by a flaw, changes the composite properties of the signal in wavelet space. For EMAT data, the useful information projects onto five mutually orthogonal wavelet scales. This paper reports on the use of a robust 17-dimensional feature vector that consistently distinguishes "flaw" signatures from "no-flaw" signatures in a substantial collection of experimental data.

This paper describes a wavelet-based analysis of electromagnetic acoustic transducer (EMAT) signals for in-line inspection of flaws in natural gas pipeline. The main objective of the project has been to implement the use of EMATs for pipe flaw detection, specifically the ability to detect stress corrosion cracks (SCCs) that are undetectable by current techniques. In this approach, two EMATs are used; one is the transmitter, while the second one, located a few inches away from the first, is used to receive the induced signal. Using a four-level wavelet decomposition, the EMAT data are filtered based on frequency. The features used to classify are derived from the coefficients representing each level of the four-level decomposition of the signature. The objective of the project was to detect SCC with minimal false positive even if smaller SCCs (shallow) are not identified. Although many features could be used, selecting the right features that results in maximum separation between the classes (SCC flaw, other pipe artifacts, and no flaw) was a challenge. This paper describes the process of down-selecting the feature sets and separating the classes. The results using this approach have shown promise.