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The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important(More)
We revisit a previous study on inter-session variability (McGonigle et al. [2000]: Neuroimage 11:708-734), showing that contrary to one popular interpretation of the original article, inter-session variability is not necessarily high. We also highlight how evaluating variability based on thresholded single-session images alone can be misleading. Finally, we(More)
Application of a neural network approach to data exploration and the generation of a model of system normality is described for use in novelty detection of vibration characteristics of a modern jet engine. The analysis of the shape of engine vibration signatures is shown to improve upon existing methods of engine vibration testing, in which engine(More)
We describe a new model which is able to model accurately the characteristics of subject motion, a dominant artefact in Functional Magnetic Resonance Images. Using the model, which is based on specific knowledge regarding the nature of the image acquisition, it is possible to correct for this motion which would otherwise render activation detection on the(More)
Application of novelty detection to a new class of jet engine is considered within this paper, providing a worked example of the steps necessary for constructing a model of normality. Abnormal jet engine vibration signatures are automatically identified with respect to a training set of normal examples. Pre-processing steps suitable for this area of(More)
We develop novelty detection techniques for the analysis of data from a large-vehicle engine turbocharger in order to illustrate how abnormal events of operational significance may be identified with respect to a model of normality. Results are validated using polynomial function modelling and reduced dimensionality visualisation techniques to show that(More)
Existing approaches to the problem of subject motion artefacts in FMRI data have applied rigid-body registration techniques to what is a non-rigid problem. We propose a model which can account for the non-linear characteristics of movement effects, known to result from the acquisition methods used to form these images. The model also facilitates the proper(More)
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