Past Industrial and Interdisciplinary Workshops

8 May 2009
10:00
to
11:30
Abstract
Inverse problems arise with regularity (sic!) in the context of our study of the deformation of solids, and its characterisation (in terms of diffraction and imaging) using radiation (neutrons and X-rays).<br /> <br /> I wish to introduce several examples where the advancement of inverse problem methods can make a significant impact on applicatins.<br /> <br /> 1. Inverse eigenstrain analysis of residual stress states<br /> <br /> 2. Strain tomography<br /> <br /> 3. Strain image correlation<br /> <br /> Depending on the time available, I may also mention (a) Rietveld refinement of diffraction patterns from polycrystalline aggregates, and<br /> (b) Laue pattern indexing and energy dispersive detection for single grain strain analysis.<br /> <br />
  • Industrial and Interdisciplinary Workshops
20 March 2009
10:00
Edward Stansfield
Abstract
PROBLEM STATEMENT: Consider a set of measurements made by many sensors placed in a noisy environment, the noise is both temporally and spatially correlated and has time varying statistics. Given this environment, characterised by spatial and temporal scales of correlation, the challenge is to detect the presence of a weak, stationary signal described by smaller scales of temporal and spatial correlation. Many current and future challenges involve detection of signals in the presence of other, similar, signals. The signal environment is extremely busy and thus the traditional process of detection of a signal buried in noise at reducing signal to noise ratio is no longer sufficient. Signals of interest may be at high SNR but need to be detected, classified, isolated and analysed as close to real time as is possible. All interfering signals are potentially signals of interest and all overlap in time and frequency. Can the performance of signal detection algorithms be parameterised by some characteristic(s) of the signal environment? A problem exists to detect and classify multiple signal types, but with a very low duty cycle for the receiver. In certain circumstances, very short windows of opportunity exist where the local signal environment can be sampled and the duty cycle of observation opportunities can be as low as 10%. The signals to be detected may be continuous or intermittent (burst) transmissions. Within these short windows, it is desirable to detect and classify multiple transmissions in terms of signal type (e.g. analogue or digital comms, navigation etc.) and location of transmitters. The low duty cycle of observations for the receiver makes this a challenging prospect. Again, can the performance of signal detection algorithms be parameterised by some characteristic(s) of the signal environment?
  • Industrial and Interdisciplinary Workshops

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