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
27 February 2009
10:00
Abstract
About a third of us will have a cancer during our lives, and we all know someone with the disease. Despite enormous progress in recent years, so that being diagnosed with cancer is not the death sentence that it once was, treatment can be aggressive, leading to short and long term reductions in quality of life. Cancer and its treatment is still feared, and rightly so - it is a major health concern. Destroying cancer non-invasively using protons or charged light ions such as carbon (Particle Therapy Cancer Research or PTCR) offers advantages over conventional radiotherapy using x-rays, since far lower radiation dose is delivered to healthy normal tissues. PT is also an alternative to radical cancer surgery. Most radiotherapy uses a small electron linear accelerator to accelerate an electron beams to a few million volts and then to generate hard x-rays, whereas CPT uses cyclotrons or synchrotrons to accelerate protons to a few hundred million volts, which themselves sterilise the tumour. More recently, a new concept in accelerators – the “non-scaling Fixed Field Alternating Gradient” accelerator – has been advanced, which offers the prospect of developing relatively compact, high acceleration rate accelerators for a variety of purposes, from neutrino factories and muon acceleration to cancer therapy. However, there are formidable technical challenges to be overcome, including resonance crossing. We have recently been awarded funding in the UK to construct a demonstrator non-scaling FFAG at the Daresbury laboratory (EMMA, the Electron Model with Many Applications), and to design a prototype machine for proton and carbon ion cancer therapy (PAMELA, the Particle Accelerator for MEdicaL Applications). I will describe some of the motivations for developing this new type of accelerator. Finally, although the physics of CPT says that it should be qualitatively and quantitatively better than conventional radiotherapy, the robust clinical analyses (for example, randomised control trials) have not been done, and the meta-analyses seem to suffer from large sample biases. The Particle Therapy Cancer Research Institute (part of the James Martin 21st Century School in Oxford) will study the clinical effectiveness of charged particle therapy to treat cancer, promoting its use in the UK and elsewhere on the basis of robust clinical evidence and analysis.
  • Industrial and Interdisciplinary Workshops

Pages