University of Birmingham

Birmingham, United Kingdom

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School of Biosciences
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School of Psychology
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School of Geography, Earth and Environmental Sciences
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    ABSTRACT: A Generalised Cornu Spiral (GCS) is a planar curve defined to have a monotonic rational linear curvature profile and as such these curves are considered fair. However, their implementation in current CAD systems is not straight forward partly due to not being in the usual polynomial form. A GCS cannot be expressed exactly using a finite polynomial and so a compromise can be achieved by instead approximating the GCS with a suitable polynomial. An efficient robust approximation of the GCS using quintic polynomials is presented. The approximation satisfies the G2G2 continuity conditions at the end points and the remaining four degrees of freedom are argued for by looking at G3G3 approximations. The method begins by reparameterising the GCS in terms of more intuitive geometric descriptions; the winding angle, change in curvature and a shape factor. The G3G3 approximations provide insight to help define values for the free parameters, and the new geometric form allows for the shortcomings in the G3G3 approximations to be controlled. The efficiency of the approximation is improved compared to earlier methods which required a numerical search. Also, there is strong evidence that the method guarantees a satisfactory approximation when the GCS lies within certain identified bounds.
    Journal of Computational and Applied Mathematics 01/2015; 273:1–12.
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    ABSTRACT: This research studied the effect of κ-carrageenan concentration and emulsifier mixture of soybean lecithin (LEC) and/or polyglycerol polyricinoleate (PGPR) on the physical properties of water-in-cocoa butter emulsions. Emulsions were prepared using bench scale margarine line process, consisting of a scraped surface heat exchanger and a pin stirrer. Results show that droplet size increases as the concentration of κ-carrageenan and/or LEC increases. Emulsions crystallise mainly in form V (β2), however when the concentration of κ-carrageenan increases to 1.5 wt% less stable polymorphic forms (II) were also observed. The rheological properties of the emulsions at 40 °C were mainly controlled by the concentration of LEC which causes droplets to flocculate and as consequence viscosity increases. Finally, behaviour under large deformation showed that the presence of water droplets weakens the emulsions structure, due to the reduction in the density of the cocoa butter matrix bearing the load.
    Journal of Food Engineering 12/2014;
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    ABSTRACT: The classical machinery of supervised learning machines relies on a correct set of training labels. Unfortunately, there is no guarantee that all of the labels are correct. Labelling errors are increasingly noticeable in today׳s classification tasks, as the scale and difficulty of these tasks increases so much that perfect label assignment becomes nearly impossible. Several algorithms have been proposed to alleviate the problem of which a robust Kernel Fisher Discriminant is a successful example. However, for classification, discriminative models are of primary interest, and rather curiously, the very few existing label-robust discriminative classifiers are limited to linear problems. In this paper, we build on the widely used and successful kernelising technique to introduce a label-noise robust Kernel Logistic Regression classifier. The main difficulty that we need to bypass is how to determine the model complexity parameters when no trusted validation set is available. We propose to adapt the Multiple Kernel Learning approach for this new purpose, together with a Bayesian regularisation scheme. Empirical results on 13 benchmark data sets and two real-world applications demonstrate the success of our approach.
    Pattern Recognition. 11/2014; 47(11):3641–3655.


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12/2007: pages 35-50;
Qualitative Research in Psychology 01/2006; 3(2):102-120.

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