Keith Hopcraft

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Essential criteria for the emergence of wave-like manifestations occurring in an entirely discrete system are identified using a simple model for the movement of particles through a network. The dynamics are entirely stochastic and memoryless involving a birth-death-migration process. The requirements are that the network should have at least three nodes,(More)
The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques suitable for non-intermittent time-series data, these approaches are not always appropriate for intermittent(More)
Laser Doppler flowmetry relies upon the use of the first moment of the power spectrum of the photocurrent and numerous methods of providing an estimate of this have been implemented. One, operating in the time domain and using only a few, simple processing steps, is claimed to be both fast and accurate (Draijer et al. in Med Biol Eng Comput(More)
The stochastic point processes formed by the zero crossings or extremal points of differentiable, stationary Gaussian processes are studied as a function of their autocorrelation function. The properties of these point processes are mapped to the space formed by the parameters appearing in the autocorrelation function, their adopted form being sensitive to(More)
We report analytical calculations for the propagation of electromagnetic radiation through an inhomogeneous layer whose refractive index varies in one dimension situated between bulk right-and left-handed media. Significant field localization is generated in the layer that is caused by the coherent superposition of evanescent waves. The strength of the(More)
Non-Gaussian height fluctuations occurring on the fueling time scale of a slowly driven rice pile match those observed in some turbulent/critical phenomena, forming an anticorrelated random fractal process with Hurst exponent H=0.2. Inspired by this observation, the concept of fractional Brownian motion (FBM) is extended to treat stochastic processes with(More)
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