Hybrid deterministic-statistical modelling of built-up structures

When used correctly, Statistical Energy Analysis (SEA) can provide good predictions of high frequency vibration levels in built-up structures. Unfortunately, the assumptions that underlie SEA break down as the frequency of excitation is reduced, and the method does not yield accurate predictions at "medium" frequencies (and neither does the Finite Element Method, which is limited to low frequencies). A basic problem is that parts of the system have a short wavelength of deformation and meet the requirements of SEA, while other parts of the system do not - this is often referred to as the "mid-frequency" problem, and there is a broad class of mid-frequency vibration problems that are of great concern to industry. In this paper, a coupled deterministic-statistical approach referred to as the Hybrid Method (Shorter & Langley, 2004) is briefly described, and some results that demonstrate how the method overcomes the aforementioned difficulties are presented.

Active learning of model evidence using Bayesian quadrature

Numerical integration is a key component of many problems in scientific computing, statistical modelling, and machine learning. Bayesian Quadrature is a modelbased method for numerical integration which, relative to standard Monte Carlo methods, offers increased sample efficiency and a more robust estimate of the uncertainty in the estimated integral. We propose a novel Bayesian Quadrature approach for numerical integration when the integrand is non-negative, such as the case of computing the marginal likelihood, predictive distribution, or normalising constant of a probabilistic model. Our approach approximately marginalises the quadrature model's hyperparameters in closed form, and introduces an active learning scheme to optimally select function evaluations, as opposed to using Monte Carlo samples. We demonstrate our method on both a number of synthetic benchmarks and a real scientific problem from astronomy.

Statistical image modelling using interscale phase relationships of complex wavelet coefficients

A novel method for modelling the statistics of 2D photographic images useful in image restoration is defined. The new method is based on the Dual Tree Complex Wavelet Transform (DT-CWT) but a phase rotation is applied to the coefficients to create complex coefficients whose phase is shift-invariant at multiscale edge and ridge features. This is in addition to the magnitude shift invariance achieved by the DT-CWT. The increased correlation between coefficients adjacent in space and scale provides an improved mechanism for signal estimation. © 2006 IEEE.

Modelling of EDC-enabled multimode fibre links

The application of electronic dispersion compensation (EDC) technology to extended-reach operation of multimode-fibre (MMF) links is considered. The essential theory is described in the context of MMF and the application of statistical techniques to predict supported link lengths for EDC-enabled MMF links is presented.