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Most quasi-static ultrasound elastography methods image only the axial strain, derived from displacements measured in the direction of ultrasound propagation. In other directions, the beam lacks high resolution phase information and displacement estimation is therefore less precise. However, these estimates can be improved by steering the ultrasound beam through multiple angles and combining displacements measured along the different beam directions. Previously, beamsteering has only considered the 2D case to improve the lateral displacement estimates. In this paper, we extend this to 3D using a simulated 2D array to steer both laterally and elevationally in order to estimate the full 3D displacement vector over a volume. The method is tested on simulated and phantom data using a simulated 6-10MHz array, and the precision of displacement estimation is measured with and without beamsteering. In simulations, we found a statistically significant improvement in the precision of lateral and elevational displacement estimates: lateral precision 35.69μm unsteered, 3.70μm steered; elevational precision 38.67μm unsteered, 3.64μm steered. Similar results were found in the phantom data: lateral precision 26.51μm unsteered, 5.78μm steered; elevational precision 28.92μm unsteered, 11.87μm steered. We conclude that volumetric 3D beamsteering improves the precision of lateral and elevational displacement estimates.

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Most quasi-static ultrasound elastography methods image only the axial strain, derived from displacements measured in the direction of ultrasound propagation. In other directions, the beam lacks high resolution phase information and displacement estimation is therefore less precise. However, these estimates can be improved by steering the ultrasound beam through multiple angles and combining displacements measured along the different beam directions. Previously, beamsteering has only considered the 2D case to improve the lateral displacement estimates. In this paper, we extend this to 3D using a simulated 2D array to steer both laterally and elevationally in order to estimate the full 3D displacement vector over a volume. The method is tested on simulated and phantom data using a simulated 6-10 MHz array, and the precision of displacement estimation is measured with and without beamsteering. In simulations, we found a statistically significant improvement in the precision of lateral and elevational displacement estimates: lateral precision 35.69 μm unsteered, 3.70 μm steered; elevational precision 38.67 μm unsteered, 3.64 μm steered. Similar results were found in the phantom data: lateral precision 26.51 μm unsteered, 5.78 μm steered; elevational precision 28.92 μm unsteered, 11.87 μm steered. We conclude that volumetric 3D beamsteering improves the precision of lateral and elevational displacement estimates. © 2012 Elsevier B.V. All rights reserved.

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Various MgB2 wires with different sheath materials provided by Hyper Tech Research Inc., have been tested in the superconducting fault current limiter (SFCL) desktop tester at 24-26K in a self-field. Samples 1 and 2 are similarly fabricated monofilamentary MgB2 wires with a sheath of CuNi, except that sample 2 is doped with SiC and Mg addition. Sample 3 is a CuNi sheathed multifilamentary wire with Cu stabilization and Mg addition. All the samples with Nb barriers have the same diameter of 0.83mm and superconducting fractions ranging from 15% to 27% of the total cross section. They were heat-treated at temperatures of 700 °C for a hold time of 20-40min. Current limiting properties of MgB2 wires subjected to pulse overcurrents have been experimentally investigated in an AC environment in the self-field at 50Hz. The quench currents extracted from the pulse measurements were in a range of 200-328A for different samples, corresponding to an average engineering critical current density (Je) of around 4.8 × 10 4Acm-2 at 25K in the self-field, based on the 1νVcm-1 criterion. This work is intended to compare the quench behaviour in the Nb-barrier monofilamentary and multifilamentary MgB2 wires with CuNi and Cu/CuNi sheaths. The experimental results can be applied to the design of fault current limiter applications based on MgB2 wires. © IOP Publishing Ltd.

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Most modern design codes do not allow for movement between a shallow foundation and the underlying soil during seismic loading. Consequently, the full magnitude of seismic energy is transmitted from the soil to the foundation during an earthquake. This energy either has to be dissipated before reaching the superstructure via engineering solutions such as base isolation systems, or the structure itself must withstand the full impact of the earthquake resulting in high material usage and expensive design. However, the inherent hysteric behaviour of soil can be used to isolate a foundation from the underlying soil. As part of a study into the soil-structure-interaction of shallow foundations, methods to optimise foundation isolation were investigated. In this paper the results from centrifuge tests investigating two of these methods are compared to results when no special foundation layout was implemented and the impact of the proposed isolation methods is discussed. © 2010 Taylor & Francis Group, London.

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Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.