Development of the microstructure of uranium-doped Nd-Ba-Cu-O

Melt grown Nd-Ba-Cu-O (NdBCO) has been reported to exhibit higher values of critical current density, Jc and irreversibility field, Hirr, than other (RE)BCO superconductors, such as YBCO. The microstructure of NdBCO typically contains 5-10 μm sized inclusions of the Nd4Ba2Cu2O10 phase (Nd-422) in a superconducting NdBa2Cu3O7-δ phase (Nd-123) matrix. The average size of these inclusions is characteristically larger than that of the Y2BaCuO5 (Y-211) inclusions in YBCO. As a result, there is scope to further refine the Nd-422 size to enhance Jc in NdBCO. Large grain samples of NdBCO superconductor doped with various amounts of depleted UO2 and containing excess Nd-422 have been fabricated by top seeded melt growth under reduced oxygen partial pressure. The effect of the addition of depleted UO2 on the NdBCO microstructure has been studied systematically in samples with and without added CeO2. It is observed that the addition of UO2 refines the NdBCO microstructure via the formation of uranium-containing phase particles in the superconducting matrix. These particles are of approximately spherical geometry with dimensions of around 1 μm. The average size of the nonsuperconducting phase particles in the uranium-doped microstructure is an order of magnitude less than their size in un-doped Nd-123 prepared with excess Nd-422. The critical current density of uranium-doped NdBCO is observed to increase significantly compared to the undoped material.

Adaptive BDDC in Three Dimensions

The adaptive BDDC method is extended to the selection of face constraints in three dimensions. A new implementation of the BDDC method is presented based on a global formulation without an explicit coarse problem, with massive parallelism provided by a multifrontal solver. Constraints are implemented by a projection and sparsity of the projected operator is preserved by a generalized change of variables. The effectiveness of the method is illustrated on several engineering problems.

Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization

Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However sometimes images or 3D data are only available at a lower sampling rate due to physical constraints of the imaging system. In this paper, we model the under-sampled observation as the result of combining convolution and subsampling. Because the wavelet coefficients of piecewise smooth images tend to be sparse and well modelled by tree-like structures, we propose the L0 reweighted-L2 minimization (L0RL2 ) algorithm to solve this problem. This promotes model-based sparsity by minimizing the reweighted L2 norm, which approximates the L0 norm, and by enforcing a tree model over the weights. We test the algorithm on 3 examples: a simple ring, the cameraman image and a 3D microscope dataset; and show that good results can be obtained. © 2010 IEEE.

Augmented attribute representations

We propose a new learning method to infer a mid-level feature representation that combines the advantage of semantic attribute representations with the higher expressive power of non-semantic features. The idea lies in augmenting an existing attribute-based representation with additional dimensions for which an autoencoder model is coupled with a large-margin principle. This construction allows a smooth transition between the zero-shot regime with no training example, the unsupervised regime with training examples but without class labels, and the supervised regime with training examples and with class labels. The resulting optimization problem can be solved efficiently, because several of the necessity steps have closed-form solutions. Through extensive experiments we show that the augmented representation achieves better results in terms of object categorization accuracy than the semantic representation alone. © 2012 Springer-Verlag.

Half-bridge SIC inverter for hybrid electric vehicles: Design, development and testing at higher operating temperature

In recent years, Silicon Carbide (SiC) semiconductor devices have shown promise for high density power electronic applications, due to their electrical and thermal properties. In this paper, the performance of SiC JFETs for hybrid electric vehicle (HEV) applications is investigated at heatsink temperatures of 100 °C. The thermal runaway characteristics, maximum current density and packaging temperature limitations of the devices are considered and the efficiency implications discussed. To quantify the power density capabilities of power transistors, a novel 'expression of rating' (EoR) is proposed. A prototype single phase, half-bridge voltage source inverter using SiC JFETs is also tested and its performance at 25 °C and 100 °C investigated.

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.