Distribution of critical current density in large YBa2Cu3O7-δ grains fabricated using seeded peritectic solidification

High quality large grain high Tc superconducting ceramics offer enormous potential as 'permanent' magnets and in magnetic screening applications at 77K. This requires sample dimensions -cm with uniform high critical current densities of the order 105 A/cm2 in applied magnetic fields of IT. We report a study of the magnetic characterisation of a typical large YBa2Cu3O7-δ grain, prepared by seeded peritectic solidification, and correlate the magnetically determined critical current density, Jc, with microstuctural features from different regions of the bulk sample. From this data we extract the temperature, field and positional dependence of the critical current density of the samples and the irreversibility line. We find that whilst the bulk sample exhibits a good Jc of order 104 A/cm2 (77K, 1T), the local Jc is strongly correlated with the sample microstructure towards the edge of the sample and more severely at the centre of the sample by the presence of SmBa2Cu3O7-δ seed crystal. © 1997 IEEE.

Nonparametric Bayesian Density Modeling with Gaussian Processes

We present the Gaussian process density sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a distribution defined by a density that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.

Response probability distribution of built-up vibro-acoustic systems

The vibro-acoustic response of built-up structures, consisting of stiff components with low modal density and flexible components with high modal density, is sensitive to small imperfections in the flexible components. In this paper, the uncertainty of the response is considered by modeling the low modal density master system as deterministic and the high modal density subsystems in a nonparametric stochastic way, i.e., carrying a diffuse wave field, and by subsequently computing the response probability density function. The master system's mean squared response amplitude follows a singular noncentral complex Wishart distribution conditional on the subsystem energies. For a single degree of freedom, this is equivalent to a chi-square or an exponential distribution, depending on the loading conditions. The subsystem energies follow approximately a chi-square distribution when their relative variance is smaller than unity. The results are validated by application to plate structures, and good agreement with Monte Carlo simulations is found. © 2012 Acoustical Society of America.

The Gaussian process density sampler

We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We can also infer the hyperparameters of the Gaussian process. We compare this density modeling technique to several existing techniques on a toy problem and a skullreconstruction task.

On the applicability of the lognormal distribution in random dynamical systems

This paper is concerned with the probability density function of the energy of a random dynamical system subjected to harmonic excitation. It is shown that if the natural frequencies and mode shapes of the system conform to the Gaussian Orthogonal Ensemble, then under common types of loading the distribution of the energy of the response is approximately lognormal, providing the modal overlap factor is high (typically greater than two). In contrast, it is shown that the response of a system with Poisson natural frequencies is not approximately lognormal. Numerical simulations are conducted on a plate system to validate the theoretical findings and good agreement is obtained. Simulations are also conducted on a system made from two plates connected with rotational springs to demonstrate that the theoretical findings can be extended to a built-up system. The work provides a theoretical justification of the commonly used empirical practice of assuming that the energy response of a random system is lognormal.

Localized tail state distribution in amorphous oxide transistors deduced from low temperature measurements

In this paper, we extract density of localized tail states from measurements of low temperature conductance in amorphous oxide transistors. At low temperatures, trap-limited conduction prevails, allowing extraction of the trapped carrier distribution with energy. Using a test device with a-InGaZnO channel layer, the extracted tail state energy and density at the conduction band minima are 20 meV and 2 × 10 19 cm -3 eV -1, respectively, which are consistent with values reported in the literature. Also, the field-effect mobility as a function of temperature from 77 K to 300 K is retrieved for different gate voltages, yielding the activation energy and the percolation threshold. © 2012 American Institute of Physics.

Electroluminescence analysis and simulation of the effects of injection and temperature on carrier distribution in InGaN-based light-emitting diodes with color-coded quantum wells

This paper reports on an extensive analysis of the electroluminescence characteristics of InGaN-based LEDs with color-coded structure, i.e., with a triple quantum well structure in which each quantum well has a different indium content. The analysis is based on combined electroluminescence measurements and two-dimensional simulations, carried out at different current and temperature levels. Results indicate that (i) the efficiency of each of the quantum wells strongly depends on device operating conditions (current and temperature); (ii) at low current and temperature levels, only the quantum well closer to the p-side has a significant emission; (iii) emission from the other quantum wells is favored at high current levels. The role of carrier injection, hole mobility, carrier density and non-radiative recombination in determining the relative intensity of the quantum wells is discussed in the text. © 2013 The Japan Society of Applied Physics.

Fourier spectrum based extraction of an equivalent trap state density in indium gallium zinc oxide transistors

Segregating the dynamics of gate bias induced threshold voltage shift, and in particular, charge trapping in thin film transistors (TFTs) based on time constants provides insight into the different mechanisms underlying TFTs instability. In this Letter we develop a representation of the time constants and model the magnitude of charge trapped in the form of an equivalent density of created trap states. This representation is extracted from the Fourier spectrum of the dynamics of charge trapping. Using amorphous In-Ga-Zn-O TFTs as an example, the charge trapping was modeled within an energy range of ΔEt 0.3 eV and with a density of state distribution as Dt(Et-j)=Dt0exp(-ΔEt/ kT)with Dt0 = 5.02 × 1011 cm-2 eV-1. Such a model is useful for developing simulation tools for circuit design. © 2014 AIP Publishing LLC.