Packing-dependent pore structure in single-walled carbon nanotube arrays

Based on a self-similar array model of single-walled carbon nanotubes (SWNTs), the pore structure of SWNT bundles is analyzed and compared with that obtained from the conventional triangular model and adsorption experimental results. In addition to the well known cylindrical endo-cavities and interstitial pores, two types of newly defined pores with diameters of 2-10 and 8-100 nm are proposed, inter-bundle pores and inter-array pores. In particular, the relationship between the packing configuration of SWNTs and their pore structures is systematically investigated. (c) 2005 American Institute of Physics.

Enhanced detection-guided NLMS estimation of sparse FIR-modeled signal channels

In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm. © 2006 IEEE.

Interfacial behavior of tetrapyridylporphyrin monolayer arrays

The behavior of monolayer films of free base 5,10,15,20-tetrapyridylporphinato (TPyP) and 5,10,15,20-tetrapyridylporphinato zinc(II) (ZnTPyP) on pure water, 0.1 M CdCl2, and 0.1 M CuCl2 subphases was investigated by surface pressure-area isotherms, specular X-ray reflectometry, and polarized total reflection X-ray absorption spectroscopy (PTRXAS). Surface pressure-area isotherms showed significant differences in the area per molecule on pure water compared to that on salt subphases, with a marked increase in the area observed on the salt solutions. This behavior was noted for both forms of the porphyrin and both salts investigated. Modeling of specular X-ray reflectometry data indicated that thinner and more electron dense layers on salt subphases best fit the observed profiles. These data suggest that the porphyrin macrocycle is oriented parallel to the interface on salt subphases and takes on a tilted conformation on pure water. In the case of ZnTPyP, PTRXAS was used to determine the orientation of the porphyrin moiety relative to the surface and to probe the coordination of the central Zn ion. In agreement with the pressure-area isotherms and reflectometry, the PTRXAS data indicate a change in orientation on the salt subphases.

Constructing internationally comparable real income aggregates by combining sparse benchmark data with annual national accounts data: A state-space approach

The importance of availability of comparable real income aggregates and their components to applied economic research is highlighted by the popularity of the Penn World Tables. Any methodology designed to achieve such a task requires the combination of data from several sources. The first is purchasing power parities (PPP) data available from the International Comparisons Project roughly every five years since the 1970s. The second is national level data on a range of variables that explain the behaviour of the ratio of PPP to market exchange rates. The final source of data is the national accounts publications of different countries which include estimates of gross domestic product and various price deflators. In this paper we present a method to construct a consistent panel of comparable real incomes by specifying the problem in state-space form. We present our completed work as well as briefly indicate our work in progress.

Microphone and loudspeaker arrays for room acoustic measurements

A multichannel spherical speaker array allows, together with a spherical microphones array, the measurement of the MIMO (Multiple Input Multiple Output) acoustic impulse response of an environment capturing meaningful information about propagation of sound between source an receiver. The mathematical framework for extracting arbitrary directivity virtual microphones from real microphones array signals is recalled and the application of the same method to the speakers array to generate arbitrary directivity source is presented. A convenient solutions for the construction and calibration of speakers spherical array for measurement purposes is illustrated. The postprocessing technique developed to compute and visualize acoustic path between source and receiver from measured MIMO impulse response is discussed. Real word results from measurement in a small theater are shown.

Sparse representation for Gaussian process models

We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the model. Experimental results on toy examples and large real-world datasets indicate the efficiency of the approach.

Sparse on-line Gaussian processes

We develop an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the GP model. By using an appealing parametrisation and projection techniques that use the RKHS norm, recursions for the effective parameters and a sparse Gaussian approximation of the posterior process are obtained. This allows both for a propagation of predictions as well as of Bayesian error measures. The significance and robustness of our approach is demonstrated on a variety of experiments.

Gaussian processes:iterative sparse approximations

In recent years there has been an increased interest in applying non-parametric methods to real-world problems. Significant research has been devoted to Gaussian processes (GPs) due to their increased flexibility when compared with parametric models. These methods use Bayesian learning, which generally leads to analytically intractable posteriors. This thesis proposes a two-step solution to construct a probabilistic approximation to the posterior. In the first step we adapt the Bayesian online learning to GPs: the final approximation to the posterior is the result of propagating the first and second moments of intermediate posteriors obtained by combining a new example with the previous approximation. The propagation of em functional forms is solved by showing the existence of a parametrisation to posterior moments that uses combinations of the kernel function at the training points, transforming the Bayesian online learning of functions into a parametric formulation. The drawback is the prohibitive quadratic scaling of the number of parameters with the size of the data, making the method inapplicable to large datasets. The second step solves the problem of the exploding parameter size and makes GPs applicable to arbitrarily large datasets. The approximation is based on a measure of distance between two GPs, the KL-divergence between GPs. This second approximation is with a constrained GP in which only a small subset of the whole training dataset is used to represent the GP. This subset is called the em Basis Vector, or BV set and the resulting GP is a sparse approximation to the true posterior. As this sparsity is based on the KL-minimisation, it is probabilistic and independent of the way the posterior approximation from the first step is obtained. We combine the sparse approximation with an extension to the Bayesian online algorithm that allows multiple iterations for each input and thus approximating a batch solution. The resulting sparse learning algorithm is a generic one: for different problems we only change the likelihood. The algorithm is applied to a variety of problems and we examine its performance both on more classical regression and classification tasks and to the data-assimilation and a simple density estimation problems.