Single frequency inverse obstacle scattering: a sparsity constrained linear sampling method approach

The linear sampling method (LSM) offers a qualitative image reconstruction approach, which is known as a viable alternative for obstacle support identification to the well-studied filtered backprojection (FBP), which depends on a linearized forward scattering model. Of practical interest is the imaging of obstacles from sparse aperture far-field data under a fixed single frequency mode of operation. Under this scenario, the Tikhonov regularization typically applied to LSM produces poor images that fail to capture the obstacle boundary. In this paper, we employ an alternative regularization strategy based on constraining the sparsity of the solution's spatial gradient. Two regularization approaches based on the spatial gradient are developed. A numerical comparison to the FBP demonstrates that the new method's ability to account for aspect-dependent scattering permits more accurate reconstruction of concave obstacles, whereas a comparison to Tikhonov-regularized LSM demonstrates that the proposed approach significantly improves obstacle recovery with sparse-aperture data.     

Neural Network Modelling with Input Uncertainty: Theory and Application

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network's weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.     

On the variability of manual spike sorting

The analysis of action potentials, or "spikes," is central to systems neuroscience research. Spikes are typically identified from raw waveforms manually for off-line analysis or automatically by human-configured algorithms for on-line applications. The variability of manual spike "sorting" is studied and its implications for neural prostheses discussed. Waveforms were recorded using a micro-electrode array and were used to construct a statistically similar synthetic dataset. Results showed wide variability in the number of neurons and spikes detected in real data. Additionally, average error rates of 23% false positive and 30% false negative were found for synthetic data.     

A model for residual life prediction based on Brownian motion with an adaptive drift

A degradation model is presented in this paper for the prediction of the residual life using an adapted Brownian motion-based approach with a drifting parameter. This model differs from other Brownian motion-based approaches in that the drifting parameter of the degradation process is adapted to the history of monitored information. This adaptation is performed by Kalman filtering. We also use a threshold distribution instead of the usual single threshold line which is sometime difficult to obtain in practice. We demonstrate the model using some examples and show that the model performs reasonably well and has a better prediction ability than the standard Brownian motion-based model. The model is then fitted to the data generated from a simulator using the expectation-maximization algorithm. We also fit a standard Brownian motion-based model to the same data to compare the difference and performance. The result shows that the adapted model performs better in terms of certain test statistics and the total mean square errors.     

Laser transfer of sol-gel ferroelectric thin films using an ITO release layer

A new laser transfer process is reported which allows damage-free transfer of ferroelectric thin films from a growth substrate directly to a target substrate. The thin film ferroelectric material is deposited on a fused silica growth substrate with a sacrificial release layer of ITO (indium tin oxide). Regions of the film that are to be transferred are then selectively metallised, and bonded to the target substrate. Separation from the growth substrate is achieved by laser ablation of the ITO release layer by a single pulse from a KrF excimer laser, with the laser light being incident through the growth substrate. The residual ITO on the transferred ferroelectric layer is electrically conducting, and may be suitable for incorporation into the final device, depending on the application. The new process has been demonstrated for 500 nm-thick layers of sol-gel PZT which were thermosonically bonded to a silicon target substrate prior to laser release. The transferred films show ferroelectric behaviour and have a slightly reduced permittivity compared to the as-deposited material.     

Bayesian regularization applied to ultrasound strain imaging

Noise artifacts due to signal decorrelation and reverberation are a considerable problem in ultrasound strain imaging. For block-matching methods, information from neighboring matching blocks has been utilized to regularize the estimated displacements. We apply a recursive Bayesian regularization algorithm developed by Hayton et al. [Artif. Intell., vol. 114, pp. 125-156, 1999] to phase-sensitive ultrasound RF signals to improve displacement estimation. The parameter of regularization is reformulated, and its meaning examined in the context of strain imaging. Tissue-mimicking experimental phantoms and RF data incorporating finite-element models for the tissue deformation and frequency-domain ultrasound simulations are used to compute the optimal parameter with respect to nominal strain and algorithmic iterations. The optimal strain regularization parameter was found to be twice the nominal strain and did not vary significantly with algorithmic iterations. The technique demonstrates superior performance over median filtering in noise reduction at strains 5% and higher for all quantitative experiments performed. For example, the strain SNR was 11 dB higher than that obtained using a median filter at 7% strain. It has to be noted that for applied deformations lower than 1%, since signal decorrelation errors are minimal, using this approach may degrade the displacement image.     

Large-scale,adhesive-free and omnidirectional 3D nanocone anti-reflection films for high performance photovoltaics

An effective and low-cost front-side anti-reflection(AR) technique has long been sought to enhance the performance of highly efficient photovoltaic devices due to its capability of maximizing the light absorption in photovoltaic devices. In order to achieve high throughput fabrication of nanostructured flexible and anti-reflection films, large-scale, nano-engineered wafer molds were fabricated in this work. Additionally, to gain in-depth understanding of the optical and electrical performance enhancement with AR films on polycrystalline Si solar cells, both theoretical and experimental studies were performed. Intriguingly,the nanocone structures demonstrated an efficient light trapping effect which reduced the surface reflection of a solar cell by17.7% and therefore enhanced the overall electric output power of photovoltaic devices by 6% at normal light incidence. Notably, the output power improvement is even more significant at a larger light incident angle which is practically meaningful for daily operation of solar panels. The application of the developed AR films is not only limited to crystalline Si solar cells explored here, but also compatible with any types of photovoltaic technology for performance enhancement.     

Advancing Tailored Health Communication: A Persuasion and Message Effects Perspective

Tailored health communication (THC) is any combination of information and behavior change strategies intended to reach one specific person based on information unique to that person, related to the outcome of interest, and derived from an individual assessment. THCs have been studied as a means to facilitate behavior change by influencing some key intermediate steps that precede the behavioral outcome. These include the extent to which people attend to communications, think about them, find them relevant and salient, and intend to take action. Evidence shows that THCs have achieved modest success in changing a number of cancer-related behaviors, including smoking, diet, exercise, and cancer screening. However, it is likely that THCs could be more effective if they were developed with a greater understanding of message effects and what we refer to as the behavioral pathway. Instead of using unidimensional approaches to influence behavior change, a message effects approach would help researchers identify key leverage points for impact on such intermediate outcomes as persuasion and yielding. Such a strategy also might be used to determine when THCs are the preferred approach and when generic, targeted, or combinations of THCs and targeted communications might be appropriate. Viewing THCs from the perspective of the behavioral pathway might indicate use of different messages, sources, and formats to influence different people at different points on the pathway. We provide a brief history of THCs and suggest how integrating a broader perspective of health behavior and health communication theories could enrich THCs.     

Inside Front Cover: Three‐Dimensionally Ordered Gold Nanocrystal/Silica Superlattice Thin Films Synthesized via Sol–Gel Self‐Assembly (Adv. Funct. Mater. 7/2006)

The synthesis of three‐dimensionally ordered, transparent gold‐nanocrystal (NC)/silica superlattice thin films using the self‐assembly (by spin‐coating) of water‐soluble gold nanocrystal micelles and soluble silica is reported by Fan and co‐workers on p. 891. The robust, 3D NC/silica superlattice films are of interest for the development of collective optical and electronic phenomena, and, importantly, for the integration of NC arrays into device architectures. Nanocrystals and their ordered arrays hold many important applications in fields such as catalysis, surface‐enhanced Raman spectroscopy based sensors, memory storage, and electronic and optical nanodevices. Herein, a simple and general method to synthesize ordered, three‐dimensional, transparent gold nanocrystal/silica superlattice thin films by self‐assembly of gold nanocrystal micelles with silica or organosilsesquioxane by spin‐coating is reported. The self‐assembly process is conducted under acidic sol–gel conditions (ca. pH 2), ensuring spin‐solution homogeneity and stability and facilitating the formation of ordered and transparent gold nanocrystal/silica films. The monodisperse nanocrystals are organized within inorganic host matrices as a face‐centered cubic mesostructure, and characterized by transmission electron spectroscopy and X‐ray diffraction.