A multiple circular path convolution neural network system for detection of mammographic masses

A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.     

The Effect of Nanoparticle Shape on the Photocarrier Dynamics and Photovoltaic Device Performance of Poly(3‐hexylthiophene):CdSe Nanoparticle Bulk Heterojunction Solar Cells

The charge separation and transport dynamics in CdSe nanoparticle:poly(3‐hexylthiophene) (P3HT) blends are reported as a function of the shape of the CdSe‐nanoparticle electron acceptor (dot, rod, and tetrapod). For optimization of organic photovoltaic device performance it is crucial to understand the role of various nanostructures in the generation and transport of charge carriers. The sample processing conditions are carefully controlled to eliminate any processing‐related effects on the carrier generation and on device performance with the aim of keeping the conjugated polymer phase constant and only varying the shape of the inorganic nanoparticle acceptor phase. The electrodeless, flash photolysis time‐resolved microwave conductivity (FP‐TRMC) technique is used and the results are compared to the efficiency of photovoltaic devices that incorporate the same active layer. It is observed that in nanorods and tetrapods blended with P3HT, the high aspect ratios provide a pathway for the electrons to move away from the dissociation site even in the absence of an applied electric field, resulting in enhanced carrier lifetimes that correlate to increased efficiencies in devices. The processing conditions that yield optimum performance in high aspect ratio CdSe nanoparticles blended with P3HT result in poorly performing quantum dot CdSe:P3HT devices, indicating that the latter devices are inherently limited by the absence of the dimensionality that allows for efficient, prolonged charge separation at the polymer:CdSe interface.     

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.     

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.     

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.