Surface grid generation for complex three-dimensional geometries

An outline is presented for the creation of surface grids from primitive geometry data such as obtained from CAD/CAM systems. The general procedure is applicable to any geometry including full aircraft with wing, nacelle, and empennage. When developed in an interactive graphics environment, a code based on this procedure is expected to substantially improve the turn around time for generating surface grids on complex geometries. Results are shown for a general hypersonic airplane geometry

SECONIC : towards multi-compartmental models for ultrasonic brain stimulation by intramembrane cavitation

Objective. To design a computationally efficient model for ultrasonic neuromodulation (UNMOD) of morphologically realistic multi-compartmental neurons based on intramembrane cavitation.Approach. A Spatially Extended Neuronal Intramembrane Cavitation model that accurately predicts observed fast Charge Oscillations (SECONIC) is designed. A regular spiking cortical Hodgkin-Huxley type nanoscale neuron model of the bilayer sonophore and surrounding proteins is used. The accuracy and computational efficiency of SECONIC is compared with the Neuronal Intramembrane Cavitation Excitation (NICE) and multiScale Optimized model of Neuronal Intramembrane Cavitation (SONIC).Main results. Membrane charge redistribution between different compartments should be taken into account via fourier series analysis in an accurate multi-compartmental UNMOD-model. Approximating charge and voltage traces with the harmonic term and first two overtones results in reasonable goodness-of-fit, except for high ultrasonic pressure (adjusted R-squared >= 0.61). Taking into account the first eight overtones results in a very good fourier series fit (adjusted R-squared >= 0.96) up to 600 kPa. Next, the dependency of effective voltage and rate parameters on charge oscillations is investigated. The two-tone SECONIC-model is one to two orders of magnitude faster than the NICE-model and demonstrates accurate results for ultrasonic pressure up to 100 kPa.Significance. Up to now, the underlying mechanism of UNMOD is not well understood. Here, the extension of the bilayer sonophore model to spatially extended neurons via the design of a multi-compartmental UNMOD-model, will result in more detailed predictions that can be used to validate or falsify this tentative mechanism. Furthermore, a multi-compartmental model for UNMOD is required for neural engineering studies that couple finite difference time domain simulations with neuronal models. Here, we propose the SECONIC-model, extending the SONIC-model by taking into account charge redistribution between compartments

i-RheoFT: Fourier transforming sampled functions without artefacts

In this article we present a new open-access code named “i-RheoFT” that implements the analytical method first introduced in [PRE, 80, 012501 (2009)] and then enhanced in [New J Phys 14, 115032 (2012)], which allows to evaluate the Fourier transform of any generic time-dependent function that vanishes for negative times, sampled at a finite set of data points that extend over a finite range, and need not be equally spaced. I-RheoFT has been employed here to investigate three important experimental factors: (i) the ‘density of initial experimental points’ describing the sampled function, (ii) the interpolation function used to perform the “virtual oversampling” procedure introduced in [New J Phys 14, 115032 (2012)], and (iii) the detrimental effect of noises on the expected outcomes. We demonstrate that, at relatively high signal-to-noise ratios and density of initial experimental points, all three built-in MATLAB interpolation functions employed in this work (i.e., Spline, Makima and PCHIP) perform well in recovering the information embedded within the original sampled function; with the Spline function performing best. Whereas, by reducing either the number of initial data points or the signal-to-noise ratio, there exists a threshold below which all three functions perform poorly; with the worst performance given by the Spline function in both the cases and the least worst by the PCHIP function at low density of initial data points and by the Makima function at relatively low signal-to-noise ratios. We envisage that i-RheoFT will be of particular interest and use to all those studies where sampled or time-averaged functions, often defined by a discrete set of data points within a finite time-window, are exploited to gain new insights on the systems’ dynamics

Alternative Fillers in Asphalt Concrete Mixtures: Laboratory Investigation and Machine Learning Modeling towards Mechanical Performance Prediction

In recent years, due to the reduction in available natural resources, the attention of many researchers has been focused on the reuse of recycled materials and industrial waste in common engineering applications. This paper discusses the feasibility of using seven different materials as alternative fillers instead of ordinary Portland cement (OPC) in road pavement base layers: namely rice husk ash (RHA), brick dust (BD), marble dust (MD), stone dust (SD), fly ash (FA), limestone dust (LD), and silica fume (SF). To exclusively evaluate the effect that selected fillers had on the mechanical performance of asphalt mixtures, we carried out Marshall, indirect tensile strength, moisture susceptibility, and Cantabro abrasion loss tests on specimens in which only the filler type and its percentage varied while keeping constant all the remaining design parameters. Experimental findings showed that all mixtures, except those prepared with 4% RHA or MD, met the requirements of Indian standards with respect to air voids, Marshall stability and quotient. LD and SF mixtures provided slightly better mechanical strength and durability than OPC ones, proving they can be successfully recycled as filler in asphalt mixtures. Furthermore, a Machine Learning methodology based on laboratory results was developed. A decision tree Categorical Boosting approach allowed the main mechanical properties of the investigated mixtures to be predicted on the basis of the main compositional variables, with a mean Pearson correlation and a mean coefficient of determination equal to 0.9724 and 0.9374, respectively

High order discretizations for spatial dependent SIR models

In this paper, an SIR model with spatial dependence is studied and results regarding its stability and numerical approximation are presented. We consider a generalization of the original Kermack and McKendrick model in which the size of the populations differs in space. The use of local spatial dependence yields a system of integro-differential equations. The uniqueness and qualitative properties of the continuous model are analyzed. Furthermore, different choices of spatial and temporal discretizations are employed, and step-size restrictions for population conservation, positivity, and monotonicity preservation of the discrete model are investigated. We provide sufficient conditions under which high order numerical schemes preserve the discrete properties of the model. Computational experiments verify the convergence and accuracy of the numerical methods.Comment: 33 pages, 5 figures, 3 table

Experimental and Machine Learning Approach to Investigate the Mechanical Performance of Asphalt Mixtures with Silica Fume Filler

This study explores the potential in substituting ordinary Portland cement (OPC) with industrial waste silica fume (SF) as a mineral filler in asphalt mixtures (AM) for flexible road pavements. The Marshall and indirect tensile strength tests were used to evaluate the mechanical resistance and durability of the AMs for different SF and OPC ratios. To develop predictive models of the key mechanical and volumetric parameters, the experimental data were analyzed using artificial neural networks (ANN) with three different activation functions and leave-one-out cross-validation as a resampling method. The addition of SF resulted in a performance comparable to, or slightly better than, OPC-based mixtures, with a maximum indirect tensile strength of 1044.45 kPa at 5% bitumen content. The ANN modeling was highly successful, partly due to an interpolation-based data augmentation strategy, with a correlation coefficient RCV of 0.9988

Experimental and Machine Learning Approach to Investigate the Mechanical Performance of Asphalt Mixtures with Silica Fume Filler

This study explores the potential in substituting ordinary Portland cement (OPC) with industrial waste silica fume (SF) as a mineral filler in asphalt mixtures (AM) for flexible road pavements. The Marshall and indirect tensile strength tests were used to evaluate the mechanical resistance and durability of the AMs for different SF and OPC ratios. To develop predictive models of the key mechanical and volumetric parameters, the experimental data were analyzed using artificial neural networks (ANN) with three different activation functions and leave-one-out cross-validation as a resampling method. The addition of SF resulted in a performance comparable to, or slightly better than, OPC-based mixtures, with a maximum indirect tensile strength of 1044.45 kPa at 5% bitumen content. The ANN modeling was highly successful, partly due to an interpolation-based data augmentation strategy, with a correlation coefficient R-CV of 0.9988

Positioning by multicell fingerprinting in urban NB-IoT networks

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments

Fast and robust methods for missing data recovery in image processing.

by Wong Yin Shung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 62-64).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.7Chapter 2 --- Fundamentals --- p.9Chapter 2.1 --- Representation of a digital image --- p.9Chapter 2.2 --- Salt-and-pepper --- p.10Chapter 2.3 --- Resolution of a gray digital image --- p.11Chapter 3 --- Filters --- p.14Chapter 3.1 --- Median filter --- p.15Chapter 3.2 --- Adaptive median filter --- p.15Chapter 3.3 --- Multi-state median filter --- p.16Chapter 3.4 --- Directional difference-based switching median filter --- p.18Chapter 3.5 --- Improved switching median filters --- p.20Chapter 3.6 --- Variational method --- p.21Chapter 3.7 --- Two-phase method --- p.22Chapter 4 --- New Two Phase Methods --- p.25Chapter 4.1 --- Triangule-based interpolation --- p.25Chapter 4.1.1 --- Delaunay triangulation --- p.26Chapter 4.1.2 --- Linear interpolation --- p.28Chapter 4.1.3 --- Cubic interpolation --- p.29Chapter 4.2 --- Gradient estimation --- p.32Chapter 4.3 --- Regularization method --- p.33Chapter 4.3.1 --- Least square method with Laplacian regularization --- p.33Chapter 4.3.2 --- Lagrange multipliers --- p.35Chapter 4.4 --- Fast transform for finding the inverse of Laplacian matrix --- p.38Chapter 5 --- Inpainting and Zooming --- p.39Chapter 5.1 --- Inpainting --- p.39Chapter 5.2 --- Zooming --- p.40Chapter 5.2.1 --- Bilinear interpolation --- p.40Chapter 5.2.2 --- Bicubic interpolation --- p.41Chapter 6 --- Results --- p.46Chapter 6.1 --- Results of denoising --- p.47Chapter 6.2 --- Results of inpainting --- p.47Chapter 6.3 --- Results of zooming --- p.48Chapter 6.4 --- Conclusions --- p.5